Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I used for teaching SOC 4015 / SOC 
    5050 at Saint Louis University (SLU). The package also contains an R Markdown
    template with the required formatting for assignments in my courses 
    SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
Tests the hypothesis  that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test.
             The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}.
tester allows you to test characteristics of common R objects.
Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.
Test functions are often used to test computer code.
  They are used in optimization to test algorithms and in
  metamodeling to evaluate model predictions. This package provides
  test functions that can be used for any purpose.
  Some functions are taken from <https://www.sfu.ca/~ssurjano>, but
  their R code is not used.
Develop, evaluate, and score multiple choice examinations, 
 psychological scales, questionnaires, and similar types of data involving
 sequences of choices among one or more sets of answers.
 This version of the package should be considered as brand new.  Almost all
 of the functions have been changed, including their argument list.
 See the file NEWS.Rd in the Inst folder for more information.
 Using the package does not require any formal statistical knowledge 
 beyond what would be provided by a first course in statistics in a 
 social science department.  There the user would encounter the concept 
 of probability and how it is used to model data and make decisions, 
 and would become familiar with basic mathematical and statistical notation.
 Most of the output is in graphical form.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
Software testing is important, but, in part because it is
    frustrating and boring, many of us avoid it. 'testthat' is a testing
    framework for R that is easy to learn and use, and integrates with
    your existing 'workflow'.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.
Uses the Distorted Wave Born Approximation (DWBA) to compute the
  acoustic backward scattering, the geometry of the object is formed by a
  volumetric mesh, composed of tetrahedrons. This computation is done efficiently
  through an analytical 3D integration that allows for a solution which is
  expressed in terms of elementary functions for each tetrahedron.
  It is important to note that this method is only valid for objects whose
  acoustic properties, such as density and sound speed, do not vary significantly
  compared to the surrounding medium.
When using the R package  'exams'  to write mathematics questions in 'Sweave' files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the  topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra.  The output of the developed functions can be used in 'Sweave' files.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
Articles in the 'R Journal' were first authored in 'LaTeX', which performs 
    admirably for 'PDF' files but is less than ideal for modern online interfaces.
    The 'texor' package does all the transitional chores and conversions necessary
    to move to the online versions.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>. (If the Zelig package, which this package enhances, cannot be found on CRAN, you can find it at <https://github.com/IQSS/Zelig>. If the mnlogit package, which this package enhances, cannot be found on CRAN, you can find an old version in the CRAN Archive at <https://cran.r-project.org/src/contrib/Archive/mnlogit/>.)
Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see  <https://www.r-text.org>.
Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings.
    The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. 
    This package implements the same logic for sequences of words and letters instead of molecular sequences.
The United Nations’ Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information regarding the methodology see Meier, Mata & Wulff (2022) <arXiv:2110.05856>.
This is a companion package for the 'text2sdg' package. It contains the trained ensemble models needed by the 'detect_sdg' function from the 'text2sdg' package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.
Converts text into speech using various text-to-speech (TTS) engines and provides an unified interface for accessing their functionality.
  With this package, users can easily generate audio files of spoken words, phrases, or sentences from plain text data. The package supports multiple TTS engines, 
  including Google's 'Cloud Text-to-Speech API', 'Amazon Polly', Microsoft's 'Cognitive Services Text to Speech REST API',  and a free TTS engine called 'Coqui TTS'.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the 'LaTeX' tabular environment using '.tex' code. It can compile '.tex' code to 'PDF' automatically.
Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text
    datasets on the disk and load them when needed. Includes various
    sentiment lexicons and labeled text data sets for classification and
    analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
Implement text and sentiment analysis with 'texter'. 
             Generate sentiment scores on text data and also visualize sentiments.
             'texter' allows you to quickly generate insights on your data.
             It includes support for lexicons such as 'NRC' and 'Bing'.
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
The software application Praat can be used to annotate
    waveform data (e.g., to mark intervals of interest or to label events).
    (See <http://www.fon.hum.uva.nl/praat/> for more information about Praat.)
    These annotations are stored in a Praat TextGrid object, which consists of
    a number of interval tiers and point tiers. An interval tier consists of
    sequential (i.e., not overlapping) labeled intervals. A point tier consists
    of labeled events that have no duration. The 'textgRid' package provides
    S4 classes, generics, and methods for accessing information that is stored
    in Praat TextGrid objects.
Contains real images of the same textile material 
    with/without local defects, which were used in 
    Bui and Apley (2018) <doi:10.1080/00401706.2017.1302362>.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Visualise complex relations in texts. This is done by providing functionalities for displaying 
    text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering and semantic text 'embeddings'. 
    Feel free to join the effort of providing interesting text visualisations.
The 'textrank' algorithm is an extension of the 'Pagerank' algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the 'Pagerank' algorithm which identifies the most important sentences in your text and ranks them. 
    In a similar way 'textrank' can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the 'Pagerank' algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords.  
    More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.
Converting text to numerical features requires specifically
    created procedures, which are implemented as steps according to the
    'recipes' package. These steps allows for tokenization, filtering,
    counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Provides access to the text shaping functionality in the
    'HarfBuzz' library and the bidirectional algorithm in the 'Fribidi'
    library.  'textshaping' is a low-level utility package mainly for
    graphic devices that expands upon the font tool-set provided by the
    'systemfonts' package.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
A framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. 
  Includes various metrics for word counts/frequencies that scale efficiently. Quickly 
  analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). 
  Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports, e.g. for expanding
  and aligning strings that form the lines of such
  reports. The package also provides generic functions for
  transforming R objects to HTML and to plain text.
R implementation of 'TFactS' to predict which are the 
    transcription factors (TFs), regulated in a biological condition based on
    lists of differentially expressed genes (DEGs) obtained from transcriptome
    experiments. This package is based on the 'TFactS' concept by 
    Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands
    it. It allows users to perform 'TFactS'-like enrichment approach. The
    package can import and use the original catalogue file from the 'TFactS'
    as well as users' defined catalogues of interest that are not supported 
    by 'TFactS' (e.g., Arabidopsis).
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for
    building complex input pipelines from simple, re-usable pieces.
    See <https://www.tensorflow.org/guide> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/guide/estimator>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. 
  Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training 
  progress of machine learning models.
'TensorFlow' Hub is a library for the publication, discovery, and
    consumption of reusable parts of machine learning models. A module is a 
    self-contained piece of a 'TensorFlow' graph, along with its weights and 
    assets, that can be reused across different tasks in a process known as
    transfer learning. Transfer learning train a model with a smaller dataset,
    improve generalization, and speed up training.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15. <doi:10.1186/1748-7188-2-15>.
Provides a framework for the creation and use of Neural ordinary
    differential equations with the 'tensorflow' and 'keras' packages. 
    The idea of Neural ordinary differential equations comes from 
    Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and 
    presents a novel way of learning and solving differential systems.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the 'tframe' package
	which provides a programming kernel for time series. Extensions to
	'tframe' provided in 'tframePlus' can also be used. See the Guide
	vignette for examples.
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow'
    that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU').
    'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers,
    variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Creates a framework to store and apply display metadata to Analysis
    Results Datasets (ARDs). The use of 'tfrmt' allows users to define table 
    format and styling without the data, and later apply the format to the data.
Provides an interactive interface to the 'tfrmt' package. Users 
    can import, modify, and export tables and templates with little to no code.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
Extension of 'funHDDC' Schmutz et al. (2018) 
    <doi:10.1007/s00180-020-00958-4> for cases including
    outliers by fitting t-distributions for robust groups. 'TFunHDDC' can cluster
    univariate or multivariate data produced by the 'fda' package for data using
    a b-splines or Fourier basis.
Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Efficient implementation of K-Means++ algorithm. For more
    information see (1) "kmeans++ the advantages of the k-means++
    algorithm" by David Arthur and Sergei Vassilvitskii (2007),
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
    algorithms, Society for Industrial and Applied Mathematics,
    Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of
    Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval
    Rabani, Leonard J. Schulman and Chaitanya Swamy
    <doi:10.1145/2395116.2395117>.
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.  For details and tutorials, 
 see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) 
 <doi:10.18637/jss.v033.i06>.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.
Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
A collection of high performance utilities to compute
    distance, correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis
    of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad
    Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).
Turing Geovisualization Engine R package for geospatial visualization and analysis.
Contains data sets used in other packages Torsten Hothorn
  maintains.
Provide R Markdown templates and LaTeX preamble
    which are necessary for creating PDF from R Markdown documents in Thai language.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood using posterior samples and
    unnormalized log posterior values via reciprocal importance sampling.
    Metodiev, Perrot-Dockès, Ouadah, Irons, & Raftery (2023) <arXiv:2305.08952>.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
A tool for starring GitHub repositories.
Consolidates and calculates different sets of time-series features from multiple
    'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>,
    'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>,
    'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020)
    <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J.,
    and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020)
    <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021)
    <https://facebookresearch.github.io/Kats/>. Provides a standardised workflow from feature calculation to
    feature processing, machine learning classification procedures, and the production of statistical graphics.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
Theme 'ggplot2', 'lattice', and 'base' graphics based on a
    few choices, including foreground color, background color, accent
    color, and font family. Fonts that aren't available on the system, but
    are available via download on 'Google Fonts', can be automatically
    downloaded, cached, and registered for use with the 'showtext' and
    'ragg' packages.
A dataset with an uneven number of cases in each class is
    said to be unbalanced. Many models produce a subpar performance on
    unbalanced datasets. A dataset can be balanced by increasing the
    number of minority cases using SMOTE 2011 <arXiv:1106.1813>,
    BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008
    <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the
    number of majority cases using NearMiss 2003
    <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
    link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Utilizing the 'OpenAI' API as the back end (<https://platform.openai.com/docs/api-reference>), 'TheOpenAIR' offers 'R' wrapper functions for the 'ChatGPT' endpoint and several high-level functions that enable the integration of 'ChatGPT' capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
    v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions.
    Recommend update for use with long distance calculations. v.4.1.3 changed to frameLocates to reflect change to as.character() to format().
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).
A user-friendly R data package that is intended to make Turkish higher education statistics more accessible.
Plot your data using color palettes for 'ggplot2' inspired by the TV show, book series and video games "The Witcher".
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
Some tools for cleaning up messy 'Excel' files to be suitable
    for R. People who have been working with 'Excel' for years built more
    or less complicated sheets with names, characters, formats that are
    not homogeneous. To be able to use them in R nowadays, we built a set
    of functions that will avoid the majority of importation problems and
    keep all the data at best.
Determine the path of the executing script. Compatible
        with a few popular GUIs: 'Rgui', 'RStudio', 'VSCode',
        'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible with
        several functions and packages: 'source()', 'sys.source()',
        'debugSource()' in 'RStudio', 'compiler::loadcmp()',
        'box::use()', 'knitr::knit()', 'plumber::plumb()',
        'shiny::runApp()', 'package:targets', and
        'testthat::source_file()'.
Functions for admin needs of employees of Thomas Jefferson
        University and Thomas Jefferson University Hospital, Philadelphia,
        PA.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://www.symas.com/lmdb>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Creates some 'WebGL' shaders. They can be used as the background of a 'Shiny' app. They also can be visualized in the 'RStudio' viewer pane or included in 'Rmd' documents, but this is pretty useless, besides contemplating them.
A tool that allows users to estimate tree height in the long-term forest experiments in Sweden. It utilizes the multilevel nonlinear mixed-effect height models developed for the forest experiments and consists of four functions for the main species, other conifer species, and other broadleaves. Each function within the system returns a data frame that includes the input data and the estimated heights for any missing values. Ogana et al. (2023) <doi:10.1016/j.foreco.2023.120843>\n Arias-Rodil et al. (2015) <doi:10.1371/JOURNAL.PONE.0143521>.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/tds/>) 
  catalogs, and access dataset metadata and resources.
Design and analyze three-arm non-inferiority or superiority trials
    which follow a gold-standard design, i.e. trials with an experimental treatment,
    an active, and a placebo control. Method for the following distributions are implemented:
    Poisson (Mielke and Munk (2009) <arXiv:0912.4169>), negative binomial (Muetze et al. (2016) <doi:10.1002/sim.6738>),
    normal (Pigeot et al. (2003) <doi:10.1002/sim.1450>; Hasler et al. (2009) <doi:10.1002/sim.3052>), 
    binary (Friede and Kieser (2007) <doi:10.1002/sim.2543>), nonparametric (Muetze et al. (2017) <doi:10.1002/sim.7176>),
    exponential (Mielke and Munk (2009) <arXiv:0912.4169>).
A fast, interactive cross-platform, and easy to share 
    'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or
    'AFNI/SUMA' surfaces. The viewer widget can be either standalone or 
    embedded into 'R-shiny' applications. The standalone version only require
    a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 
    'Safari'), and can be inserted into any websites. The 'R-shiny' 
    support allows the 3D viewer to be dynamically generated from reactive user 
    inputs. This feature has been fully adopted by 'RAVE' 
    <https://openwetware.org/wiki/RAVE>, an interactive toolbox to 
    analyze 'iEEG' data. Documentation about 'threeBrain' is provided
    by <https://dipterix.org/threeBrain/> and several vignettes included 
    in this package. To cite the package, please check our 'NeuroImage' paper 
    by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>),
    or see 'citation("threeBrain")' for details.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Access open data from <https://www.threesixtygiving.org>, a 
    database of charitable grant giving in the UK operated by '360Giving'.
    The package provides functions to search and retrieve data on charitable 
    grant giving, and process that data into tidy formats. It relies on the 
    '360Giving' data standard, described at 
    <https://standard.threesixtygiving.org/>.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.
We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.
Allow to compute and visualise convective parameters commonly
  used in the operational prediction of severe convective storms. Core algorithm
  is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly
  efficient engine allows to derive thermodynamic and kinematic parameters from
  large numerical datasets such as reanalyses or operational Numerical Weather 
  Prediction models in a reasonable amount of time. Package has been developed
  since 2017 by research meteorologists specializing in severe thunderstorms.
  The most relevant methods used in the package based on the following publications
  Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>,
  McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>,
  Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>,
  Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>,
  Showalter (1953) <doi:10.1175/1520-0477-34.6.250>,
  Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>,
  Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>,
  Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>,
  Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>,
  Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>,
  Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.
Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References:
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.;
  Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional
    data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
A tool to rectangle a nested list, that is to convert it into
    a tibble. This is done automatically or according to a given
    specification.  A common use case is for nested lists coming from
    parsing JSON files or the JSON response of REST APIs. It is supported
    by the 'vctrs' package and therefore offers a wide support of vector
    types.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
Code execution timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'StackList', which is a
    stack implemented as a
    list, both of which support operations 'push', 'pop', 'first_element',
    'last_element' and 'clear'.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps using the harmonic representation of inequalities.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Provides 'Apache Spark' style window aggregation for R dataframes and remote 'dbplyr' tables via 'mutate' in 'dplyr' flavour.
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Tools to download data series from 'Banco de España' ('BdE')
    on 'tibble' format. 'Banco de España' is the national central bank
    and, within the framework of the Single Supervisory Mechanism ('SSM'),
    the supervisor of the Spanish banking system along with the European
    Central Bank. This package is in no way sponsored endorsed or
    administered by 'Banco de España'.
Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.
Provides users a quick exploratory dive into common
    visualizations without writing a single line of code given the users
    data follows the Analysis Data Model (ADaM) standards put forth by the
    Clinical Data Interchange Standards Consortium (CDISC)
    <https://www.cdisc.org>. Prominent modules/ features of the
    application are the Table Generator, Population Explorer, and the
    Individual Explorer. The Table Generator allows users to drag and drop
    variables and desired statistics (frequencies, means, ANOVA, t-test,
    and other summary statistics) into bins that automagically create
    stunning tables with validated information. The Population Explorer
    offers various plots to visualize general trends in the population
    from various vantage points. Plot modules currently include scatter
    plot, spaghetti plot, box plot, histogram, means plot, and bar plot.
    Each plot type allows the user to plot uploaded variables against one
    another, and dissect the population by filtering out certain subjects.
    Last, the Individual Explorer establishes a cohesive patient
    narrative, allowing the user to interact with patient metrics (params)
    by visit or plotting important patient events on a timeline. All
    modules allow for concise filtering & downloading bulk outputs into
    html or pdf formats to save for later.
An integrated R interface to several United States Census Bureau 
    APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's 
    geographic boundary files. Allows R users to return Census and ACS data as 
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping 
    and spatial analysis.
There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
A common interface to specifying clustering models, in the
    same style as 'parsnip'. Creates unified interface across different
    functions and computational engines.
Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Provides convenience functions for common data
    modification and analysis tasks in communication research. This
    includes functions for univariate and bivariate data analysis, index
    generation and reliability computation, and intercoder reliability
    tests. All functions follow the style and syntax of the tidyverse, and
    are construed to perform their computations on multiple variables at
    once. Functions for univariate and bivariate data analysis comprise
    summary statistics for continuous and categorical variables, as well
    as several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).
Core parts of the C API of R are wrapped in a C++ namespace via a set
 of inline functions giving a tidier representation of the underlying data structures
 and functionality using a header-only implementation without additional dependencies.
The Common Workflow Language <https://www.commonwl.org/> is an
    open standard for describing data analysis workflows. This package takes
    the raw Common Workflow Language workflows encoded in JSON or 'YAML'
    and turns the workflow elements into tidy data frames or lists.
    A graph representation for the workflow can be constructed and visualized
    with the parsed workflow inputs, outputs, and steps. Users can embed the
    visualizations in their 'Shiny' applications, and export them
    as HTML files or static images.
Provides model specifications, tuning parameters for models in 
    'dann' package. Models based on Hastie (1996) 
    <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
Visualize your 'Tidyverse' data analysis pipelines via the 
    'Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.
To make it easy to generate random numbers based upon the underlying stats 
    distribution functions. All data is returned in a tidy and structured
    format making working with the data simple and straight forward. Given that the
    data is returned in a tidy 'tibble' it lends itself to working with the rest of the
    'tidyverse'.
Utils for basic statistical experiments, that can be used for teaching 
    introductory statistics. Each experiment generates a tibble.
    Dice rolls and coin flips are simulated using sample().
    The properties of the dice can be changed, like the number of sides.
    A coin flip is simulated using a two sided dice.
    Experiments can be combined with the pipe-operator.
Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'mds', 'Rtsne'. etc.
Takes objects of class edsurvey.data.frame and converts them to a data.frame within the calling environment of 'dplyr' and 'ggplot2' functions. Additionally, for plotting with 'ggplot2', users can map aesthetics to subject scales and all plausible values will be used. This package supports student level data; to work with school or teacher level data, see '?EdSurvey::getData'.
Unicodes are not friendly to work with, and not all Unicodes are
    Emoji per se, making obtaining Emoji statistics a difficult task. This
    tool can help your experience of working with Emoji as smooth as possible,
    as it has the 'tidyverse' style.
The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.
Provides the function 'tidyformula()', which translates formulas containing 'tidyselect'-style selection helpers. It expands these helpers by evaluating 'dplyr::select()' with the relevant selection helper and a supplied data frame. The package contains methods for traversing abstract syntax trees from Wickham, Hadley (2019) <doi:10.1201/9781351201315>.
A toolkit of tidy data manipulation verbs with 'data.table' as the backend.
  Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 
  'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain.
  This package is an extension of 'data.table'. While enjoying a tidy syntax, 
  it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Tidy syntax for 'data.table', using modification by reference whenever possible.
 This toolkit is designed for big data analysis in high-performance desktop or laptop computers.
 The syntax of the package is similar or identical to 'tidyverse'.
 It is user friendly, memory efficient and time saving. For more information,
 check its ancestor package 'tidyfst'.
Provides functions that compute predictions from Generalised
    Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble.
    These can be plotted with a generic plot()-method that uses 'ggplot2' or
    plotted as any other data frame. The main function is predict_gam().
A toolset that allows you to easily import and tidy data sheets retrieved
    from Gapminder data web tools. It will therefore contribute to reduce the time
    used in data cleaning of Gapminder indicator data sheets as they are very messy.
It interactively or programmatically label points within custom gates on two dimensions <https://github.com/stemangiola/tidygate>. 
    The information is added to your tibble. It is based on the package 'gatepoints' from Wajid Jawaid (who is also author of this package). The code of 'gatepoints' was nto integrated in 'tidygate'. 
    The benefits are (i) in interactive mode you can draw your gates on extensive 'ggplot'-like scatter plots; 
    (ii) you can draw multiple gates; and (iii) you can save your gates and apply the programmatically.
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
An intuitive interface for getting data from geocoding services.
In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.
A graph, while not "tidy" in itself, can be thought of as two
    tidy data frames describing node and edge data respectively.
    'tidygraph' provides an approach to manipulate these two virtual data
    frames using the API defined in the 'dplyr' package, as well as
    provides tidy interfaces to a lot of common graph algorithms.
This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.
Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (n.d.) <arXiv:2004.14497>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and
    <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Construct and analyse indexes in a pipeline tidy workflow.
    'tidyindex' contains modules for transforming variables, aggregating 
    variables across time, reducing data dimension through weighting, and 
    fitting distributions. A manuscript describing the methodology can be 
    found at <https://github.com/huizezhang-sherry/paper-tidyindex>.
Turn complex 'JSON' data into tidy data frames.
Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning based on ongoing and, as yet, unpublished research.
How can we measure how the usage or frequency of some
    feature, such as words, differs across some group or set, such as
    documents? One option is to use the log odds ratio, but the log odds
    ratio alone does not account for sampling variability; we haven't
    counted every feature the same number of times so how do we know which
    differences are meaningful? Enter the weighted log odds, which
    'tidylo' provides an implementation for, using tidy data principles.
    In particular, here we use the method outlined in Monroe, Colaresi,
    and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio
    by a prior. By default, the prior is estimated from the data itself,
    an empirical Bayes approach, but an uninformative prior is also
    available.
Provides feedback about 'dplyr' and 'tidyr' operations.
An interface to the 'mclust' package to easily
    carry out latent profile analysis ("LPA"). Provides functionality to
    estimate commonly-specified models. Follows a tidy approach, in that
    output is in the form of a data frame that can subsequently be
    computed on. Also has functions to interface to the commercial 'MPlus'
    software via the 'MplusAutomation' package.
Framework to run Monte Carlo simulations over a parameter grid. 
             Allows to parallelize the simulations. 
             Generates plots and 'LaTeX' tables
             summarizing the results from the simulation.
The tidy modeling "verse" is a collection of packages for
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Provides the means to convert 'multiqc_data.json' files,
    produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream
    analysis in R. This analysis might involve cohort analysis, quality control visualisation,
    change-point detection, statistical process control, clustering, or any other
    type of quality analysis.
Provides functions for visualising generalised
    additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Tidy tools for 'NetCDF' data sources. Explore the contents of a 
 'NetCDF' source (file or URL) presented as variables organized by grid with a 
 database-like interface. The hyper_filter() interactive function translates the 
 filter value or index expressions to array-slicing form. No data is read until 
 explicitly requested, as a data frame or list of arrays via hyper_tibble() or 
 hyper_array().
Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.
The goal of 'tidyplate' is to help researchers convert different types of
    microplates into tidy dataframes which can be used in data analysis. It accepts xlsx and
    csv files formatted in a specific way as input. It supports all types of 
    standard microplate formats such as 6-well, 12-well, 24-well, 48-well, 96-well, 384-well,
    and, 1536-well plates.
Provides functions such as str_crush(), add_missing_column(), 
  coalesce_data() and drop_na_all() that complement 'tidyverse' functionality
  or functions that provide alternative behaviors such as if_else2()
  and str_detect2().
Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.
Bayesian analysis used here to answer the question: "when
    looking at resampling results, are the differences between models
    'real'?" To answer this, a model can be created were the performance
    statistic is the resampling statistics (e.g. accuracy or RMSE). These
    values are explained by the model types. In doing this, we can get
    parameter estimates for each model's affect on performance and make
    statistical (and practical) comparisons between models. The methods
    included here are similar to Benavoli et al (2017)
    <https://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula in
    'Tidy Eval' code that calculates the predictions.  It works with
    several databases back-ends because it leverages 'dplyr' and 'dbplyr'
    for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(),
    xgb.Booster.complete(), cubist(), and ctree() models.
Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.
Use 'SQL' 'SELECT' statements to query 'R' data
    frames.
The purpose of this package is to access the 
    United States Census Bureau's Quarterly Workforce Indicator data. Additionally, 
    the data will be retrieved in a tidy format for further manipulation with full variable
    descriptions added if desired. Information about the United States Census Bureau's 
    Quarterly Workforce Indicator is available at 
    <https://www.census.gov/data/developers/data-sets/qwi.html>.
Tools to help to create tidy data, where each column is a
    variable, each row is an observation, and each cell contains a single
    value.  'tidyr' contains tools for changing the shape (pivoting) and
    hierarchy (nesting and 'unnesting') of a dataset, turning deeply
    nested lists into rectangular data frames ('rectangling'), and
    extracting values out of string columns. It also includes tools for
    working with missing values (both implicit and explicit).
Helper functions for processing 'REDCap' data in R. 'REDCap' is a 
    web-enabled application for building and managing surveys and databases 
    developed at Vanderbilt University.
Provides 'tidyverse' methods for wrangling
    and analyzing 'Earth Engine' <https://earthengine.google.com/> data. These methods help the user with filtering, 
    joining and summarising 'Earth Engine' image collections.
With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.
Utility to convert text based summary of rule based models to a tidy dataframe (where each row represents a rule) with related metrics such as support, confidence and lift. Rule based models from these packages are supported: 'C5.0', 'rpart' and 'Cubist'.
Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  as well as a number of specialised functions to process presences and pseudoabsences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.
A backend for the selecting functions of the 'tidyverse'.  It
    makes it easy to implement select-like functions in your own packages
    in a way that is consistent with other 'tidyverse' interfaces for
    selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
    Includes functionality to estimate latent class analyses.
It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.
Tidy standardized mean differences ('SMDs'). 'tidysmd' uses
    the 'smd' package to calculate standardized mean differences for
    variables in a data frame, returning the results in a tidy format.
A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.
Save the output of statistical tests in an organized file that can 
  be shared with others or used to report statistics in scientific papers.
Functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.
A tidy interface to 'data.table',
  giving users the speed of 'data.table' while using tidyverse-like syntax.
The 'tidytags' package coordinates the simplicity of collecting tweets 
    over time with a 'Twitter Archiving Google Sheet' (TAGS; <https://tags.hawksey.info/>) 
    and the utility of the 'rtweet' package (<https://docs.ropensci.org/rtweet/>) 
    for processing and preparing additional 'Twitter' metadata. 'tidytags' also 
    introduces functions developed to facilitate systematic yet flexible analyses 
    of data from 'Twitter'.
Extension of the 'tidyverse' for 'SpatRaster' and
    'SpatVector' objects of the 'terra' package. It includes also new
    'geom_' functions that provide a convenient way of visualizing 'terra'
    objects with 'ggplot2'.
Using tidy data principles can make many text mining tasks
    easier, more effective, and consistent with tools already in wide use.
    Much of the infrastructure needed for text mining with tidy data
    frames already exists in packages like 'dplyr', 'broom', 'tidyr', and
    'ggplot2'. In this package, we provide functions and supporting data
    sets to allow conversion of text to and from tidy formats, and to
    switch seamlessly between tidy tools and existing text mining
    packages.
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages.
  This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of
  'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count,
  various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks,
  saving to PDF and PNG from a pipe and various small utilities.
Generate tables, listings, and graphs (TLG) using 'tidyverse.'
  Tables can be created functionally, using a standard TLG process, or by
  specifying table and column metadata to create generic analysis summaries.
  The 'envsetup' package can also be leveraged to create environments for table
  creation.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
'TidyTuesday' is a project by the 'R4DS Online Learning Community' in which they 
  post a weekly dataset onto post a weekly dataset in a public data repository 
  (<https://github.com/rfordatascience/tidytuesday>) for people to
  analyze and visualize. This package provides the tools to easily download this data and the 
  description of the source.
Provides a consistent API to pull United States Department of
    Agriculture census and survey data from the National Agricultural
    Statistics Service (NASS) QuickStats service.
The 'tidyverse' is a set of packages that work in harmony
    because they share common data representations and 'API' design. This
    package is designed to make it easy to install and load multiple
    'tidyverse' packages in a single step. Learn more about the
    'tidyverse' at <https://www.tidyverse.org>.
Query 'Wikidata' API <https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.
Imports non-tabular from Excel files into R.  Exposes cell content,
    position and formatting in a tidy structure for further manipulation.
    Tokenizes Excel formulas.  Supports '.xlsx' and '.xlsm' via the embedded
    'RapidXML' C++ library <https://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
Functions to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors, including native image representation.
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
The R implementation of TIGER. 
    TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters.
    TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared.
    Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.
Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas (Chamberlain and Duquette, 2007, <doi:2014/41271>), barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.
A collection of data sets and functions that are useful in the
    teaching of statistics at an elementary level to students who may have
    little or no previous experience with the command line.  The functions for
    elementary inferential procedures follow a uniform interface for user
    input.  Some of the functions are instructional applets that can only be
    run on the R Studio integrated development environment with package
    'manipulate' installed.  Other instructional applets are Shiny apps
    that may be run locally. In teaching the package is used alongside of
    package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
    dependencies.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of 
    any novel alleles. This information is then used to correct existing V 
    allele calls from among the sample sequences.
    Citations:
    Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>,
    Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.
The functions needed to perform tight clustering Algorithm.
Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>).  The main function is tighten().  The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.
Write modelling results into a database for
    'tigreBrowser', a web-based tool for browsing figures and summary
    data of independent model fits, such as Gaussian process models
    fitted for each gene or other genomic element. The browser is
    available at <https://github.com/PROBIC/tigreBrowser>.
Download TIGER/Line shapefiles from the United States Census Bureau 
    (<https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>) 
    and load into R as 'sf' objects.
Collect  marketing data from TikTok Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
The modern database 'TileDB' introduces a powerful on-disk
 format for multi-modal data based on dimensional arrays. It supports
 dense and sparse arrays, dataframes and key-values stores, cloud
 storage ('S3', 'GCS', 'Azure'), chunked arrays, multiple compression,
 encryption and checksum filters, uses a fully multi-threaded
 implementation, supports parallel I/O, data versioning ('time
 travel'), metadata and groups. It is implemented as an embeddable
 cross-platform C++ library with APIs from several languages, and
 integrations.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
Implements an algorithm for generating maps, known as tile maps,
    in which each region is represented by a single tile of the same shape and
    size. The algorithm was first proposed in "Generating Tile Maps" by Graham
    McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow
    users to generate, plot, and compare square or hexagon tile maps.
Creates geographic map tiles from geospatial map files or 
    non-geographic map tiles from simple image files. This package provides a 
    tile generator function for creating map tile sets for use with packages 
    such as 'leaflet'. In addition to generating map tiles based on a common 
    raster layer source, it also handles the non-geographic edge case, producing 
    map tiles from arbitrary images. These map tiles, which have a 
    non-geographic, simple coordinate reference system (CRS), can also be used 
    with 'leaflet' when applying the simple CRS option. Map tiles can be created 
    from an input file with any of the following extensions: tif, grd and nc for 
    spatial maps and png, jpg and bmp for basic images. This package requires 
    'Python' and the 'gdal' library for 'Python'. 'Windows' users are 
    recommended to install 'OSGeo4W' (<https://trac.osgeo.org/osgeo4w/>) as an 
    easy way to obtain the required 'gdal' support for 'Python'.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.
Provides data frames for forest or tree data structures. You can 
    create forest data structures from data frames and process them based on 
    their hierarchies.
Generate weekly timetables as a ggplot2 layer. Add
    informative timeslots with elements such as title, key-value pairs, or
    colour to reveal trends.
Efficient routines for manipulation of date-time objects while
   accounting for time-zones and daylight saving times. The package includes
   utilities for updating of date-time components (year, month, day etc.),
   modification of time-zones, rounding of date-times, period addition and
   subtraction etc. Parts of the 'CCTZ' source code, released under the Apache
   2.0 License, are included in this package. See
   <https://github.com/google/cctz> for more details.
The 'timeDate' class fulfils the conventions of the ISO 8601 
	standard as well as of the ANSI C and POSIX standards. Beyond
	these standards it provides the "Financial Center" concept
	which allows to handle data records collected in different time 
	zones and mix them up to have always the proper time stamps with 
	respect to your personal financial center, or alternatively to the GMT
	reference time. It can thus also handle time stamps from historical 
	data records from the same time zone, even if the financial 
	centers changed day light saving times at different calendar
	dates.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.
Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits.
  The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes.
  The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes.
  References:
  Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x>
  Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814>
  Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311>
  Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.
Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.
Produce an HTML page containing horizontal strips that symbolize events in a person's lsife. Since this is entirely a visualization, the image <https://barryzee.github.io/henry-timeline/henry.html> will show the basic use to show a timeline of events. The image <https://barryzee.github.io/vermeer/cssOverlay.html> shows how to correlate two timelines of events. A brief description is available at <https://barryzee.github.io/timeLineGraphics_manuscript/golden_age.html>.
An easy tool for plotting annotated timelines, grouped timelines, and exploratory graphics (boxplot/histogram/density plot/scatter plot/line plot). Filter, summarize date data by duration and convert to calendar units.
Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.
Simple definition of time intervals for the current, previous, and next week, month, quarter and year.
A set of fast tidy functions for wrangling, completing and
    summarising date and date-time data. It combines 'tidyverse' syntax
    with the efficiency of 'data.table' and speed of 'collapse'.
Provides a 'timeR' class that makes timing codes easier. One can create 'timeR' objects and use them to record all timings, and extract recordings as data frame for later use.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
    Models for Survival Data', Springer Verlag.  Plus more recent developments.
    Additive survival model, semiparametric proportional odds model, fast
    cumulative residuals, excess risk models and more. Flexible competing risks
    regression including GOF-tests. Two-stage frailty modelling. PLS for the
    additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.
A negative binomial mixed-effects (NBME) model to detect
    nonparallel differential expression(NPDE) genes and parallel differential
    expression(PDE) genes in the time course RNA-seq data.
'S4' classes and various tools for financial time series:
  Basic functions such as scaling and sorting, subsetting,
  mathematical operations and statistical functions.
Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.
Easy visualization, wrangling, and feature engineering of time series data for 
    forecasting and machine learning prediction. Consolidates and extends time series functionality 
    from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.
Computes how the correlation between 2 time-series changes over time.
    To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>.
    It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy).
    An automatic selection procedure for the bandwidth parameter is implemented.
    Alternative kernels can be used (Epanechnikov, box and normal).
    Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps or R markdown documents.
    'timevis' includes an extensive API to manipulate a timeline after creation,
    and supports getting data out of the visualization into R. Based on the
    'vis.js' Timeline JavaScript library.
Estimates time varying regression effects under Cox type models in
    survival data using classification and regression tree. The codes in this package were 
    originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression
    Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315.
    Development of this package was supported by NIH grants AG053983 and AG057707,
    and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442.
    The content is solely the responsibility of the authors and does not necessarily
    represent the official views of the NIH.
    The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).
A problem solving environment (PSE) for fitting separable
    nonlinear models to measurements arising in physics and chemistry
    experiments, as described by Mullen & van Stokkum (2007)
    <doi:10.18637/jss.v018.i03> for its use in fitting time resolved
    spectroscopy data, and as described by Laptenok et al. (2007)
    <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence
    Lifetime Imaging Microscopy (FLIM) data, in the study of Förster
    Resonance Energy Transfer (FRET).  `TIMP` also serves as the
    computation backend for the `GloTarAn` software, a graphical user
    interface for the package, as described in Snellenburg et al. (2012)
    <doi:10.18637/jss.v049.i03>.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
A universal non-uniform random number generator
  for quite arbitrary distributions with piecewise twice
  differentiable densities.
Parsing '(R)Markdown' files with numerous regular expressions can
    be fraught with peril, but it does not have to be this way. Converting
    '(R)Markdown' files to 'XML' using the 'commonmark' package allows
    in-memory editing via of 'markdown' elements via 'XPath' through the
    extensible 'R6' class called 'yarn'. These modified 'XML' representations
    can be written to '(R)Markdown' documents via an 'xslt' stylesheet which
    implements an extended version of 'GitHub'-flavoured 'markdown' so that you
    can tinker to your hearts content.
Instead of nesting function calls, annotate and transform 
    functions using "#." comments.
A 'tufte'-alike style for 'rmarkdown'.
 A modern take on the 'Tufte' design for pdf and html vignettes,
 building on the 'tufte' package with additional contributions
 from the 'knitr' and 'ggtufte' package, and also acknowledging
 the key influence of 'envisioned css'.
Generate a palette of tints, shades or both from a single colour.
Gene Expression Omnibus(GEO) and The Cancer Genome Atlas(TCGA) are common bioinformatics public databases. We integrate the regular analysis and charts for expression data, to analyze and display the data concisely and intuitively.
Adds some functions to help in your coding etiquette.
    'tinycodet' primarily focuses on 4 aspects.
    1) Safer decimal (in)equality testing, safer atomic conversions, and other functions for safer coding.
    2) A new package import system,
    that attempts to combine the benefits of using a package without attaching it,
    with the benefits of attaching a package.
    3) Extending the string manipulation capabilities of the 'stringi' R package.
    4) Reducing repetitive code.
    Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Assign, extract, or remove variable labels from R vectors.
  Lightweight and dependency-free.
Creates useful files and folders for data
  analysis  projects and provides functions to manage data,
  scripts and output files. Also provides a project
  template for 'Rstudio'.
Provides functions to get personal 'Google Scholar'
    profile data from web API and show it in table or figure format.
Snapshots for unit tests using the 'tinytest' framework for R. Includes expectations to test base R and 'ggplot2' plots as well as console output from print().
An R wrapper for the 'Spotify' Web API 
  <https://developer.spotify.com/web-api/>.
Provides a lightweight (zero-dependency) and easy to use 
    unit testing framework. Main features: install tests with 
    the package. Test results are treated as data that can be stored and 
    manipulated. Test files are R scripts interspersed with test commands, that
    can be programmed over. Fully automated build-install-test sequence for 
    packages. Skip tests when not run locally (e.g. on CRAN). Flexible and 
    configurable output printing. Compare computed output with output stored 
    with the package. Run tests in parallel. Extensible by other packages.
    Report side effects.
Unit testing is a solid component of automated CI/CD pipelines. 'tinytest' - a light-weight,
	zero-dependency  alternative to 'testthat' was developed. To be able to integrate 'tinytests' results into common CI/CD
	systems the 'tinytests'-object needs to be converted to JUnit XML format. 'tinytest2JUnit' enables this conversion
	while keeping the zero-dependency nature.
Helper functions to install and maintain the 'LaTeX' distribution
  named 'TinyTeX' (<https://yihui.org/tinytex/>), a lightweight, cross-platform,
  portable, and easy-to-maintain version of 'TeX Live'. This package also
  contains helper functions to compile 'LaTeX' documents, and install missing
  'LaTeX' packages automatically.
Themes for 'ggplot2' are a convenient way to style plots. The 'hrbrthemes' package
 contains a particularly nice one, but brings along a significant tail of dependencies. So this
 (currently experimental) package brings along just the 'theme_ipsum_rc' theme using the 'Roboto'
 'Condensed' font.  Should the font not be installed on your system, see the help in the package
 'hrbrthemes' on how on install 'Roboto Condensed'.
Download geographic shapes from the United States Census Bureau 
    TIGER/Line Shapefiles <https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>.
    Functions support downloading and reading in geographic boundary data.
    All downloads can be set up with a cache to avoid multiple downloads.
    Data is available back to 2000 for most geographies.
Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.
Accurately estimates phase shifts by accounting for period
  changes and for the point in the circadian cycle at which the stimulus occurs.
  See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.
Assists performing tip-dating of phylogenetic trees with BEAST 
    BEAST is a popular software for phylogenetic analysis.
    The package assists the implementation of various phylogenetic tip-
	dating tests using BEAST. It contains two main functions.
	The first one allows preparing date randomization analyses, 
	which assess the temporal signal of a data set. 
	The second function allows performing leave-one-out analyses,
	which test for the consistency between independent calibration sequences
	and allow pinpointing those leading to potential bias.
	The included tutorial provides detailed step-by-step instructions.
	An expanded description of the package can be found in article:
	Rieux, A. and Khatchikian, C.E. (2017), 
	TIPDATINGBEAST: an R package to assist the implementation of phylogenetic
	tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613.
	<onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.
Provides access to the complete Pali Canon, or
    Tipitaka, the canonical scripture for Theravadin Buddhists 
    worldwide. Based on the Chattha Sangayana Tipitaka version
    4 (Vipassana Research Institute, 1990).
Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.
The strength of evidence provided by epidemiological and observational 
            studies is inherently limited by the potential for unmeasured confounding. 
            We focus on three key quantities: the observed bound of the confidence 
            interval closest to the null, the relationship between an unmeasured 
            confounder and the outcome, for example a plausible residual effect 
            size for an unmeasured continuous or binary confounder, and the 
            relationship between an unmeasured confounder and the exposure, 
            for example a realistic mean difference or prevalence difference 
            for this hypothetical confounder between exposure groups. Building 
            on the methods put forth by Cornfield et al. (1959), Bross (1966), 
            Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), 
            Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), 
            VanderWeele & Ding (2017), and Ding & VanderWeele (2016), 
            we can use these quantities to assess how an unmeasured confounder
            may tip our result to insignificance.
Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.
It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process.
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include Baker and King 
  (2010) <doi:10.1111/j.2041-210X.2009.00007.x>, King and Baker (2010) 
  <doi:10.1899/09-144.1>, and Baker and King (2013) <doi:10.1899/12-142.1>.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.
Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Facilitate the management of data from knowledge
   resources that are frequently used alone or together
   in research environments.
   In 'TKCat', knowledge resources are manipulated as modeled database (MDB)
   objects. These objects provide access to the data tables along with a general
   description of the resource and a detail data model documenting the
   tables, their fields and their relationships.
   These MDBs are then gathered in catalogs that can be easily
   explored an shared.
   Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and
   create new catalogs suited for specific needs.
A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 'tkImgR' read and write different image formats (PPM/PGM, PNG and GIF) using the standard 'Tcl/Tk' distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the 'tkImg' package for 'Tcl/Tk'.
Simple mechanism for placing R graphics in a Tk widget.
Implements inverse and augmented inverse probability weighted 
    estimators for common treatment effect parameters at an interim analysis 
    with time-lagged outcome that may not be available for all enrolled subjects.  
    Produces estimators, standard errors, and information that can be used to 
    compute stopping boundaries using software that assumes that the 
    estimators/test statistics have independent increments. 
    Tsiatis, A. A. and Davidian, M., (2022) <arXiv:2204.10739> .
Implements a semiparametric estimator for the odds ratio model with
    censored, time-lagged, ordered categorical outcome in a randomized clinical
    trial that incorporates baseline and time-dependent information. 
    Tsiatis, A. A. and Davidian, M. (2021) <arXiv:2106.15559>.
Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm 
    is a major building block of the T-Rex selector (see R package 'trex').
    The package is based on the papers Machkour, Muma, and Palomar (2021) <arXiv:2110.06048>, Efron, Hastie, Johnstone, 
    and Tibshirani (2004) <doi:10.1214/009053604000000067>, and Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>.
An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
Gives a number of functions to aid common data 
    analysis processes and reporting statistical results in an 'RMarkdown' file. 
    Data analysis functions combine multiple base R functions used to describe 
    simple bivariate relationships into a single, easy to use function. 
    Reporting functions will return character strings to report p-values, 
    confidence intervals, and hypothesis test and regression results. Strings 
    will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' 
    document. The package also provides a wrapper for the CreateTableOne() 
    function in the 'tableone' package to make the results knit-able.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Calculates empirical TL-moments (trimmed L-moments) of arbitrary 
    order and trimming, and converts them to distribution parameters.
Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing	relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. 
	References used for this package:
	Foley, James, Andries van Dam, Steven Feiner, and John Hughes. 
	"Computer Graphics: Principle and Practice". Addison-Wesley 
	Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1);
	Genz, A., "Numerical computation of multivariate
	normal probabilities," Journal of Computational and
	Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low
    Rank Covariance Structures for Computing High-Dimensional Normal and Student-
    t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) 
    <doi:10.1007/s11222-020-09978-y>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: 
    Computing High-Dimensional Multivariate Normal and Student-t Probabilities 
    with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 
    1-25 (2022) <doi:10.18637/jss.v101.i04>.
Functions for point and interval estimation in
	     error-in-variables models via total least squares or
	     generalized total least squares method. See Golub and Van
	     Loan (1980) <doi:10.1137/0717073>, Gleser (1981)
	     <https://www.jstor.org/stable/2240867>, Ivan Markovsky
	     and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for
	     more information.
A framework for text mining applications within R.
This package provides a tm Source to create corpora from
  a corpus prepared in the format used by the Alceste application (i.e.
  a single text file with inline meta-data). It is able to import both
  text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining 
             in a distributed way. The package provides a convenient interface for
             handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
  articles exported from the 'Europresse' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
Provides a 'tm' Source to create corpora from
  articles exported from the Dow Jones 'Factiva' content provider as
  XML or HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author, subject,
  geographical coverage, company, industry, and various
  provider-specific fields).
Enhances 'koRpus' text object classes and methods to also support large
          corpora. Hierarchical ordering of corpus texts into arbitrary categories will
          be preserved. Provided classes and methods also improve the ability of using
          the 'koRpus' package together with the 'tm' package. To ask for help, report
          bugs, suggest feature improvements, or discuss the global development of the
          package, please subscribe to the koRpus-dev mailing list
          (<https://korpusml.reaktanz.de>).
Provides a 'tm' Source to create corpora from
  articles exported from the 'LexisNexis' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
  Note that the file format is highly unstable: there is no warranty
  that this package will work for your corpus, and you may have
  to adjust the code to adapt it to your particular format.
A plug-in for the tm text mining framework providing mail handling
  functionality.
Useful functions to connect to 'TM1' <https://www.ibm.com/uk-en/products/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
With this tool, a user should be able to quickly implement
    complex random effect models through simple C++ templates. The package combines
    'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector
    library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain
    an efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through 'BLAS' and parallel user templates.
Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.
A Text mining toolkit for Chinese, which includes facilities for 
    Chinese string processing, Chinese NLP supporting, encoding detecting and 
    converting. Moreover, it provides some functions to support 'tm' package 
    in Chinese.
Provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <arxiv:2209.15045>, Barnhill et al. (2023) <arxiv:2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <arXiv:2306.08796>, Yoshida et al. (2022) <arXiv:2206.04206>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis. See Zyla et al (2019)
  <doi:10.1093/bioinformatics/btz447>.
Define general templates with tags that can be replaced by content depending on arguments and objects to modify the final output of the document.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Sensitivity analysis using the trimmed means estimator.
Provides conditional maximum likelihood (CML) item parameter estimation of sequential as well as cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) as well as probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2021, <doi:10.31234/osf.io/ew27f>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for the simulation of several kinds of multistage designs.
Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <arXiv:2108.04731>.
Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong.    Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed.     Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
  Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows 
  truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
  transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. 
  Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
  for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Simulation of random vectors from truncated multivariate normal 
  and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.
Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.
Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.
Performing the hypothesis tests for the two
    sample problem based on order statistics and power comparisons. 
    Calculate the test statistic, density, distribution function, 
    quantile function, random number generation and others.
Provides access to the Taxonomic Name Resolution Service <https://github.com/ojalaquellueva/tnrsapi> through R.  The user supplies plant taxonomic names and the package returns resolved taxonomic names along with information on decisions.  Optionally, the package can also be used to parse taxonomic names.
Create interactive tables, calendars and charts with 'TOAST UI' <https://ui.toast.com/> libraries to
  integrate in 'shiny' applications or 'rmarkdown' 'HTML' documents.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).
This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Use the <https://toggl.com> time tracker api through R.
Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by  in Algeri and van Dyk (2018) <arXiv:1803.03858>.
Interfaces with the 'Hugging Face' tokenizers library to provide implementations
  of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm 
  <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both 
  training new vocabularies and tokenizing texts.
Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.
Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.
Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.
Email Finder R Client Library.
        Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet.
        Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name.
        Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.
Transcriptome visualization from sample trait statistics to gene expression analysis. Six categories include "Samples Statistics", "Traits Analysis", "Differential Expression Analysis", "Advanced Analysis", "GO and KEGG Enrichment", "Tables Operations", with complete sample data.
Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.
Includes functions for mapping named lists to function arguments, random strings,
  pasting and combining rows together across columns, etc.
Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in "Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm" by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).
Uplift modeling aims at predicting the causal effect of an action such as a marketing campaign on a particular individual. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) variable selection, iv) parameters estimation and, v) model validation. For more details, see <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis.  This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. A function for log-ratio principal component analysis with condition number computations has been added to the package.
Tools to calculate stability indices with parametric,
 non-parametric and probabilistic approaches. The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. 
 Function "table_stability" offers the summary table of all stability indices in this package. 
 This R package toolStability is part of the main publication: 
 Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. 
 Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG/tree/V1.0.0>. 
 Sample dataset in this package is derived from another publication:  
 Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. 
 For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. 
 Indices used in this package are from: 
 Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. 
 Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. 
 Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. 
 Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. 
 Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. 
 Lin CS, Binns MR (1988) <https://cdnsciencepub.com/doi/abs/10.4141/cjps88-018>. 
 Nassar R, Hühn M (1987). 
 Pinthus MJ (1973) <doi:10.1007/BF00021563>. 
 Römer T (1917). 
 Shukla GK (1972). 
 Wricke G (1962).
An R wrapper for using 'TooManyCells', a command line program for clustering, visualizing, and quantifying cell clade relationships. See <https://gregoryschwartz.github.io/too-many-cells/> for more details.
Language specific cardinal to ordinal number conversion.
Several datasets which describe the chef contestants in Top Chef, 
    the challenges that they compete in, and the results of those challenges. 
    This data is useful for practicing data wrangling, graphing, and analyzing 
    how each season of Top Chef played out.
The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>).  The authors published an implementation of their method as an R script (two different versions; also available in this package).  This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation.  It also provides a small number of bug fixes to the original code.
Data collected on movement behavior is often in the form of time-
    stamped latitude/longitude coordinates sampled from the underlying movement
    behavior. These data can be compressed into a set of segments via the Top-
    Down Time Ratio Segmentation method described in Meratnia and de By (2004)
    <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, 
    can both reduce the size of the data as well as provide corrective smoothing
    mechanisms to help reduce the impact of measurement error.  This is an
    improvement on the well-known Douglas-Peucker algorithm for segmentation
    that operates not on the basis of perpendicular distances. Top-Down Time
    Ratio segmentation allows for disparate sampling time intervals by
    calculating the distance between locations and segments with respect to
    time. Provided a trajectory with timestamps, tdtr() returns a set of straight-
    line segments that can represent the full trajectory. McCool, Lugtig,
    and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as
    implemented here in more detail.
Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for 
    Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
    For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), 
    pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.
Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.
Find topics in texts which are semantically embedded using techniques like word2vec or Glove. 
    This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic.
    The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <arXiv:1907.04907>.
Provides implementation of the "Topic SCORE" algorithm that is
	     proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
	     step is optimized through the usage of svds() function in 'RSpectra'
	     package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
	     error measure in the word-topic matrix A, and an algorithm for
	     recovering the topic-document matrix W given A and D based on
	     quadratic programming.
		The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package 'TopKLists' <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.
A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from <https://www.gurobi.com/>).
Set of hydrological functions including an R
        implementation of the hydrological model TOPMODEL, which is
        based on the 1995 FORTRAN version by Keith Beven. From version
        0.7.0, the package is put into maintenance mode.
A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) <doi:10.1111/j.1365-294X.2009.04338.x>), weighted topographic paths (Zhan et al. (1993) <doi:10.1007/3-540-57207-4_29>), and topographic least cost paths (Wang and Summers (2010) <doi:10.1111/j.1365-294X.2009.04465.x>). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques). The package is
             based on the method described in Massa M.S., Chiogna M., Romualdi
             C. (2010) <doi:10.1186/1752-0509-4-121>.
Flexible and ergonomic topological sorting implementation for
    R. Supports a variety of input data encoding (lists of edges or
    adjacency matrices, graphs edge direction), stable sort variants as
    well as cycle detection with detailed diagnosis.
A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.
Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.
The goal of tor (to-R) is to help you to import
    multiple files from a single directory at once, and to do so as
    quickly, flexibly, and simply as possible.
Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.
Provides access to datasets, models and processing
    facilities for deep learning in audio.
Provides datasets in a format that can be easily consumed by torch 'dataloaders'.
  Handles data downloading from multiple sources, caching and pre-processing so
  users can focus only on their model implementations.
Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
Provides access to datasets, models and preprocessing
    facilities for deep learning with images. Integrates seamlessly
    with the 'torch' package and it's 'API' borrows heavily from
    'PyTorch' vision package.
Implements additional operators for computer vision models, including
    operators necessary for image segmentation and object detection deep learning
    models.
Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.
Draws tornado plots for model sensitivity to univariate changes.  Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package.  Also draws variable importance plots.
A framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.
Operators and functions provided by base R sometimes lack some
    features found in other programming languages, such as the ability to
    concatenate strings using + or to repeat strings using *. This package aims
    at providing such functionality without breaking existing code, i.e., only
    statements, that would throw errors in pure base R are patched.
Base R sometimes requires verbose statements for simple,
    often recurring tasks, such as printing text without trailing
    space, ending with newline. This package aims at providing
    shorthands for such tasks.
A general framework of two directional simultaneous inference
    is provided for high-dimensional as well as the fixed dimensional models with manifest
    variable or latent variable structure, such as high-dimensional mean models, high-
    dimensional sparse regression models, and high-dimensional latent factors models.
    It is making the simultaneous inference on a set of parameters from two directions,
    one is testing whether the estimated zero parameters indeed are zero and the other is
    testing whether there exists zero in the parameter set of non-zero. More details can be 
    referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.
The goal of 'tosr' is to create the Tree of Science from 
             Web of Science (WoS) and Scopus data. It can read files 
             from both sources at the same time. More information 
             can be found in Valencia-Hernández (2020) 
             <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.
Two one-sided tests (TOST) procedure to test equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations. Allows you to specify equivalence bounds in raw scale units or in terms of effect sizes. See: Lakens (2017) <doi:10.1177/1948550617697177>.
Download summary files from Census Bureau <https://www2.census.gov/> 
    and extract data, in particular high resolution data at 
    block, block group, and tract level, from decennial census and 
    American Community Survey 1-year and 5-year estimates.
Measures the degree of balance for a given phylogenetic tree by 
  calculating the Total Cophenetic Index.
  Reference: A. Mir, F. Rossello, L. A. Rotger (2013).
  A new balance index for phylogenetic trees.
  Math. Biosci. 241, 125-136 <doi:10.1016/j.mbs.2012.10.005>.
Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).
R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://www.hcup-us.ahrq.gov>
    and AHRQ (Agency for Healthcare Research and Quality)
    <https://www.ahrq.gov>.  It currently contains functions for mapping ICD-9
    codes to the AHRQ comorbidity measures and translating ICD-9
    (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General
    Equivalence Mappings) from CMS (Centers for Medicare and Medicaid
    Services).
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
Non-imputational method for handling missing values in 
   a prediction context, meaning that not only are there missing
   values in the training dataset, but also some values may be missing
   in future cases to be predicted. Based on the notion of regression
   averaging (Matloff (2017, ISBN: 9781498710916)).
Computation of stopping boundaries for a single-arm trial using a
    Bayesian criterion; i.e., for each m<=n (n= total patient number of the 
    trial) the smallest number of observed toxicities is calculated
    leading to the termination of the trial/accrual according to the specified 
    criteria. The probabilities of stopping the trial/accrual at and up until 
    (resp.) the m-th patient (m<=n) is also calculated. This design is more 
    conservative than the frequentist approach (using Clopper Pearson CIs)
    which might be preferred as it concerns safety.See also Aamot et.al.(2010) 
    "Continuous monitoring of toxicity in clinical trials - simulating the risk 
    of stopping prematurely" <doi:10.5414/cpp48476>.
Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays.
Enables users to build 'ToxPi' prioritization models and provides 
  functionality within the grid framework for plotting ToxPi graphs.
  'toxpiR' allows for more customization than the 'ToxPi GUI' 
  (<https://toxpi.org>) and integration into existing workflows for greater 
  ease-of-use, reproducibility, and transparency.
  toxpiR package behaves nearly identically to the GUI; the package 
  documentation includes notes about all differences.
  The vignettes download example files from 
  <https://github.com/ToxPi/ToxPi-example-files>.
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>, see also Balboa and de Una-Alvarez (2018) <doi:10.18637/jss.v083.i10>, are included.
Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the 
  Tissue-Adjusted Pathway Analysis of cancer (TPAC) method.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc.
Constraint-based causal discovery using the PC algorithm while
    accounting for a partial node ordering, for example a partial temporal ordering
    when the data were collected in different waves of a cohort study.
    Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>  
    provide a guide how to use tpc to analyse cohort data.
A threshold partial correlation approach to selecting 
             important variables in linear models of L. and others (2017) at
             <doi:10.5705/ss.202015.0473>, and in partial linear models
             of L. and others (2018) at <doi:10.1016/j.jmva.2018.06.005>. 
             This package also extends the PC-simple algorithm of
             B. and others (2010) at <doi:10.1093/biomet/asq008> to 
             partial linear models.
Tools to calculate trait probability density functions (TPD) at
    any scale (e.g. populations, species, communities). TPD functions are used to compute
    several indices of functional diversity, as well as its partition across scales.
    These indices constitute a unified framework that incorporates the underlying
    probabilistic nature of trait distributions into uni- or multidimensional
    functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for
    further information.
We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in  Kyoto Encyclopedia of Genes and Genomes Database.
             We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.
A bioinformatics tool for the estimation of the tumor purity from sequencing data. 
    It uses the set of putative clonal somatic single nucleotide variants within copy number neutral
    segments to call tumor cellularity.
Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book 'Chakraborty' 'DP' (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.
Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable <doi:10.1016/j.crmeth.2022.100227>.
A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.
Estimation of transition probabilities for the
	illness-death model and or the three-state progressive model.
Provide data generation and estimation tools for the truncated positive normal (tpn)
             model discussed in Gomez, Olmos, Varela and Bolfarine (2018) 
             <doi:10.1007/s11766-018-3354-x>, the slash tpn distribution discussed in Gomez, 
             Gallardo and Santoro (2021) <doi:10.3390/sym13112164>, the bimodal tpn distribution 
             discussed in Gomez et al. (2022) <doi:10.3390/sym14040665> and the flexible tpn model.
Regression models for temporal process responses with
        time-varying coefficient.
Enables the acquisition of Korean financial market data, 
            designed to integrate seamlessly with the 'tidyquant' package.
Plant ecologists often need to collect "traits" data
    about plant species which are often scattered among various
    databases: TR8 contains a set of tools which take care of
    automatically retrieving some of those functional traits data
    for plant species from publicly available databases (Biolflor,
    The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, Catminat, BROT,
    PLANTS, Jepson Flora Project).
    The TR8 name, inspired by "car plates" jokes, was chosen since
    it both reminds of the main object of the package and is
    extremely short to type.
Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in  C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.
Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.
Better looking call stacks after an error.
'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'Tracer' (<https://github.com/beast-dev/tracer/>) is a GUI tool 
  to parse and analyze the files generated by 'BEAST2'.
  This package provides a way to parse and analyze 'BEAST2' input
  files without active user input, but using
  R function calls instead.
Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.
Functions for preparing and analyzing animal tracking data, 
    with the intention of identifying areas which are potentially important at 
    the population level and therefore of conservation interest. Areas identified 
    using this package may be checked against global or regionally-defined criteria,
    such as those set by the Key Biodiversity Area program. The method
    published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.
Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in  evolutionary and ecological studies.
Data frame class for storing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.
Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors’ contributions, the final document can be downloaded and rendered locally.
Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Provides an integrated user interface and workflow for
             the analysis of running, cycling and swimming data from GPS-enabled
             tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides only one way for disturbance reconstruction from tree-ring data.
Access 'Open Trade Statistics' API from R to download
    international trade data.
Contains performance analysis metrics of track records including entropy-based
            correlation and dynamic beta based on the Kalman filter. The normalized sample entropy method
            has been implemented which produces accurate entropy estimation even on smaller datasets while for
            the dynamic beta calculation the Kalman filter methodology has been utilized.
            On a separate stream, trades from the five major assets classes and also
            functionality to use pricing curves, rating tables, CSAs and add-on tables. The
            implementation follows an object oriented logic whereby each trade inherits from
            more abstract classes while also the curves/tables are objects. Furthermore, odds calculators
            and P&L back-testing functionality has been implemented for the most widely used betting/trading
            strategies including martingale, DAlembert, Labouchere and Fibonacci. Some basic functionality about climate risk 
            was also added in the latest version.
Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.
Estimation, selection and comparison of several families of
 transformations. The families of transformations included in the package are 
 the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), 
 Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), 
 Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), 
 gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), 
 Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>),
 Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), 
 Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), 
 Reciprocal and Yeo-Johnson. The package simplifies to 
 compare linear models with untransformed and transformed dependent variable 
 as well as linear models where the dependent variable is transformed with 
 different transformations. Furthermore, the package employs maximum likelihood 
 approaches, moments optimization and divergence minimization to estimate the optimal 
 transformation parameter.
Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes 
       methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>, 
       ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>, 
       Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>,
       Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>, 
       Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, 
       Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.
The goal of 'trainR' is to provide a simple interface to the 
    National Rail Enquiries (NRE) systems. There are few data feeds 
    available, the simplest of them is Darwin, which provides real-time 
    arrival and departure predictions, platform numbers, delay estimates, 
    schedule changes and cancellations. Other data feeds provide historical 
    data, Historic Service Performance (HSP), and much more. 'trainR' 
    simplifies the data retrieval, so that the users can focus on their 
    analyses. For more details visit 
    <https://www.nationalrail.co.uk/46391.aspx>.
A collection of commonly used tools for animal movement and other tracking 
 data. Variously distance, angle, bearing, distance-to, bearing-to and speed are 
 provided for geographic data that can be used directly or within 'tidyverse' 
 syntax. Distances and bearings are calculated using modern geodesic methods as 
 provided by Charles F. F. Karney (2013) <doi:10.1007/s00190-012-0578-z> 
 via the 'geodist' and 'geosphere' packages.
Species trait data from many different sources, including
    sequence data from 'NCBI' (<https://www.ncbi.nlm.nih.gov/>),
    plant trait data from 'BETYdb', data from 'EOL' 'Traitbank',
    'Birdlife' International, and more.
Calculates trait moments from trait and community data using the 
  methods developed in Maitner et al (2021) <doi:10.22541/au.162196147.76797968/v1>.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating a number of measures of change capturing various features of the trajectories; (2) using a Principal Component Analysis based dimension reduction algorithm to select a subset of measures and (3) using the K-means clustering algorithm to identify clusters of trajectories.
Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.
Find the probability and the trajectory of longitudinal mixture model. Methods used in the package refer to Nagin (2005),
      <doi:10.4159/9780674041318>,
      Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press.
      and Noel (2022),
      <https://orbilu.uni.lu/>,
      thesis.
A toolbox to assist with statistical analysis of animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. Some support is provided for 3-dimensional trajectories. 
    McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt'. Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
  transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
  <DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models 
  (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
  <DOI:10.1080/00031305.2023.2203177>) can be fitted as well.
Extends invariant causal prediction (Peters et al., 2016, 
    <doi:10.1111/rssb.12167>) to generalized linear and transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291>).
    The methodology is described in Kook et al. (2023,
    <doi:10.48550/arXiv.2309.12833>).
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as the relative frequency plot and methods for polyadic data.
Likelihood-based estimation of mixed-effects transformation models
    using the Template Model Builder ('TMB', Kristensen et al., 2016)
    <doi:10.18637/jss.v070.i05>. The technical details of transformation models
    are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood
    contributions of exact, randomly censored (left, right, interval) and
    truncated observations are supported. The random effects are assumed to be
    normally distributed on the scale of the transformation function, the
    marginal likelihood is evaluated using the Laplace approximation, and the
    gradients are calculated with automatic differentiation (Tamasi & Hothorn,
    2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be
    defined using 'mgcv'.
Partially penalized versions of specific transformation models
    implemented in package 'mlt'. Available models include a fully parametric version
    of the Cox model, other parametric survival models (Weibull, etc.), models for
    binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
    linear models, and continuous outcome logistic regression. Hyperparameter tuning
    is facilitated through model-based optimization functionalities from package 'mlrMBO'.
    The accompanying vignette describes the methodology used in 'tramnet' in detail.
    Transformation models and model-based optimization are described in
    Hothorn et al. (2019) <doi:10.1111/sjos.12291> and
    Bischl et al. (2016) <arxiv:1703.03373>, respectively.
Implements a trampoline algorithm for R that let's users write recursive functions
    that get around R's stack call limitations, enabling theoretically infinite recursion. The
    algorithm is based around generator function as implemented in the 'coro' package, and is
    based almost completely on the 'trampoline' module from Python <https://gitlab.com/ferreum/trampoline>.
Matching terminal restriction fragment length
        polymorphism ('TRFLP') profiles between unknown samples and a
        database of known samples.  'TRAMPR' facilitates analysis of
        many unknown profiles at once, and provides tools for working
        directly with electrophoresis output through to generating
        summaries suitable for community analyses with R's rich set of
        statistical functions.  'TRAMPR' also resolves the issues of
        multiple 'TRFLP' profiles within a species, and shared 'TRFLP'
        profiles across species.
Greedy optimal subset selection for transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess
    algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to
    models from packages 'tram' and 'cotram'.
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP  <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.
Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000)  <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980)  <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.
Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.
Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).
Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.
In order to smoothly animate the transformation of polygons and 
    paths, many aspects needs to be taken into account, such as differing number 
    of control points, changing center of rotation, etc. The 'transformr' 
    package provides an extensive framework for manipulating the shapes of 
    polygons and paths and can be seen as the spatial brother to the 'tweenr' 
    package.
A geomorphology-based hydrological modelling for transferring
    streamflow measurements from gauged to ungauged catchments. Inverse
    modelling enables to estimate net rainfall from streamflow measurements 
    following Boudhraâ et al. (2018) <doi:10.1080/02626667.2018.1425801>. 
    Resulting net rainfall is then estimated on the ungauged catchments 
    by spatial interpolation in order to finally simulate streamflow 
    following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.
Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.
Allows translating with formatted string literals, grouped entries, and configurable system of plurals. Have a separate file for each locale and use inheritance to handle dialect differences.
To make the semiparametric transformation models easier to apply in real studies, 
    we introduce this R package, in which the MLE in transformation models via 
    an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> 
    and adaptive lasso method in transformation models proposed by Liu XX, Zeng 
    D(2013) <doi:10.1093/biomet/ast029> are implemented.  
    C++ functions are used to compute complex loops. The coefficient vector and 
    cumulative baseline hazard function can be estimated, along with the 
    corresponding standard errors and P values.
Treatment and visualization of membrane (selective) transport 
    data. Transport profiles involving up to three species are produced as
    publication-ready plots and several membrane performance parameters 
    (e.g. separation factors as defined in Koros et al. (1996) 
    <doi:10.1351/pac199668071479> and non-linear regression parameters
    for the equations described in Rodriguez de San Miguel et al. (2014)
    <doi:10.1016/j.jhazmat.2014.03.052>) can be obtained. Many widely used 
    experimental setups (e.g. membrane physical aging) can be easily studied
    through the package's graphical representations.
A unified estimation procedure for the analysis of right censored data using linear transformation models. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.
Implementation of two transportation problem algorithms. 
             1. North West Corner Method 
             2. Minimum Cost Method or Least cost method.
             For more technical details about the algorithms please refer below URLs.
             <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
             <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.
Inference of transmission tree from a dated phylogeny. 
    Includes methods to simulate and analyse outbreaks.
    The methodology is described in
    Didelot et al. (2014) <doi:10.1093/molbev/msu121>,
    Didelot et al. (2017) <doi:10.1093/molbev/msw275>.
A set of vectorised functions to calculate medical equations used in transplantation, 
    focused mainly on transplantation of abdominal organs. These functions include donor and recipient
    risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for 
    quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), 
    a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a 
    transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. 
    All functions are designed to work with either US or international units.
    References for the equations are provided in the vignettes and function documentation.
To visualize the gene structure with multiple isoforms better, I developed this package to draw different transcript structures easily.
Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.
Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette, install 'fwelnet' from 'GitHub' <https://github.com/kjytay/fwelnet>.
Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. 
             Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. 
             Reference: 
             Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <arXiv:2211.09391>.
A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.
Univariate time series operations that follow an opinionated design. 
    The main principle of 'transx' is to keep the number of observations the same. 
    Operations that reduce this number have to fill the observations gap.
The trapezoid package provides 'dtrapezoid', 'ptrapezoid', 'qtrapezoid',
    and 'rtrapezoid' functions for the trapezoidal distribution.
Pacote para a analise de experimentos com um ou dois fatores com
             testemunhas adicionais conduzidos no delineamento inteiramente 
             casualizado ou em blocos casualizados. 
             "Package for the analysis of one or two-way experiments with 
             additional controls conducted in a completely randomized design 
             or in a randomized block design".
Simple trustworthy utility functions to use TauDEM 
    (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface.
    This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) 
    and MPI (Message Passing Interface) for different operating systems.
    Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, 
    and it provides wrapper commands for calling TauDEM methods from R.
'Travel Time' API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using ‘as the crow flies’ distance.
  Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.
Contains R functions for simulating and estimating integer-valued trawl processes as 
    described in the article Veraart (2019),"Modeling, simulation and inference for
    multivariate time series of counts using trawl processes", Journal of Multivariate Analysis,
    169, pages 110-129,
    <doi:10.1016/j.jmva.2018.08.012> and for 
    simulating random vectors from the bivariate negative binomial and the bi- and trivariate 
    logarithmic series distributions.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
Computes treatment patterns within a given cohort using the Observational
    Medical Outcomes Partnership (OMOP) common data model (CDM). As described
    in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations.  Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.
A modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions.
An R re-implementation of the 'treeinterpreter' package on PyPI
        <https://pypi.org/project/treeinterpreter/>. Each prediction can be
        decomposed as 'prediction = bias + feature_1_contribution + ... +
        feature_n_contribution'. This decomposition is then used to calculate
        the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using
        out-of-bag samples (MDI-oob) feature importance measures based on the
        work of Li et al. (2019) <arXiv:1906.10845>.
Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk.
Interface to the API for 'TreeBASE' <http://treebase.org>
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found 
    in Schmidt, Erdfelder, & Heck (2022) <DOI:10.31234/osf.io/gh8md>.
Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology,
    see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.
An implementation that combines trait data and a phylogenetic tree (or trees) into a 
    single object of class treedata.table. The resulting object can be easily 
    manipulated to simultaneously change the trait- and tree-level sampling.
    Currently implemented functions allow users to use a 'data.table' syntax when
    performing operations on the trait dataset within the treedata.table object.
Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. The methods are described in Volz, E.M. and S.D.W. Frost (2017) <doi:10.1093/ve/vex025>.
The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) <doi:10.1093/treephys/14.7-8-9.1069>. Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) <doi:10.1016/j.ufug.2006.01.007>. US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213.
Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).
Perform test to detect differences in structure between families of
             trees. The method is based on cophenetic distances and aggregated
             Student's tests.
Testing for trajectory presence and heterogeneity on
 multivariate data. Two statistical methods (Tenha & Song 2022) 
 <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
 test quantifies the statistical evidence for trajectory presence. The
 subset specificity measure summarizes pattern heterogeneity using the
 minimum subtree cover. There is no user tunable parameters for either
 method. Examples are included to illustrate how to use the methods on 
 single-cell data for studying gene and pathway expression dynamics and
 pathway expression specificity.
Implements measures of tree similarity, including 
  information-based generalized Robinson-Foulds distances
  (Phylogenetic Information Distance, Clustering Information Distance,
  Matching Split Information Distance; Smith 2020)
  <doi:10.1093/bioinformatics/btaa614>; 
  Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
  <doi:10.1007/978-3-642-40453-5_13>, 
  including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
  the Matching Split Distance (Bogdanowicz & Giaro 2012)
  <doi:10.1109/TCBB.2011.48>;
  Maximum Agreement Subtree distances;
  the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
  Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. 
  (1996) <doi:10.1007/3-540-61332-3_168>.
  Includes tools for visualizing mappings of tree space (Smith 2022)
  <doi:10.1093/sysbio/syab100>,
  for calculating the median of sets of trees,
  and for computing the information content of trees and splits.
Perform two types of analysis: 1) checking the
    goodness-of-fit of tree models to your single-cell gene expression
    data; and 2) deciding which tree best fits your data.
Creates interpretable decision tree visualizations 
    with the data represented as a heatmap at the tree's leaf nodes.
    'treeheatr' utilizes the customizable 'ggparty' package for 
    drawing decision trees.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
Miscellaneous data sets and functions to accompany Greenwell (2022) 
    "Tree-Based Methods for Statistical Learning in R" <doi:10.1201/9781003089032>.
Provides functionality for creation and comparison of polynomials that uniquely
  describe trees as introduced in Liu (2019, <arXiv:1904.03332>). The core method
  converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials 
  described with coefficient matrices. Additionally, a conversion for rooted binary trees 
  with binary trait labels is also provided. Once the polynomials of trees are calculated 
  there are functions to calculate distances, distance matrices and plot different distance 
  trees from a target tree. Manipulation and conversion to the tree defining polynomials is 
  implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 
  'RcppThread' is used to improve performance converting to polynomials and calculating distances.
The German national forest inventory uses angle count sampling, 
    a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung.
    Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 
    vom Juni 1947` and extended by Grosenbaugh
    (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>)
    as probability proportional to size sampling.
    When plots are located near stand boundaries, their sizes and hence
    their probabilities need to be corrected.
Reconstruct phylogenetic trees from discrete data.
  Inapplicable character states are handled using the algorithm of Brazeau,
  Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083> with the "Morphy"
  library, under equal or implied step weights.
  Contains a "shiny" user interface for interactive tree search and exploration
  of results, including character visualization, rogue taxon detection,
  tree space mapping, and cluster consensus trees (Smith 2022a, b)
  <doi:10.1093/sysbio/syab099>, <doi:10.1093/sysbio/syab100>.
  Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, 
  Successive Approximations (Farris, 1969) <doi:10.2307/2412182>
  and custom optimality criteria are implemented.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist.  Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12917>.
Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. It enables users to cut trees in any orientation, such as rootwardly (from root to tips) and tipwardly (from tips to its root), or allows users to define a specific time interval of interest. It can also be used to create multiple tree pieces of equal temporal width. Moreover, it allows the assessment of novel temporal rates for various phylogenetic indexes, which can be quickly displayed graphically.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a 'shiny' interface which can be started from R using
    treespaceServer(). 
    For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Combine a list of taxa with a phylogeny to generate a starting tree for use in
    total evidence dating analyses.
Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns. The methods are described in Volz, E., Wiuf, C., Grad, Y., Frost, S., Dennis, A., & Didelot, X. (2020) <doi:10.1093/sysbio/syaa009>.
Efficient implementations of functions for the creation, 
  modification and analysis of phylogenetic trees.
  Applications include:
  generation of trees with specified shapes;
  tree rearrangement;
  analysis of tree shape;
  rooting of trees and extraction of subtrees;
  calculation and depiction of split support;
  plotting the position of rogue taxa (Klopfstein & Spasojevic 2019)
  <doi:10.1371/journal.pone.0212942>;
  calculation of ancestor-descendant relationships,
  of 'stemwardness' (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>,
  and of tree balance (Mir et al. 2013) <doi:10.1016/j.mbs.2012.10.005>;
  artificial extinction (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>;
  import and export of trees from Newick, Nexus (Maddison et al. 1997)
  <doi:10.1093/sysbio/46.4.590>,
  and TNT <https://www.lillo.org.ar/phylogeny/tnt/> formats;
  and analysis of splits and cladistic information.
Provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, 
    and additional functions for generating customized palettes that are on theme. The package also offers functions for applying 
    the palettes to plots made using the 'ggplot2' package.
Provides a collection of true type and open type Star Trek-themed fonts.
Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within 'tidyverse' or 'ggplot2' workflows. Low-level functions are also provided for creating new interfaces.
An R client for the Trello API. Supports free-tier features such as
    access to private boards, creating and updating cards and other resources,
    and downloading data in a structured way.
A slightly-opinionated R interface for the 'Tremendous' API (<https://www.tremendous.com/>). 
    In addition to supporting GET and POST requests, 'tremendousr' has, dare I say, 
    tremendously intuitive functions for sending digital rewards and incentives directly from R.
Tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. 'TrenchR' focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) <doi:10.1007/978-1-4612-6024-0> and Campbell and Norman (1988) <isbn:9780387949376>.
The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package includes tests for trend detection
	     (Cox-Stuart Trend Test, Mann-Kendall Trend Test, 
	     (correlated) Hirsch-Slack Test,
             partial Mann-Kendall Trend Test, multivariate (multisite)
	     Mann-Kendall Trend Test, (Seasonal) Sen's slope, 
	     partial Pearson and Spearman correlation trend test),
             change-point detection (Lanzante's test procedures, 
	     Pettitt's test, Buishand Range Test,
	     Buishand U Test, Standard Normal Homogeinity Test),
	     detection of non-randomness (Wallis-Moore Phase Frequency Test,
	     Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test)
	     and the two sample Robust Rank-Order Distributional Test.
Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
  Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>.
  Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. 
  Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.
Provides a coherent interface for evaluating models fit with the
  trending package.  This package is part of the RECON
  (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Provides a coherent interface to multiple modelling tools for
  fitting trends along with a standardised approach for generating confidence
  and prediction intervals.
Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.
Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.
Analysis of annual average ocean water level time series, providing
    improved estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on singular spectrum analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also includes a range of diagnostic tools to inspect
    the components comprising the original time series which enables expert
    interpretation and selection of likely trend components. A wide range of
    screen and plot to file options are available in the package.
Matrix factorization for multivariate time series with both low rank and temporal structures. The procedure is the one proposed by Alquier, P. and Marie, N. Matrix factorization for multivariate time series analysis. Electronic journal of statistics, 13(2), 4346-4366 (2019).
Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) <doi:10.1080/00949655.2018.1482492>.
Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package.
    Use four spaces when indenting paragraphs within the Description.
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.
Performs fast variable selection in high-dimensional settings while controlling the 
    false discovery rate (FDR) at a user-defined target level. The package is based on the paper 
    Machkour, Muma, and Palomar (2021) <arXiv:2110.06048>.
Tri-hierarchical incomplete block design is defined as an arrangement of v  treatments each replicated r  times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.
Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2023) <https://gitlab.com/AgroSimi/triact_manuscript>.
Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.
Implements target trial emulation methods to apply randomized
    clinical trial design and analysis in an observational setting. Using
    marginal structural models, it can estimate intention-to-treat and
    per-protocol effects in emulated trials using electronic health
    records. A description and application of the method can be found in
    Danaei et al (2013) <doi:10.1177/0962280211403603>.
A collection of clinical trial designs and methods, implemented in 
    'rstan' and R, including: the Continual Reassessment Method by O'Quigley et 
    al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) 
    <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of
    Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the 
    Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and
    more. We provide functions to aid model-fitting and analysis. 
    The 'rstan' implementations may also serve as a cookbook to anyone looking 
    to extend or embellish these models. We hope that this package encourages 
    the use of Bayesian methods in clinical trials. There is a preponderance of 
    early phase trial designs because this is where Bayesian methods are used 
    most. If there is a method you would like implemented, please get in touch.
Functions and Examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.  Also includes maximum likelihood estimation of parameters.
Measuring angles between points in a landscape is much easier
    than measuring distances. When the location of three points is known the
    position of the observer can be determined based solely on the angles between
    these points as seen by the observer. This task (known as triangulation)
    however requires onerous calculations - these calculations are automated by this
    package.
A collection of high-performance functions for the triangular
    distribution that consists of the probability density function, cumulative
    distribution function, quantile function, random variate generator, moment
    generating function, characteristic function, and expected shortfall
    function. References: Samuel Kotz, Johan Ren Van Dorp (2004)
    <doi:10.1142/5720> and Acerbi, Carlo and Tasche, Dirk. (2002)
    <doi:10.1111/1468-0300.00091>.
Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( 
    Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual
    parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria 
    (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared
    (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. 
    See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.
Processing and analysis of pathomics, omics and other medical datasets.
    'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, 
    plotting and many other methods for exploratory analysis of high-dimensional datasets.
A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.
Propensity score matching for non-binary treatments.
Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.
Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total.
A lightweight toolkit to reduce the size of a list object. The
    object is minimized by recursively removing elements from the object 
    one-by-one. The process is constrained by a reference function call
    specified by the user, where the target object is given as an argument. 
    The procedure will not allow elements to be removed from the object, that 
    will cause results from the function call to diverge from the function 
    call with the original object.
Provides various commonly-used response time trimming
    methods, including the recursive / moving-criterion methods reported by
    Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
    the package will return trimmed data ready for inferential testing.
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
Access and manipulate spatial tracking data, with straightforward 
 coercion from and to other formats. Filter for speed and create time spent 
 maps from tracking data. There are coercion methods to convert between 'trip'
 and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 
 'spatstat'. Trip objects can be created from raw or grouped data frames, and 
 from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, 
 Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
Data handling and estimation functions for animal movement
    estimation from archival or satellite tags. Helper functions are included
    for making image summaries binned by time interval from Markov Chain Monte Carlo
    simulations.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
Attain excellent covariate balance by matching two treated units
    and one control unit or vice versa within strata. Using such triples, as
    opposed to also allowing pairs of treated and control units, 
    allows easier interpretation of the two possible 
    weights of observations and better insensitivity to unmeasured bias in the test
    statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio
    allows for the match to be feasible in more situations.
    The 'rrelaxiv' package, which provides an alternative solver for the underlying 
    network flow problems, carries an academic license and is not available on CRAN, but
    may be downloaded from 'GitHub' at <https://github.com/josherrickson/rrelaxiv/>.
    The 'Gurobi' commercial optimization software is required to use the two functions
    [infsentrip()] and [triplesIP()]. These functions are not essential
    to the main purpose of this package. A free academic license can be obtained at 
    <https://www.gurobi.com/features/academic-named-user-license/>. 
    The 'gurobi' R package can then be installed following 
    the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Tools for exploring effects of correlated features in predictive 
    models. The predict_triplot() function delivers instance-level explanations 
    that calculate the importance of the groups of explanatory variables. The 
    model_triplot() function delivers data-level explanations. The generic plot 
    function visualises in a concise way importance of hierarchical groups of 
    predictors. All of the the tools are model agnostic, therefore works for any
    predictive machine learning models. Find more details in Biecek (2018) 
    <arXiv:1806.08915>.
Uses read counts for biallelic single nucleotide polymorphisms (SNPs)
    to compare the likelihoods for the observed read counts given that a sample is 
    either diploid or triploid. It allows parameters to be specified to account for 
    sequencing error rates and allelic bias. For details of the algorithm, please see
    Delomas (2019) <doi:10.1111/1755-0998.13073>.
Overall predictive performance is measured by a mean score
    (or loss), which decomposes into miscalibration, discrimination, and
    uncertainty components. The main focus is visualization of these distinct
    and complementary aspects in joint displays.
    See Dimitriadis, Gneiting, Jordan, Vogel (2023) <arXiv:2301.10803>.
Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure.
Functions to estimate temporally regularized matrix factorizations (TRMF) for forecasting and imputing values in short but high-dimensional time series. Uses regularized alternating least squares to compute the factorization, allows for several types of constraints on matrix factors and natively handles weighted and missing data.
Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.
These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries.
It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 <doi: 10.1007/BFb0017135>.
Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.
The main function of the package aims to update 'lmer()'/'glmer()' models depending on their warnings, so trying to avoid convergence and singularity problems.
Provides methods for generating .dat files for use with the 'AMPL' 
    software using spatial data, particularly rasters. It includes support for 
    various spatial data formats and different problem types. By automating the 
    process of generating 'AMPL' datasets, this package can help streamline 
    optimization workflows and make it easier to solve complex optimization 
    problems. The methods implemented in this package are described in detail
    in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).
Gives the required 2^n treatment combinations in a 2^n symmetric factorial experiment in their respective standard order.
Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).
Implements Bayesian methods, described in
    Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the
    proportion of liars in coin flip-style experiments, where subjects
    report a random outcome and are paid for reporting a "good" outcome.
Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
    The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess." <https://dl.acm.org/doi/10.5555/2981562.2981605>.
Implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the 
   same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.
Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. The output is a plot with the effect size of risk variants in the Y axis, and the allele frequency spectrum in the X axis. Corte et al (2023) <doi:10.1101/2023.04.21.23288923>.
Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets 'simu', 'simu_c1', and 'simu_c2', which are used to illustrate the usage of the functions in this package.
    Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.
A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Handles truncated members from the exponential family of
	probability distributions. Contains functions such as rtruncnorm() and
	dtruncpois(), which are truncated versions of rnorm() and dpois() from the
	stats package that also offer richer output containing, for example, the
	distribution parameters. It also provides functions to retrieve the original
	distribution parameters from a truncated sample by maximum-likelihood
	estimation.
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Finds the posterior modes for the mean and standard deviation for a
  truncated normal distribution with one or two known truncation points.
  The method used extends Bayesian methods for parameter estimation for a singly
  truncated normal distribution under the Jeffreys prior (see Zhou X,
  Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated
  data with applications to operational risk measurement".
  <doi:10.1080/14697688.2012.752103>). This package additionally allows for a
  doubly truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
Does local optimization using two derivatives and trust regions.
    Guaranteed to converge to local minimum of objective function.
Trusted Timestamps (tts) are created by incorporating a hash of a file or dataset into a transaction on the decentralized blockchain (Stellar network). 
    The package makes use of a free service provided by <https://stellarapi.io>.
Trust region algorithm for nonlinear optimization. Efficient when
    the Hessian of the objective function is sparse (i.e., relatively few nonzero
    cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.
Advanced tryCatch() and try() functions for better error handling
             (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Contains R functions and datasets detailed in the book
        "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
The best ANN structure for time series data analysis is a demanding need in the present era.
    This package will find the best-fitted ANN model based on forecasting accuracy.
    The optimum size of the hidden layers was also determined after determining the number of lags to be included.
    This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978>  "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Time series toolkit with identical behavior for all
  time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo',
  'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.
Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.
Two stage curvature identification with machine learning for causal 
    inference in settings when instrumental variable regression is not suitable
    because of potentially invalid instrumental variables. Based on Guo and 
    Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: 
    Causal Inference with Possibly Invalid Instrumental Variables"
    <arXiv:2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2023) 
    "TSCI: two stage curvature identification for causal inference with 
    invalid instruments" <arXiv:2304.00513>.
A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Functions for the analysis of time series using copula models.  
    The package is based on methodology described in the following references.
    McNeil, A.J. (2021) <doi:10.3390/risks9010014>,
    Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>,
    Bladt, M., & McNeil, A.J. (2021) <arXiv:2107.00960>.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
A set of functions to implement Time Series Cointegrated System (TSCS)
    spatial interpolation and relevant data visualization.
A terribly-simple data base for numeric
  time series, written purely in R, so no external
  database-software is needed. Series are stored in
  plain-text files (the most-portable and enduring file
  type) in CSV format. Timestamps are encoded using R's
  native numeric representation for 'Date'/'POSIXct',
  which makes them fast to parse, but keeps them
  accessible with other software. The package provides
  tools for saving and updating series in this
  standardised format, for retrieving and joining data,
  for summarising files and directories, and for
  coercing series from and to other data types (such as
  'zoo' series).
ARIMA-model-based decomposition of quarterly and 
 monthly time series data.
 The methodology is developed and described, among others, in 
 Burman (1980) <DOI:10.2307/2982132> and 
 Hillmer and Tiao (1982) <DOI:10.2307/2287770>.
RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.
We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.
Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), <doi:10.32614/RJ-2016-058>.
Implements a method for identifying subgroups with superior response relative to the overall sample.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics.  On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.
Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.
Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application.
    A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ).
    In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ).
    After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule.
    'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.
Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        <http://www.mpipks-dresden.mpg.de/~tisean/>.
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Multiscale multifractal analysis (MMA) (Gierałtowski et al.,
    2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method,
    designed to describe scaling properties of fluctuations within the signal
    analyzed. The main result of this procedure is the so called Hurst surface
    h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation
    scaling exponent) on the multifractal parameter q and the scale of observation s
    (data window width).
Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average models. 
 It provides functions and methods for: TARMA model fitting and forecasting, tests for threshold effects, unit-root tests based on TARMA models.
Tools to perform some descriptive data analysis for assets. Manage the portfolio and capital of assets. It also downloads and organizes data from the Tehran Stock Exchange (TSE).
Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
A general regression neural network (GRNN) is a variant of a
    Radial Basis Function Network characterized by a fast single-pass learning.
    'tsfgrnn' allows you to forecast time series using a GRNN model Francisco 
    Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco
    Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting
    horizon is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. You can consult and plot how the
    prediction was done. It is also possible to assess the forecasting accuracy
    of the model using rolling origin evaluation.
Allows forecasting time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.
Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.
Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained.
To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).
Two-stage procedure compares hazard rate functions,
    which may or may not cross each other.
Provides a 'tbl_ts' class (the 'tsibble') for
    temporal data in an data- and model-oriented format. The 'tsibble'
    provides tools to easily manipulate and analyse temporal data, such as
    filling in time gaps and aggregating over calendar periods.
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
A shared tsibble data easily communicates between
    htmlwidgets on both client and server sides, powered by 'crosstalk'. A
    shiny module is provided to visually explore periodic/aperiodic
    temporal patterns.
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).
An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <doi:10.1371/journal.pone.0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <doi:10.1111/1467-9876.00187>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).
Tools for specifying time series regression models.
A toolkit implementing the Matrix Profile concept
    that was created by CS-UCR
    <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
Detection of outliers in time series following the 
    Chen and Liu (1993) <DOI:10.2307/2290724> procedure. 
    Innovational outliers, additive outliers, level shifts, 
    temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
    salesperson problem (also traveling salesman problem; TSP).
    The package provides some simple algorithms and
    an interface to the Concorde TSP solver and its implementation of the
    Chained-Lin-Kernighan heuristic. The code for Concorde
    itself is not included in the package and has to be obtained separately.
    Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
Prediction intervals for ARIMA and structural time series
    models using importance sampling approach with uninformative priors for model
    parameters, leading to more accurate coverage probabilities in frequentist
    sense. Instead of sampling the future observations and hidden states of the
    state space representation of the model, only model parameters are sampled,
    and the method is based solving the equations corresponding to the conditional
    coverage probability of the prediction intervals. This makes method relatively
    fast compared to for example MCMC methods, and standard errors of prediction
    limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-driven models (either statistical or machine learning) demand tuning. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data pre-processing that complies with the chosen model. Many time series frameworks have features to build and tune models. The package differs as it provides a framework that seamlessly integrates tuning data pre-processing activities with the building of models. The package provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, tuning, modeling, prediction, and accuracy assessment. More information is available at Izau et al. <doi:10.5753/sbbd.2022.224330>.
Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
    Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson),
    and forecasting accuracy measures are implemented.
Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. The estimator incorporates an instrumental variable and therefore can recover causal estimand in the presence of unmeasured confounding under some assumptions. A.Ying, R. Xu and J. Murphy. (2019) <doi:10.1002/sim.8071>.
Methods for handling the missing values outliers are introduced in
    this package. The recognized missing values and outliers are replaced 
    using a model-based approach. The model may consist of both autoregressive
    components and external regressors. The methods work robust and efficient,
    and they are fully tunable. The primary motivation for writing the package
    was preprocessing of the energy systems data, e.g. power plant production
    time series, but the package could be used with any time series data. For 
    details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.
Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion  of temperature as an additional climate variable. This allows for land  degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2,  climate change and climate variability the details of which are in press in Burrell et al., (2020).  The version under active development and additional example scripts showing  how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.
Execution of various time series models and choosing the best one
    either by a specific error metric or by picking the best one by majority vote.
    The models are based on the "forecast" package, written by Prof. Rob Hyndman.
Flexible simulation of time series using time
    series components, including seasonal, calendar and outlier effects.
    Algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.
It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). 
          This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness.
          ---
          Guerrero, V.M (2007)  <DOI:10.1016/j.spl.2007.03.006>.
          Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (2020, ISBN: 978-0-367-18733-0).
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.
Plot official statistics' time series conveniently: automatic
    legends, highlight windows, stacked bar chars with positive and
    negative contributions, sum-as-line option, two y-axes with automatic
    horizontal grids that fit both axes and other popular chart types.
    'tstools' comes with a plethora of defaults to let you plot without
    setting an abundance of parameters first, but gives you the
    flexibility to tweak the defaults. In addition to charts, 'tstools'
    provides a super fast, 'data.table' backed time series I/O that allows
    the user to export / import long format, wide format and transposed
    wide format data to various file types.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Fitting tree-structured varying coefficient models (Berger et al. (2019), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.
Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). 'tsvio' is an R wrapper to 'C' code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.
An 'RStudio' add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using 'plotly' package.
Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott.  It is helpful for data analysis and for time series instruction.
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
Wrapper of datatable widget, allowing display of data.tree objects.
            All arguments of the data.tree become columns and each node is a row.
            Adds column with buttons allowing folding and unfolding the levels.
This interface was created to develop a standard procedure 
 to analyse temporal trend in the framework of the OSPAR convention.
 The analysis process run through 4 successive steps : 1) manipulate your data, 2)
 select the parameters you want to analyse, 3) build your regulated 
 time series, 4) perform diagnosis and analysis and 5) read the results. 
 Statistical analysis call other package function such as Kendall tests
 or cusum() function.
Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.
U.S. Department of the Treasury, Alcohol and Tobacco Tax and
    Trade Bureau (TTB) collects data and reports on monthly beer
    industry production and operations. This data package includes
    a collection of 10 years (2006 - 2015) worth of data on materials
    used at U.S. breweries in pounds reported by the Brewer's Report
    of Operations and the Quarterly Brewer's Report of Operations
    forms, ready for data analysis. This package also includes historical
    tax rates on distilled spirits, wine, beer, champagne, and tobacco
    products as individual data sets.
The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.
Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.
The 'tinytest' package offers a light-weight zero-dependency unit-testing
 framework to which this package adds support of the 'diffobj' package for 'diff'-style
 comparison of R objects.
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Likelihood ratio and maximum likelihood statistics are provided 
     that can be used as alternatives to p-values Colquhoun (2017) 
     <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. 
     together with degrees of freedom. For the function 'tTOlr', the argument
     'twoSided' has the default 'twoSided = TRUE'.
A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
		Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
		areas. These techniques provide estimates of material mechanical properties
		(such as moduli) at different temperatures in a wider range of time. This
		package provides the Time-Temperature superposition Master Curve at a referred
		temperature by the three methods: the two wider used methods, Arrhenius based
		methods and WLF, and the newer methodology based on derivatives procedure.
		The Master Curve is smoothed by B-splines basis. The package output is composed
		of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
		data fitted using B-splines with bootstrap confidence intervals.
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by 'tidySingleCellExperiment' and 'tidyseurat'.
Create structured, formatted HTML tables of in a flexible and
    convenient way.
Implementation of Time to Target plot based on the work 
             of Ribeiro and Rosseti (2015) <DOI:10.1007/s11590-014-0760-8>, 
             that describe a numerical method that gives the probability of 
             an algorithm A finds a solution at least as good as a given 
             target value in smaller computation time than algorithm B.
Get comments posted on YouTube videos, information on how many 
    times a video has been liked, search for videos with particular content, and 
    much more. You can also scrape captions from a few videos. To learn more about
    the YouTube API, see <https://developers.google.com/youtube/v3/>.
Get statistics and reports from YouTube. To learn more about
    the YouTube Analytics and Reporting API, see <https://developers.google.com/youtube/reporting/>.
Performs Three-Mode Principal Components Analysis,
    which carries out Tucker Models.
Helper functions for 'TUFLOW FV' models. Current functionality includes reading in and plotting
    output POINTS files and generating initial conditions based on point observations.
Provides R Markdown output formats to use Tufte styles for
    PDF and HTML output.
Custom template and output formats for use with rmarkdown. Produce
    Edward Tufte-style handouts in html formats with full support for rmarkdown
    features
Perform the conventional Tukey test from formula, 
             lm, aov, aovlist and lmer objects.
Tukey regions are polytopes in the Euclidean space, viz. 
    upper-level sets of the Tukey depth function on given data. The bordering 
    hyperplanes of a Tukey region are computed as well as its vertices, facets, 
    centroid, and volume. In addition, the Tukey median set, which is the 
    non-empty Tukey region having highest depth level, and its barycenter 
    (= Tukey median) are calculated. Tukey regions are visualized in dimension 
    two and three. For details see Liu, Mosler, and Mozharovskyi 
    (2019, <doi:10.1080/10618600.2018.1546595>). See file LICENSE.note for 
    additional license information.
Provides wrapper functions to the multiple marginal model function mmm() of package 'multcomp' to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.
Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
The ability to tune models is important. 'tune' contains
    functions and classes to be used in conjunction with other
    'tidymodels' packages for finding reasonable values of
    hyper-parameters in models, pre-processing methods, and
    post-processing steps.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).
Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ...
        Also contains functions ported from the 'rastamat' 'Matlab' package.
Tuning random forest with one line. The package is mainly based on the packages 'ranger' and 'mlrMBO'.
Functions to find all matches or non-matches, orphans, and
    duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM and MM algorithms. It can be used to accelerate any smooth,
        linearly convergent acceleration scheme.  A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("turboEM").
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    <http://en.wikipedia.org/wiki/Turtle_graphics>.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
Helps the R users to get data from 'Tushare Pro'<https://tushare.pro>.
    'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area.
    We support financial data such as stock price, financial report statements and digital coins data.
Helper functions for creating, editing, and testing tutorials
    created with the 'learnr' package. Provides a simple method for allowing
    students to download their answers to tutorial questions. For examples
    of its use, see the 'r4ds.tutorials' and 'all.primer.tutorials' packages.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model can also be run in a semi-distributed fashion and with dual representation of soil layer.
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Create a time-varying dataset using features, exposure, and
    look back specifications.
Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time.  These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>.  We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Ter{\"a}svirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.
Probability mass (d), distribution (p), quantile (q), and random 
    number generating (r and rt) functions for the time-varying right-truncated 
    geometric (tvgeom) distribution. Also provided are functions to calculate the first 
    and second central moments of the distribution. The tvgeom distribution
    is similar to the geometric distribution, but the probability 
    of success is allowed to vary at each time step, and there are a limited 
    number of trials. This distribution is essentially a Markov chain, and it
    is useful for modeling Markov chain systems with a set 
    number of time steps.
Functions for managing cashflows and interest rate curves.
Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money.
    Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417).
    Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219).
    Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313).
    S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015).
Provides functions for estimating mediation effects that vary over 
   time as described in Cai X, Coffman DL, Piper ME, Li R. 
   Estimation and inference for the mediation effect in a time-varying mediation model. 
   BMC Med Res Methodol. 2022;22(1):1-12.
This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).
Provides tools for denoising noisy signal and images via
    Total Variation Regularization. Reducing the total variation of
    the given signal is known to remove spurious detail while preserving
    essential structural details. For the seminal work on the topic,
    see Rudin et al (1992) <doi:10.1016/0167-2789(92)90242-F>.
Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.
Contains various 'ggplot2' themes and color palettes based on TV shows 
    such as 'Game of Thrones', 'Brooklyn Nine-Nine', 'Avatar: The Last Airbender',
    'Spongebob Squarepants', and more.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.
Provides functions for propensity score
        estimation and weighting for continuous exposures as described in Zhu, Y., 
        Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for
        estimating generalized propensity scores with continuous treatments.
        Journal of Causal Inference, 3(1), 25-40. <doi:10.1515/jci-2014-0022>.
Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.
Provides functions for gradient boosted weighting to correct linkage 
    failures or generate comparison groups.
Implements Time-Weighted Dynamic Time Warping (TWDTW), 
    a measure for quantifying time series similarity. The TWDTW algorithm, 
    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
    Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional 
    time series of various resolutions. It is particularly suitable for comparing 
    time series with seasonality for environmental and ecological data analysis, 
    covering domains such as remote sensing imagery, climate data, hydrology, 
    and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, 
    efficient 'Fortran' routines for TWDTW calculations, flexible time weighting 
    definitions, as well as utilities for time series preprocessing and visualization.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion  (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that can be used as basis for animation. Furthermore 
    it adds a number of vectorized interpolaters for common R data 
    types such as numeric, date and colour.
An interface to 'twitter-text', a 'JavaScript' library which
   is responsible for determining the length/validity of a tweet and 
   identifying/linking any URLs or special tags (e.g. mentions or
   hashtags) which may be present.
Generates a game of 2048 that can be played in the
    console.  Supports grids of arbitrary sizes, undoing the last move,
    and resuming a game that was exited during the current session.
An R wrapper around the API of TheyWorkForYou, a parliamentary 
    monitoring site that scrapes and repackages Hansard (the UK's parliamentary 
    record) and augments it with information from the Register of Members' 
    Interests, election results, and voting records to provide a unified 
    source of information about UK legislators and their activities. See 
    <http://www.theyworkforyou.com> for details.
The Twilio web service provides an API for computer programs
    to interact with telephony. The included functions wrap the SMS and MMS 
    portions of Twilio's API, allowing users to send and receive text messages
    from R. See <https://www.twilio.com/docs/> for more information.
An efficient algorithm for data twinning. This work is supported by U.S. National Science 
	Foundation grants DMREF-1921873 and CMMI-1921646.
Tailored for topic modeling with tweets and fit for visualization tasks in R.
    Collect, pre-process and analyze the contents of tweets using 
    LDA and structural topic models (STM). Comes with visualizing capabilities like tweet and hashtag maps 
    and built-in support for 'LDAvis'.
Provides an interface to the Twitter web API.
Collect  your data on digital marketing campaigns from Twitter Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides an integration to the 'metatrader 5'. 
    The functionalities carry out automated trading using
    sentiment indexes computed from 'twitter' and/or 'stockwits'. 
    The sentiment indexes are based on the ph.d. dissertation 
    "Essays on Economic Forecasting Models" (Godeiro,2018) <https://repositorio.ufpb.br/jspui/handle/123456789/15198>
    The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage.
Include the Twitter status widgets in HTML pages created
  using R markdown. The package uses the Twitter javascript APIs to
  embed in your document Twitter cards associated to specific statuses.
  The main targets are regular HTML pages or dashboards.
Implementation of a Bayesian two-way latent structure model for integrative genomic clustering.  The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation.  A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler.  The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density.  Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources.  Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU.  See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.
The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming 
    taxons in Dutch Watermanagement. This package makes it easier to use the 
    TWN-list for ecological analyses. It  consists of two parts. First it makes the 
    TWN-list itself available in R. Second, it has a few functions that make it 
    easy to perform some basic and often recurring tasks for checking and consulting
    taxonomic data from the TWN-list.
A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and
  confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Density, distribution function, quantile function, 
    and random generation function, maximum likelihood estimation (MLE), 
    penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM),
    and median rank estimation (MEDRANK) for the two-parameter exponential distribution.
    MLE and PMLE are based on Mengjie Zheng (2013)<https://scse.d.umn.edu/sites/scse.d.umn.edu/files/mengjie-thesis_masters-1.pdf>.
    QM is based on Entisar Elgmati and Nadia Gregni (2016)<doi:10.5539/ijsp.v5n5p12>.
    MEDRANK is based on Matthew Reid (2022)<doi:10.5281/ZENODO.3938000>.
Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics <doi:10.1111/j.1541-0420.2008.01046.x>.
Fast randomization based two sample tests. 
  Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. 
  The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.
For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.
Implements a variety of non-parametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Carries out analyses of two-way tables with one observation per cell, together with graphical displays
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.
Estimates the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions.
    Clément de Chaisemartin, Xavier D'Haultfœuille (2020) <DOI: 10.1257/aer.20181169>.
Performs two-way tests in independent groups designs. These are two-way ANOVA, two-way ANOVA under heteroscedasticity: parametric bootstrap based generalized test and generalized pivotal quantity based generalized test (Ananda et al., 2022) <doi:10.1080/03610926.2022.2059682>, two-way ANOVA for medians, trimmed means, M-estimators (Wilcox, 2011; ISBN:978-0-12-386983-8). The package performs descriptive statistics and graphical approaches. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots. All 'twowaytests' functions are designed for two-way layout.
A collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.
Efficient estimation of the population-level causal effects of
    stochastic interventions on a continuous-valued exposure. Both one-step and
    targeted minimum loss estimators are implemented for the counterfactual mean
    value of an outcome of interest under an additive modified treatment policy,
    a stochastic intervention that may depend on the natural value of the
    exposure. To accommodate settings with outcome-dependent two-phase
    sampling, procedures incorporating inverse probability of censoring
    weighting are provided to facilitate the construction of inefficient and
    efficient one-step and targeted minimum loss estimators.  The causal
    parameter and its estimation were first described by Díaz and van der Laan
    (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
    estimation procedure and its application to data from two-phase sampling
    designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
    and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be
    enhanced through the Super Learner ensemble model in 'sl3', available for
    download from GitHub using 'remotes::install_github("tlverse/sl3")'.
Provides functions to produce rudimentary ascii graphics
        directly in the terminal window. Provides a basic plotting
        function (and equivalents of curve, density, acf and barplot)
        as well as boxplot and image functions.
This queue is a data structure that lets
  parallel processes send and receive messages,
  and it can help coordinate the work
  of complicated parallel tasks.
  Processes can push new messages to the queue,
  pop old messages, and obtain a
  log of all the messages ever pushed. File locking
  preserves the integrity of the data even when
  multiple processes access the queue simultaneously.
A type system for R. It supports setting variable types in a script or the body of a function, so variables can't be assigned illegal values. Moreover it supports setting argument and return types for functions.
Type hints are special comments within a function body indicating the intended nature of the function's arguments in terms of data types, dimensions and permitted values. The actual parameters with which the function is called are evaluated against these type hint comments at run-time.
Provides a simple type annotation for R that is usable in scripts,
    in the R console and in packages. It is intended as a convention to allow other
    packages to use the type information to provide error checking,
    automatic documentation or optimizations.
The 'R' language includes a set of defined types, but the language
    itself is "absurdly dynamic" (Turcotte & Vitek (2019)
    <doi:10.1145/3340670.3342426>), and lacks any way to specify which types are
    expected by any expression. The 'typetracer' package enables code to be
    traced to extract detailed information on the properties of parameters
    passed to 'R' functions. 'typetracer' can trace individual functions or
    entire packages.
Provides an up-to-date copy of the Internet Assigned Numbers
    Authority (IANA) Time Zone Database. It is updated periodically to
    reflect changes made by political bodies to time zone boundaries, UTC
    offsets, and daylight saving time rules. Additionally, this package
    provides a C++ interface for working with the 'date' library. 'date'
    provides comprehensive support for working with dates and date-times,
    which this package exposes to make it easier for other R packages to
    utilize. Headers are provided for calendar specific calculations,
    along with a limited interface for time zone manipulations.
Download and compile any version of the
  IANA Time Zone Database (also known as Olson database)
  and make it current in your R session.
  Beware: on Windows 'Cygwin' is required!
Contains functions for calculating under-five child mortality estimates using the Trussell version of the Brass method (United Nations (1990) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf> and United Nations (1983) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf>) as well as applying the cohort-derived methods by Rajaratnam and colleagues (Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) "Measuring Under-Five Mortality: Validation of New Low-Cost Methods" <doi:10.1371/journal.pmed.1000253>).
Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. Tools are provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
The Unmanned Aerial Vehicle Mission Planner provides an easy to use work flow for planning autonomous obstacle avoiding surveys of (almost) ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller as used in the 3DR-Solo. Additionally it contains some useful tools for digitizing and data manipulation.
The framework proposed in Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>, together with an interactive Shiny dashboard. 'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'.
Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
Fit Bayesian hierarchical models of animal abundance and occurrence
    via the 'rstan' package, the R interface to the 'Stan' C++ library.
    Supported models include single-season occupancy, dynamic occupancy, and
    N-mixture abundance models. Covariates on model parameters are specified
    using a formula-based interface similar to package 'unmarked', while also
    allowing for estimation of random slope and intercept terms. References:
    Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>;
    Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Calculates the Urban Centrality Index (UCI) as in Pereira et al., 
             (2013) <doi:10.1111/gean.12002>. The UCI measures the 
             extent to which the spatial organization of a city or region varies 
             from extreme polycentric to extreme monocentric in a continuous 
             scale from 0 to 1. Values closer to 0 indicate more polycentric patterns
             and values closer to 1 indicate a more monocentric urban form.
Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.
Clustering and classification inference for high dimension low sample size (HDLSS)
    data with U-statistics. The package contains implementations of nonparametric statistical
    tests for sample homogeneity, group separation, clustering, and classification of 
    multivariate data. The methods have high statistical power and are tailored for data
    in which the dimension L is much larger than sample size n. See Gabriela B. Cybis,
    Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387>, Marcio 
    Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio 
    Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.
An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.
Comprehensive analysis and forecasting 
             of univariate time series using automatic 
             unobserved components models and algorithms.
             Harvey, AC (1989) <doi:10.1017/CBO9781107049994>.
             Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>.
             Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>.
             Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>.
Changes the column names of the inputted dataset to the correct
    names from the Uniform Crime Report codebook for the "Offenses Known and
    Clearance by Arrest" datasets from 1998-2014.
Provides functions and a Shiny application for downloading,
    analyzing and visualizing datasets from UCSC Xena
    (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public
    databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Download and explore datasets from UCSC Xena data hubs, which
    are a collection of UCSC-hosted public databases such as TCGA, ICGC,
    TARGET, GTEx, CCLE, and others.  Databases are normalized so they can
    be combined, linked, filtered, explored and downloaded.
The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.
This natural language processing toolkit provides language-agnostic
    'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
    parsing' of raw text. Next to text parsing, the package also allows you to train
    annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
    at <https://universaldependencies.org/format.html>. The techniques are explained
    in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
    with UDPipe', available at <doi:10.18653/v1/K17-3009>. 
    The toolkit also contains functionalities for commonly used data manipulations on texts 
    which are enriched with the output of the parser. Namely functionalities and algorithms 
    for collocations, token co-occurrence, document term matrix handling, 
    term frequency inverse document frequency calculations,
    information retrieval metrics (Okapi BM25), handling of multi-word expressions,
    keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) 
    sentiment scoring and semantic similarity analysis.
Provides simple bindings to Unidata's udunits library.
Enables the user to calculate Value at Risk (VaR) and Expected 
    Shortfall (ES) by means of various parametric and semiparametric 
    GARCH-type models. For the latter the estimation of the nonparametric scale
    function is carried out by means of a data-driven smoothing approach. Model
    quality, in terms of forecasting VaR and ES, can be assessed by means of 
    various backtesting methods such as the traffic light test for VaR and a 
    newly developed traffic light test for ES. The approaches implemented in 
    this package are described in e.g. Feng Y., Beran J., Letmathe S. and 
    Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as 
    Letmathe S., Feng Y. and Uhde A. (2021) 
    <https://ideas.repec.org/p/pdn/ciepap/141.html>.
This is a new version of the 'userfriendlyscience' package,
    which has grown a bit unwieldy. Therefore, distinct functionalities
    are being 'consciously uncoupled' into different packages. This package
    contains the general-purpose tools and utilities (see the
    'behaviorchange' package, the 'rosetta' package, and the
    soon-to-be-released 'scd' package for other functionality), and
    is the most direct 'successor' of the original 'userfriendlyscience' package.
    For example, this package contains a number of basic functions to create
    higher level plots, such as diamond plots, to easily plot sampling
    distributions, to generate confidence intervals, to plan study sample sizes
    for confidence intervals, and to do some basic operations such as
    (dis)attenuate effect size estimates.
An R API providing easy access to a relational database with macroeconomic, 
             financial and development related time series data for Uganda. 
             Overall more than 5000 series at varying frequency (daily, monthly, 
             quarterly, annual in fiscal or calendar years) can be accessed through 
             the API. The data is provided by the Bank of Uganda, 
             the Ugandan Ministry of Finance, Planning and Economic Development,
             the IMF and the World Bank. The database is being updated once a month.
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR),
    Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, 
    quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. 
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, 
    Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>,
    Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.
Unit-Gompertz density, cumulative distribution, quantile functions and random deviate
   	generation of the unit-Gompertz distribution. In addition, there are a function for fitting the 
   	Generalized Additive Models for Location, Scale and Shape.
Construct a Hidden Markov Model with states learnt by unsupervised classification.
Implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters when outcome is missing not at random (non-ignorable missingness) introduced in Genbaeck, M., Stanghellini, E., de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbaeck, M., Ng, N., Stanghellini, E., de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>; and (ii) double robust and outcome regression estimators of average causal effects (on the treated) with possibly unobserved confounding introduced in Genbaeck, M., de Luna, X. (2018) <doi:10.1111/biom.13001>.
A set of custom 'R' 'Markdown' templates for documents and
   presentations with the University of Illinois at Urbana-Champaign (UIUC)
   color scheme and identity standards.
Functions for converting between UK and US spellings of English words.
Process UK Biobank COVID-19 test result data for susceptibility, severity and mortality analyses, perform potential non-genetic COVID-19 risk factor and co-morbidity association tests. Wang et al. (2021) <doi:10.5281/zenodo.5174381>.
Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.
A suite of utilities for working with the UK Biobank 
    <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) 
    metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. 
    Includes functions for extracting biomarkers from decoded UK Biobank field 
    data, removing unwanted technical variation from biomarker concentrations,
    computing an extended set of lipid, fatty acid, and cholesterol fractions, 
    and for re-deriving composite biomarkers and ratios after adjusting data 
    for unwanted biological variation. For further details on methods see
    Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.
A set of tools to create a UK Biobank <http://www.ukbiobank.ac.uk/> dataset from a UKB fileset (.tab, .r, .html),
    visualize primary demographic data for a sample subset, query ICD diagnoses,
    retrieve genetic metadata, read and write standard file formats for genetic analyses.
Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 12.1. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Proposal of the extreme rank plot for extreme value analysis: with an emphasis on flood frequency studies", Hammond (2019, <doi:10.2166/nh.2019.157>). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "Improving the FEH statistical procedures for flood frequency estimation", Environment Agency (2008, ISBN: 978 1 84432 920 5). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Wallingford HydroSolutions, (2016, <http://software.hydrosolutions.co.uk/winfap4/Urban-Adjustment-Procedure-Technical-Note.pdf>). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).
Allows users to access live UK energy market information via various APIs.
A time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.
Access data from Land Registry Open Data
    <http://landregistry.data.gov.uk/> through 'SPARQL' queries. 'uklr'
    supports the house price index, transaction and price paid data.
Downloads data from the 'UK Police' public data API, 
    the full docs of which are available at <https://data.police.uk/docs/>. 
    Includes data on police forces and police force areas, crime reports, 
    and the use of stop-and-search powers.
Universally unique identifiers ('UUIDs') can be suboptimal
        for many uses-cases because they aren't the most character
        efficient way of encoding 128 bits of randomness; v1/v2 versions
        are impractical in many environments, as they require access to a
        unique, stable MAC address; v3/v5 versions require a unique seed
        and produce randomly distributed IDs, which can cause
        fragmentation in many data structures; v4 provides no other
        information than randomness which can cause fragmentation in many
        data structures. 'ULIDs' (<https://github.com/ulid/spec>) have
        128-bit compatibility with 'UUID', 1.21e+24 unique 'ULIDs' per
        millisecond, are lexicographically sortable, canonically encoded
        as a 26 character string, as opposed to the 36 character 'UUID',
        use Crockford's 'base32' for better efficiency and readability (5
        bits per character), are case insensitive, have no special
        characters (i.e. are 'URL' safe) and have a onotonic sort order
        (correctly detects and handles the same millisecond).
A tool to define rare biosphere. 'ulrb' solves the problem of the 
    definition of rarity by replacing human decision with an unsupervised machine 
    learning algorithm (partitioning around medoids, or k-medoids). This algorithm 
    works for any type of microbiome data, provided there is an abundance score 
    for each phylogenetic unit. For validation of this method to several kinds of 
    molecular data and environments, please see Pascoal et al, 2023 (in preparation).
    Preliminary data suggest this method also works well for non-microbiome data, 
    if there is a species abundance table.
A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.
Uniform manifold approximation and projection is a technique
    for dimension reduction. The algorithm was described by McInnes and
    Healy (2018) in <arXiv:1802.03426>. This package provides an interface
    for two implementations. One is written from scratch, including components
    for nearest-neighbor search and for embedding. The second implementation
    is a wrapper for 'python' package 'umap-learn' (requires separate
    installation, see vignette for more details).
By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'.
A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc.
Tools for analyzing sequencing data containing unique
    molecular identifiers generated by 'UMIErrorCorrect'
    (<https://github.com/stahlberggroup/umierrorcorrect>).
Calculates one-sample unbiased central moment estimates and 
    two-sample pooled estimates up to 6th order, including estimates of 
    powers and products of central moments. Provides the machinery for 
    obtaining unbiased central moment estimators beyond 6th order by generating
    expressions for expectations of raw sample moments and their powers and 
    products. 
    Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>)
    for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
The Ultimate Microrray Prediction, Reality and Inference
  Engine (UMPIRE) is a package to facilitate the simulation of realistic
  microarray data sets with links to associated outcomes. See Zhang and
  Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the
  ability to simulate realistic mixed-typed clinical data.
Quickly create, run, and report structural equation models, and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
    Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R.
    Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
If a procedure consists of several stages and there are several models that can be selected for each stage,
	uncertainty of the procedure can be decomposed by stages or models.
	This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method.
	Yongdai Kim et al. (2019) <doi:10.1016/j.hydroa.2019.100024> is a related paper which is accessible via the URL below.
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Consensus clustering by the unification of clustering results from multiple datasets using external specifications.
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Implements Minimum Torsion for portfolio diversification as
    described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.
Model data with a suspected clustering structure (either in 
    co-variate space, regression space or both) using a Bayesian product model 
    with a logistic regression likelihood. Observations are represented 
    graphically and clusters are formed through various edge removals or 
    additions. Cluster quality is assessed through the log Bayesian evidence of 
    the overall model, which is estimated using either a Sequential Monte Carlo 
    sampler or a suitable transformation of the Bayesian Information Criterion 
    as a fast approximation of the former. The internal Iterated Batch 
    Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is 
    made available as a free standing function.
Calculate several understandability metrics of BPMN models. BPMN stands for business process modelling notation and is a language for expressing business processes into business process diagrams. Examples of these understandability metrics are: average connector degree, maximum connector degree, sequentiality, cyclicity, diameter, depth, token split, control flow complexity, connector mismatch, connector heterogeneity, separability, structuredness and cross connectivity. See R documentation and paper on metric implementation included in this package for more information concerning the metrics.
Use syntax inspired by the package 'glue' to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.
Versatile method for ungrouping histograms (binned count data) 
 assuming that counts are Poisson distributed and that the underlying sequence 
 on a fine grid to be estimated is smooth. The method is based on the composite 
 link model and estimation is achieved by maximizing a penalized likelihood. 
 Smooth detailed sequences of counts and rates are so estimated from the binned 
 counts. Ungrouping binned data can be desirable for many reasons: Bins can be 
 too coarse to allow for accurate analysis; comparisons can be hindered when 
 different grouping approaches are used in different histograms; and the last 
 interval is often wide and open-ended and, thus, covers a lot of information 
 in the tail area. Age-at-death distributions grouped in age classes and 
 abridged life tables are examples of binned data. Because of modest assumptions, 
 the approach is suitable for many demographic and epidemiological applications. 
 For a detailed description of the method and applications see 
 Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.
A 'ggplot2' theme and color palettes following
    the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.
Verb-like functions to work with messy data, often derived from 
             spreadsheets or parsed PDF tables. Includes functions for unwrapping 
             values broken up across rows, relocating embedded grouping values, 
             and to annotate meaningful formatting in spreadsheet files.
A classification (decision) tree is constructed from survival data with high-dimensional covariates.
 The method is a robust version of the logrank tree, where the variance is stabilized.
 The main function "uni.tree" returns a classification tree for a given survival dataset.
 The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests.
 The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user).
 This tree construction algorithm is proposed by Emura et al. (2021, in review).
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
Data from Unicode 15.1.0 and related utilities.
Most universities use specific color combinations to express their unique brand identity. The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.
Testing whether two discrete variables have a functional
  relationship under null distributions where the two variables are
  statistically independent with fixed marginal counts.
  The fast enumeration algorithm was based on (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>.
Uniform sampling of Directed Acyclic Graphs (DAG) using exact
 enumeration by relating each DAG to a sequence of outpoints (nodes with no
 incoming edges) and then to a composition of integers as suggested by
 Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.
Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).
In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules.
Functions for the creation and manipulation of scenes and objects
    within the 'Unity' '3D' video game engine (<https://unity.com/>). Specific
    focuses include the creation and import of terrain data and 'GameObjects' as
    well as scene management.
Uniform sampling on various geometric shapes, such as
    spheres, ellipsoids, simplices.
Perform L1 or L2 isotonic and unimodal regression on 1D weighted or unweighted input vector and isotonic regression on 2D weighted or unweighted input vector. It also performs L infinity isotonic and unimodal regression on 1D unweighted input vector. Reference: Quentin F. Stout (2008) <doi:10.1016/j.csda.2008.08.005>. Spouge, J., Wan, H. & Wilbur, W.(2003) <doi:10.1023/A:1023901806339>. Q.F. Stout (2013) <doi:10.1007/s00453-012-9628-4>.
Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.
Implementation of "light" stemmers for French, German, Italian, Spanish, Portuguese, Finnish, Swedish. 
  They are based on the same work as the "light" stemmers found in 'SolR' <https://lucene.apache.org/solr/> or 'ElasticSearch' <https://www.elastic.co/fr/products/elasticsearch>. 
  A "light" stemmer consists in removing inflections only for noun and adjectives. 
  Indexing verbs for these languages is not of primary importance compared to nouns and adjectives. 
  The stemming procedure for French is described in (Savoy, 1999) <doi:10.1002/(SICI)1097-4571(1999)50:10%3C944::AID-ASI9%3E3.3.CO;2-H>.
Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
	self-defined penalties on the B-spline coefficients.
Provides the ability to read 'Unisens' data into R. 'Unisens' is a universal data format for multi sensor data.
Any package (subsequently called 'target package') is enabled to provide its users an easily accessible, user-friendly and human readable text file where key=value pairs (used by functions defined in the target package) can be saved. This settings file lives in a location defined by the user of the target package, and its user-defined values remain unchanged even when the author of the target package is introducing or deleting keys, or when the target package is updated or re-installed.
Routines to interact with the Uniswap trading platform and its API <https://uniswap.org>.
  The package contains codebase to interact with the uniswap platform directly from R console, Ability 
  to pull and export data related to the platform and analyse some aspects.
The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.
United is a software tool which can be downloaded at the following
    website <http://www.schroepl.net/pbm/software/united/>. In general, it is
    a virtual manager game for football teams. This package contains helpful
    functions for determining an optimal formation for a virtual match in
    United. E.g. knowing that the opponent has a strong defensive it is
    advisable to beat him in the midfield. Furthermore, this package contains
    functions for computing the optimal usage of hardness in a game.
Simplifies regression tests by comparing objects produced by test
    code with earlier versions of those same objects.  If objects are unchanged
    the tests pass, otherwise execution stops with error details.  If in
    interactive mode, tests can be reviewed through the provided interactive
    environment.
A collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.
Support for measurement units in R vectors, matrices
    and arrays: automatic propagation, conversion, derivation
    and simplification of units; raising errors in case of unit
    incompatibility. Compatible with the POSIXct, Date and difftime 
    classes. Uses the UNIDATA udunits library and unit database for 
    unit compatibility checking and conversion.
    Documentation about 'units' is provided in the paper by Pebesma, Mailund &
    Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
    vignette; see 'citation("units")' for details.
A test to understand the stability of the underlying 
    stochastic data. Helps the user’s understand whether the random variable under 
    consideration is stationary or non-stationary without any manual interpretation 
    of the results. It further ensures to check all the prerequisites and assumptions which 
    are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all
    the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.
Concise TAP <http://testanything.org/> compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Assess essential unidimensionality using external validity
    information  using the procedure proposed by Ferrando & Lorenzo-Seva
    (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing
    differential  and incremental validity, both based on a second-order 
    modelling schema for the general factor.
User-friendly maximum likelihood estimation (Fisher (1921) 
    <doi:10.1098/rsta.1922.0009>) of univariate densities.
Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them.
    The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).
    The details of the indices in this package can be found in:
    C. Alok. (2010) <https://hdl.handle.net/10603/93443>,
    J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>,
    T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>,
    C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>,
    D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>,
    J. C. Dunn (1973) <doi:10.1080/01969727308546046>,
    F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>,
    M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>,
    S. H. Kwon (1998) <doi:10.1049/EL:19981523>,
    S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>,
    G. W. Miligan (1980) <doi:10.1007/BF02293907>,
    M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>,
    M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>,
    S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>,
    A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>,
    Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>,
    N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>,
    N. Wiroonsri, O. Preedasawakul (2023) <arXiv:2308.14785>,
    C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495> and
    X. Xie, G. Beni (1991) <doi:10.1109/34.85677>.
Provides S3 generic methods and some default implementations
    for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC)
    samples.  The purpose of 'universals' is to reduce package
    dependencies and conflicts.  The 'nlist' package implements many of
    the methods for its 'nlist' class.
Well known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.
Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.
Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.
Separate a data frame in two based on key columns. The function
 unjoin() provides an inside-out version of a nested data frame. This is used
 to identify duplication and normalize it (in the database sense) by linking
 two tables with the redundancy removed. This is a basic requirement for
 detecting topology within spatial structures that has motivated the need for
 this package as a building block for workflows within more applied projects.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Fit and assess Bayesian multi-staged regression models that account 
    for unmeasured confounders using JAGS.
Fast flattening of hierarchical data structures (e.g. JSON, XML)
             into data.frames with a flexible spec language.
Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.
Tools for converting data from complex or irregular layouts to a
    columnar structure.  For example, tables with multilevel column or row
    headers, or spreadsheets.  Header and data cells are selected by their
    contents and position, as well as formatting and comments where available,
    and are associated with one other by their proximity in given directions.
    Functions for data frames and HTML tables are provided.
Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.
Wraps the 'unrtf' utility <https://www.gnu.org/software/unrtf/> to 
    extract text from RTF files. Supports document conversion to HTML, LaTeX or
    plain text. Output in HTML is recommended because 'unrtf' has limited 
    support for converting between character encodings.
Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.
Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.
Hubbell's Unified Neutral Theory of Biodiversity.
Calculates a Mahalanobis distance for every row of a set of
    outcome variables (Mahalanobis, 1936
    <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
    distance is calculated using a conditional covariance matrix (i.e., a
    covariance matrix of the outcome variables after controlling for a set
    of predictors). Plotting the output of the cond_maha() function can
    help identify which elements of a profile are unusual after
    controlling for the predictors.
Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.
We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.
Provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.
When updating major or minor R versions all packages should
    be re-installed. The utilities in this package assist in getting a
    user up-and-running again by installing all previously installed R
    packages. The package uses 'renv' to install; immediately replenishing
    your 'renv' package cache.
Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allele bias, overdispersion, and sequencing
       error. The main functions are flexdog() and multidog(), 
       which allow the specification
       of many different genotype distributions. Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See
       Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
       Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details 
       on the implemented methods.
Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".
An implementation of the UPMASK method for performing membership
    assignment in stellar clusters in R. It is prepared to use photometry and
    spatial positions, but it can take into account other types of data. The
    method is able to take into account arbitrary error models, and it is
    unsupervised, data-driven, physical-model-free and relies on as few
    assumptions as possible. The approach followed for membership assessment is
    based on an iterative process, dimensionality reduction, a clustering
    algorithm and a kernel density estimation.
Up-and-Down is the most popular design approach for dose-finding, but has been severely under-served by the statistical computing community. This is the first package to address Up-and-Down's needs. For a recent methodological tutorial on Up-and-Down, see Oron et al. (2022) <doi:10.1097/ALN.0000000000004282>.
'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets.
  This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'.
Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.
Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on canonical analysis. These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>.
Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.
Variance approximations for the 
    Horvitz-Thompson total estimator in Unequal Probability Sampling
    using only first-order inclusion probabilities. 
    See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.
'Elasticsearch' is an open-source, distributed, document-based datastore
    (<https://www.elastic.co/products/elasticsearch>).
    It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that
    'API' was not designed for common data science workflows like pulling large batches of
    records and normalizing those documents into a data frame that can be used as a training
    dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch'
    that is explicitly designed to make these data science workflows easy and fun.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. Goehring (2023) <https://bengoehring.github.io/files/ps-paper-anon-word-ca.docx> argues that supervisors---especially supervisors of small teams---should utilize computational tools to monitor reliability in real time. As such, this package provides easy-to-use functions for calculating inter-rater reliability statistics and measuring the reliability of one coder compared to the rest of the team.
Calculate unified measures
  that quantify the effect of a covariate on a binary dependent variable
  (e.g., for meta-analyses).
  This can be particularly important
  if the estimation results are obtained
  with different models/estimators
  (e.g., linear probability model, logit, probit, ...)
  and/or with different transformations of the explanatory variable of interest
  (e.g., linear, quadratic, interval-coded, ...).
  The calculated unified measures are:
  (a) semi-elasticities of linear, quadratic, or interval-coded covariates and
  (b) effects of linear, quadratic, interval-coded, or categorical covariates
  when a linear or quadratic covariate changes between distinct intervals,
  the reference category of a categorical variable
  or the reference interval of an interval-coded variable needs to be changed,
  or some categories of a categorical covariate
  or some intervals of an interval-coded covariate need to be grouped together.
  Approximate standard errors of the unified measures are also calculated.
  All methods that are implemented in this package 
  are described in the 'vignette'  
  "Extracting and Unifying Semi-Elasticities and Effect Sizes
  from Studies with Binary Dependent Variables"
  that is included in this package.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Provide the URL checking tools available in R 4.1+ as a
    package for earlier versions of R. Also uses concurrent requests so
    can be much faster than the serial versions.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth 2.0) and 'is.gd' (no API key).
A toolkit for all URL-handling needs, including encoding and decoding,
    parsing, parameter extraction and modification. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
Conduct unit root tests based on 'EViews' (<https://eviews.com>) routines and report them in tables. 'EViews' (Econometric Views) is a commercial software for econometrics.
S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
A simple interface to the Geographic Header information
  from the "2010 US Census Summary File 2".  The entire Summary File 2
  is described at
  <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>,
  but note that this package only provides access to parts of the
  geographic header ('geoheader') of the file.  In particular, only
  the first 101 columns of the geoheader are included and, more
  importantly, only rows with summary levels (SUMLEVs) 010 through 050
  (nation down through county level) are included.  In addition to
  access to (part of) the geoheader, the package also provides a
  decode function that takes a column name and value and, for certain
  columns, returns "the meaning" of that column (i.e., a "SUMLEV"
  value of 40 means "State"); without a value, the decode function
  attempts to describe the column itself.
Updated versions of the 1970's "US State Facts and
    Figures" objects from the 'datasets' package included with R. The new
    data is compiled from a number of sources, primarily from United
    States Census Bureau or the relevant federal agency.
Contains a WGS84 datum map of the USA, which includes all
    Commonwealth and State boundaries & also includes Puerto Rico and the U.S.
    Virgin Islands. This map is a reprojection of the NAD83 datum map from the
    USGS National Map. This package contains a subset of the data included in
    the 'USA.state.boundaries.data' package, which is available in a 'drat'
    repository. To install that data package, please follow the instructions at
    <https://gitlab.com/iembry/usa.state.boundaries.data>.
Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.
Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR')
  Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. 
  Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.
An alternative for downloading various United States Department of
    Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. 
    You must sign up for an API token from the mentioned website in order for 
    this package to work.
Demographic data on the United States at the county and state levels spanning multiple years.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
Functions to re-arrange, extract, and work with distances.
Consistent with 'knitr' syntax highlighting, 'usedthese' adds a
    summary table of R package & function usage to a Quarto document and 
    enables aggregation of usage across a Quarto website. Learn more about
    'usedthese' at <https://cgoo4.github.io/usedthese/>.
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
A set of general functions that I have used in various 
  projects and other R packages. Miscellaneous operations
  on data frames, matrices and vectors, ROC and PR statistics.
Code snippets to fit models using the tidymodels framework
    can be easily created for a given data set.
Automate package and project setup tasks that are otherwise
    performed manually. This includes setting up unit testing, test
    coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp',
    'RStudio' projects, and more.
Compiled and cleaned the county-level estimates of fertilizer, 
    nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA).  The commercial fertilizer data were originally 
    generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated
    based on county-level population data of livestock, poultry, and other animals.
    See the user manual for detailed data sources and cleaning methods.  
    'usfertilizer' utilized the tidyverse to clean the original data and provide 
    user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.
Provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.
Provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.
Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.
Calculates federal and state income taxes in the United States. It acts as a wrapper 
    to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts 
    the calculations, while 'usincometaxes' prepares the data for TAXSIM 35, sends the data to 
    TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it 
    into a data frame. All without the user worrying about this process.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law (Gunther 2007)
    <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Obtain United States map data frames of varying region types (e.g. county,
    state). The map data frames include Alaska and Hawaii conveniently placed to the
    bottom left, as they appear in most maps of the US. Convenience functions for plotting
    choropleths and working with FIPS codes are also provided.
Provides a container for data used by the 'usmap' package.
    The data used by 'usmap' has been extracted into this package so that the
    file size of the 'usmap' package can be reduced greatly. The data in this
    package will be updated roughly once per year (plus bug fixes) as new
    shape files are provided by the US Census bureau.
Implements various independence tests for discrete, continuous, and infinite-dimensional data. The tests are based on a U-statistic permutation test, the USP of Berrett, Kontoyiannis and Samworth (2020) <arXiv:2001.05513>, and shown to be minimax rate optimal in a wide range of settings. As the permutation principle is used, all tests have exact, non-asymptotic Type I error control at the nominal level.
Centers of population (centroid) data for census areas in the
    United States.
The "ussher" data set is drawn from original chronological textual historic events. Commonly known as James Ussher's Annals of the World, the source text was originally written in Latin in 1650, and published in English translation in 1658.The data are classified by index, year, epoch (or one of the 7 ancient "Ages of the World"), Biblical source book if referenced (rarely), as well as alternate dating mechanisms, such as "Anno Mundi" (age of the world) or "Julian Period" (dates based upon the Julian calendar). Additional file "usshfull" includes variables that may be of further interest to historians, such as Southern Kingdom and Northern Kingdom discrepant dates, and the original amalgamated dating mechanic used by Ussher in the original text. The raw data can also be called using "usshraw", as described in: Ussher, J. (1658) <https://archive.org/stream/AnnalsOfTheWorld/Annals_djvu.txt>.
A set of functions leading to multivariate response L1 regression. 
    This includes functions on computing Euclidean inner products and norms, 
    weighted least squares estimates on multivariate responses, function to compute 
    fitted values and residuals. This package is a companion to the book "U-Statistics,
    M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear 
    in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of 
    Hindustan Book Agency and Springer-Verlag.
Make requests from the US Treasury Fiscal Data API endpoints.
Forms a query to submit for US Treasury yield curve data, posting
    this query to the US Treasury web site's data feed service.  By default the
    download includes data yield data for 12 products from January 1, 1990,
    some of which are NA during this span.  The caller can pass parameters to
    limit the query to a certain year or year and month, but the full download
    is not especially large.  The download data from the service is in XML
    format.  The package's main function transforms that XML data into a numeric 
    data frame with treasury product items (constant maturity yields for 12 kinds 
    of bills, notes, and bonds) as columns and dates as row names. The function 
    returns a list which includes an item for this data frame as well as query-related
    values for reference and the update date from the service.
Three functions are provided: first function changes time from local to UTC, other changes from UTC to local and third returns difference between local and UTC. %h+% operator is also provided it adds hours to a time.
Process and print 'UTF-8' encoded international
    text (Unicode). Input, validate, normalize, encode, format, and
    display.
Functions for building customized ready-to-export tables for publication.
Tools for formatting and summarizing data for outcomes research.
A set of functions to aid in the production of visuals in ggplot2.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
Contains functions for simulating phase 12 trial designs described by Chapple and Thall (2019) including simulation and the EFF-TOX trial and simulation and implementation of the U12 trial. Functions for implementing the EFF-TOX trial are found in the package 'Phase123'.
Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.
The most used functions on IPEA (Instituto de Pesquisa Economica Aplicada). 
             Most of functions deal with brazilian names. 
             It can guess the women single's name, extract prepositions or extract the first name.
Multi-label learning strategies and others procedures to support multi-
  label classification in R. The package provides a set of multi-label procedures such as
  sampling methods, transformation strategies, threshold functions, pre-processing 
  techniques and evaluation metrics. A complete overview of the matter can be seen in
  Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and 
  Ventura, S. (2015) A Tutorial on Multi-label Learning.
A fast, user-friendly tool for annotating potential deleterious variants in the untranslated regions for both human and mouse species. Y Liu, JD Dougherty (2021) <doi:10.1101/2021.06.23.449510>.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A tool for checking how much information is disclosed when
    reporting summary statistics.
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
An implementation of the Uniform Manifold Approximation and 
    Projection dimensionality reduction by McInnes et al. (2018) 
    <arXiv:1802.03426>. It also provides means to transform new data and to 
    carry out supervised dimensionality reduction. An implementation of the 
    related LargeVis method of Tang et al. (2016) <arXiv:1602.00370> is also 
    provided. This is a complete re-implementation in R (and C++, via the 'Rcpp'
    package): no Python installation is required. See the uwot website 
    (<https://github.com/jlmelville/uwot>) for more documentation and examples.
Provides convenience functions for user experience research
    with an emphasis on quantitative user experience testing and reporting.
    The functions are designed to translate statistical approaches to 
    applied user experience research.
An R interface to V8 <https://v8.dev>: Google's open source JavaScript
    and WebAssembly engine. This package can be compiled either with V8 version 6 
    and up or NodeJS when built as a shared library.
Various semiparametric and nonparametric statistical tools for
    immune correlates analysis of vaccine clinical trial data. This includes
    calculation of summary statistics and estimation of risk, vaccine efficacy,
    controlled effects (controlled risk and controlled vaccine efficacy), and
    mediation effects (natural direct effect, natural indirect effect,
    proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022)
    <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023)
    <doi:10.48550/arXiv.2208.06465>.
An implementation of three procedures developed by 
    John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM 
    (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. 
    Combined, they provide a way to identify, treat, and analyze outliers 
    in two-way (i.e., contingency) tables, as described in his 
    landmark paper "The Future of Data Analysis", Tukey, John W. (1962) 
    <https://www.jstor.org/stable/2237638>.
A lexicon and rule-based sentiment analysis tool that is specifically 
    attuned to sentiments expressed in social media, and works well on texts from other 
    domains. Hutto & Gilbert (2014) <https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109/8122>.
Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If
    you want to explore the full VAERS data for 1990 - Present (data, symptoms,
    and vaccines), then check out the 'vaersND' package from the URL below. The
    URL and BugReports below correspond to the 'vaersND' package, of which
    'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN
    due to the large size of the data set. To install the Suggested 'vaers' and
    'vaersND' packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to
    explore the full VAERS data for 1990 - Present (data, symptoms, and
    vaccines), then check out the 'vaers' package from the URL below. The URL
    and BugReports below correspond to the 'vaers' package, of which 'vaersvax'
    is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the
    large size of the data set. To install the Suggested 'vaers' and 'vaersND'
    packages, use the following R code:
    'devtools::install_git("<https://gitlab.com/iembry/vaers.git>",
    build_vignettes = TRUE)' and
    'devtools::install_git("<https://gitlab.com/iembry/vaersND.git>",
    build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS)
    is a national early warning system to detect possible safety problems in
    U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control
    and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For
    more information about the data, visit <https://vaers.hhs.gov/>. For
    information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html#vaccine>.
Provides access to the 'Vagalume' API <https://api.vagalume.com.br>. 
    The data extracted is basically lyrics of songs and information about
    artists/bands.
Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) <doi:10.1080/01621459.2018.1518235>.
Estimates joint marker (longitudinal) and
    and survival (time-to-event) outcomes using variational approximations.
    The package supports multivariate markers allowing for
    correlated error terms and multiple types of survival outcomes which may be
    left-truncated, right-censored, and recurrent. Time-varying fixed and
    random covariate effects are supported along with non-proportional hazards.
A set of basic tools to transform functions into functions with
    input validation checks, in a manner suitable for both programmatic and
    interactive use.
A binding for the 'valection' program which offers various ways to 
  sample the outputs of competing algorithms or parameterizations, and fairly 
  assess their performance against each other. The 'valection' C library is 
  required to use this package and can be downloaded from:
  <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.
The Bank of Canada updated their Valet API
             <https://www.bankofcanada.ca/valet/docs>, and no R client
             currently exists. This provides access to all of Valet's endpoints
             and serves responses in wide format easy for researchers to
             handle but also provides tools to access API responses as a list.
An interface to the 'Valhalla' routing engine’s
    application programming interfaces (APIs) for turn-by-turn routing,
    isochrones, and origin-destination analyses. Also includes several
    user-friendly functions for plotting outputs, and strives to follow
    "tidy" design principles. Please note that this package requires
    access to a running instance of 'Valhalla', which is open source and
    can be downloaded from <https://github.com/valhalla/valhalla>.
Methods and tools for analysing and validating the outputs
    and modelled functions of artificial neural networks (ANNs) in terms
    of predictive, replicative and structural validity. Also provides a
    method for fitting feed-forward ANNs with a single hidden layer.
Functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?
Declare data validation rules and data quality indicators;
        confront data with them and analyze or visualize the results.
        The package supports rules that are per-field, in-record,
        cross-record or cross-dataset. Rules can be automatically
        analyzed for rule type and connectivity. Supports checks implied
        by an SDMX DSD file as well. See also Van der Loo
        and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6
        and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.
An implementation of statistical tests for the validation of rating systems as described in the ECB Working paper ''Advances in multivariate back-testing for credit risk underestimation'', by F. Coppens, M. Mayer, L. Millischer, F.  Resch, S. Sauer, K. Schulze (ECB WP series, forthcoming).
Generate suggestions for validation rules 
  from a reference data set, which can be used as a starting point for domain specific 
   rules to be checked with package 'validate'.
Rule sets with validation rules may contain redundancies or contradictions. 
  Functions for finding redundancies and problematic rules are provided, 
  given a set a rules formulated with 'validate'.
Given a partition resulting from any clustering algorithm, the implemented tests allow valid post-clustering inference by testing if a given variable significantly separates two of the estimated clusters. 
             Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). 
             "Post-clustering difference testing: valid inference and practical considerations", <arXiv:2210.13172>.
Functions for metrics and plots for model evaluation. Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). <doi:10.1111/SUM.12694>.
The algorithm implemented in this package was
    designed to quickly estimates the distribution of the 
    log-rank especially for heavy unbalanced groups. VALORATE 
    estimates the null distribution and the p-value of the 
    log-rank test based on a recent formulation. For a given 
    number of alterations that define the size of survival 
    groups, the estimation involves a weighted sum of 
    distributions that are conditional on a co-occurrence term 
    where mutations and events are both present. The estimation 
    of conditional distributions is quite fast allowing the 
    analysis of large datasets in few minutes 
    <http://bioinformatica.mty.itesm.mx/valorate>.
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Read and manipulate genome intervals and signals. Provides
    functionality similar to command-line tool suites within R, enabling
    interactive analysis and visualization of genome-scale data.  Riemondy
    et al. (2017) <doi:10.12688/f1000research.11997.1>.
Two methods are implemented to cluster data with finite mixture
    regression models. Those procedures deal with high-dimensional covariates and
    responses through a variable selection procedure based on the Lasso estimator.
    A low-rank constraint could be added, computed for the Lasso-Rank procedure.
    A collection of models is constructed, varying the level of sparsity and the
    number of clusters, and a model is selected using a model selection criterion
    (slope heuristic, BIC or AIC). Details of the procedure are provided in
    "Model-based clustering for high-dimensional data. Application to functional data"
    by Emilie Devijver (2016) <arXiv:1409.1333v2>,
    published in Advances in Data Analysis and Clustering.
EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life 
    often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L)
    contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: 
    the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values 
    using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system
    scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/>
    The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references).
    The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources.
    The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references).
    The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. 
    The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
You can easily visualize your 'sf' polygons or data.frame with h3 address.
    While 'leaflet' package is too raw for data analysis, 
    this package can save data analysts' efforts & time with pre-set visualize options.
Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.
Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of 
    variable annuities, which reflect realistic features of common annuity contracts in practice. 
    It aims to facilitate the development and dissemination of research related to the efficient 
    valuation of a portfolio of large variable annuities. The main valuation methodology was 
    proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Vampirize the response biases from a dataset! Performs 
    factor analysis controlling the effects of social desirability
    and acquiescence using the method described in Ferrando, 
    Lorenzo-Seva & Chico (2009) <doi:10.1080/10705510902751374>.
Wrapper around the 'City of Vancouver' Open Data API <https://opendata.vancouver.ca/api/v2/console> to simplify and standardize access to 'City of Vancouver' open data. 
  Functionality to list the data catalogue and access data and geographic records.
Collection of functions to evaluate presence-absence models. The 
    main function corrects discrimination for the representativeness effect 
    following: 
    Jiménez-Valverde (2022) "The uniform AUC: dealing with the 
    representativeness effect in presence-absence models. Methods Ecol. Evol, 
    accepted on 28 January 2022.
Palettes generated from Vincent van Gogh's paintings.
Simplifies functions assess normality for bivariate and multivariate statistical techniques. Includes functions designed to replicate plots and tables that would result from similar calls in 'SPSS', including hst(), box(), qq(), tab(), cormat(), and residplot(). Also includes simplified formulae, such as mode(), scatter(), p.corr(), ow.anova(), and rm.anova().
Provides low-level access to 'GDAL' functionality.  
  'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats 
  that presents a single raster abstract data model and single vector abstract data model to the calling application 
  for all supported formats <https://gdal.org/>. This package is focussed on providing exactly and only what GDAL does, to enable
  developing further tools.
A collection of the functions for estimation, hypothesis testing, prediction for stationary vector autoregressive models.
Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.
Tool for easy and efficient discretization of continuous and categorical data. 
    The package calculates the most optimal binning of a given explanatory variable with respect to a 
    user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value 
    to each of the calculated binpoints in order to recode the original variable. 
    The package allows users to impose certain restrictions on the functional form on the resulting 
    binning while maximizing the overall information value in the original data. 
    The package is well suited for logistic scoring models where input variables may be subject to 
    restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in 
    detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring 
    is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and 
    Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum 
    information value.
Fast algorithms for fitting Bayesian variable selection
    models and computing Bayes factors, in which the outcome (or
    response variable) is modeled using a linear regression or a
    logistic regression. The algorithms are based on the variational
    approximations described in "Scalable variational inference for
    Bayesian variable selection in regression, and its accuracy in
    genetic association studies" (P. Carbonetto & M. Stephens, 2012,
    <DOI:10.1214/12-BA703>). This software has been applied to large
    data sets with over a million variables and thousands of samples.
Performs clustering of quantitative variables,
    assuming that clusters lie in low-dimensional subspaces. Segmentation of
    variables, number of clusters and their dimensions are selected based on
    BIC. Candidate models are identified based on many runs of K-means
    algorithm with different random initializations of cluster centers.
Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.
Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.
Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.
Captures and compiles the genes and variants related to a disease, a phenotype or a clinical feature from the public databases including HPO (Human Phenotype Ontology, <http://human-phenotype-ontology.github.io/about.html>), Orphanet <http://www.orpha.net/consor/cgi-bin/index.php>, OMIM (Online Mendelian Inheritance in Man, <http://www.omim.org>), ClinVar <http://www.ncbi.nlm.nih.gov/clinvar>, and UniProt (Universal Protein Resource, <http://www.uniprot.org>) and PubMed abstracts. HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. HPO currently contains approximately 11,000 terms and over 115,000 annotations to hereditary diseases. Orphanet is the reference portal for information on rare diseases and orphan drugs, whose aim is to help improve the diagnosis, care and treatment of patients with rare diseases. OMIM is a continuously updated catalog of human genes and genetic disorders and traits, with particular focus on the molecular relationship between genetic variation and phenotypic expression. ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. UniProt focuses on amino acid altering variants imported from Ensembl Variation databases. For Homo sapiens, the variants including human polymorphisms and disease mutations in the UniProt are manually curated  from UniProtKB/Swiss-Prot. Additionally, PubMed provides the primary and latest source of the information. Text mining was employed to capture the information from PubMed abstracts.
Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).
Abstract descriptions of (yet) unobserved variables.
Implements variable screening techniques for ultra-high
    dimensional regression settings.  Techniques for independent (iid) data,
    varying-coefficient models, and longitudinal data are implemented. The package
     currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
     and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
     simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
    Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
    Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>,
    Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086>
    Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and
    Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
Provides functions for the variance gamma
        distribution. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizations for the variance gamma distribution.
This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is 
  a framework based on 'scala' and 'spark' to analyze genome datasets, 
  see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples 
  each one containing 80 million features in either unsupervised clustering approaches 
  and supervised applications, like classification and regression. The genome datasets
  are usually writing in VCF, a specific text file format used 
  in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great 
  tool for genome research, because it is able to read VCF files, run analyses and return 
  the output in a 'spark' data frame.
Enables computationally efficient parameters-estimation by variational Bayesian 
    methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models
    for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. 
    Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements,
    variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods 
    e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>,
    Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. 
    To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test.
Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for women military service 
    members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the 
    existing American College of Cardiology/American Heart Association CVD risk assessment
    tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic 
    minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. 
    (2021) <doi:10.1161/JAHA.120.019217>.
Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information.
The goal of the package is to equip the 'jmcm' package (current version 0.2.1) 
    with estimations of the covariance of estimated parameters. Two methods are provided. 
    The first method is to use the inverse of estimated Fisher's information matrix, see 
    M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and 
    J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) 
    <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) 
    <doi:10.1214/aos/1176351062> for reference.
A continuous version of the receiver operating characteristics (ROC) curve to visualize and assess the classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.
A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.
In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you.
    We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates,
    Outer Control Variates and Importance Sampling algorithms are available in the framework. User can
    write its own simulation function and use the Variance Reduction techniques in this package to
    obtain more efficient simulations. An implementation of Asian Option simulation is already 
    available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Utilizes multiple variable selection methods to estimate Average Treatment Effect.
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
Creates a summary dataframe that can be used in 'RStudio' similar to the variable
    explorer in 'Stata', but which also includes the summary statistics. By default the result 
    is shown in the 'RStudio' Viewer Pane as a searchable data table. This is useful particularly 
    if you have a large dataset with a very large number of labelled variables with hard to 
    remember names. Can also be used to generate a table of summary statistics.
Vector autoregressive (VAR) model is a fundamental and effective approach
    for multivariate time series analysis. Shrinkage estimation methods can be
    applied to high-dimensional VAR models with dimensionality greater than
    the number of observations, contrary to the standard ordinary least squares
    method. This package is an integrative package delivering nonparametric,
    parametric, and semiparametric methods in a unified and consistent manner,
    such as the multivariate ridge regression in Golub, Heath, and Wahba (1979)
    <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in
    Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and
    Bayesian estimation methods using noninformative and informative priors
    in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and
    Ni and Sun (2005) <doi:10.1198/073500104000000622>.
An implementation of the Likelihood ratio Test (LRT) for testing that,
    in a (non)linear mixed effects model, the variances of a subset of the random
    effects are equal to zero. There is no restriction on the subset of variances
    that can be tested: for example, it is possible to test that all the variances
    are equal to zero. Note that the implemented test is asymptotic.
    This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'.
    Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.
Implements a maximum likelihood estimation (MLE) method for
    estimation and prediction of Gaussian process-based spatially varying
    coefficient (SVC) models (Dambon et al. (2021a)
    <doi:10.1016/j.spasta.2020.100470>).  Covariance tapering (Furrer et
    al. (2006) <doi:10.1198/106186006X132178>) can be applied such that
    the method scales to large data. Further, it implements a joint
    variable selection of the fixed and random effects (Dambon et al.
    (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its 
    capabilities are described in (Dambon et al. (2021c) <arXiv:2106.02364>).
Provide a collection of miscellaneous R functions
    related to the Vasicek distribution with the intent to make
    the lives of risk modelers easier.
Vasicek density, cumulative distribution, quantile functions and random deviate
   	generation of Vasicek distribution. In addition, there are two functions for fitting the 
   	Generalized Additive Models for Location Scale and Shape introduced by Rigby and 
   	Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Some functions 
   	are written in C++ using 'Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).
An R client for the 'vatcheckapi.com' VAT number validation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://vatcheckapi.com/docs> .
Provides an interface to a 'HashiCorp' vault server over
  its http API (typically these are self-hosted; see
  <https://www.vaultproject.io>).  This allows for secure storage and
  retrieval of secrets over a network, such as tokens, passwords and
  certificates.  Authentication with vault is supported through
  several backends including user name/password and authentication via
  'GitHub'.
The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. 
    One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large.  
    This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) <doi:10.1002/sim.9619>, which is based on Gaussian variational approximate inference and is computationally efficient.
Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.
Contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.
Time series decomposition for univariate time series using the
    "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
    described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
    Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
    und Finanzwissenschaften', by
    Hebbel and Steuer, Springer Berlin Heidelberg, 2022
    <doi:10.1007/978-3-662-65638-9>, or
    'Decomposition of Time Series using the Generalised Berlin 
    Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
    Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
    Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
    Series: Advanced Studies in Theoretical and Applied Econometrics.
    Springer 2014, p. 9-40.
ANOVA and REML estimation of linear mixed models is implemented, once following
 Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
 The primary objective of this package is to perform a variance component analysis (VCA)
 according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
 Procedures" (2014). There are plotting methods for visualization of an experimental design,
 plotting random effects and residuals. For ANOVA type estimation two methods for computing
 ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of 
 variance components can be derived, which is used in estimating confidence intervals. Linear
 hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
 values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
 more comprehensive description of the features.
Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).
Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
    and the 'gnm' package for Generalized Nonlinear Models.
	In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
	adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
	'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
  Michael Friendly and David Meyer.
The 'vcfpp.h' (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use 'C++' 'API' of 'htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF/BCF files. Explore a range of powerful features for efficient VCF data manipulation.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files. Once
    VCF data is read into R a parser function extracts matrices of data. This
    information can then be used for quality control or other purposes. Additional
    functions provide visualization of genomic data. Once processing is complete
    data may be written to a VCF file (*.vcf.gz). It also may be converted into
    other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between
    VCF data and familiar R software.
Implements functions for varying coefficient meta-analysis methods. 
  These methods do not assume effect size homogeneity. Subgroup effect size 
  comparisons, general linear effect size contrasts, and linear models of 
  effect sizes based on varying coefficient methods can be used to describe 
  effect size heterogeneity. Varying coefficient meta-analysis methods do not 
  require the unrealistic assumptions of the traditional fixed-effect and 
  random-effects meta-analysis methods.  
  For details see:  Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.
Methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the 'stats' package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.
Method to perform penalized variance component analysis.
Record test suite 'HTTP' requests and replays them during
    future runs. A port of the Ruby gem of the same name
    (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr'
    R package for matching 'HTTP' requests by various rules ('HTTP' method,
    'URL', query parameters, headers, body, etc.), and then caching
    real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests
    matching any previous requests in the same 'cassette' use a cached
    'HTTP' response.
Defines new notions of prototype and size that are used to
    provide tools for consistent and well-founded type-coercion and
    size-recycling, and are in turn connected to ideas of type- and
    size-stability useful for analysing function interfaces.
A way of visualizing collections of time series and, optionally
    their future values, forecasts for their future values and prediction
    intervals for the forecasts. A web-based GUI can be used to display the
    information in a collection of time series.
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019) <doi:10.1111/2041-210X.13253>. Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A worked sample on the body shape of cichlid fishes is included, based on the dataset from Kerschbaumer et al. (2013) <doi:10.5061/dryad.fc02f>.
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
    The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
    The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.
Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
An extension to the 'testthat' package that makes it easy
    to add graphical unit tests. It provides a Shiny application to
    manage the test cases.
To ease the visualization of outputs from Diversity Motif Analyser ('DiMA';
    <https://github.com/BVU-BILSAB/DiMA>). 'vDiveR' allows visualization of the diversity 
    motifs (index and its variants – major, minor and unique) for elucidation of 
    the underlying inherent dynamics. Please refer <https://vdiver-manual.readthedocs.io/en/latest/>
    for more information.
Contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.
Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.
Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection,  regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.
Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
Automates set operations (i.e., comparisons of overlap) between multiple vectors.
    It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
The 'base'  tools  union() intersect(), etc.,  follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::sets (alias for all the items) tools functionality.
A tool for fast, efficient bitwise operations along the
    elements within a vector. Provides such functionality for AND, OR and
    XOR, as well as infix operators for all of the binary bitwise
    operations.
Facilitate the analysis of inter-limb and intra-limb coordination in human movement. 
  It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks.
  Needham, R., Naemi, R., & Chockalingam, N. (2014) <doi:10.1016/j.jbiomech.2013.12.032>.
  Needham, R., Naemi, R., & Chockalingam, N. (2015) <doi:10.1016/j.jbiomech.2015.07.023>.
  Tepavac, D., & Field-Fote, E. C. (2001) <doi:10.1123/jab.17.3.259>.
  Park, J.H., Lee, H., Cho, Js. et al. (2021) <doi:10.1038/s41598-020-80237-w>.
New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) 
  to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. 
  This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.
Provides platform for Vedic calendar system having several
    functionalities to facilitate conversion between Gregorian and Vedic
    calendar systems, and helpful in examining its impact in the
    time series analysis domain.
The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar
    for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega'
    specifications. Functions exist which enable building a valid 'spec' from scratch
    or importing a previously created 'spec' file. Functions also exist to export 'spec'
    files and to generate code which will enable plots to be embedded in properly
    configured web pages. The default behavior is to generate an 'htmlwidget'.
Ordination methods, diversity analysis and other
  functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
    results and in diversity analysis, especially with the vegan package.
'Vega' and 'Vega-Lite' parse text in 'JSON' notation to render 
  chart-specifications into 'HTML'. This package is used to facilitate the 
  rendering. It also provides a means to interact with signals, events,
  and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'.
A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.
Handling of vegetation data from different sources (
	Turboveg 2.0 <https://www.synbiosys.alterra.nl/turboveg/>; 
	the German national repository <https://www.vegetweb.de> and others. 
    Taxonomic harmonization (given appropriate taxonomic lists,
    e.g. the German taxonomic standard list "GermanSL", <https://germansl.infinitenature.org>).
Collection of common methods to determine growing season length in
  a simple manner. Start and end dates of the vegetation periods are calculated
  solely based on daily mean temperatures and the day of the year.
Import and handling data from vegetation-plot databases, especially
    data stored in 'Turboveg 2' (<https://www.synbiosys.alterra.nl/turboveg/>).
    Also import/export routines for exchange of data with 'Juice'
    (<https://www.sci.muni.cz/botany/juice/>) are implemented.
Elaboration of vehicular emissions inventories,
    consisting in four stages, pre-processing activity data, preparing 
    emissions factors, estimating the emissions and post-processing of emissions 
    in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>.
    Before using VEIN you need to know the vehicular composition of your study area, in other words,
    the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to
    start with the project to download a template to create a structure of directories and scripts.
Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny applications.
A close to zero dependency package to draw and display Venn 
    diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams.
Calculate and plot Venn diagrams in 2D and 3D.
Turn R analysis outputs into full sentences, by writing vectors into in-sentence lists, pluralising words conditionally, spelling out numbers if they are at the start of sentences, writing out dates in full following US or UK style, and managing capitalisations in tidy data.
Describe in words the genealogical relationship between two
    members of a given pedigree. 'verbalisr' is part of the 'ped suite'
    collection of packages for pedigree analysis. For a demonstration of
    'verbalisr', see the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>.
An implementation of the 'Verhoeff' algorithm for calculating 
    check digits (Verhoeff, J. (1969) <doi:10.1002/zamm.19710510323>). 
    Functions are provided to calculate a check digit given an input number, 
    calculate and append a check digit to an input number, and validate that a 
    check digit is correct given an input number.
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
The 'biomarker' data set by Vermeulen et al. (2009)
    <doi:10.1016/S1470-2045(09)70154-8> is provided. The data
    source, however, is by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>. The original data set may
    be downloaded from
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This data set is for a real-time quantitative polymerase chain reaction
    ('PCR') experiment that comprises the raw fluorescence data of 24,576
    amplification curves. This data set comprises 59 genes of interest and 5
    reference genes. Each gene was assessed on 366 neuroblastoma complementary
    DNA ('cDNA') samples and on 18 standard dilution series samples (10-fold
    5-point dilution series x 3 replicates + no template controls ('NTC') x 3
    replicates).
R data pipelines commonly require reading and writing data to versioned 
    directories. Each directory might correspond to one step of a multi-step process,
    where that version corresponds to particular settings for that step and a chain of
    previous steps that each have their own versions. This package creates a configuration
    object that makes it easy to read and write versioned data, based on YAML
    configuration files loaded and saved to each versioned folder.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
A lightweight package for sorting version codes in various forms. No strong dependencies guaranteed.
A toolset for interactively exploring the differences between two data frames.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Permits to apply the 'Vertex Sort' algorithm (Jothi et al. (2009) <10.1038/msb.2009.52>) to a graph in order to elucidate its hierarchical structure. It also allows graphic visualization of the sorted graph by exporting the results to a cytoscape friendly format. Moreover, it offers five different algorithms of graph randomization: 1) Randomize a graph with preserving node degrees, 2) with preserving similar node degrees, 3) without preserving node degrees, 4) with preserving node in-degrees and 5) with preserving node out-degrees.
Multi-precision library that allows to store and operate with arbitrarily big integers without
    loss of precision. It includes a large list of tools to work with them, like:
      - Arithmetic and logic operators
      - Modular-arithmetic operators
      - Computer Number Theory utilities
      - Probabilistic primality tests
      - Factorization algorithms
      - Random generators of diferent types of integers.
Simple functions for calculating the image gradient, image hessian, volume ratio filter, and Frangi vesselness filter of 3-dimensional volumes.
The goal of 'vetiver' is to provide fluent tooling to
    version, share, deploy, and monitor a trained model. Functions handle
    both recording and checking the model's input data prototype, and
    predicting from a remote API endpoint. The 'vetiver' package is
    extensible, with generics that can support many kinds of models.
Declarative template-based framework for verifying that objects
  meet structural requirements, and auto-composing error messages when they do
  not.
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Implements methods for inference on potential waning of vaccine
    efficacy and for estimation of vaccine efficacy at a user-specified time
    after vaccination based on data from a randomized, double-blind,
    placebo-controlled vaccine trial in which participants may be unblinded
    and placebo subjects may be crossed over to the study vaccine.  The
    methods also allow adjustment for possible confounding via inverse
    probability weighting through specification of models for the trial
    entry process, unblinding mechanisms, and the probability an unblinded
    placebo participant accepts study vaccine:  Tsiatis, A. A. and Davidian,
    M. (2021) <arXiv:2102.13103> .
Implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine.  The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine.
Computation the value of one of two uniformly 
    distributed marginals if the copula probability value is known
    and the value of the second marginal is also known.
    Computation and plotting corresponding cumulative
    distribution function or survival function.
    The numerical definition of a common area limited by lines
    of the cumulative distribution function and survival function.
    Approximate quantification of the probability of this area.
    In addition to 'amh', the copula dimension may be larger than 2.
A set of visual input controls for Shiny apps to facilitate filtering across multiple outputs.
Variance function estimation for models proposed by W. Sadler in his variance function program 
  ('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is
  to fit multiple variance functions to a data set and consequently assess which function reflects
  the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship
  is of great importance when individual test-results are used for defining follow-up treatment of patients.
Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
Empirical models for runoff, erosion, and phosphorus loss 
    across a vegetated filter strip, given slope, soils, climate, and 
    vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. 
    It also includes functions for deriving climate parameters from 
    measured daily weather data, and for simulating rainfall. Models 
    implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 
    2009 <doi:10.2134/jeq2008.0337>).
An implementation of about 6 major classes of
    statistical regression models. The central algorithm is
    Fisher scoring and iterative reweighted least squares.
    At the heart of this package are the vector generalized linear
    and additive model (VGLM/VGAM) classes. VGLMs can be loosely
    thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs that use smoothing. The book "Vector Generalized
    Linear and Additive Models: With an Implementation in R"
    (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
    the statistical framework and the package. Currently only
    fixed-effects models are implemented. Many (100+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE. The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Hauck-Donner effect detection is implemented.
    Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.
Mainly data sets to accompany the VGAM package and
	the book "Vector Generalized Linear and
	Additive Models: With an Implementation in R" (Yee, 2015)
	<DOI:10.1007/978-1-4939-2818-7>.
	These are used to illustrate vector generalized
	linear and additive models (VGLMs/VGAMs), and associated models
	(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
	Interaction Models, and constrained and unconstrained ordination
	models in ecology). This package now contains some
	old VGAM family functions which have been replaced by newer
	ones (often because they are now special cases).
Extending the functionalities of the 'VGAM' package with
         additional functions and datasets. At present, 'VGAMextra'
         comprises new family functions (ffs) to estimate several time
         series models by maximum likelihood using Fisher scoring, 
         unlike popular packages in CRAN relying on optim(), including
         ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non-
         seasonal), the Order-(p) VAR model, error correction models
         for cointegrated time series, and ARMA-structures with Student-t 
         errors. For independent data, new ffs to estimate the inverse-
         Weibull, the inverse-gamma, the generalized beta of the second
         kind and the general multivariate normal distributions are
         available. In addition, 'VGAMextra' incorporates new VGLM-links
         for the mean-function, and the quantile-function (as an alternative
         to ordinary quantile modelling) of several 1-parameter distributions,
         that are compatible with the class of VGLM/VGAM family functions.
         Currently, only fixed-effects models are implemented. All functions
         are subject to change; see the NEWS for further details on the
         latest changes.
Estimates hierarchical models using mean-field variational Bayes. 
    At present, it can estimate logistic, linear, and negative binomial models. 
    It can accommodate models with an arbitrary number of random effects and 
    requires no integration to estimate. It also provides the ability to improve 
    the quality of the approximation using marginal augmentation. 
    Goplerud (2022) <doi:10.1214/21-BA1266> provides details on the variational
    algorithms.
Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) <doi:10.1093/oxfordjournals.molbev.a004201>, codon adaptation index (CAI) Sharp  and  Li (1987) <doi:10.1093/nar/15.3.1281>, relative  codon deoptimization index (RCDI) Puigbò et al (2010) <doi:10.1186/1756-0500-3-87>, similarity index (SiD) Zhou et al (2013) <doi:10.1371/journal.pone.0077239>,  synonymous codon usage orderliness (SCUO) Wan et al (2004) <doi:10.1186/1471-2148-4-19> and, relative synonymous    codon usage (RSCU) Sharp et al (1986) <doi:10.1093/nar/14.13.5125>. Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot() and PR2.plot().
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
The base class 'VirtualArray' is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived 'XArray' class is defined to be used for homogeneous lists that contain a single class of objects. The 'RasterArray' and 'SfArray' classes enable the use of stacked spatial data instead of lists.
Provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to <https://viaf.org/>.
The Variable Infiltration Capacity (VIC) model is a macroscale
    hydrologic model that solves full water and energy balances, originally
    developed by Xu Liang at the University of Washington (UW).
    The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>,
    see Hamman et al. (2018).
    Development and maintenance of the current official version
    of the VIC model at present is led by the UW Hydro (Computational Hydrology
    group) in the Department of Civil and Environmental Engineering at UW. VIC is
    a research model and in its various forms it has been applied to most of the
    major river basins around the world, as well as globally 
    <http://vic.readthedocs.io/en/master/Documentation/References/>. 
    References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and
    S. J. Burges (1994), A simple hydrologically based model of land surface water
    and energy fluxes for general circulation models, J. Geophys. Res., 99(D7),
    14415-14428, <doi:10.1029/94JD00483>"; 
    "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The
    Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements
    for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, 
    <doi:10.5194/gmd-11-3481-2018>".
Compute group-specific intraclass correlation coefficients, 
    Bayesian testing of homogenous within-group variance, and spike-and-slab
    model selection to determine which groups share a common within-group 
    variance in a one-way random effects model <10.31234/osf.io/hpq7w>.
A shiny app for accurate estimation of vaccine induced immunogenicity 
    with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, 
    Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 
    477:112711. <doi:10.1016/j.jim.2019.112711>.
Easily interfaces R to spatial datasets available through 
  the Victorian Government's WFS (Web Feature Service): <https://opendata.maps.vic.gov.au/geoserver/ows?request=GetCapabilities&service=wfs>, 
  which allows users to read in 'sf' data from these sources. VicmapR uses the lazy querying approach and code developed by Teucher et al. (2021) for the 'bcdata' R package <doi:10.21105/joss.02927>.
Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. For the details of the methods, see the reference section of 'GitHub' README.md <https://github.com/rikenbit/Vicus>.
Video interactivity within 'shiny' applications using 'video.js'. Enables
    the status of the video to be sent from the UI to the server, and allows events
    such as playing and pausing the video to be triggered from the server.
Converts Vietnam's provinces' names and ID
    across different formats. Handles diacritics and different spellings.
Conversion of characters from unsupported Vietnamese character encodings to Unicode characters. These Vietnamese encodings (TCVN3, VISCII, VPS) are not natively supported in R and lead to printing of wrong characters and garbled text (mojibake). This package fixes that problem and provides readable output with the correct Unicode characters (with or without diacritics).
Debugging pipe chains often consists of viewing the output after 
  each step. This package adds RStudio addins and two functions that allow 
  outputing each or select steps in a convenient way.
Provides a graphical user interface to integrate, visualize and explore results 
            from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid 
            species. The app is meant for interactive use and allows users to optionally upload different sources 
            of information, including  gene annotation and alignment files, enabling the exploitation and search for 
            candidate genes in a genome browser. In its current version, 'VIEWpoly' supports inputs from 'MAPpoly', 
            'polymapR', 'diaQTL', 'QTLpoly' and 'polyqtlR' packages.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.
This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.
New tools for the visualization of missing and/or imputed values
    are introduced, which can be used for exploring the data and the structure of
    the missing and/or imputed values. Depending on this structure of the missing
    values, the corresponding methods may help to identify the mechanism generating
    the missing values and allows to explore the data including missing values.
    In addition, the quality of imputation can be visually explored using various
    univariate, bivariate, multiple and multivariate plot methods. A graphical user
    interface available in the separate package VIMGUI allows an easy handling of
    the implemented plot methods.
To computed the variability independent of mean (VIM) or variation
    independent of mean (VIM). The methodology can be found at
    Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.
Calculate point estimates of and valid confidence intervals for
    nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
    using flexible estimators of the underlying regression functions. For more information
    about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
An implementation of methods related to sparse clustering and variable importance 
    in clustering. The package currently allows to perform sparse k-means clustering with a group 
    penalty, so that it automatically selects groups of numerical features. It also allows to 
    perform sparse clustering and variable selection on mixed data (categorical and numerical 
    features), by preprocessing each categorical feature as a group of numerical features.
    Several methods for visualizing and exploring the results are also provided. 
    M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.
Provides a programmatic interface in R for the US Department of 
    Transportation (DOT) National Highway Transportation Safety Administration 
    (NHTSA) vehicle identification number (VIN) API, located at 
    <https://vpic.nhtsa.dot.gov/api/>. 
    The API can decode up to 50 vehicle identification numbers in one call, and 
    provides manufacturer information about the vehicles, including make, model, 
    model year, and gross vehicle weight rating (GVWR).
Provides tools for the statistical analysis of regular vine copula 
    models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and 
    Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.
Implements D-vine quantile regression models with
  parametric or nonparametric pair-copulas. See 
  Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and
  Schallhorn et al. (2017) <arXiv:1705.08310>.
Implementation of the vine graphical model for building
    high-dimensional probability distributions as a factorization of
    bivariate copulas and marginal density functions. This package
    provides S4 classes for vines (C-vines and D-vines) and methods
    for inference, goodness-of-fit tests, density/distribution
    function evaluation, and simulation.
Produces violin plots with optional nonparametric (Mann-Whitney test) and parametric (Tukey's honest significant difference) mean comparison and linear regression. This package aims to be a simple and quick visualization tool for comparing means and assessing trends of categorical factors.
A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.
A general framework for constructing variable importance plots from 
  various types of machine learning models in R. Aside from some standard model-
  specific variable importance measures, this package also provides model-
  agnostic approaches that can be applied to any supervised learning algorithm.
  These include 1) an efficient permutation-based variable importance measure, 
  2) variable importance based on Shapley values (Strumbelj and Kononenko, 
  2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based 
  approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A 
  variance-based method for quantifying the relative strength of interaction 
  effects is also included (see the previous reference for details).
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Provides methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. These methods help determine the extent of extrapolation when making predictions.
Provides a comprehensive framework for building, evaluating, and visualizing regression models for analyzing viral load and CD4 (Cluster of Differentiation 4) lymphocytes data. It leverages the principles of the tidymodels ecosystem of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org> to offer a user-friendly experience in model development. This package includes functions for data preprocessing, feature engineering, model training, tuning, and evaluation, along with visualization tools to enhance the interpretation of model results. It is specifically designed for researchers in biostatistics, computational biology, and HIV research who aim to perform reproducible and rigorous analyses to gain insights into disease dynamics. The main focus is on improving the understanding of the relationships between viral load, CD4 lymphocytes, and other relevant covariates to contribute to HIV research and the visibility of vulnerable seropositive populations.
A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) <arXiv:2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.
Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi.org/10.1016/j.eneco.2012.03.003>.
Color maps designed to improve graph readability for readers with 
    common forms of color blindness and/or color vision deficiency. The color 
    maps are also perceptually-uniform, both in regular form and also when 
    converted to black-and-white for printing. This package also contains 
    'ggplot2' bindings for discrete and continuous color and fill scales. A lean
    version of the package called 'viridisLite' that does not include the 
    'ggplot2' bindings can be found at 
    <https://cran.r-project.org/package=viridisLite>.
Color maps designed to improve graph readability for readers with 
    common forms of color blindness and/or color vision deficiency. The color 
    maps are also perceptually-uniform, both in regular form and also when 
    converted to black-and-white for printing. This is the 'lite' version of the 
    'viridis' package that also contains 'ggplot2' bindings for discrete and 
    continuous color and fill scales and can be found at 
    <https://cran.r-project.org/package=viridis>.
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Generates lifespans and fertility histories in continuous time using individual-level state transition (multi-state) models and data from the Human Mortality Database and the Human Fertility Database. To facilitate virtual population analysis, data on virtual individuals are stored in a data structure commonly used in sample surveys. Life histories are generated for multiple generations. The genealogies that result facilitate the study of family ties.
Provides a framework for generating virtual species distributions,
    a procedure increasingly used in ecology to improve species distribution
    models. This package integrates the existing methodological approaches 
    with the objective of generating virtual species distributions with 
    increased ecological realism.
Provides users with a simple and convenient
             mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface)
             compatible 'ODBC' (Open Database Connectivity) interface.
             'Virtuoso' is a high-performance "universal server," which can act
             as both a relational database, supporting standard Structured Query
             Language ('SQL') queries, while also supporting data following the
             Resource Description Framework ('RDF') model for Linked Data.
             'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language)
             queries, a graph-based query that supports semantic reasoning.
             This allows users to leverage the performance of local or remote 'Virtuoso' servers using
             popular 'R' packages such as 'DBI' and 'dplyr', while also providing a 
             high-performance solution for working with large 'RDF' 'triplestores' from 'R.'
             The package also provides helper routines to install, launch, and manage
             a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using
             the standard interactive installers from the 'R' command-line.  By 
             automatically handling these setup steps, the package can make using 'Virtuoso'
             considerably faster and easier for a most users to deploy in a local
             environment. Managing the bulk import of triples
             from common serializations with a single intuitive command is another key
             feature of this package.  Bulk import performance can be tens to
             hundreds of times faster than the comparable imports using existing 'R' tools,
             including 'rdflib' and 'redland' packages.
Use VirusTotal, a Google service that analyzes files and URLs 
    for viruses, worms, trojans etc., provides category of the content hosted by a 
    domain from a variety of prominent services, provides passive DNS information,
    among other things. See <http://www.virustotal.com> for more information.
Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with 
    a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data 
    in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization 
    of spectra and results of analysis in the 'ggplot2' style.
Provides a set of wrapper functions for 'Visa Chart Components'.
  'Visa Chart Components' <https://github.com/visa/visa-chart-components> is an accessibility focused,
  framework agnostic set of data experience design systems components for the web.
Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.
Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) <doi:10.1198/016214501753168262> and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) <doi:10.1214/17-AOS1570>. The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+).
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or
  generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009),
  "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>.
  These include better tabular presentation of collinearity diagnostics that highlight the important numbers,
  a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient,
  and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: 
   - components descriptive analysis
   - heat plot of the two-by-two component combinations 
   - leaving one component combination out scatter plot 
   - violin plot for specific component combinations' effects
   - density plot for components' effects  
   - waterfall plot for the interventions' effects that differ by a certain component combination 
   - network graph of components
   - rank heat plot of components for multiple outcomes.
   The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.
Functionality for creating phase portraits of functions in the
    complex number plane. Works with R base graphics, whose full 
    functionality is available. Parallel processing is used for optimum 
    performance.
Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.
A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions.
Phase I clinical trials are the first step in drug development to test
    a new drug or drug combination on humans. Typical designs of Phase I 
    trials use toxicity as the primary endpoint and aim to find the maximum 
    tolerable dosage. However, these designs are poorly applicable for the 
    development of cancer therapeutic vaccines because the expected safety 
    concerns for these vaccines are not as much as cytotoxic agents. The 
    primary objectives of a cancer therapeutic vaccine phase I trial thus 
    often include determining whether the vaccine shows biologic activity 
    and the minimum dose necessary to achieve a full immune or even clinical 
    response. This package implements a Bayesian Phase I cancer vaccine
    trial design that allows simultaneous evaluation of safety and
    immunogenicity outcomes. See Wang et al. (2019) <DOI:10.1002/sim.8021>
    for further details.
Performs modeling and forecasting of park visitor counts
	using social media data and (partial) on-site visitor counts.
	Specifically, the model is built based on an automatic decomposition
	of the trend and seasonal components of the social media-based park visitor counts,
	from which short-term forecasts of the visitor counts and percent changes
	in the visitor counts can be made. A reference for generating social media-based
	visitor counts can be found at
	Wood, Guerry, Silver, and Lacayo (2013) <doi:10.1038/srep02976>.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
To enable fit-for-purpose, reusable clinical and medical
    research focused visualizations and tables with sensible defaults and
    based on graphical principles as described in: "Vandemeulebroecke et
    al. (2018)" <doi:10.1002/pst.1912>, "Vandemeulebroecke et al. (2019)"
    <doi:10.1002/psp4.12455>, and "Morris et al. (2019)"
    <doi:10.1136/bmjopen-2019-030215>.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)
Visualization of the most powerful statistical hypothesis test.
    The function vistat() visualizes the statistical hypothesis testing between the 
    dependent variable (response) varsample and the independent variable (feature) varfactor. 
    The statistical hypothesis test (including the eventual corresponding post-hoc analysis) with
    the highest statistical power fulfilling the assumptions of the corresponding test is chosen based on a decision tree.
    A graph displaying the raw data accordingly to the chosen test is generated, the test statistics including eventual 
    post-hoc-analysis are returned. The automated workflow is especially suited for browser based interfaces to 
    server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test().
    Implemented tests to check the normal distribution of standardized residuals: shapiro.test() and ad.test().
    Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test().
    For the comparison of averages, the following algorithm is implemented: 
    If the p-values of the standardized residuals of both shapiro.test() or ad.test() are smaller 
    than 1-conf.level, kruskal.test() resp. wilcox.test() are performed, otherwise the oneway.test()
    and aov() resp. t.test() are performed and displayed. Exception: 
    If the sample size is bigger than 100, t.test() is always performed and wilcox.test() is never executed 
    (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>).
    For the test of independence of count data, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is implemented: 
    If more than 20 percent of all cells have a count smaller than 5, fisher.test() is performed and displayed,
    otherwise chisqu.test(). In both cases case an additional mosaic plot is generated.
A library for creating time based charts, like Gantt or timelines. Possible outputs 
  include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be
  used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the 
  interactive outputs created by vistime() and hc_vistime(), you can interact with the 
  plot using mouse hover or zoom.
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs.
  In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits.
  Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.
Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.
Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.
Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martínez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martínez (2023, in press).
A collection of tools for analyzing the field of vision. It
    provides a framework for development and use of innovative methods for
    visualization, statistical analysis, and clinical interpretation of
    visual-field loss and its change over time. It is intended to be a tool for
    collaborative research. The package is described in Marin-Franch and Swanson
    (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative
    (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].
Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.
Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror.
To visualize the probabilities of early
      termination, fail and success of Simon's two-stage
      design.  To evaluate and visualize the operating
      characteristics of Simon's two-stage design.
Visual 2D point and contour plots for binary classification modeling under algorithms such as glm(), randomForest(), gbm(), nnet() and svm(), presented over two dimensions generated by FAMD and MCA methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses.
Visualizes vowel variation in f0, F1, F2, F3 and duration.
A Shiny application and functions for visual exploration of hierarchical clustering with numeric datasets. Allows users to iterative set hyperparameters, select features and evaluate results through various plots and computation of evaluation criteria.
Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    for the permutation variable importance measure in a random forest and the
    PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>.
    Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    do not use the "standard" permutation variable
    importance but the cross-validated permutation variable
    importance for the novel test approach. The cross-validated
    permutation variable importance is not based on the out-of-bag
    observations but uses a similar strategy which is inspired by
    the cross-validation procedure. The novel test approach can be
    applied for classification trees as well as for regression
    trees. However, the use of the novel testing approach has not
    been tested for regression trees so far, so this routine is
    meant for the expert user only and its current state is rather
    experimental.
Provides templates and functions to simplify the production and maintenance of curriculum vitae.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.
Analysis of minor alleles in Illumina sequencing data of viral
    genomes. Functions in 'vivaldi' primarily operate on vcf files.
A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.
Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.
Interactive visualization for Bayesian prior and posterior distributions. 
    This package facilitates an animated transition between prior and posterior distributions. 
    Additionally, it splits the distribution into bars based on the provided 'breaks,' displaying 
    the probability for each region. If no 'breaks' are provided, it defaults to zero.
Provides an interface to the VK API <https://vk.com/dev/methods>.
      VK <https://vk.com/> is the largest European online social networking
      service, based in Russia.
Contains functions to set up risk-adjusted quality control charts in health care. For the variable life adjusted display (VLAD) proposed by Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> signaling rules derived in Wittenberg et al. (2018) <doi:10.1002/sim.7647> are implemented. Additionally, for the risk-adjusted cumulative sum chart based on log-likelihood ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441> average run length and control limits can be computed with fast and accurate Markov chain approximations developed in Knoth et al. (2019) <doi:10.1002/sim.8104>.
Assists in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) using a consistent calling scheme for longitudinal data. It provides the ability to projection supplementary information (supplementary objects and variables) that can often occur in longitudinal data to graphs, as well as provides a new interactive implementation to perform the additional interpretation, so it is also useful for longitudinal data visuals analysis (see <http://lib.pusan.ac.kr/resource/e-article/?app=eds&mod=detail&record_id=edsker.000004649097&db_id=edsker> for more information).
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992>.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.
'RcppArmadillo' implementation for the Matlab code of the 'Variational Mode Decomposition' and 'Two-Dimensional Variational Mode Decomposition'. For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, <doi:10.1109/TSP.2013.2288675>; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, <doi:10.1007/978-3-319-14612-6_15>.
Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii)  Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.
A variety of tools to allow the quantification of videos of the 
    lymphatic vasculature taken under an operating microscope. Lymphatic vessels
    that have been injected with a variety of blue dyes can be tracked throughout
    the video to determine their width over time. Code is optimised
    for efficient processing of multiple large video files. Functions to calculate
    physiologically relevant parameters and generate graphs from these values 
    are also included.
Provides fast sampling from von Mises-Fisher distribution using the method proposed by Andrew T.A Wood (1994) <doi:10.1080/03610919408813161>.
Manage, provision and use Virtual Machines pre-configured for R.
             Develop, test and build package in a clean environment.
             'Vagrant' tool and a provider (such as 'Virtualbox') have to be installed.
An implementation of the variable neighborhood trust region 
    algorithm Bierlaire et al. (2009) "A Heuristic for Nonlinear Global 
    Optimization" <doi:10.1287/ijoc.1090.0343>.
Create adjacency matrices of vocalisation graphs from
  dataframes containing sequences of speech and silence intervals,
  transforming these matrices into Markov diagrams, and generating
  datasets for classification of these diagrams by 'flattening' them
  and adding global properties (functionals) etc.  Vocalisation
  diagrams date back to early work in psychiatry (Jaffe and Feldstein,
  1970) and social psychology (Dabbs and Ruback, 1987) but have only
  recently been employed as a data representation method for machine
  learning tasks including meeting segmentation (Luz, 2012)
  <doi:10.1145/2328967.2328970> and classification (Luz,
  2013) <doi:10.1145/2522848.2533788>.
Methods to calculate the expected value of information from a decision-analytic model.  This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS).  A range of alternative computational methods are provided under the same user interface.  See Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.
Tools for voice analysis, speaker recognition and mood inference. 
    Gathers 'R' and 'Python' tools to solve problems concerning voice and audio 
    in general.
Simplifies and largely automates practical voice analytics for social science research. This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 <doi:10.1016/j.jbusres.2020.09.020>.
'R' implementation of the 'vol2bird' software for generating vertical profiles
    of birds and other biological signals in weather radar data. See Dokter et al.
    (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.
Trading Strategies for high Option Volatility environment are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Generates interactive plots for analysing and visualising 
    three-class high dimensional data. It is particularly suited to visualising 
    differences in continuous attributes such as gene/protein/biomarker 
    expression levels between three groups. Differential gene/biomarker 
    expression analysis between two classes is typically shown as a volcano 
    plot. However, with three groups this type of visualisation is particularly 
    difficult to interpret. This package generates 3D volcano plots and 3-way 
    polar plots for easier interpretation of three-class data.
Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the 'safetyGraphics' R package.
Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume
             of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments
             (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms
             for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for
             estimating copulas useful in computational finance. Methods implemented in 'volesti' are described
             in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.
Volleyball match statistics of the German volleyball first division league (seasons 2013/2014 to 
    2018/2019). The data has been collected from the official volleyball first division homepage 
    (<www.volleyball-bundesliga.de>) and contains information on teams, staff, sets, matches, and player-in-match 
    statistics (extracted automatically from the official match reports).
Facilitates modeling species' ecological niches and 
  geographic distributions based on occurrences and environments that 
  have a vertical as well as horizontal component, and projecting models 
  into three-dimensional geographic space. Working in three dimensions is 
  useful in an aquatic context when the organisms one wishes to model can 
  be found across a wide range of depths in the water column. The package
  also contains functions to automatically generate marine training
  model training regions using machine learning, and interpolate and smooth
  patchily sampled environmental rasters using thin plate splines.
  Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) <doi:10.1038/ncomms11484>.
  Nychka D, Furrer R, Paige J, Sain S (2021) <doi:10.5065/D6W957CT>.
  Pateiro-Lopez B, Rodriguez-Casal A (2022) <https://CRAN.R-project.org/package=alphahull>.
The d3.js framework with the plugins d3-voronoi-map, d3-voronoi-treemap and d3-weighted-voronoi
        are used to generate Voronoi treemaps in R and in a shiny application.
        The computation of the Voronoi treemaps are based on Nocaj and Brandes (2012)
        <doi:10.1111/j.1467-8659.2012.03078.x>.
Export dataframes and automatically start importing into 
    'Vorteks'. 'Vorteks Visualization Environment (VVE)' and 'Vorteks Data Manager (VDM)' will start an import. 'Vorteks Processing Environment (VPE)' will start a new project and
    add a file reader with the dataframe file already set.
    Warning: WINDOWS ONLY. Requires installation of 'Vorteks' software.
Contains selected data from two publications,
    Campbell 'et' 'al'. (2016) <DOI:10.1080/14486563.2015.1028486>
    and 'Pacioni' 'et' 'al'. (2017) <DOI:10.1071/PC17002>.
    The data is provided both as raw outputs from the population viability
    analysis software 'Vortex' and packaged as R objects.
    The R package 'vortexR' uses the raw data provided here to illustrate its
    functionality of parsing raw 'Vortex' output into R objects.
Collects tweets and metadata for threaded conversations and
    generates networks.
A 'Shiny' application for the interactive visualisation and
    analysis of networks that also provides a web interface for collecting
    social media data using 'vosonSML'.
A suite of easy to use functions for collecting social media
    data and generating networks for analysis. Supports Twitter, YouTube,
    Reddit and web site data sources.
Generating realizations of a fractal Brownian function on uniform 1D & 2D grid with classic and generic versions of the Voss algorithm (random sequential additions).
Counting election votes and determining election results by different methods, including
    the single transferable vote or ranked choice, approval, score, plurality, condorcet and two-round runoff methods (Raftery et al., 2021 <doi:10.32614/RJ-2021-086>).
'ProPublica' <https://projects.propublica.org/represent/> makes United States
    Congress member votes available and has developed their own unique cartogram to visually
    represent this data. Tools are provided to retrieve voting data, prepare voting data 
    for plotting with 'ggplot2', create vote cartograms and theme them.
Provide functions to generate random simulated evaluations on candidates by voters for evaluation-based elections. Functions are based on several models for continuous or discrete evaluations.
An R interface to the Project 'VoteSmart'<https://justfacts.votesmart.org/> API.
Various methods to count ballots in voting systems are provided.
	Functions to check validity of ballots are also provided to ensure flexibility.
Calculates voter transitions comparing two elections, using the function solve.QP() in package 'quadprog'.
A wrapper around a CSS library called 'vov.css', intended for use 
  in 'shiny' applications. Simply wrap a UI element in one of the animation 
  functions to see it move.
Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data.  vowels is the backend for the NORM website.
Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.
Tools for 3D point cloud voxelisation, projection, geometrical and morphological description of trees (DBH, height, volume, crown diameter), analyses of temporal changes between different measurement times, distance based clustering and visualisation of 3D voxel clouds and 2D projection. Most analyses and algorithms provided in the package are based on the concept of space exploration and are described in Lecigne et al. (2018, <doi:10.1093/aob/mcx095>).
Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.
Variable Penalty Dynamic Time Warping (VPdtw) for aligning chromatographic signals. With an appropriate penalty this method performs good alignment of chromatographic data without deforming the peaks (Clifford, D., Stone, G., Montoliu, I., Rezzi S., Martin F., Guy P., Bruce S., and Kochhar S.(2009) <doi:10.1021/ac802041e>; Clifford, D. and Stone, G. (2012) <doi:10.18637/jss.v047.i08>).
An oceanographic data processing package for analyzing and visualizing Video 
    Plankton Recorder data.  This package was developed at 'Bedford Institute of Oceanography'.
    Functions are designed to process automated image classification output and create organized and 
    easily portable data products.
Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.
The goal of 'vroom' is to read and write data (like 'csv',
    'tsv' and 'fwf') quickly. When reading it uses a quick initial
    indexing step, then reads the values lazily , so only the data you
    actually use needs to be read.  The writer formats the data in
    parallel and writes to disk asynchronously from formatting.
A  collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.
Performs variable selection/feature reduction under a clustering or 
  classification framework. In particular, it can be used in an automated fashion 
  using mixture model-based methods ('teigen' and 'mclust' are currently supported).
  Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix').
  See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023)
  <doi:10.48550/arXiv.2305.16464>.
Provides a shim command for survival analysis graphic generation.
Provides decomposition methods for the skewness or variance of a variable (e.g., wage).
             By breaking distribution moments into independent components, users can 
             analyze changes in distributions across time or between groups.
An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2020) <doi:10.18637/jss.v096.c01>.
A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
Provides fast spectral estimation of latent factors in random
    dot product graphs using the vsp estimator. Under mild assumptions,
    the vsp estimator is consistent for (degree-corrected) stochastic
    blockmodels, (degree-corrected) mixed-membership stochastic
    blockmodels, and degree-corrected overlapping stochastic blockmodels.
A nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <arXiv:2303.10018>.
Implementation of 'Azure DevOps' <https://azure.microsoft.com/> API calls. 
    It enables the extraction of information about repositories, build and release definitions and individual releases. 
    It also helps create repositories and work items within a project without logging into 'Azure DevOps'. 
    There is the ability to use any API service with a shell for any non-predefined call.
Three steps variable selection procedure based on random forests.
    Initially developed to handle high dimensional data (for which number of
    variables largely exceeds number of observations), the package is very
    versatile and can treat most dimensions of data, for regression and
    supervised classification problems. First step is dedicated to eliminate
    irrelevant variables from the dataset. Second step aims to select all
    variables related to the response for interpretation purpose. Third step
    refines the selection by eliminating redundancy in the set of variables
    selected by the second step, for prediction purpose.
    Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015)
    <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges, and summary statistics. See the vignette "vtable" for a package overview.
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
    'vtreat' prepares variables so that data has fewer exceptional cases, making
    it easier to safely use models in production. Common problems 'vtreat' defends
    against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
    categorical levels (levels seen during application, but not during training). Reference: 
    "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.
A tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame.
An interactive document on  the topic of variance analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.
Estimates the type of variables in non-quality controlled data. The prediction is based on a random forest model, trained on over 5000 medical variables with accuracy of 99%. The accuracy can hardy depend on type and coding style of data.
Contains functions for visualization univariate data: ccdplot and qddplot.
Make it easy to use 'vue' in R with helper
              dependency functions and examples.
Contains functions for analysis and summary of tidal datasets. Also provides access to tidal data collected by the National Oceanic and Atmospheric Administration's Center for Operational Oceanographic Products and Services and the Permanent Service for Mean Sea Level. For detailed description and application examples, see Hill, T.D. and S.C. Anisfeld (2021) <doi:10.6084/m9.figshare.14161202.v1> and Hill, T.D. and S.C. Anisfeld (2015) <doi:10.1016/j.ecss.2015.06.004>.
Calculates the volume under the ROC surface and its (co)variance for ordered multi-class ROC analysis as well as certain bivariate ordinal measures of association.
Offers a comprehensive set of assertion tests to help users validate the integrity of their data. 
    These tests can be used to check for specific conditions or properties within a dataset and 
    help ensure that data is accurate and reliable. 
    The package is designed to make it easy to add quality control checks to data analysis 
    workflows and to aid in identifying and correcting any errors or inconsistencies in data.
Allow R users to interact with the 'Canvas' Learning Management System (LMS) API (see
    <https://canvas.instructure.com/doc/api/all_resources.html> for details).
    It provides a set of functions to access and manipulate course data, assignments, grades, users, 
    and other resources available through the 'Canvas' API.
Provides a set of functions for data transformations.
    Transformations are performed on character and numeric data. As the scope of the package is
    within Student Analytics, there are functions focused around the academic year.
Provides numerous functions to fill data.
    These can be applied either to missing or skewed data.
    The functions are designed within the scope of Student Analytics.
Offers a wide range of functions for reading and writing data in various file formats, 
    including CSV, RDS, Excel and ZIP files. 
    Additionally, it provides functions for retrieving metadata associated with files, 
    such as file size and creation date, making it easy to manage and organize large data sets. 
    This package is designed to simplify data import and export tasks, 
    and provide users with a comprehensive set of tools to work with different types of data files.
Provides a set of functions for manipulating data frames in accordance with specific business rules. 
    In addition, it includes wrapper functions for commonly used functions from the popular 'tidyverse' package, 
    making it easy to integrate these functions into data analysis workflows. 
    The package is designed to streamline data preprocessing and help users quickly and efficiently 
    perform data transformations that are specific to their business needs.
Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.
Provides an R interface for interacting with the 'Tableau' Server.
    It allows users to perform various operations such as publishing workbooks, refreshing data extracts, 
    and managing users using the 'Tableau' REST API (see <https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_ref.htm> for details). Additionally, 
    it includes functions to perform manipulations on local 'Tableau' workbooks.
Provides an R interface for interacting with the 'Semestry TermTime' services.
    It allows users to retrieve scheduling data from the API. see
    <https://github.com/vusaverse/vvtermtime/blob/main/openapi_7.7.0.pdf> for details.
Provides R functions to draw lines and curves with the width
             of the curve allowed to vary along the length of the curve.
Gaze data from the Visual World Paradigm requires significant
    preprocessing prior to plotting and analyzing the data. This package 
    provides functions for preparing visual world eye-tracking data for 
    statistical analysis and plotting. It can prepare data for linear 
    analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as
    well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM).
    Additionally, it contains various plotting functions for creating grand average and
    conditional average plots. See the vignette for samples of the functionality.
    Currently, the functions in this package are designed for handling data
    collected with SR Research Eyelink eye trackers using Sample Reports created
    in SR Research Data Viewer.  While we would like to add functionality 
    for data collected with other systems in the future, the current package is 
    considered to be feature-complete; further updates will mainly entail maintenance
	and the addition of minor functionality.
Set of functions that improves the graphical presentations of the functions: wave.correlation and spin.correlation (waveslim package, Whitcher 2012) and the wave.multiple.correlation and wave.multiple.cross.correlation (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in a scientific paper: Polanco-Martinez and Fernandez-Macho (2014), <doi:10.1109/MCSE.2014.96>.
R interface to a W3C Markup Validation service.
  See <https://validator.w3.org/> for more information.
Provides a set of utility function to prevent the spread of
    utilities script in W4M (Workflow4Metabolomics) scripts, and centralize
    them in a single package.
    Some are meant to be replaced by real packages in a near future, like
    the parse_args() function: it is here only to prepare the ground for
    more global changes in W4M scripts and tools.
Contains inferential and graphical routines for multi-group analysis of while-alive loss (or event) rate for possibly recurrent nonfatal event in the presence of death.
Color palettes taken from the landscapes and cities of Washington 
    state. Colors were extracted from a set of photographs, and then combined to 
    form a set of continuous and discrete palettes.  Continuous palettes were 
    designed to be perceptually uniform, while discrete palettes were chosen to
    maximize contrast at several different levels of overall brightness and 
    saturation. Each palette has been evaluated to ensure colors are 
    distinguishable by colorblind people.
A multivariate weather generator for daily climate variables based 
    on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). 
    It uses a Markov chain for modeling the succession of weather states. 
    Conditionally to the weather states, the multivariate variables are modeled using the family 
    of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must 
    include the variable 'Rain' and can accept as many other variables as desired.
A user-friendly factor-like interface for converting strings of
    text into numeric vectors and rectangular data structures.
Square pie charts (a.k.a. waffle charts) can be used
    to communicate parts of a whole for categorical quantities. To emulate the
    percentage view of a pie chart, a 10x10 grid should be used with each square
    representing 1% of the total. Modern uses of waffle charts do not
    necessarily adhere to this rule and can be created with a grid of any
    rectangular shape. Best practices suggest keeping the number of categories
    small, just as should be done when creating pie charts. Tools are provided
    to create waffle charts as well as stitch them together, and to use glyphs
    for making isotype pictograms.
Full screen and partial loading screens for 'Shiny' with spinners, progress bars, and notifications.
Generates random data sets including: data.frames, lists,
        and vectors.
Read 'Quake' assets including bitmap images and textures in 'wal' file format. This package also provides support for extracting these assets from 'WAD' and 'PAK' file archives. It can also read models in 'MDL' and 'MD2' formats.
Compare complex R objects and reveal the key differences.
    Designed particularly for use in testing packages where being able to
    quickly isolate key differences makes understanding test failures much
    easier.
Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.
Efficient Bayesian generalized linear models with time-varying coefficients 
    as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial 
    observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, the package uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
Easily collect walk scores, bike scores, and transit scores (where
    available) from the 'Walk Score' API <https://www.walkscore.com/professional/api.php>, 
    a proprietary API that assigns locations a walkability score between 0 and 100.
A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.
The 'shiny' application Wallace is a modular platform for 
    reproducible modeling of species niches and distributions. Wallace
    guides users through a complete analysis, from the acquisition of species
    occurrence and environmental data to visualizing model predictions on an 
    interactive map, thus bundling complex workflows into a single, 
    streamlined interface. An extensive vignette, which guides users through 
    most package functionality can be found on the package's GitHub Pages 
    website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.
Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.
A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.
Provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, 
    that contains data about stores, Value of the day and products which 
    includes names, sale prices, shipping rates and taxonomies.
A toolbox of common robust statistical tests, including robust
  descriptives, robust t-tests, and robust ANOVA. It is also available as a
  module for 'jamovi' (see <https://www.jamovi.org> for more information).
  Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on
  the scripts and work of Rand Wilcox. These analyses are described in depth in
  the book 'Introduction to Robust Estimation & Hypothesis Testing'.
Implements Weighted-Average Least Squares model averaging
    for negative binomial regression models of Huynh (2023) (mimeo),
    generalized linear models of De Luca, Magnus, Peracchi (2018) 
    <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of 
    Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also 
    Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares
    for the linear regression model is based on the original 'MATLAB' code by 
    Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also 
    Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and 
    De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.
The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.
'MIME' types are shorthand descriptors for
      file contents and can be determined from "magic"
      bytes in file headers, file contents or intuited
      from file extensions. Tools are provided to
      perform curated "magic" tests as well as mapping
      'MIME' types from a database of over 1,500
      extension mappings.
It generates the roster of turn for an outlet which is flowing 
        (water) 24X7 or 168 hours towards the area under command or agricutural 
        area (to be irrigated). The area under command is differentially owned 
        by different individual farmers. The Outlet runs for free of cost to 
        irrigate the area under command 24X7.
        So, flow time of the outlet has to be divided based on an area owned by 
        an individual farmer and the location of his land or farm. This roster 
        is known as 'warabandi' and its generation in agriculture practices is a 
        very tedious task. Calculations of time in microseconds are more 
        error-prone, especially whenever it is performed by hands. That division
        of flow time for an individual farmer can be calculated by 'warabandi'. 
        However, it generates a full publishable report for an outlet and all the 
        farmers who have farms subjected to be irrigated. 
        It reduces error risk and makes a more reproducible roster. For more 
        details about warabandi system you can found elsewhere in 
        Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.
Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. 'warbleR' makes use of the basic sound analysis tools from the package 'seewave', and offers new tools for acoustic structure analysis. The main features of the package are the use of loops to apply tasks through acoustic signals referenced in a selection (annotation) table and the production of spectrograms in image files that allow to organize data and verify acoustic analyzes. The package offers functions to explore, organize and manipulate multiple sound files, explore and download 'Xeno-Canto' recordings, detect signals automatically, create spectrograms of complete recordings or individual signals, run different measures of acoustic signal structure, evaluate the performance of measurement methods, catalog signals, characterize different structural levels in acoustic signals, run statistical analysis of duet coordination and consolidate databases and annotation tables, among others.
This estimates precise weaning ages
	for a given skeletal population
	by analyzing the stable nitrogen isotope ratios of them.
	Bone collagen turnover rates estimated anew and
	the approximate Bayesian computation (ABC)
	were adopted in this package.
Tooling to group dates by a variety of periods including:
    yearly, monthly, by second, by week of the month, and more.  The
    groups are defined in such a way that they also represent the distance
    between dates in terms of the period. This extracts valuable
    information that can be used in further calculations that rely on a
    specific temporal spacing between observations.
Mixed effects modeling with warping for functional data using B-
    spline. Warping coefficients are considered as random effects, and warping
    functions are general functions, parameters representing the projection onto B-
    spline basis of a part of the warping functions. Warped data are modelled by a
    linear mixed effect functional model, the noise is Gaussian and independent from
    the warping functions.
Urban water and sanitation survey dataset collected by Water and
    Sanitation for the Urban Poor (WSUP) with technical support from 
    Valid International. These citywide surveys have been collecting data 
    allowing water and sanitation service levels across the entire city to be 
    characterised, while also allowing more detailed data to be collected in 
    areas of the city of particular interest. These surveys are intended to 
    generate useful information for others working in the water and sanitation
    sector. Current release version includes datasets collected from a survey 
    conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of 
    a series of surveys to be conducted by WSUP in various
    cities in which they operate including Accra, Ghana; Nakuru, Kenya; 
    Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This 
    package will be updated once the surveys in other cities are completed and 
    datasets have been made available.
Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.
Gets data from the Washington State Legislature.
Create plots and tables in a consistent style with WaSHI
    (Washington Soil Health Initiative) branding. Use 'washi' to easily
    style your 'ggplot2' plots and 'flextable' tables.
The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.
Provides a set of functions to implement decision-making systems
    based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment),
    Chakraborty and Zavadskas (2012) <doi:10.5755/j01.eee.122.6.1810>.
    So this package offers functions that analyze and validate the
    raw data, which must be entered in a determined format;
    extract specific vectors and matrices from this raw database;
    normalize the input data; calculate rankings by intermediate methods;
    apply the lambda parameter for the main method; and a function that does
    everything at once. The package has an example database called choppers,
    with which the user can see how the input data should be organized so that
    everything works as recommended by the decision methods based on multiple
    criteria that this package solves. Basically, the data are composed of a set
    of alternatives, which will be ranked, a set of choice criteria, a matrix
    of values for each Alternative-Criterion relationship, a vector of weights
    associated with the criteria, since certain criteria are considered more
    important than others, as well as a vector that defines each criterion as
    cost or benefit, this determines the calculation formula, as there are those
    criteria that we want the highest possible value (e.g. durability)
    and others that we want the lowest possible value (e.g. price).
Functions to compute Wasserstein barycenters
    of subset posteriors using the swapping algorithm developed by Puccetti, 
    Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The 
    Wasserstein barycenter is a geometric approach for combining subset 
    posteriors. It allows for parallel and distributed computation of the 
    posterior in case of complex models and/or big datasets, thereby increasing
    computational speed tremendously.
Imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services (see <https://waterservices.usgs.gov/> for more information), plots the data, addresses some common data problems, and calculates and plots anomalies.
Provides support for creating waterfall charts in R
    using both traditional base and lattice graphics.
A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
The main purpose of waterquality is to quickly and easily convert
    satellite-based reflectance imagery into one or many well-known water quality
    algorithms designed for the detection of harmful algal blooms or the following
    pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity.
    Johansen et al. (2019) <doi:10.21079/11681/35053>.
Provides Water Year Hydrologic Classification Indices based on measured 
    unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources
    and subject to revision.
Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
    Longitudinal trajectories are shown in both Cartesian and polar coordinates.
    In many scenarios, a WATS plot more clearly shows the existence and effect size of
    of an intervention.
    This package accompanies
    "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs"
    by Rodgers, Beasley, & Schuelke (2014)
    <doi:10.1080/00273171.2014.946589>;
    see 'citation("Wats")' for details.
Tools for fitting and simulating mixtures of Watson distributions. 
             The random sampling scheme of the package offers two sampling 
             algorithms that are based of the results of Sablica, Hornik and Leydold (2022)
             <https://research.wu.ac.at/en/publications/random-sampling-from-the-watson-distribution>.
             What is more, the package offers a smart tool to combine these two methods,
             and based on the selected parameters, it approximates the relative sampling
             speed for both methods and picks the faster one. In addition, the package
             offers a fitting function for the mixtures of Watson distribution, that
             uses the expectation-maximization (EM) algorithm. Special features are
             the possibility to use multiple variants of the E-step and M-step,
             sparse matrices for the data representation and state of the art methods
             for numerical evaluation of needed special functions using the results of
             Sablica and Hornik (2022) <https://www.ams.org/journals/mcom/2022-91-334/S0025-5718-2021-03690-X/>.
Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. 
  Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit 
  PCM and other encodings.
Computes Bayesian wavelet shrinkage credible intervals for
	nonparametric regression.
	The method uses cumulants to derive Bayesian credible intervals for
	wavelet regression estimates.
	The first four cumulants of the posterior distribution of the
	estimates are expressed in terms of the observed data and integer
	powers of the mother wavelet functions.
	These powers are closely approximated by linear combinations of
	wavelet scaling functions at an appropriate finer scale.
	Hence, a suitable modification of the discrete wavelet transform allows
	the posterior cumulants to be found efficiently for any data set.
	Johnson transformations then yield the credible intervals themselves.
	Barber, S., Nason, G.P. and Silverman, B.W. (2002)
	<doi:10.1111/1467-9868.00332>.
Makes available code necessary to reproduce figures and
        tables in papers on the WaveD method for wavelet deconvolution
        of noisy signals as presented in The WaveD Transform in R,
        Journal of Statistical Software Volume 21, No. 3, 2007.
The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS  (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.
Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <DOI:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>.
Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021)  <doi:10.1007/s00500-021-06087-4>.
Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.
The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the  supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.
Contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.
The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.
Wavelet routines that calculate single sets of wavelet
    multiple regressions and correlations, and cross-regressions and
    cross-correlations from a multivariate time series.  Dynamic versions
    of the routines allow the wavelet local multiple (cross-)regressions
    and (cross-)correlations to evolve over time.
Functions for calculating the
    fetch (length of open water distance along given directions)
    and estimating wave energy from wind and wave monitoring data.
For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.
Originally designed application in the context of
    resource-limited plant research and breeding programs, 'waves'
    provides an open-source solution to spectral data processing and model
    development by bringing useful packages together into a streamlined
    pipeline.  This package is wrapper for functions related to the
    analysis of point visible and near-infrared reflectance measurements.
    It includes visualization, filtering, aggregation, preprocessing,
    cross-validation set formation, model training, and prediction
    functions to enable open-source association of spectral and reference
    data. This package is documented in a peer-reviewed manuscript in the
    Plant Phenome Journal <doi:10.1002/ppj2.20012>.  Specialized
    cross-validation schemes are described in detail in Jarquín et al.
    (2017) <doi:10.3835/plantgenome2016.12.0130>. Example data is from
    Ikeogu et al. (2017) <doi:10.1371/journal.pone.0188918>.
Spatial data are generally auto-correlated, meaning that if two 
  units selected are close to each other, then it is likely that they share the
  same properties. For this reason, when sampling in the population it is often
  needed that the sample is well spread over space. A new method to draw a sample
  from a population with spatial coordinates is proposed. This method is called
  wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
  to a spatial weights matrix to update the inclusion probabilities vector
  into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.
Basic wavelet routines for time series (1D), image (2D) and array 
  (3D) analysis.  The code provided here is based on wavelet methodology 
  developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); 
  the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as
  implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  
  All figures in chapters 4-7 of GSW (2001) are reproducible using this package 
  and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.
Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).
Computation of approximate potentials for both gradient and non
    gradient fields. It is known from physics that only gradient fields, also
    known as conservative, have a well defined potential function. Here we
    present an algorithm, based on the classical Helmholtz decomposition, to
    obtain an approximate potential function for non gradient fields.
    More information in
    Rodríguez-Sánchez (2020) <doi:10.1371/journal.pcbi.1007788>.
Assessing predictive models of spatial data can be
    challenging, both because these models are typically built for
    extrapolating outside the original region represented by training data
    and due to potential spatially structured errors, with "hot spots" of
    higher than expected error clustered geographically due to spatial
    structure in the underlying data. Methods are provided for assessing
    models fit to spatial data, including approaches for measuring the
    spatial structure of model errors, assessing model predictions at
    multiple spatial scales, and evaluating where predictions can be made
    safely. Methods are particularly useful for models fit using the
    'tidymodels' framework. Methods include Moran's I ('Moran' (1950)
    <doi:10.2307/2332142>), Geary's C ('Geary' (1954)
    <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)
    <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from
    'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement
    metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>)
    and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an
    implementation of the area of applicability methodology from 'Meyer'
    and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an
    implementation of multi-scale assessment as described in 'Riemann'
    'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
The BACON algorithms are methods for multivariate outlier
    nomination (detection) and robust linear regression by Billor, Hadi,
    and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension
    to weighted problems is due to Beguin and Hulliger (2008)
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see
    also <doi:10.21105/joss.03238>.
Provides efficient implementation of the Wild Binary Segmentation and Binary
    Segmentation algorithms for estimation of the number and locations of
    multiple change-points in the piecewise constant function plus Gaussian
    noise model.
Implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" <arXiv:2005.04089>. This diagnostic can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided.
Search and download data from the World Bank Data API.
Implements detection for the number and locations of
    the change-points in a time series using the Wild Binary Segmentation and
    the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) <doi:10.5705/ss.202015.0262>.
Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <http://dataexplorer.wittgensteincentre.org/>.
A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.
K-means clustering, hierarchical clustering, and PCA with observational 
    weights and/or variable weights. It also includes the corresponding functions 
    for data nuggets which serve as representative samples of large datasets.
    Cherasia et al., (2022) <doi:10.1007/978-3-031-22687-8_20>. 
    Amaratunga et al., (2009) <doi:10.1002/9780470317129>.
Retrieval the leaf area index (LAI) and soil moisture (SM) from  microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.
Implements inferential and graphic procedures for the semiparametric proportional 
  means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 
  2016, <doi:10.1093/biostatistics/kxv050>).
Allows users to create weighted confusion matrices and accuracy
    metrics that help with the model selection process for classification
    problems, where distance from the correct category is important. The
    package includes several weighting schemes which can be parameterized, as
    well as custom configuration options. Furthermore, users can decide
    whether they wish to positively or negatively affect the accuracy score
    as a result of applying weights to the confusion matrix. 'wconf' integrates
    well with the 'caret' package, but it can also work standalone when
    provided data in matrix form.
    References:
    Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package"
    <doi:10.18637/jss.v028.i05>
    Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization
    (and the mewto R package)" <doi:10.2139/ssrn.3805911>
    Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023)
    "A general framework for implementing distances for categorical variables"
    <arXiv:2301.02190v1>.
Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
A new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.
Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.
Provides efficient implementations of weighted dependence measures
  and related asymptotic tests for independence. Implemented measures are
  the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and
  Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and
  Hollander et al. (2015, ISBN:9780470387375).
There are a number of binary files associated with the
    'Webdriver'/'Selenium' project. This package provides functions to download
    these binaries and to manage processes involving them.
Implementations of network analysis including
    (1) assortativity coefficient of weighted and directed networks, 
    Yuan, Yan and Zhang (2021) <doi:10.1093/comnet/cnab017>,
    (2) centrality measures for weighted and directed networks,
    Opsahl, Agneessens and Skvoretz (2010) <doi:10.1016/j.socnet.2010.03.006>,
    Zhang, Wang and Yan (2022) <doi:10.1016/j.physa.2021.126438>,
    (3) clustering coefficient of weighted and directed networks, 
    Fagiolo (2007) <doi:10.1103/PhysRevE.76.026107> and 
    Clemente and Grassi (2018) <doi:10.1016/j.chaos.2017.12.007>,
    (4) rewiring networks with given assortativity coefficients,
    Wang, Yan, Yuan and Zhang (2022) <doi:10.1007/s11222-022-10161-8>,
    (5) preferential attachment network generation,
    Yuan, Wang, Yan and Zhang (2023) <doi:10.6339/23-JDS1110>.
Functions for finding and pulling data from the
  'Wisconsin Department of Natural Resources ArcGIS REST APIs'
  <https://dnrmaps.wi.gov/arcgis/rest/services> and 
  <https://dnrmaps.wi.gov/arcgis2/rest/services>.
Fetch and clean data from the World Database on Protected
    Areas (WDPA) and the World Database on Other Effective Area-Based
    Conservation Measures (WDOECM). Data is obtained from Protected Planet
    <https://www.protectedplanet.net/en>. To augment data cleaning procedures,
    users can install the 'prepr' R package (available at
    <https://github.com/dickoa/prepr>). For more information on this package,
    see Hanson (2022) <doi:10.21105/joss.04594>.
Functions are collected to analyse weather data for agriculture 
    purposes including to read weather records in multiple formats, 
    calculate extreme climate index.
Numerous time series admit autoregressive moving average (ARMA)
  representations, in which the errors are uncorrelated but not necessarily
  independent.
  These models are called weak ARMA by opposition to the standard ARMA models, 
  also called strong ARMA models, in which the error terms are supposed to be 
  independent and identically distributed (iid).
  This package allows the study of nonlinear time series models through weak 
  ARMA representations.
  It determines identification, estimation and validation for ARMA models and 
  for AR and MA models in particular. 
  Functions can also be used in the strong case.
  This package also works on white noises by omitting arguments 'p', 'q', 'ar'
  and 'ma'.
  See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and 
  Boubacar Maïnassara, Y. and Saussereau, B. (2018)
  <doi:10.1080/01621459.2017.1380030> for more details.
Package to read Empatica E4 data, perform several transformations, perform signal
    processing and analyses, including batch analyses.
Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, <doi:10.54302/mausam.v31i4.3477>. Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,<https://www.environmentandecology.com/volume-40-2022>.
Functions to convert between weather metrics, including conversions
    for metrics of temperature, air moisture, wind speed, and precipitation.
    This package also includes functions to calculate the heat index from
    air temperature and air moisture.
Provides Apache and IIS log analytics for transaction performance, client populations and workload definitions.
Chemical information from around the web. This package interacts 
    with a suite of web services for chemical information. Sources include: Alan
    Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver,
    ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet,
    NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata.
Organizational framework for web development in R including functions to  
    serve static and dynamic content via HTTP methods, includes the html5 package to
    create HTML pages, and offers other utility functions for common tasks related 
    to web development.
A client for the 'WebDriver' 'API'. It allows driving a
    (probably headless) web browser, and can be used to test web
    applications, including 'Shiny' apps. In theory it works with any
    'WebDriver' implementation, but it was only tested with 'PhantomJS'.
Functions for easily creating interactive web pages using
    'R Markdown' that students can use in self-guided learning.
Create a web app that makes it easier to test web clients
    without using the internet. It includes a web app framework with path
    matching, parameters and templates. Can parse various 'HTTP' request
    bodies. Can send 'JSON' data or files from the disk. Includes a web
    app that implements the 'httpbin.org' web service.
The web version WebGestalt <https://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.
Displays geospatial data on an interactive 3D globe in the web browser.
Creates interactive web maps using the 'JavaScript' 'Leaflet'
    library with base layers of 'The National Map' ('TNM'). 'TNM' services
    provide access to base geospatial information that describes the landscape
    of the United States and its territories. This package is dependent on, and
    intended to be used with, the 'leaflet' package.
Stubbing and setting expectations on 'HTTP' requests.
    Includes tools for stubbing 'HTTP' requests, including expected
    request conditions and response conditions. Match on
    'HTTP' method, query parameters, request body, headers and
    more. Can be used for unit tests or outside of a testing 
    context.
Create reproducible image stimuli, 
    specialised for face images with 'psychomorph' or 'webmorph' templates.
Lossless webp images are 26% smaller in size compared to PNG. Lossy
    webp images are 25-34% smaller in size compared to JPEG. This package reads
    and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using
    conventions from the 'jpeg' and 'png' packages.
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Several analysis-related functions for the book entitled
    "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9)
    by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for
    the object of class 'htest'.
R is used by a vast array of people for a vast array of purposes
    - including web analytics. This package contains functions for consuming and
    munging various common forms of request log, including the Common and Combined
    Web Log formats and various Amazon access logs.
A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.
Functions that allow for accessing domains and a number of search engines.
Takes screenshots of web pages, including Shiny applications and R
    Markdown documents.
Takes screenshots of web pages, including Shiny applications
    and R Markdown documents. 'webshot2' uses headless Chrome or Chromium
    as the browser back-end.
Provides a 'WebSocket' client interface for R.
    'WebSocket' is a protocol for low-overhead real-time communication:
    <https://en.wikipedia.org/wiki/WebSocket>.
Data structures and methods to work with web tracking data. The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 <doi:10.17605/OSF.IO/M3U9P>; Stier et al., 2022 <doi:10.1017/S0003055421001222>).
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Parses http request data in application/json, multipart/form-data, 
    or application/x-www-form-urlencoded format. Includes example of hosting
    and parsing html form data in R using either 'httpuv' or 'Rhttpd'.
Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.
Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.
Calculates the WEGE (Weighted Endemism including Global 
    Endangerment index) index for a particular area. Additionally it also 
    calculates rasters of KBA's (Key Biodiversity Area) criteria (A1a, A1b, A1e, 
    and B1), Weighted endemism (WE), the EDGE (Evolutionarily Distinct and
    Globally Endangered) score, Evolutionary Distinctiveness (ED) and Extinction
    risk (ER). Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C.,
    Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020)
    <doi:10.1101/2020.01.17.910299>.
A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://stats.wnba.com/> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.
Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" <doi:10.18637/jss.v043.i09>.
Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations.
  For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, 
  Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. 
  This work was supported by the National Research Foundation of Korea (NRF) grants funded by 
  the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).
Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).
Graphical data analysis of accelerated life tests. Methods derived from  Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6).
An interactive introduction to Life Data Analysis that depends on 'WeibullR' 
  by David Silkworth and Jurgen Symynck (2022) <https://CRAN.R-project.org/package=WeibullR>, 
  a R package for Weibull Analysis, and 'learnr' by Garrick Aden-Buie et al. (2023) 
  <https://CRAN.R-project.org/package=learnr>, a framework for building interactive learning 
  modules in R.
Build interactive Weibull Probability 
  Plots with 'WeibullR' by David Silkworth and Jurgen Symynck (2022) 
  <https://CRAN.R-project.org/package=WeibullR>, an R package for 
  Weibull analysis, and 'plotly' by Carson Sievert (2020) <https://plotly-r.com>, 
  an interactive web-based graphing library.
A 'Shiny' web application for life data analysis that depends on 'WeibullR' by David Silkworth and Jurgen Symynck (2022) 
  <https://CRAN.R-project.org/package=WeibullR>, an R package for Weibull analysis.
Weighted descriptive statistics is the discipline of quantitatively describing the main features of real-valued fuzzy data which usually given from a fuzzy population. One can summarize this special kind of fuzzy data numerically or graphically using this package. To interpret some of the properties of one or several sets of real-valued fuzzy data, numerically summarize is possible by some weighted statistics which are designed in this package such as mean, variance, covariance and correlation coefficent. Also, graphically interpretation can be given by weighted histogram and weighted scatter plot using this package to describe properties of real-valued fuzzy data set.
Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2022) <arXiv:2203.04619>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.
Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided.
The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.
A conditional independence test that can be applied both to
    univariate and multivariate random variables. The test is based on a
    weighted form of the sample covariance of the residuals after a
    nonlinear regression on the conditioning variables. Details are
    described in Scheidegger, Hoerrmann and Buehlmann (2021) "The Weighted
    Generalised Covariance Measure" <arXiv:2111.04361>. The test is a
    generalisation of the Generalised Covariance Measure (GCM) implemented
    in the R package 'GeneralisedCovarianceMeasure' by Jonas Peters and
    Rajen D. Shah based on Shah and Peters (2020) "The Hardness of
    Conditional Independence Testing and the Generalised Covariance
    Measure" <arXiv:1804.07203>.
An implementation of the Weighted Portmanteau Tests described
      in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing"
      published by the Journal of the American Statistical Association, Volume 107, 
      Issue 498, pages 777-787, 2012.
Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2018) <doi:10.1214/18-AOAS1153>.  The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>.  The main functions are wgtRank() and wgtRanktt() and ef2C().
Fast computation of
 Receiver Operating Characteristic (ROC) curves
 and Area Under the Curve (AUC)
 for weighted binary classification problems
 (weights are example-specific cost values).
The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011)  <doi:10.1093/biostatistics/kxr005>, Nikoloulopoulos (2016) <doi:10.1002/sim.6871> and Nikoloulopoulos (2017) <arXiv:1510.07376>.
Treemaps are a visually appealing graphical representation of
    numerical data using a space-filling approach. A plane or 'map' is
    subdivided into smaller areas called cells.  The cells in the map are
    scaled according to an underlying metric which allows to grasp the
    hierarchical organization and relative importance of many objects at
    once. This package contains two different implementations of treemaps,
    Voronoi treemaps and Sunburst treemaps.  The Voronoi treemap function
    subdivides the plot area in polygonal cells according to the highest
    hierarchical level, then continues to subdivide those parental cells
    on the next lower hierarchical level, and so on. The Sunburst treemap
    is a computationally less demanding treemap that does not require
    iterative refinement, but simply generates circle sectors that are
    sized according to predefined weights.  The Voronoi tesselation is
    based on functions from Paul Murrell (2012)
    <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.
Calculates z-differences (O.Kuss (2013) <doi:10.1016/j.jclinepi.2013.06.001>) for each variable scale (continuous, binary, ordinal and nominal) with or without weights (e.g. generated by propensity score methods).
Generates balancing weights for causal effect estimation in observational studies with
             binary, multi-category, or continuous point or longitudinal treatments by easing and
             extending the functionality of several R packages and providing in-house estimation methods.
             Available methods include propensity score weighting using generalized linear models, gradient
             boosting machines, the covariate balancing propensity score algorithm, Bayesian additive regression trees, and
             SuperLearner, and directly estimating balancing weights using entropy balancing, energy balancing, and optimization-based weights. Also
             allows for assessment of weights and checking of covariate balance by interfacing directly
             with the 'cobalt' package. See the vignette "Installing Supporting Packages" for instructions on how
             to install any package 'WeightIt' uses, including those that may not be on CRAN.
Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.
Estimates the Vevea and Hedges (1995)
    weight-function model. By specifying arguments, users can
    also estimate the modified model described in Vevea and Woods (2005), 
    which may be more practical with small datasets. Users 
    can also specify moderators to estimate a linear model. 
    The package functionality allows users to easily extract the 
    results of these analyses as R objects for other uses. In addition, 
    the package includes a function to launch both models as 
    a Shiny application. Although the Shiny application is also available online, 
    this function allows users to launch it locally if they choose.
Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights. NOTE: Weighted partial correlation calculations pulled to address a bug.
Functions for subject/instance weighted support vector machines (SVM). 
    It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing 
    any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some 
    improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. 
    The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and
    published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.
Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).
Palettes generated mostly from 'Wes Anderson' movies.
The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.
A suite of routines for Weyl algebras.  Notation follows
   Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic
   D-Modules").  Uses 'disordR' discipline
   (Hankin 2022 <doi:10.48550/ARXIV.2210.03856>).  To cite
   the package in publications, use Hankin
   2022 <doi:10.48550/ARXIV.2212.09230>.
Provides a computationally efficient way of fitting
	     weighted linear fixed effects estimators for causal
	     inference with various weighting schemes. Weighted linear
	     fixed effects estimators can be used to estimate the
	     average treatment effects under different identification
	     strategies. This includes stratified randomized
	     experiments, matching and stratification for
	     observational studies, first differencing, and
	     difference-in-differences. The package implements methods
	     described in Imai and Kim (2017) "When should We Use
	     Linear Fixed Effects Regression Models for Causal
	     Inference with Longitudinal Data?", available at
	     <https://imai.fas.harvard.edu/research/FEmatch.html>.
Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.
Provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder.
Provides a convenient data set, a set of helper functions, and a benchmark function for
    economically (profit) driven wind farm layout optimization. This enables researchers in the field
    of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization
    to focus on their optimization methodology contribution and also provides a realistic benchmark
    setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020)
    <doi:10.1016/j.energy.2020.119244>.
A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Weighted generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale correlation structures are estimated based on quasi-likelihood. Multiple model selection criterion are provided for selection of mean model and working correlation structure based on WGEE/GEE.
Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test.
Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.
An enhanced implementation of Whittaker-Henderson smoothing for the gradation 
    of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks.
    'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>.
    Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, 
    automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.
Inferences about counterfactuals are essential for prediction,
      answering what if questions, and estimating causal effects.
      However, when the counterfactuals posed are too far from the data at
      hand, conclusions drawn from well-specified statistical analyses
      become based largely on speculation hidden in convenient modeling
      assumptions that few would be willing to defend. Unfortunately,
      standard statistical approaches assume the veracity of the model
      rather than revealing the degree of model-dependence, which makes this
      problem hard to detect. WhatIf offers easy-to-apply methods to
      evaluate counterfactuals that do not require sensitivity testing over
      specified classes of models. If an analysis fails the tests offered
      here, then we know that substantive inferences will be sensitive to at
      least some modeling choices that are not based on empirical evidence,
      no matter what method of inference one chooses to use. WhatIf
      implements the methods for evaluating counterfactuals discussed in
      Gary King and Langche Zeng, 2006, "The Dangers of Extreme
      Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; 
      and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The 
      Pitfalls of Counterfactual Inference," International Studies 
      Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.
Imports 'WhatsApp' chat logs and parses them into
    a usable dataframe object. The parser works on chats exported 
    from Android or iOS phones and on Linux, macOS and Windows. The parser has  multiple options for extracting smileys and emojis
    from the messages, extracting URLs and domains from the messages, extracting names and types of sent 
    media files from the messages, extracting timestamps from messages, extracting and anonymizing author 
    names from messages. Can be used to create anonymized versions of data.
Use the 'what3words' API 
    <https://developer.what3words.com/public-api> to return three words which 
    uniquely identify every 3m x 3m square on Earth. It is also possible to 
    return coordinates from any valid three words location. Supports multiple 
    languages.
Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as 'LeftOf', 'RightOf', 'TopOf', and 'Beneath' and more. Other features include clustering, dendrograms and integration with 'ggplot2' generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.
In Multidimensional Systems the When dimension allows us to
    express when the analysed facts have occurred. The purpose of this
    package is to provide support for implementing this dimension in the
    form of date and time tables for Relational On-Line Analytical Processing 
    star database systems.
Robust and reliable functions to return informative outputs to 
        console with the run or source location of a command. This can be from
        the  'RScript'/R terminal commands or 'RStudio' console, source editor, 
        'Rmarkdown' document and a Shiny application.
Retrieve geographical information for airports using their IATA or ICAO codes.
Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.
An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.
Provides a set of functions to make tracking the hidden movements 
  of the 'Jack' player easier. By tracking every possible path Jack might have 
  traveled from the point of the initial murder including special movement such 
  as through alleyways and via carriages, the police can more accurately narrow 
  the field of their search. Additionally, by tracking all possible hideouts from 
  round to round, rounds 3 and 4 should have a vastly reduced field of search.
Implements the whitening methods (ZCA, PCA, Cholesky,
 ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
 "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
 as well as the whitening approach to canonical correlation analysis allowing
 negative canonical correlations described in Jendoubi and Strimmer (2019)
 "A whitening approach to probabilistic canonical correlation analysis for omics
 data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers
 functions to simulate random orthogonal matrices, compute (correlation) loadings
 and explained variation. It also contains four example data sets (extended UCI 
 wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).
Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.
Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008>
    introduced 'WhiteStripe', an intensity-based normalization of T1 
    and T2 images, where normal 
    appearing white matter performs well, but requires segmentation.
    This method performs white matter mean and standard deviation
    estimates on data that has been rigidly-registered to the 'MNI'
    template and uses histogram-based methods.
Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 <doi:10.32614/RJ-2021-048>). In addition to parallel methods, data wrangling and additional statistical attributes are provided.
This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth.  It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) <doi:10.1111/eva.12659>, and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin.
Look up the username and full name of the current user,
    the current user's email address and 'GitHub' username,
    using various sources of system and configuration information.
The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey
    instrument to assess the distribution of disability within a country or 
    region, grounded in the International Classification of Functioning, 
    Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions 
    for calculating and presenting the results from this survey, as used by 
    the WHO. The package primarily provides functions for implementing
    Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to
    calculate a metric scale for disability.
Interactive tools for generating random samples. Users select an
    .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the
    sample type (Simple Random Sample or Stratified), the number of backups
    desired, and a "stratify_on" value (if desired). The sample size is determined
    using a normal approximation to the hypergeometric distribution based on
    Nicholson (1956) <doi:10.1214/aoms/1177728270>. An .xlsx file is created
    with the sample and key metadata for reference. It is menu-driven and lets
    users pick an output directory. See vignettes for a detailed walk-through.
Providing a user-friendly interface to digitize wood imagery such as tree ring scans. The package offers a web-based application that the user can run locally from their computers to analyze wood characteristics.
Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.
Provides two functions 'frameableWidget()', and 'frameWidget()'.
  The 'frameableWidget()' is used to add extra code to a 'htmlwidget' which
  allows is to be rendered correctly inside a responsive 'iframe'.
  The 'frameWidget()' is a 'htmlwidget' which displays content of another 'htmlwidget'
  inside a responsive 'iframe'.
  These functions allow for easier embedding of 'htmlwidgets' in content management systems
  such as 'wordpress', 'blogger' etc.
  They also allow for separation of widget content from main HTML content where
  CSS of the main HTML could interfere with the widget.
Encapsulates the pattern of untidying data into a wide
    matrix, performing some processing, then turning it back into a tidy
    form. This is useful for several operations such as co-occurrence
    counts, correlations, or clustering that are mathematically convenient
    on wide matrices.
Retrieve and import data from different databases of the Federal Statistical Office of Germany (DESTATIS) using their SOAP XML web service <https://www-genesis.destatis.de/>.
The german Wikibook "GNU R" introduces R to new users. This package is a collection of functions and datas used in the german WikiBook "GNU R".
An API client for the 'Wikidata Query Service'
    <https://query.wikidata.org/>.
Read from, interogate, and write to Wikidata <https://www.wikidata.org> -
    the multilingual, interdisciplinary, semantic knowledgebase. Includes functions to:
    read from wikidata (single items, properties, or properties); query wikidata (retrieving
    all items that match a set of criterial via Wikidata SPARQL query service); write to
    Wikidata (adding new items or statements via QuickStatements); and handle and manipulate
    Wikidata objects (as lists and tibbles). Uses the Wikidata and Quickstatements APIs.
Query Wikidata and get facts from current and historic Wikipedia main pages.
Scrape lake metadata tables from Wikipedia <https://www.wikipedia.org/>.
A wrapper for the MediaWiki API, aimed particularly at the
    Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
    page text, information about users or the history of pages, and elements of
    the category tree.
Queries online 'WikiPathway' graphics and allows mapping user data (e.g., expression values) on the graph. The package designs a grammar of graphic syntax that using pipe operator to add graphic layer.
'Taxonomic' information from 'Wikipedia', 'Wikicommons',
    'Wikispecies', and 'Wikidata'. Functions included for getting
    taxonomic information from each of the sources just listed, as
    well performing taxonomic search.
A set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.
Access 'Wikipedia' through the several 'MediaWiki' APIs
    (<https://www.mediawiki.org/wiki/API>), as well as through the
    'XTools' API (<https://www.mediawiki.org/wiki/XTools/API>). Ensure
    your API calls are correct, and receive results in tidy tibbles.
An implementation of the 1-Sample Wilcoxon Sign rank test for medians. 
              It includes 2 functions, W_stat(), which computes the 
              exact probabilities of the Wilcoxon Sign Rank Test Statistic, W.
              The second function, Wilcox.m.test() allows the user to conduct the
              1-Sample Wilcoxon Sign Rank hypothesis test for medians, this also 
              allows the user to conduct the hypothesis test for the normal approximation, 
              based on the techniques of Bickel and Doksum (1973, ISBN:013850363X).
Dynamic interaction refers to spatial-temporal associations
    in the movements of two (or more) animals. This package provides tools for
    calculating a suite of indices used for quantifying dynamic interaction with
    wildlife telemetry data. For more information on each of the methods employed
    see the references within. The package (as of version >= 0.3) also has new tools for
    automating contact analysis in large tracking datasets. The package draws 
    heavily on the classes and methods developed in the 'adehabitat' packages.
Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.
Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game.  At this time the package does not support player tactics, so draw poker variants are not included.
Fast Routines to Compute Romano-Wolf corrected p-Values 
             (Romano and Wolf (2005a) <DOI:10.1198/016214504000000539>, 
             Romano and Wolf (2005b) <DOI:10.1111/j.1468-0262.2005.00615.x>) 
             for objects of type 'fixest' and 'fixest_multi' from the 'fixest' 
             package via a wild (cluster) bootstrap.
Fetches data from three disparate data sources and allows user to perform analyses on them. It offers two core components: 1. A robust data retrieval and preparation infrastructure for wildfire, climate, and air quality index data and 2. A simple, informative, and interactive visualizations of the aforementioned datasets for California counties from 2011 through 2015. The sources of data are: wildfire data from Kaggle <https://www.kaggle.com/rtatman/188-million-us-wildfires>, climate data from the National Oceanic and Atmospheric Administration  <https://www.ncdc.noaa.gov/cdo-web/token>, and air quality data from the Environmental Protection Agency <https://aqs.epa.gov/aqsweb/documents/data_api.html>.
Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data.
    This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.
Obtain the native stack trace and fuse it with R's
    stack trace for easier debugging of R packages with native code.
Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.
Provides a tool to fit and compare the wind turbine power curves 
    with successful curve fitting techniques. Facilitates to examine and compare 
    the performance of a user-defined power curve fitting techniques. 
    Also, provide features to generate power curve discrete points from a graphical 
    power curves. Data on the power curves of the wind turbine from major 
    manufacturers are provided.
Analysing convergent evolution using the Wheatsheaf index, described in Arbuckle et al. (2014) <doi: 10.1111/2041-210X.12195>, and some other unrelated but perhaps useful functions.
Evaluation of prediction performance of smaller regions of
    spectra for Chemometrics. Segmentation of spectra, evolving dimensions
    regions and sliding windows as selection methods. Election of the best
    model among those computed based on error metrics. Chen et al.(2017)
    <doi:10.1007/s00216-017-0218-9>.
Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the 'Windsor.ai' API <https://www.windsor.ai/api-fields/>.
Computes inequality measures of a given variable taking into account weights. Bootstrap method provides distribution of inequality measures and several additional statistics.
Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the 'WINFAP' software, hence the name of the package.
Calculate the win ratio for prioritized outcomes and the 95% confidence interval based on Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>. Three type of outcomes can be analyzed: survival "failure-time" events, repeated survival "failure-time" events and continuous or ordinal "non-failure time" events that are captured at specific time-points in the study.
Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis and inverse probability of censoring weighting (IPCW) analysis.
Provides simple, fast functions for maximum likelihood and Bayesian estimates of wildlife population parameters, suitable for use with simulated data or bootstraps. Early versions were indeed quick and dirty, but optional error-checking routines and meaningful error messages have been added. Includes single and multi-season occupancy, closed capture population estimation, survival, species richness and distance measures.
In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others.
    It could be that some individuals are known to harbor more micro-environmental variance than others.
    In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means.
    Package 'wISAM' handles this situation by allowing for weighting of each observation according to residual variance.
    Specifically, the 'weight' parameter to the function conduct_scan() takes the precision of each observation (one over the variance).
Adds ... to a function's argument list so that it can
    tolerate non-matching arguments.
A set of functions to run code 'with' safely and
    temporarily modified global state. Many of these functions were
    originally a part of the 'devtools' package, this provides a simple
    package with limited dependencies to provide access to these
    functions.
Provides a minimal R and C++ API for parsing
  well-known binary and well-known text representation of
  geometries to and from R-native formats.
  Well-known binary is compact
  and fast to parse; well-known text is human-readable
  and is useful for writing tests. These formats are
  useful in R only if the information they contain can be
  accessed in R, for which high-performance functions
  are provided here.
Utility functions to convert between the 'Spatial' classes
  specified by the package 'sp', and the well-known binary '(WKB)'
  representation for geometry specified by the 'Open Geospatial Consortium'.
  Supports 'Spatial' objects of class 'SpatialPoints',
  'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
  'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
  types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
  'MultiPolygon'. Includes extensions to enable creation of maps with
  'TIBCO Spotfire'.
Converts weekly data to monthly data.
     Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.
Provides extra utilities for well-known formats in the
  'wk' package that are outside the scope of that package. Utilities
  to parse coordinates from data frames, plot well-known geometry
  vectors, extract meta information from well-known geometry vectors,
  and calculate bounding boxes are provided.
It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
Use various regression models for the analysis of win loss endpoints 
             adjusting for non-binary and multivariate covariates.
Calculate magnetic field at a given location and time according to 
  the World Magnetic Model (WMM). Both the main field and secular variation 
  components are returned. This functionality is useful for physicists and 
  geophysicists who need orthogonal components from WMM. Currently, this package 
  supports annualized time inputs between 2000 and 2025. If desired, users can
  specify which WMM version to use, e.g., the original WMM2015 release or the 
  recent out-of-cycle WMM2015 release. Methods used to implement WMM, including 
  the Gauss coefficients for each release, are described in the following 
  publications: Chulliat et al (2020) <doi:10.25923/ytk1-yx35>,
  Chulliat et al (2019) <doi:10.25921/xhr3-0t19>, 
  Chulliat et al (2015) <doi:10.7289/V5TB14V7>, 
  Maus et al (2010) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf>, 
  McLean et al (2004) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf>,
  and Macmillian et al (2000) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf>.
Power calculator for the two-sample Wilcoxon-Mann-Whitney rank-sum test for a continuous outcome (Mollan, Trumble, Reifeis et. al., Mar. 2020) <doi:10.1080/10543406.2020.1730866> <arXiv:1901.04597>, (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.
Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test
    that is needed for a given power and two sided type I error rate. The method works for metric data with and without
    ties, count data, ordered categorical data, and even dichotomous data.
    But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect.
    For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
New tools for the imputation of missing values in high-dimensional
    data are introduced using the non-parametric nearest neighbor methods. It
    includes weighted nearest neighbor imputation methods that use specific
    distances for selected variables. It includes an automatic procedure of cross
    validation and does not require prespecified values of the tuning parameters.
    It can be used to impute missing values in high-dimensional data when the sample
    size is smaller than the number of predictors. For more information see Faisal
    and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
Estimates Poole and Rosenthal's (1985 <doi:10.2307/2111172>, 1991 <doi:10.2307/2111445>)
	W-NOMINATE scores from roll call votes supplied though a 'rollcall'
	object from the 'pscl' package.
Descriptive statistics for large data tend to be low resolution on the tails. 
    Whisker Odds generate a table of descriptive statistics for large data. This is the same as
    letter-values, but with an alternative naming of depths which allow for depths beyond 26. For 
    a reference to letter-values see 'Heike Hofmann' and 'Hadley Wickham' and 'Karen Kafadar' (2017) <doi:10.1080/10618600.2017.1305277>.
Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
Implements an automated binning of numeric variables and factors with
 respect to a dichotomous target variable.
 Two approaches are provided: An implementation of fine and coarse classing that
 merges granular classes and levels step by step. And a tree-like approach that
 iteratively segments the initial bins via binary splits. Both procedures merge,
 respectively split, bins based on similar weight of evidence (WOE) values and
 stop via an information value (IV) based criteria.
 The package can be used with single variables or an entire data frame. It provides
 flexible tools for exploring different binning solutions and for deploying them to
 (new) data.
Implements a spatiotemporal boundary detection model with a dissimilarity
    metric for areal data with inference in a Bayesian setting using Markov chain
    Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
    probit or Tobit link and spatial correlation is introduced at each time point
    through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
    through a hierarchical structure and can be specified as exponential or first-order
    autoregressive. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in "Diagnosing Glaucoma 
    Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", 
    by Berchuck et al (2018), <arXiv:1805.11636>. The paper is in press at the Journal of 
    the American Statistical Association.
A hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.
Tools for generating simulated sawn timber
    strength grading data with a main focus on statistical simulation based on
    covariance matrices. Simulation data
    for Norway spruce sawn timber from Austria and reference values of means and
    standard deviations of grade determining properties from literature
    for a number of European countries are provided, as well.
Monetary valuation of wood in German forests
             (stumpage values), including estimations of harvest quantities, 
             wood revenues, and harvest costs. The functions are sensitive to
             tree species, mean diameter of the harvested trees, stand quality,
             and logging method. The functions include estimations for the
             consequences of disturbances on revenues and costs. The underlying
             assortment tables are taken from Offer and Staupendahl (2018) with
             corresponding functions for salable and skidded volume derived in
             Fuchs et al. (in preparation). Wood revenue and harvest cost
             functions were taken from v. Bodelschwingh (2018). The consequences
             of disturbances refer to Dieter (2001), Moellmann and Moehring
             (2017), and Fuchs et al. (2022a, 2022b). For the full references 
             see documentation of the functions, package README, and Fuchs et
             al. (in preparation). Apart from Dieter (2001) and Moellmann and
             Moehring (2017), all functions and factors are based on data from
             HessenForst, the forest administration of the Federal State of
             Hesse in Germany.
Students learning both econometrics and R may find the introduction 
    to both challenging. The wooldridge data package aims to lighten the task by efficiently 
    loading any data set found in the text with a single command. Data sets have been 
    compressed to a fraction of their original size. Documentation files contain page numbers, 
    the original source, time of publication, and notes from the author suggesting avenues for 
    further analysis and research. If one needs an introduction to R model syntax, a 
    vignette contains solutions to examples from chapters of the text. 
    Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), 
    and are backwards compatible with all previous versions of the text.
Create reproducible and transparent research projects in 'R'.
    This package is based on the Workflow for Open
    Reproducible Code in Science (WORCS), a step-by-step procedure based on best
    practices for
    Open Science. It includes an 'RStudio' project template, several
    convenience functions, and all dependencies required to make your project
    reproducible and transparent. WORCS is explained in the tutorial paper
    by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021).
    <doi:10.3233/DS-210031>.
For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers one-to-many and symmetrization alignments. Moreover, it evaluates the quality of word alignment based on this package and some other software. It also builds an automatic dictionary of two languages based on given parallel corpus.
Learn vector representations of words by continuous bag of words and skip-gram implementations of the 'word2vec' algorithm. 
    The techniques are detailed in the paper "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al. (2013), available at <arXiv:1310.4546>.
Connecting to Wordbank, an open repository for developmental
    vocabulary data. For more information on the underlying data, see
    <http://wordbank.stanford.edu>.
Functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.
A fast visualization tool for creating wordcloud
    by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create 
    wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.
The 'Wordle' game. Players have six attempts to guess a 
    five-letter word. After each guess, the player is informed which 
    letters in their guess are either: anywhere in the word; in the right 
    position in the word. This can be used to inform the next guess. Can be 
    played interactively in the console, or programmatically. Based on Josh 
    Wardle's game <https://www.powerlanguage.co.uk/wordle/>.
An interface to WordNet using the Jawbone Java API to WordNet.
  WordNet (<https://wordnet.princeton.edu/>) is a large lexical database of
  English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
  cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
  are interlinked by means of conceptual-semantic and lexical relations.
  Please note that WordNet(R) is a registered tradename.  Princeton
  University makes WordNet available to research and commercial users
  free of charge provided the terms of their license
  (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed,
  and proper reference is made to the project using an appropriate
  citation (<https://wordnet.princeton.edu/citing-wordnet>).
Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks.
Apply 'Wordpiece' (<arXiv:1609.08144>) tokenization to input text, 
 given an appropriate vocabulary. The 'BERT' (<arXiv:1810.04805>) tokenization 
 conventions are used by default.
Provides data to be used by the wordpiece algorithm in order to 
    tokenize text into somewhat meaningful chunks. Included vocabularies were 
    retrieved from 
    <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and 
    <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed
    into an R-friendly format.
Collects several classical word pools used most often
    to provide lists of words in psychological studies of learning and memory. It
    provides a simple function, 'pickList' for selecting random samples of words
    within given ranges.
A framework for developing n-gram models for text prediction.
    It provides data cleaning, data sampling, extracting tokens from text,
    model generation, model evaluation and word prediction. For information on how n-gram models 
    work we referred to: "Speech and Language Processing"
    <https://web.stanford.edu/~jurafsky/slp3/3.pdf>. For optimizing R code and
    using R6 classes we referred to "Advanced R" 
    <https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to 
    "R Packages", <https://r-pkgs.org/index.html>.
The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. If all letters are revealed before your guesses run out, you win this game; otherwise you fail. You may run multiple games to guess different words.
Serves for rendering MS Word documents with R inline code and inserting tables and plots.
List of english scrabble words as listed in the OTCWL2014
    <https://www.scrabbleplayers.org/w/Official_Tournament_and_Club_Word_List_2014_Edition>.
    Words are collated from the 'Word Game Dictionary' <https://www.wordgamedictionary.com/word-lists/>.
Provides access to various word embedding methods (GloVe, 
    fasttext and word2vec) to extract word vectors using a unified framework to
    increase reproducibility and correctness.
An interactive laboratory for research on distributional semantic models ('DSM',
             see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
Provides a workflow for your analysis projects by combining
  literate programming ('knitr' and 'rmarkdown') and version control
  ('Git', via 'git2r') to generate a website containing time-stamped,
  versioned, and documented results.
Managing both a 'parsnip' model and a preprocessor, such as a
    model formula or recipe from 'recipes', can often be challenging. The
    goal of 'workflows' is to streamline this process by bundling the
    model alongside the preprocessor, all within the same object.
A workflow is a combination of a model and preprocessors
    (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021)
    <https://www.tmwr.org/>). In order to try different combinations of
    these, an object can be created that contains many workflows. There
    are functions to create workflows en masse as well as training them
    and visualizing the results.
Functions for the import, transformation, and analysis of data 
    from muscle physiology experiments. The work loop technique is used to 
    evaluate the mechanical work and power output of muscle. Josephson (1985) 
    <doi:10.1242/jeb.114.1.493> modernized the technique for
    application in comparative biomechanics. Although our initial motivation 
    was to provide functions to analyze work loop experiment data, as we 
    developed the package we incorporated the ability to analyze data from 
    experiments that are often complementary to work loops. There are currently 
    three supported experiment types: work loops, simple twitches, and tetanus 
    trials. Data can be imported directly from .ddf files or via an object 
    constructor function. Through either method, data can then be cleaned or 
    transformed via methods typically used in studies of muscle physiology. 
    Data can then be analyzed to determine the timing and magnitude of force 
    development and relaxation (for isometric trials) or the magnitude of work, 
    net power, and instantaneous power among other things (for work loops). 
    Although we do not provide plotting functions, all resultant objects are 
    designed to be friendly to visualization via either base-R plotting or 
    'tidyverse' functions.
 This package has been peer-reviewed by rOpenSci (v. 1.1.0).
World Flora Online is an online flora of all known plants, available from <https://www.worldfloraonline.org/>. Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (<http://www.theplantlist.org/>), a working list of plant names that has become static since 2013.
Allow users to obtain clean and tidy
    football (soccer) game, team and player data. Data is collected from a
    number of popular sites, including 'FBref',
    transfer and valuations data from
    'Transfermarkt'<https://www.transfermarkt.com/> and shooting location
    and other match stats data from 'Understat'<https://understat.com/>
    and 'fotmob'<https://www.fotmob.com/>. It gives users the
    ability to access data more efficiently, rather than having to export
    data tables to files before being able to complete their analysis.
Functions to import data from more than 30,000 surface
    meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface
    Database (ISD).
A toolkit to detect clusters from distance matrices. 
    The distance matrices are assumed to be calculated between the cells of 
    multiple animals ('Caenorhabditis elegans') from input time-series matrices. 
    Some functions for generating distance matrices, performing clustering, 
    evaluating the clustering, and visualizing the results of clustering and 
    evaluation are available. We're also providing the download function to 
    retrieve the calculated distance matrices from 
    'figshare' <https://figshare.com>.
Client for World Register of Marine Species
    (<https://www.marinespecies.org/>). Includes functions for each
    of the API methods, including searching for names by name, date and
    common names, searching using external identifiers, fetching
    synonyms, as well as fetching taxonomic children and
    taxonomic classification.
Generate wordsearch and crossword puzzles using
    custom lists of words (and clues).  Make them easy or hard,
    and print them to solve offline with paper and pencil!
R clients to the 'Web of Science' and 'InCites' 
  <https://clarivate.com/products/data-integration/> APIs, which 
  allow you to programmatically download publication and citation data
  indexed in the 'Web of Science' and 'InCites' databases.
It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.
Introduce weights into Ordered Weighted Averages and extend bivariate means based on n-ary tree construction. Please refer to the following:
	G. Beliakov, H. Bustince, and T. Calvo (2016, ISBN: 978-3-319-24753-3),
	G. Beliakov(2018) <doi:10.1002/int.21913>,
	G. Beliakov, J.J. Dujmovic (2016) <doi:10.1016/j.ins.2015.10.040>,
	J.J. Dujmovic and G. Beliakov (2017) <doi:10.1002/int.21828>.
This method generates a tour path by interpolating between d-D frames in p-D using Givens rotations. The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See Buja et al (2005) <doi:10.1016/S0169-7161(04)24014-7>.
Opinionated functions that enable easier and faster
    analysis of Viva Insights data. There are three main types of functions in 'wpa':
    (i) Standard functions create a 'ggplot' visual or a summary table based on a specific
    Viva Insights metric; (2) Report Generation functions generate HTML reports on
    a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover
    more specific applications (e.g. Subject Line text mining) of Viva Insights data.
    This package adheres to 'tidyverse' principles and works well with the pipe syntax.
    'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for
    simplicity.
Supplies permutation-test alternatives to traditional hypothesis-test
  procedures such as two-sample tests for means, medians, and standard deviations;
  correlation tests; tests for homogeneity and independence; and more. Suitable for
  general audiences, including individual and group users, introductory statistics
  courses, and more advanced statistics courses that desire an introduction to
  permutation tests.
Weighted Piecewise Kernel Density Estimation for large data.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
        2010
Data from the United Nation's World Population Prospects 2012
Provides data from the United Nation's World Population Prospects 2015.
Provides data from the United Nation's World Population Prospects 2017.
Provides data from the United Nation's World Population Prospects 2019.
Explore data in the 'wpp2019' (or 2017, 2015, ...) package using a 'shiny' interface.
Functions to assist in the processing and
    exploration of data from environmental monitoring programs.
    The package name stands for "water quality" and reflects the
    original focus on time series data for physical and chemical
    properties of water, as well as the biota. Intended for
    programs that sample approximately monthly, quarterly or
    annually at discrete stations, a feature of many legacy data
    sets. Most of the functions should be useful for analysis of
    similar-frequency time series regardless of the subject
    matter.
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. (2021) <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. (2015) <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. (2022) <doi:10.1289/EHP10570>).
Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using
  Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error
  Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>.
  Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and
  visualization of results.
Implements various win ratio methodologies for composite endpoints of death and 
  non-fatal events, including the (stratified) proportional win-fractions (PW) regression models 
  (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly 
  recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 
  2021 <doi:10.1111/biom.13501>).
Supports systematic scrutiny, modification, and integration of
    data. The function status() counts rows that have missing values in 
    grouping columns (returned by na() ), have non-unique combinations of 
    grouping columns (returned by dup() ), and that are not locally sorted
    (returned by unsorted() ). Functions enumerate() and itemize() give 
    sorted unique combinations of columns, with or without occurrence counts,
    respectively. Function ignore() drops columns in x that are present in y,
    and informative() drops columns in x that are entirely NA; constant() returns
    values that are constant, given a key.  Data that have defined unique 
    combinations of grouping values behave more predictably during merge operations.
The main functionalities of 'wrappedtools' are:
    adding backticks to variable names; rounding to desired precision 
    with special case for p-values; 
    selecting columns based on pattern and storing their position, name, 
    and backticked name; computing and formatting of descriptive statistics 
    (e.g. mean±SD), comparing groups and creating publication-ready tables with 
    descriptive statistics and p-values; creating specialized plots for 
    correlation matrices. Functions were mainly written for my own daily work or 
    teaching, but may be of use to others as well.
Helper functions to easily add functionality to functions.  The package can 
  assign functions to have an lazy evaluation allowing you to save and update the 
  arguments before and after each function call.  You can set a temporary working 
  directory within functions and wrap console messages around other functions.
Tools for writing and debugging R code. Provides: 
    '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return),
    'build_frame()'/'draw_frame()' ('data.frame' example tools),
    'qc()' (quoting concatenate), 
    ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard
    evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.
A wrapper around Michel Scheffers's 'libassp' (<https://libassp.sourceforge.net/>).
    The 'libassp' (Advanced Speech Signal Processor) library aims at providing
    functionality for handling speech signal files in most common audio formats
    and for performing analyses common in phonetic science/speech science. This
    includes the calculation of formants, fundamental frequency, root mean
    square, auto correlation, a variety of spectral analyses, zero crossing
    rate, filtering etc. This wrapper provides R with a large subset of
    'libassp's signal processing functions and provides them to the user in a
    (hopefully) user-friendly manner.
A collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.
Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) <doi:10.1002/sim.9297>.
Additional options for making graphics in the context of analyzing high-throughput data are available here.
  This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, 
  automatic checking for optimal location of legends in plots, small histograms to insert as legends, 
  histograms re-transforming axis labels to linear when plotting log2-transformed data,
  a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats, 
  principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples, 
  generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>, 
  staggered count plots and generation of mouse-over interactive html pages.
Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
        or matrix.
Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'. 
    Fast and no Java or Excel required.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
             files from one or more data frames. Each data frame will be
             written to a separate named worksheet in the Excel spreadsheet.
             The worksheet name will be the name of the data frame it contains
             or can be specified by the user.
The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. 
  Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc.  
  Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM)  
  for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). 
  Other functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. 
  Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. 
  Many times large experimental datasets need some additional filtering, adequate functions are provided. 
  Batch reading (or writing) of sets of files and combining data to arrays is supported, too. 
  Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Water resources system simulator is a tool for simulation and analysis of large-scale water resources systems. 'WRSS' proposes functions and methods for construction, simulation and analysis of primary storage and hydropower water resources features (e.g. reservoirs, aquifers, and etc.) based on Standard Operating Policy (SOP).
A collection of implementations of classical and
    novel algorithms for weighted sampling without replacement.
An adaptation for estuaries (tidal waters) of weighted regression
    on time, discharge, and season to evaluate trends in water quality time series. 
    Please see Beck and Hagy (2015) <doi:10.1007/s10666-015-9452-8> for 
    details.
Top-Down mass spectrometry aims to identify entire proteins as well as their (post-translational) modifications or ions bound (eg Chen et al (2018) <doi:10.1021/acs.analchem.7b04747>). 
    The pattern of internal fragments (Haverland et al (2017) <doi:10.1007/s13361-017-1635-x>) may reveal important information about the original structure of the proteins studied 
    (Skinner et al (2018) <doi:10.1038/nchembio.2515> and Li et al (2018) <doi:10.1038/nchem.2908>). 
    However, the number of possible internal fragments gets huge with longer proteins and subsequent identification of internal fragments remains challenging, 
    in particular since the the accuracy of measurements with current mass spectrometers represents a limiting factor.   
    This package attempts to deal with the complexity of internal fragments and allows identification of terminal and internal fragments from deconvoluted mass-spectrometry data.
Predicts individual race/ethnicity using surname, first name, middle name, geolocation,
    and other attributes, such as gender and age. The method utilizes Bayes'
    Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given
    individual. The package implements methods described in Imai and Khanna (2016)
    "Improving Ecological Inference by Predicting Individual Ethnicity from Voter
    Registration Records" Political Analysis <DOI:10.1093/pan/mpw001>.
Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.
Includes a variety of methods to compute objects related to the 'Wasserstein distance' (also known as 'Kantorovich distance' or 'Earth-Mover distance'). The main effort of this package is to allow for computations of 'Wasserstein barycenter' using regularised, unregularised and stochastic methods. It also provides convenient wrappers to call the 'transport' package with more general inputs. Handy visual tools are provided to showcase, barycenters, animations of optimal transport geodesics and animations of principal components in the 'Wasserstein space'. It also includes tools to compute 'Kantorovich-Rubinstein' distances and barycenters.
Easily override the default visual choices in 'ggplot2' to make 
    your time series plots look more like the Wall Street Journal. Specific 
    theme design choices include omitting x-axis grid lines and displaying 
    sparse light grey y-axis grid lines. Additionally, this allows to label 
    the y-axis scales with your units only displayed on the top-most number, 
    while also removing the bottom most number (unless specifically 
    overridden). The goal is visual simplicity, because who has time to waste 
    looking at a cluttered graph?
Entropy weighted k-means (ewkm) by Liping Jing, Michael
        K. Ng and Joshua Zhexue Huang (2007)
        <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering
        algorithm that is well suited to very high dimensional data.
        Weights are calculated as the importance of a variable with
        regard to cluster membership.  The two-level variable
        weighting clustering algorithm tw-k-means (twkm) by Xiaojun
        Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013)
        <doi:10.1109/TKDE.2011.262> introduces two types of weights,
        the weights on individual variables and the weights on
        variable groups, and they are calculated during the clustering
        process.  The feature group weighted k-means (fgkm) by Xiaojun
        Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012)
        <doi:10.1016/j.patcog.2011.06.004> extends this concept by
        grouping features and weighting the group in addition to
        weighting individual features.
A weighted selection probability to locate rare variants associated with multiple phenotypes.
A parallel implementation of Weighted Subspace Random Forest.  The
    Weighted Subspace Random Forest algorithm was proposed in the
    International Journal of Data Warehousing and Mining by Baoxun Xu,
    Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
    (2012) <DOI:10.4018/jdwm.2012040103>.  The algorithm can classify
    very high-dimensional data with random forests built using small
    subspaces.  A novel variable weighting method is used for variable
    subspace selection in place of the traditional random variable
    sampling.This new approach is particularly useful in building
    models from high-dimensional data.
Tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.
Perform the calculation of W-test, diagnostic checking, calculate
    minor allele frequency (MAF) and odds ratio.
Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.  More about the methodology can be found at <arXiv:1711.04702>.
Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).
Allows form managers to download entries from their respondents
    using Wufoo JSON API (<https://www.wufoo.com>). Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.
Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'.  
   Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, 
   conditioned scatter plot with marginal densities,
   box and stem with matching theoretical distribution, and density with matching theoretical distribution.
Book is "Linear Mixed Models: A Practical Guide Using
        Statistical Software" published in 2006 by Chapman Hall / CRC
        Press.
Provides a collection of white noise hypothesis tests for functional time series and related visualizations. 
  These include tests based on the norms of autocovariance operators that are built under both strong and weak 
  white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
  dimensional reduction are included, which are built under strong white noise assumptions. 
  Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
  These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) 
  <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, 
  and Kim et al. (2023) <doi: 10.1214/23-SS143>
  respectively.
A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 20 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc.
    Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. <https://www.usbr.gov/lc/phoenix/programs/lscrbasin/LSCRBS_Hydroclimate_2021.pdf>.
    Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.
Meta testing is the ability to test a function without having to 
    provide its parameter values.
    Those values will be generated, based on semantic naming of parameters, as 
    introduced by package 'wyz.code.offensiveProgramming'.
    Value generation logic can be completed with your own data types 
    and generation schemes. This to meet your most specific requirements and to 
    answer to a wide variety of usages, from general use case to very specific
    ones.
    While using meta testing, it becomes easier to generate stress test 
    campaigns, non-regression test campaigns and robustness test campaigns, as 
    generated tests can be saved and reused from session to session. 
    Main benefits of using 'wyz.code.metaTesting' is ability to discover valid 
    and invalid function parameter combinations, ability to infer valid 
    parameter values, and to provide smart summaries that allows you to focus
    on dysfunctional cases.
Allows to turn standard R code into offensive programming code. 
    Provides code instrumentation to ease this change and tools to assist and 
    accelerate code production and tuning while using offensive programming code 
    technics. 
    Should improve code robustness and quality. Function calls can be easily 
    verified on-demand or in batch mode to assess parameter types and length 
    conformities. 
    Should improve coders productivity as offensive programming reduces the code
    size due to reduced number of controls all along the call chain. 
    Should speed up processing as many checks will be reduced to one single check.
Allows to generate on-demand or by batch, any R documentation file,
    whatever is kind, data, function, class or package. It populates
    documentation sections, either automatically or by considering
    your input. Input code could be standard R code or offensive programming code. 
    Documentation content completeness depends on the type of code you use. With
    offensive programming code, expect generated documentation to be fully 
    completed, from a format and content point of view. With some standard R 
    code, you will have to activate post processing to fill-in any section that 
    requires complements. Produced manual page validity is automatically tested 
    against R documentation compliance rules. Documentation language 
    proficiency, wording style, and phrasal adjustments remains your job.
Allows to generate automatically 'testthat' code files from offensive 
    programming test cases. Generated test files are complete and ready to run.
    Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of
    errors in test case production, be able to test immediately generated files 
    without any need to view or modify them, and enter a zero time latency between 
    code implementation and industrial testing. As with 'testthat', you may
    complete provided test cases according to your needs to push testing further, 
    but this need is nearly void when using 'wyz.code.offensiveProgramming'.
Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. 'x.ent' uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction.
The 'X13-ARIMA-SEATS' <https://www.census.gov/data/software/x13as.html> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.
The US Census Bureau provides a seasonal adjustment program now
 called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and
 X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau
 offers both source and binary versions -- which this package integrates for
 use by other R packages.
The x3p file format  is specified in ISO standard 5436:2000 to 
    describe 3d surface measurements. 'x3ptools' allows reading, writing and 
    basic modifications to the 3D surface measurements.
A few functions which provide a quick way of subsetting
    genomic admixture data and generating customizable stacked barplots.
Create HTML5 slides with R Markdown and the JavaScript library
    'remark.js' (<https://remarkjs.com>).
Extras and extensions for 'xaringan' slides. Navigate your
    slides with tile view. Make your slides editable, live! Announce slide
    changes with subtle tones. Animate slide transitions with
    'animate.css'. Add tabbed panels to slides with 'panelset'. Use the
    'Tachyons CSS' utility toolkit for rapid slide development. Scribble
    on your slides. Add a copy button to your code chunks with
    'clipboard'. Add a logo or top or bottom banner to every slide.
    Broadcast slides to stay in sync with remote viewers. Include yourself
    in your slides with 'webcam'.  Plus a whole lot more!
Create beautifully color-coordinated and customized themes
    for your 'xaringan' slides, without writing any CSS. Complete your
    slide theme with 'ggplot2' themes that match the font and colors used
    in your slides.  Customized styles can be created directly in your
    slides' 'R Markdown' source file or in a separate external script.
Functions to extract business financial information from
  an Extensible Business Reporting Language ('XBRL') instance file and the
  associated collection of files that defines its 'Discoverable' Taxonomy
  Set ('DTS').
Functions for Estimating a (c)DCC-GARCH Model in large dimensions
    based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>.
    This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf>
    and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>,
    <doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).
Miscellaneous functions used for x-engineering (feature engineering) or 
  for supporting in other packages maintained by 'Shichen Xie'.
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
Read and write XES Files to create event log objects used by the 'bupaR' framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).
Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.
This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 
  'XGBoost' <https://xgboost.readthedocs.io/en/latest/index.html> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms
  in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, 
  and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Extreme Gradient Boosting, which is an efficient implementation
    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
    This package is its R interface. The package includes efficient linear
    model solver and tree learning algorithms. The package can automatically
    do parallel computation on a single machine which could be more than 10
    times faster than existing gradient boosting packages. It supports
    various objective functions, including regression, classification and ranking.
    The package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
Compute surrogate explanation groves for predictive machine learning models and analyze complexity vs. explanatory power of an explanation according to Szepannek, G. and von Holt, B. (2023) <doi:10.1007/s41237-023-00205-2>.
Supports a structured approach
    for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also
    contains helper functions for enabling the modeler to follow best R
    practices (by appending the program name, figure name location, and
    draft status to each plot). In addition, it enables the modeler to
    follow best graphical practices (by providing a theme that
    reduces chart ink, and by providing time-scale, log-scale, and
    reverse-log-transform-scale functions for more readable axes).
    Finally, it provides some data checking and summarizing functions for
    rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.
Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relax the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. A proposed excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>.
Computes robust association measures that do not 
    presuppose linearity. The xi correlation (xicor) is based
    on cross correlation between ranked increments.
    The reference for the methods implemented here is 
    Chatterjee, Sourav (2020) <arXiv:1909.10140>
    This package includes the Galton peas example.
Provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back
                    to XML code. It's not as powerful as the 'XML' package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for
                    the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin development easier.
Plotting ggplot2 graphs using the XKCD style.
The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.
Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.
Provides comprehensive functionality to read, write and format Excel data.
Parse entire folders of non-rectangular 'xlsx' files into a single
  rectangular and tidy 'data.frame' based on a custom template file defining the 
  column names of the output.
The expression of X-chromosome undergoes three possible biological processes: X-chromosome
             inactivation (XCI), escape of the X-chromosome inactivation (XCI-E),and skewed X-chromosome                    
             inactivation (XCI-S). To analyze the X-linked genetic association for phenotype such as                         
             continuous, binary, and time-to-event outcomes with the actual process unknown, we propose a unified            
             approach of maximizing the likelihood or partial likelihood over all of the potential biological             
             processes. The methods are described in Wei Xu, Meiling Hao (2017) <doi:10.1002/gepi.22097>. And 
             also see Dongxiao Han, Meiling Hao, Lianqiang Qu, Wei Xu (2019) <doi:10.1177/0962280219859037>.
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
Reading and writing sheets of a single 'Excel' file into and from a list of data frames. Eases I/O of tabular data in bioinformatics while keeping them in a human readable format.
The xlsxjars package collects all the external jars
  required for the xlxs package. This release corresponds to POI
  3.10.1.
A toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.
Many approaches for both reading and
        creating XML (and HTML) documents (including DTDs), both local
        and accessible via HTTP or FTP.  Also offers access to an
        'XPath' "interpreter".
Work with XML files using a simple, consistent interface.
    Built on top of the 'libxml2' C library.
XML2R is a framework that reduces the effort required to transform
    XML content into number of tables while preserving parent to child
    relationships.
Import an XML document with nested object structures and convert
    it into a relational data model. The result is a set of R dataframes 
    with foreign key relationships. The data model and the data can be exported as
    SQL code of different SQL flavors.
Converts XML documents to R dataframes and dataframes to XML documents.
  A wide variety of options allows for different XML formats and flexible control
  of the conversion process. Results can be exported to CSV and Excel, if desired. 
  Also converts XML data to R lists.
Convert the output of 'utils::getParseData()' to an 'XML'
    tree, that one can search via 'XPath', and easier to manipulate in
    general.
'XML' package for creating and reading and manipulating 'XML', with an object model based on 'Reference Classes'.
The 'XML-RPC' is a remote procedure call protocol 
    based on 'XML'. The 'xmlrpc2' package is inspired by the 'XMLRPC'
	package but uses the 'curl' and 'xml2' packages instead 'RCurl' and 'XML'.
Edit 'XMP' metadata <https://en.wikipedia.org/wiki/Extensible_Metadata_Platform> 
    in a variety of media file formats as well as 
    edit bookmarks (aka outline aka table of contents) and documentation info entries in 'pdf' files.
    Can detect and use a variety of command-line tools to perform these operations such as
    'exiftool' <https://exiftool.org/>, 'ghostscript' <https://www.ghostscript.com/>, 
    and/or 'pdftk' <https://gitlab.com/pdftk-java/pdftk>.
XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.
Fit a two-step kernel ridge regression model for
        predicting edges in networks, and carry out cross-validation
        using shortcuts for swift and accurate performance assessment
        (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.
Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model. See, for example,
    Broman and Weber (2000) <doi:10.1086/302923>.
Cross platform solution to open files, directories or 'URLs'
    with their associated programs.
Helps systematize and ease the process of 
    building unit tests with the 'testthat' package by providing 
    tools for generating expectations.
Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The 'xplain' interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.
Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.
Provides a set of functions devoted to multivariate exploratory statistics on textual data. Classical methods such as correspondence analysis and agglomerative hierarchical clustering are available. Chronologically constrained agglomerative hierarchical clustering enriched with labelled-by-words trees is offered. Given a division of the corpus into parts, their characteristic words and documents are identified. Further, accessing to 'FactoMineR' functions is very easy. Two of them are relevant in textual domain. MFA() addresses multiple lexical table allowing applications such as dealing with multilingual corpora as well as simultaneously analyzing both open-ended and closed questions in surveys. See <http://xplortext.unileon.es> for examples.
Tools to build CDISC compliant data sets and check for CDISC compliance.
Diagnostics for non-linear mixed-effects (population) 
    models from 'NONMEM' <https://www.iconplc.com/solutions/technologies/nonmem/>. 
    'xpose' facilitates data import, creation of numerical run summary 
    and provide 'ggplot2'-based graphics for data exploration and model 
    diagnostics.
Extension to 'xpose' to support 'nlmixr2'. Provides functions to import 'nlmixr2' fit data into an 'xpose' data object, allowing the use of 'xpose' for 'nlmixr2' model diagnostics.
A model building aid for nonlinear mixed-effects 
    (population) model analysis using NONMEM, facilitating data set 
    checkout, exploration and visualization, model diagnostics, candidate 
    covariate identification and model comparison. The methods are described 
    in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) 
    <doi:10.1016/s0169-2607(98)00067-4>.
Support for interfaces from R to other languages,
    built around a class for evaluators and a combination of functions, classes and
    methods for communication. Will be used through a specific language interface
    package. Described in the book "Extending R".
Tools to analyze datasets previous to any statistical modeling.
    Has various functions designed to find inconsistencies and understanding the distribution of the data.
An implementation of the RuleFit algorithm as described in Friedman & Popescu 
  (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used 
  to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result
  is a model that learns similarly to a tree ensemble, while often offering improved interpretability
  and achieving improved scoring runtime in live applications. Several algorithms for
  reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to 
  several million rows and support sparse representations to handle tens of thousands of dimensions.
Contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.
A Julia interface structured according to the general
	     form described in package 'XR' and in the book "Extending R".
Fits hierarchical regularized regression models
    to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. 
    Utilizes coordinate descent to efficiently fit regularized regression models both with and without
    external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). 
    Support for standard R matrices, sparse matrices and big.matrix objects.
A 'Python' interface structured according to the general
    form described in package 'XR' and in the book "Extending R".
This is a set of statistical quality control functions,  that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.
An extension for the 'xml2' package to transform XML documents
    by applying an 'xslt' style-sheet.
The circadian period of a time series data is predicted and the statistical significance of the periodicity are calculated using the chi-square periodogram.
Tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.
This is the tool set for crystallographer to design and analyze crystallization experiments, especially for ribosome from Mycobacterium tuberculosis.
There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Based on 'STATA' xtsum command, it is used to compute summary statistics for a panel data set. It generates overall, between-group, and within-group statistics for specified variables in a panel data set, as presented in S. Porter (2023) <https://stephenporter.org/files/xtsum_handout.pdf>, StataCorp (2023) <https://www.stata.com/manuals/xtxtsum.pdf>.
Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.
This is a collection of some useful functions when dealing with text data. 
    Currently it only contains a very efficient function of decoding HTML entities
    in character vectors by 'Rcpp' routine.
Calculates a number of valuation adjustments including CVA, DVA,
    FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For
    the KVA calculation three regulatory frameworks are supported: CEM, (simplified) SA-CCR, OEM
	and IMM. The probability of default is implied through the credit spreads curve.
    The package supports an exposure calculation based on SA-CCR which includes several trade types
    and a simulated path which is currently available only for IRSwaps. The latest regulatory capital charge methodologies
    have been implementing including BA-CVA & SA-CVA.
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.
A suite of psychometric analysis tools for research and operation, including:
    (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM;
    (2) parameter estimation using joint or marginal likelihood estimation method;
    (3) simulation of computerized adaptive testing using built-in or customized algorithms;
    (4) assembly and simulation of multistage testing. 
    The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.
An alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods.  A method for generating helio plots is also included.
Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. In case of computational heavy load, the procedures can run in parallel.
Another implementation of general regression neural network in R
    based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the 
    functional approximation or the classification.
Obtain historical and near real time data related to stocks, index 
    and currencies from the Yahoo Finance API. This package is community maintained 
    and is not officially supported by 'Yahoo'. The accuracy of data is only as 
    correct as provided on <https://finance.yahoo.com/>.
Performs nearest neighbor-based imputation using one or more alternative 
    approaches to processing multivariate data. These include methods based on canonical 
    correlation: analysis, canonical correspondence analysis, and a multivariate adaptation 
    of the random forest classification and regression techniques of Leo Breiman and Adele 
    Cutler. Additional methods are also offered. The package includes functions for 
    comparing the results from running alternative techniques, detecting imputation targets 
    that are notably distant from reference observations, detecting and correcting 
    for bias, bootstrapping and building ensemble imputations, and mapping results.
This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The big.read.table() function provided here may be
    useful for large files when only a subset is needed (but please
    see the note in the help page for this function).
Implements the 'libyaml' 'YAML' 1.1 parser and emitter
  (<https://pyyaml.org/wiki/LibYAML>) for R.
A YAML-based
 mechanism for working with table metadata. Supports
 compact syntax for creating, modifying, viewing, exporting,
 importing, displaying, and plotting metadata coded as column 
 attributes. The 'yamlet' dialect is valid 'YAML' with
 defaults and conventions chosen to improve readability. 
 See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.
Setting layout through 'YAML' headers in 'R-Markdown' documents,
    enabling their automatic generation.
    Functions and methods may summarize 'R' objects in automatic reports, for
    instance check-lists and further reports applied to the packages 'taxlist'
    and 'vegtable'.
Tidy tools for quantifying how well model fits to a data set
    such as confusion matrices, class probability curve summaries, and
    regression metrics (e.g., RMSE).
A parser and a writer for 'WEKA' Attribute-Relation File Format
    <https://waikato.github.io/weka-wiki/arff_stable/> in pure R, with no dependencies. 
    As opposed to other R implementations, this package can read standard
    (dense) as well as sparse files, i.e. those where each row does only contain 
    nonzero components. Unlike 'RWeka', 'yarr' does not require any 'Java' installation
    nor is dependent on external software. This implementation is generalized from 
    those in packages 'mldr' and 'mldr.datasets'.
Contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://www.thepiratesguidetor.com>.
A collection of string functions designed for writing 
    compact and expressive R code. 'yasp' (Yet Another String Package) is simple,
    fast, dependency-free, and written in pure R. The package provides: a
    coherent set of abbreviations for paste() from package 'base' with a
    variety of defaults, such as p() for "paste" and pcc() for "paste and
    collapse with commas"; wrap(), bracket(), and others for wrapping a
    string in flanking characters; unwrap() for removing pairs of characters
    (at any position in a string); and sentence() for cleaning whitespace
    around punctuation and capitalization appropriate for prose sentences.
Provides functions to manage taxonomy when lineages are
    described with strings and ranks separated with special patterns like
    "|*__" or ";*__".
Determines the sum of squares of the (2^n)-1 factorial effects in a 2^n factorial experiment using Yates' algorithm.
Nonparametric estimation of the discount rate and yield curve. 
    Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> 
    describe the application with the Center for Research in Security Prices (CRSP) Bond Data 
    and document the methods of this package.
Asks Yes-No questions with variable or custom responses.
Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, 
 a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system
 and support for parallel computation.
The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.
Deploy, maintain, and invoke models via the Yhat
    REST API.
Modelling the yield curve with some parametric models.
        The models implemented are: 
		Nelson, C.R., and A.F. Siegel (1987) <doi: 10.1086/296409>, 
		Diebold, F.X. and Li, C. (2006) <doi: 10.1016/j.jeconom.2005.03.005> 
		and Svensson, L.E. (1994) <doi: 10.3386/w4871>. 
		The package also includes the data of the term structure of interest rate of Federal Reserve Bank and European Central Bank.
Compute the standard expected years of life lost (YLL),
    as developed by the Global Burden of Disease Study
    (Murray, C.J., Lopez, A.D. and World Health Organization, 1996).
    The YLL is based on comparing the age of death to an external standard life
    expectancy curve. It also computes the average YLL, which highlights premature
    causes of death and brings attention to preventable deaths
    (Aragon et al., 2008).
Convert 'YMD' format number or string to Date efficiently, using Rust's
    standard library. It also provides helper functions to handle Date, e.g., quick
    finding the beginning or end of the given period, adding months to Date, etc.
The Author's personal R Package that contains miscellaneous functions. 
    The current version of package contains miscellaneous functions for brain data 
    to compute Asymmetry Index (AI) and bilateral (L+R) measures and reshape the data.
Write 'YAML' front matter for R Markdown and related
    documents. Work with 'YAML' objects more naturally and write the
    resulting 'YAML' to your clipboard or to 'YAML' files related to your
    project.
Provides a collection of lightweight helper functions (imps) both
  for interactive use and for inclusion within other packages. These include
  minimal assertion functions with a focus on informative error messaging for
  both missing and incorrect function arguments as well as other functions for
  visualising colour palettes, quoting user input and searching rows of a data
  frame.
Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <https://cricsheet.org/>.
Dataset from the young elite swimmers study.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
For any spending function specified by the user, this 
    package provides corresponding boundaries for interim testing using
    the adaptively weighted log-rank test developed by Yang and Prentice
    (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). 
    The package uses a re-sampling method to obtain stopping boundaries 
    at the interim looks.The output consists of stopping boundaries 
    and observed values of the test statistics at the interim looks, 
    along with nominal p-values defined as the probability of the test 
    exceeding the specific observed test statistic value or critical 
    value, regardless of the test behavior at other looks. 
    The asymptotic validity of the stopping boundaries is established
    in Yang (2018 <doi:10.1002/sim.7958>).
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
Provides covariate-adjusted comparison of two groups of right 
    censored data, where the binary group variable has separate short-term 
    and long-term effects on the hazard function, while effects of covariates 
    such as age, blood pressure, etc. are proportional on the hazard. The model 
    was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends 
    the two sample version of the short-term and long-term hazard ratio model
    proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model 
    extends the usual Cox proportional hazards model to allow more flexible 
    hazard ratio patterns, such as gradual onset of effect, diminishing effect, 
    and crossing hazard or survival functions. This package provides 
    the following: 1) point estimates and confidence intervals for model 
    parameters; 2) point estimate and confidence interval of the average hazard 
    ratio; and 3) plots of estimated hazard ratio function with point-wise and 
    simultaneous confidence bands.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
An implementation of equilibrium-based yield per recruit
    methods.  Yield per recruit methods can used to estimate the optimal
    yield for a fish population as described by Walters and Martell (2004)
    <isbn:0-691-11544-3>.  The yield can be based on the number of fish
    caught (or harvested) or biomass caught for all fish or just large
    (trophy) individuals.
An extension for 'NetSurfP-2.0' (Klausen et al. (2019) <doi:10.1002/prot.25674>)
    which is specifically designed to analyze the results of bottom-up-proteomics that is
    primarily analyzed with 'MaxQuant' (Cox, J., Mann, M. (2008) <doi:10.1038/nbt.1511>).
    This tool is designed to process a large number of yeast peptides that produced as a
    results of whole yeast cell-proteome digestion and provide a coherent picture of
    secondary structure of proteins.
Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
Simplify working with the 'YouTube Analytics' API <https://developers.google.com/youtube/analytics>. Collect traffic, 
    time period, location, and other data quickly and efficiently.
    For working with the 'YouTube Data' API, use the 'tuber' 'R' Package.
Simulation and Inference for SDEs and Other Stochastic Processes.
Provides a graphical user interface for the 'yuima' package.
Miscellaneous functions commonly used by 'YuLab-SMU'.
Provides a number of functions to facilitate
  extracting information in 'YAML' fragments from one or 
  multiple files, optionally structuring the information
  in a 'data.tree'. 'YAML' (recursive acronym for "YAML ain't
  Markup Language") is a convention for specifying structured
  data in a format that is both machine- and human-readable.
  'YAML' therefore lends itself well for embedding (meta)data
  in plain text files, such as Markdown files. This principle
  is implemented in 'yum' with minimal dependencies (i.e. only
  the 'yaml' packages, and the 'data.tree' package can be
  used to enable additional functionality).
Provides simple statistics from instruments and observations at sites in the NEON network, and acts as a simple interface for v0 of the National Ecological Observatory Network (NEON) API.
  Statistics are generated for meteorologic and soil-based observations, and are presented for daily, annual, and one-time observations at all available NEON sites.
  Users can also retrieve any dataset publicly hosted by NEON. Metadata for NEON sites and data products can be returned, as well as information on data product availability by site and date. 
  For more information on NEON, please visit <https://www.neonscience.org>. For detailed data product information, please see the NEON data product catalog at <https://data.neonscience.org/data-product-catalog>.
A suite of statistics for identifying areas of the genome under selective pressure. See Jacobs, Sluckin and Kivisild (2016) <doi:10.1534/genetics.115.185900>.
Principled methods for the imputation of zeros, left-censored and missing data in
    compositional data sets (Palarea-Albaladejo and Martin-Fernandez (2015) <doi:10.1016/j.chemolab.2015.02.019>).
Contains the US Census Bureau's 2020 ZCTA to County Relationship 
    File, as well as convenience functions to translate between States, Counties
    and ZIP Code Tabulation Areas (ZCTAs).
An implementation of z-curves - a method for estimating expected discovery 
    and replicability rates on the bases of test-statistics of published studies. The package 
    provides functions for fitting the new density and EM version 
    (Bartoš & Schimmack, 2020, <doi:10.31234/osf.io/urgtn>), censored observations,  
    as well as the original density z-curve (Brunner & Schimmack, 2020, <doi:10.15626/MP.2018.874>). 
    Furthermore, the package provides summarizing and plotting functions for the fitted z-curve objects. 
    See the aforementioned articles for more information about the z-curves, expected discovery 
    and replicability rates, validation studies, and limitations.
Facilitates making a connection to the 
  'Zendesk' API and executing various queries. You can use it to
  get ticket, ticket metrics, and user data. The 'Zendesk' documentation is 
  available at <https://developer.zendesk.com/rest_api
  /docs/support/introduction>. This package is not supported by 
  'Zendesk' (owner of the software).
Provides a %<-% operator to perform multiple,
    unpacking, and destructuring assignment in R. The 
    operator unpacks the right-hand side of an assignment
    into multiple values and assigns these values to 
    variables on the left-hand side of the assignment.
R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.
Implements the estimation of local (and global) association measures: Lewontin's D, Ducher's Z, pointwise mutual information, normalized pointwise mutual information and chi-squared residuals. The significance of local (and global) association is accessed using p-values estimated by permutations.
Use behavioural variables to compute period, rhythmicity and other circadian parameters.
  Methods include computation of chi square periodograms (Sokolove and Bushell (1978) <DOI:10.1016/0022-5193(78)90022-X>),
  Lomb-Scargle periodograms (Lomb (1976) <DOI:10.1007/BF00648343>, Scargle (1982) <DOI:10.1086/160554>, Ruf (1999) <DOI:10.1076/brhm.30.2.178.1422>),
  and autocorrelation-based periodograms.
Provides an Interface to 'Zenodo' (<https://zenodo.org>) REST API, 
  including management of depositions, attribution of DOIs by 'Zenodo' and 
  upload and download of files.
This package provides an R wrapper for the Zendesk API
Graphical tools for visualizing high-dimensional data along a path
 of alternating one- and two-dimensional plots. Note that this
 includes interactive graphics plots based on 'loon' in turn based on 'tcltk'
 (included as part of the standard R distribution).  It also requires 'graph' from Bioconductor.
 For more detail on use and algorithms, see <doi:10.18637/jss.v095.i04>.
Uses bootstrap to test zero order correlation being equal to a 
    partial or semi-partial correlation (one or two tailed). Confidence 
    intervals for the parameter (zero order minus partial) can also be
    determined. Implements the bias-corrected and accelerated bootstrap
    method as described in "An Introduction to the Bootstrap" Efron (1983) 
    <0-412-04231-2>.
A system for creating the bilateral trade flow between a country
    pair equal to zero. You provide the data, tell get_zerotradeflow() which 
    variables are of interest and it expands the base by creating the bilateral
    zero trade flow. The bases on the flow of trade between countries only report
    positive trade (greater than zero), however, for some analyzes of gravitacional
    models, data on zero flow is also necessary. Some examples for Gravity Model:
    Figueiredo and Loures (2016) <doi:10.5935/0034-7140.20160015> and
    Yotov, Piermartini, Monteiro and Larch <https://vi.unctad.org/tpa/web/docs/vol2/book.pdf>.
Functions to compute compositional turnover using zeta-diversity,
    the number of species shared by multiple assemblages. The package includes
    functions to compute zeta-diversity for a specific number of
    assemblages and to compute zeta-diversity for a range of numbers of
    assemblages. It also includes functions to explain how zeta-diversity
    varies with distance and with differences in environmental variables
    between assemblages, using generalised linear models, linear models
    with negative constraints, generalised additive models,shape
    constrained additive models, and I-splines.
The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
Improve the usage of model fitting functions within a piped
    work flow.
A two-part zero-inflated Beta regression model with random 
    effects (ZIBR) for testing the association between microbial abundance 
    and clinical covariates for longitudinal microbiome data. Eric Z. Chen 
    and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.
Detects abundance differences across clinical conditions. Besides, it takes the sparse nature of metagenomic data into account and handles compositional data efficiently.
Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.
Zillow, an online real estate company, provides real estate
    and mortgage data for the United States through a REST API. The
    ZillowR package provides an R function for each API service, making it
    easy to make API calls and process the response into convenient,
    R-friendly data structures.  See
    <https://www.zillow.com/howto/api/APIOverview.htm> for the Zillow API
    Documentation. NOTE: Zillow deprecated their API on 2021-09-30, and
    this package is now deprecated as a result.
Analyze count time series with excess zeros. 
    Two types of statistical models are supported: Markov regression by Yang et al.
    (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. 
    (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and 
    parameter-driven models respectively in the time series literature. The functions used for 
    Markov regression or observation-driven models can also be used to fit ordinary regression models 
    with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) 
    assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, 
    quantile, and generate random numbers from ZIP and ZINB distributions.
Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) <doi:10.1007/978-3-030-82110-4_2>.
Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) <doi:10.1080/00949655.2020.1754819>.
Cross-Platform 'zip' Compression Library. A replacement
    for the 'zip' function, that does not require any additional
    external tools on any platform.
Some data treated by the Japanese R user require
    unique operations and processing. These are caused by address, Kanji,
    and traditional year representations. 'zipangu' transforms specific
    to Japan into something more general one.
Implementation of zero-inflated Poisson models under Bayesian framework using data augmentation as discussed in Chapter 5 of Zhang (2020) <https://hdl.handle.net/10012/16378>. This package is constructed in accommodating four different scenarios: the general scenario, the scenario with measurement error in responses, the external validation scenario, and the internal validation scenario.
Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. 
             Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating
             ZIP codes to Census data, geocoding & distance calculations.
Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. 
    It provides estimates of coefficients in a new type of zero-inflated regression. 
    It provides a cross-validation method to determine the potential rank of the data in the ZIPFA 
    and conducts zero-inflated Poisson factor analysis based on the determined rank.
Implementation of four extensions of the Zipf distribution: the Marshall-Olkin 
  Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE), the 
  Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. 
  In log-log scale, the two first extensions allow for top-concavity 
  and top-convexity while the third one only allows for top-concavity. 
  All the extensions maintain the linearity associated with the Zipf model in the tail.
Statistical models and utilities for the analysis of word frequency distributions.
	The utilities include functions for loading, manipulating and visualizing word frequency
	data and vocabulary growth curves.  The package also implements several statistical 
	models for the distribution of word frequencies in a population.  (The name of this package 
	derives from the most famous word frequency distribution, Zipf's law.)
Implements Python-style zip for R. Is a more flexible version of cbind.
Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: <https://hal.archives-ouvertes.fr/hal-02936779v3>.
This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018)<https://repository.ipb.ac.id/handle/123456789/92308>. For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015)<doi:10.1016/j.csda.2015.07.013> and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016)<doi:10.1515/jos-2016-0051>.
Assesses evidence for Zipf's Law of Abbreviation in animal vocalisation using IDs, note class and note duration. The package also provides a webplot function for visualisation. Davis, M. K., and Chen, G. (2007) <doi:10.2307/2346786>.
The 'zlib' package for R aims to offer an R-based equivalent of 'Python's' built-in 'zlib' module for data compression and decompression. This package provides a suite of functions for working with 'zlib' compression, including utilities for compressing and decompressing data streams, manipulating compressed files, and working with 'gzip', 'zlib', and 'deflate' formats.
Transformation of laboratory measurements into z or z(log)-value
    based on given or empirical reference limits as proposed in
    Hoffmann et al. 2017 <doi:10.1515/labmed-2016-0087>.
A collection of utility functions that facilitate looking up
    vector values from a lookup table, annotate values in at table for 
    clearer viewing, and support a safer approach to vector sampling, 
    sequence generation, and aggregation.
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included.
'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results
    in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time
    series forecasts for prediction challenges of interest to the modeling community. This package provides functions
    for working with the 'Zoltar' API, including connecting and authenticating, getting information about projects,
    models, and forecasts, deleting and uploading forecast data, and downloading scores.
Functions, documentation and example data to help divide
    geographic space into discrete polygons (zones).
    The functions are motivated by research into the merits of different zoning systems
    <doi:10.1068/a090169>. A flexible 'ClockBoard' zoning system is
    provided, which breaks-up space by concentric rings
    and radial lines emanating from a central point.
    By default, the diameter of the rings grow according the triangular number sequence
    <doi:10.1080/26375451.2019.1598687> with the first 4 'doughnuts'
    (or 'annuli') measuring 1, 3, 6, and 10 km wide.
    These annuli are subdivided into equal segments (12 by default), creating the
    visual impression of a dartboard. Zones are labelled according to
    distance to the centre and angular distance from North, creating a simple
    geographic zoning and labelling system useful for visualising geographic
    phenomena with a clearly demarcated central location such as cities.
Computes a zonohedron from real vector generators.  The package also computes zonogons (2D zonotopes) and zonosegs (1D zonotopes).  An elementary S3 class for matroids is included, which supports matroids with rank 3, 2, and 1.  Optimization methods are taken from Heckbert (1985) <https://www.cs.cmu.edu/~ph/zono.ps.gz>.
An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.
This tool provides functions to load, segment and classify zooplankton images. 
  The image processing algorithms and the machine learning classifiers in this package are (will be, since these have not been added yet) direct ports of 
  an early 'python' implementation that can be found at <https://github.com/arickGrootveld/ZooID>. The model weights and datasets (also not added yet) 
  that are a part of this package can also be found at Arick Grootveld, Eva R. Kozak, Carmen Franco-Gordo (2023) <doi:10.5281/zenodo.7979996>.
A free (open source) solution for analyzing digital
  images of plankton. In combination with ImageJ, a free image analysis
  system, it processes digital images, measures individuals, trains for
  automatic classification of taxa, and finally, measures plankton samples
  (abundances, total and partial size spectra or biomasses, etc.).
Includes functions and reference data to 
    generate and manipulate log-ratios (also known as log size index (LSI)
    values) from measurements obtained on zooarchaeological material. Log
    ratios are used to compare the relative (rather than the absolute)
    dimensions of animals from archaeological contexts 
    (Meadow 1999, ISBN: 9783896463883).
    zoolog is also able to seamlessly integrate data and references 
    with heterogeneous nomenclature, which is internally managed by a 
    zoolog thesaurus. 
    A preliminary version of the zoolog methods was first used by 
    Trentacoste, Nieto-Espinet, and Valenzuela-Lamas (2018) 
    <doi:10.1371/journal.pone.0208109>.
You can call zm(), when displaying any active plot to enter an
    interactive session to zoom/navigate any plot. The development
    version, as well as binary releases can be found at
    <https://github.com/cbarbu/R-package-zoom>.
Provides utilities for processing and analyzing the files that are exported from a recorded 'Zoom' Meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.
Facilitates making a connection to the 'Zoom' API and executing
  various queries. You can use it to get data on 'Zoom' webinars and 'Zoom'
  meetings. The 'Zoom' documentation is available at 
  <https://developers.zoom.us/docs/api/>. This package is 
  not supported by 'Zoom' (owner of the software).
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
A tool for calculating z-scores and centiles for weight-for-age, 
    length/height-for-age, weight-for-length/height, BMI-for-age, 
    head circumference-for-age, age circumference-for-age, 
    subscapular skinfold-for-age, triceps skinfold-for-age based on the 
    WHO Child Growth Standards.
Generates well-known integer sequences. 'gmp' package is adopted for computing with arbitrarily large numbers. Every function has hyperlink to its corresponding item in OEIS (The On-Line Encyclopedia of Integer Sequences) in the function help page. For interested readers, see Sloane and Plouffe (1995, ISBN:978-0125586306).
Makes zebra-striped tables (tables with alternating row colors)
    in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
    glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
Functions for obtaining the density, random variates
             and maximum likelihood estimates of the Zero-truncated Poisson lognormal
             distribution and their mixture distribution.
Read '.xls' and '.sbj' files which are written by the
   Microsoft Windows program 'z-Tree'. The latter is a software for
   developing and carrying out economic experiments
   (see <http://www.ztree.uzh.ch/> for more information).
How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels.
Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2018 <arXiv:1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2020 <arXiv:2002.00033>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.
An efficient implementation of the slope method described by Sen (1968) <doi:10.1080/01621459.1968.10480934> plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) <doi:10.1080/07055900.2000.9649654> and Yue, Pilon, Phinney, and Cavadias (2002) <doi:10.1002/hyp.1095>.
A minor collection of HTTP wrappers for the 'Zamzar File Conversion'
    API. The wrappers makes it easy to utilize the API and thus convert
    between more than 100 different file formats (ranging from audio files,
    images, movie formats, etc., etc.) through an R session.  
    For specifics regarding the API, please see <https://developers.zamzar.com/>.
Functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Application" by A. C. Davison and
D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Various functions for classification, including k-nearest
neighbour, Learning Vector Quantization and Self-Organizing Maps.
Methods for Cluster analysis.  Much extended the original from
Peter Rousseeuw, Anja Struyf and Mia Hubert,
based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Reading and writing data stored by some versions of
'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
and for reading and writing some 'dBase' files.
Functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
A rich hierarchy of sparse and dense matrix classes,
including general, symmetric, triangular, and diagonal matrices
with numeric, logical, or pattern entries.  Efficient methods for
operating on such matrices, often wrapping the 'BLAS', 'LAPACK',
and 'SuiteSparse' libraries.
Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017) <doi:10.1201/9781315370279> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Recursive partitioning for classification,
regression and survival trees.  An implementation of most of the
functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.
Please send bug reports to ismrp@grp.ism.ac.jp.
Please send questions and comments regarding pbdR to
RBigData@gmail.com
Please send bug reports to ismrp@jasp.ism.ac.jp
Please send questions and comments to wccsnow@gmail.com
Please send questions and comments about Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding RInside to
rcpp-devel@lists.r-forge.r-project.org
Please send bug reports to <ismrp@grp.ism.ac.jp>.
Dane R. Van Domelen <vandomed@gmail.com>
Renata T C Yokota <renatayokota@gmail.com>
Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>
Marcelino De la Cruz <marcelino.delacruz@urjc.es>
Carlos J. Gil Bellosta <cgb@datanalytics.com>
Cesar de Oliveira Ferreira Silva <cesaroliveira.f.silva@gmail.com>
Rodrigo Yudi Palhaci Marubayashi <marubayashi@uel.br>
Berent Ånund Strømnes Lunde <lundeberent@gmail.com>
Jose Roberto Ayala Solares <ichbinjras@gmail.com>
Miguel Angelo Lellis Moreira <miguellellis@hotmail.com>
Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>
Almut E. D. Veraart <a.veraart@imperial.ac.uk>
Luis D. Verde Arregoitia <luis@liomys.mx>
Rafael H. M. Pereira <rafa.pereira.br@gmail.com>
Robin K. S. Hankin <hankin.robin@gmail.com>
Gabriel F. R. Vasconcelos <gabrielrvsc@yahoo.com.br>
Pedro Nascimento de Lima <pedrolima.n@gmail.com>
Pierre Lafaye de Micheaux <lafaye@unsw.edu.au>
Paul E. Santos Andrade <paulefrens@gmail.com>
Caspar J van Lissa <c.j.vanlissa@tilburguniversity.edu>
Edwin Javier Castillo Carreño <edjcastilloca@unal.edu.co>
Stephanie van der Pas <s.l.vanderpas@amsterdamumc.nl>
Ricardo de Matos Simoes <ricardo_dematossimoes@dfci.harvard.edu>
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>
Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com>
Blondeau Da Silva Stephane <Stephane.Blondeau-Da-Silva@ac-limoges.fr>
Emilio Akira Morones Ishikawa <emiliomorones@gmail.com>
Luiz Antonio de Jesus Rocha <luiz.rocha@unifesp.br>
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
Ana Belen Nieto Librero <ananieto@usal.es>
Ricardo Puziol de Oliveira <rpuziol.oliveira@gmail.com>
Rafael de Andrade Moral <rafael.deandrademoral@mu.ie>
Andrey Duvan Rincon Torres <adrincont@unal.edu.co>
Guillaume Kon Kam King <guillaume.konkamking.work@gmail.com>
David Alejandro Martell Juarez <alex91599@gmail.com>
Jianzhao Gao and Xiaodan Fan <gaojz@nankai.edu.cn>
A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
Rahim Alhamzawi  (University of Al-Qadisiyah) <rahim.alhamzawi@qu.edu.iq>
José Carlos Soage González <jsoage@uvigo.es>
Sy Han (Steven) Chiou <schiou@hsph.harvard.edu>
Francisco H. C. de Alencar <hildemardealencar@gmail.com>
Ivo Van Walle Developer <ivo.van.walle@gmail.com>
Jorge L. C. Musaja <musajajorge@gmail.com>
Ahmad Hakeem Abdul Wahab <hakeemwahab00@gmail.com>
Ting Fung (Ralph) Ma <tingfung.ma@wisc.edu>
Claucio Antonio Rank Filho <claucio.filho@hitechnologies.com.br>
Dr. T. Ian Simpson <ian.simpson@ed.ac.uk>
Luis Alfonso Perez Martos <lapm0001@gmail.com>
G. C. Livingston Jr <Glen.LivingstonJr@newcastle.edu.au>
L. A. van der Ark <L.A.vanderArk@uva.nl>
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>
M. A. J. Zondervan-Zwijnenburg <m.zondervan@vilans.nl>
Joyce Moonen - van Loon <j.moonen@maastrichtuniversity.nl>
K. Gerald van den Boogaart <support@boogaart.de>
P. Lafaye de Micheaux <lafaye@unsw.edu.au>
José Manuel Monroy Kuhn <nolozz@gmail.com>
Pierre Lafaye De Micheaux <lafaye@unsw.edu.au>
Wim Van der Elst <Wim.vanderelst@gmail.com>
Nelson Alirio Cruz Gutierrez <neacruzgu@unal.edu.co>
Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
James A. Fellows Yates <jfy133@gmail.com>
Wagner Martins dos Santos <wagnnerms97@gmail.com>
Luna L. Sanchez Reyes <sanchez.reyes.luna@gmail.com>
Carl A. B. Pearson <carl.pearson@lshtm.ac.uk>
Eduardo L. T. Conceicao <mail@eduardoconceicao.org>
Kevin Allan Sales Rodrigues <kevin.asr@outlook.com>
Ricardo Daniel Marques Salgado <ricardodansalgado@gmail.com>
Jonas Gregorio de Souza <jonas.gregorio@gmail.com>
Alec James van Rassel <alec.van-rassel.1@ulaval.ca>
Philippe J.S. De Brouwer <philippe@de-brouwer.com>
Sean A. S. Anderson <sean.as.anderson@gmail.com>
Marcelino de la Cruz Rot <marcelino.delacruz@urjc.es>
Pedro H. C. Sant'Anna <pedrosantanna@causal-solutions.com>
Don van den Bergh <donvdbergh@hotmail.com>
Roberto de la Banda <dualScale@gmail.com>
Kaique dos S. Alves <kaiquedsalves@gmail.com>
Alexandre Adalardo de Oliveira <ecovirtualpackage@gmail.com>
Mirrelijn M. van Nee <m.vannee@amsterdamumc.nl>
Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
Ardo van den Hout <Ardo.vandenHout@ucl.ac.uk>
Matheus H. J. Saldanha <mhjsaldanha@gmail.com>
Justin A. G. Hubbard <justin.hubbard@mail.utoronto.ca>
Hendrik van der Wurp <vanderwurp@statistik.tu-dortmund.de>
Jesús María Méndez Pérez <jesus.mendez@ticarum.es>
Manuel Oviedo de la Fuente <manuel.oviedo@udc.es>
Caíque de Oliveira de Souza <forestgrowthsoftware@gmail.com>
Clayton Vieira Fraga Filho <forestgrowthsoftware@gmail.com>
Nina M. D. Schiettekatte <nina.schiettekatte@gmail.com>
Agnese Maria Di Brisco <agnese.dibrisco@unimib.it>
"Vincent ten Cate, PhD" <vincent10kd@gmail.com>
Helgem de Souza Ribeiro Martins <helgem.souza@gmail.com>
S M Mashrur Arafin Ayon <mashrur399@gmail.com>
Shaiful Anuar Abu Bakar <saab@um.edu.my>
Juan Pablo Marin Diaz <jpmarindiaz@gmail.com>
Paulo Justiniano Ribeiro Jr <paulojus@ufpr.br>
Javier De La Hoz Maestre <jdelahozmaestre@gmail.com>
Teun van den Brand <tahvdbrand@gmail.com>
Vincent T van Hees <v.vanhees@accelting.com>
Xavier Fernández i Marín <xavier.fim@gmail.com>
Caspar J. van Lissa <c.j.vanlissa@tilburguniversity.edu>
Rogelio Salinas Gutiérrez Developer <rsalinas@correo.uaa.mx>
Joseph Emile Honour Percival <ipercival@gmail.com>
Anders Gonçalves da Silva <andersgs@gmail.com>
Per August Jarval Moen <pamoen@math.uio.no>
Martijn van de Pol <martijn@myscience.eu>
Rafael de Andrade Moral <rafael_moral@yahoo.com.br>
Stephanie van der Pas <svdpas@math.leidenuniv.nl>
"Stefan Rass, on behalf of the Austrian Institute of Technology" <stefan.rass@jku.at>
Sai Thein Than Tun <theinthantun.sai@gmail.com>
Marcelino de la Cruz <marcelino.delacruz@urjc.es>
Dr. M. Iqbal Jeelani <jeelani.miqbal@gmail.com>
Luiz Eduardo S. Gomes <gomes.leduardo@gmail.com>
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Luiz Henrique Keng Queiroz Junior <keng@ufg.br>
Tri Ha Minh Nguyen <tringuyen_2023@depauw.edu>
Kleber Morais de Sousa <kleberfinancas@gmail.com>
Yves Ismael Ngounou Bakam <yvesngounou20@gmail.com>
Thiago de Paula Oliveira <thiago.paula.oliveira@alumni.usp.br>
Danilo Roberti Alves de Almeida <daniloflorestas@gmail.com>
Carlos Javier Hellin Asensio <carlos.hellin@uah.es>
Rosineide Fernando da Paz <rfpaz2@gmail.com>
"Antoine Tremblay, Statistics Canada" <trea26@gmail.com>
Rune Haubo Bojesen Christensen <Rune.Haubo@gmail.com>
Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>
Vito M. R. Muggeo <vito.muggeo@unipa.it>
Sergio Andrés Cabrera Miranda <sergio05acm@gmail.com>
Mauricio van den Berg <mauricio.vandenberg@telethonkids.org.au>
Euloge C. Kenne Pagui <kenne@stat.unipd.it>
Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
Antonio Hermes M. da Silva-Junior <hermes@ccet.ufrn.br>
Joseph de la Torre Dwyer <JosephD@BRdata.com>
Caspar J. van Lissa <c.j.vanlissa@gmail.com>
João Victor Gomes de Araujo Santana <jvg.santana@gmail.com>
Stephen L R Ellison <s.ellison@lgcgroup.com>
Héctor Manuel Sánchez Castellanos <sanchez.hmsc@berkeley.edu>
Bruno André Rodrigues coelho <bruno@brodrigues.co>
Ludwig Kian Soon Hoon <ldwgkshoon@gmail.com>
Krishna Harsha K H <khkrishnaharsha123@gmail.com>
Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>
L. Andries van der Ark <L.A.vanderArk@uva.nl>
Jose Luis Vicente Villardon <villardon@usal.es>
Mark A. van de Wiel <mark.vdwiel@amsterdamumc.nl>
Hannah De los Santos <hdelossantos653@gmail.com>
Sebastian J. Teran Hidalgo <sebastianteranhidalgo@gmail.com>
Eliot J B McIntire <eliot.mcintire@canada.ca>
Don van den Bergh <d.vandenbergh@uva.nl>
Niels van der Velden <n.s.j.vandervelden@gmail.com>
Pedro Augusto Borges Santos <pabsantos21@gmail.com>
Carlos de la Calle-Arroyo <carlos.calle.arroyo@gmail.com>
Pedro Nascimento de Lima <plima@rand.org>
Rune Haubo Bojesen Christensen <rune.haubo@gmail.com>
Joao Pedro Oliveira dos Santos <joaopsantos@pm.me>
Bruno do Rosario Petrucci <petrucci@iastate.edu>
"Australian e-Health Research Centre, CSIRO" <ontoserver-support@csiro.au>
Luna Luisa Sanchez Reyes <sanchez.reyes.luna@gmail.com>
Wouter van der Bijl <wouter@zoology.ubc.ca>
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Joaquin Bienvenido Ordieres Mere <j.ordieres@upm.es>
Marieke van der Maaten-Theunissen <marieke.theunissen@tu-dresden.de>
Wessel N. van Wieringen <w.vanwieringen@vumc.nl>
Gualberto Segundo Agamez Montalvo <gsagamez@gmail.com>
Paul van der Laken <paulvanderlaken@gmail.com>
Mijael Rodrigo Vargas Godoy <mirovago@gmail.com>
Thaís de Bessa Gontijo de Oliveira <thais.bgo@gmail.com>
Juan Carlos Gonçalves Dosantos <juan.carlos.goncalves@udc.es>
Blondeau Da Silva Stephane <blondeaudasilva@xlim.fr>
Pedro H. C. Sant'Anna <pedro.h.santanna@vanderbilt.edu>
Robbie C.M. van Aert <R.C.M.vanAert@tilburguniversity.edu>
Jose Sergio Case de Oliveira <js_cdo@hotmail.com>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
Wagner Santos de Lima <wagnersantos.ufs@hotmail.com>
Maria de los Angeles Resa <maria@stat.columbia.edu>
Gabriel de Siqueira Gesteira <gdesiqu@ncsu.edu>
Achmad Fauzi Bagus F <fauzi.pandya@gmail.com>
Manuel A. Mosquera Rodríguez <mamrguez@uvigo.es>
Katherine A. L. Valeriano <katandreina@gmail.com>
Pedro Mac Dowell Innecco <dmu@pedroinnecco.com>
Christian Alexander Mongeau Ospina <christian+rpkg-repec@mongeau.net>
"Michael Barr, ACAS, MAAA, CPCU" <mike@bikeactuary.com>
Peter van den Berg <peter_v_d_berg@hotmail.com>
Jose Miguel Cotes Torres <jmcotes@unal.edu.co>
Lucas Sebastião de Paula <lucas@ime.eb.br>
Maria del Carmen Calatrava Moreno
<mc.calatrava.moreno@gmail.com>
Pedro Rafael D. Marinho <pedro.rafael.marinho@gmail.com>
Manuel David Peris Diaz <madape@alumni.uv.es>
Mohammad Hadi Foroughmand Araabi <foroughmand@gmail.com>
Laercio Junio da Silva <laerciojdsilva@gmail.com>
Lucas van der Meer <luukvandermeer@live.nl>
M. Yoga Satria Utama <221709801@stis.ac.id>
Niels J. de Winter <niels.de.winter@vub.be>
Douglas Kiarelly Godoy de Araujo <dkg.araujo@gmail.com>
Lucas da Cunha Godoy <lucasdac.godoy@gmail.com>
Julia de Paula Soares Valente <juliadepaulasoaresvalente@gmail.com>
Jose Manuel Vera Oteo <vera.josemanuel@gmail.com>
Sy Han (Steven) Chiou <schiou@utdallas.edu>
Dr. Owais Ali Wani <owaisaliwani@skuastkashmir.ac.in>
Francisco Jesús Palomares Alabarce <fpalomares@correo.ugr.es>
Fernando de Souza Bastos <fernando.bastos@ufv.br>
Stephen A Sefick Jr. <ssefick@gmail.com>
Wim Van Der Elst <wim.vanderelst@gmail.com>
Vinicius Quintas Souto Maior <vinicius@de.ufpe.br>
Jesus Maria Rodriguez Rodriguez <jesusmro@gmail.com>
Gabriel Henrique Oliveira Assuncao <gabrielhenryoliver@hotmail.com>
K. Gerald van den Boogaart <boogaart@math.tu-freiberg.de>
K. Bret Staudt Willet <bret.staudtwillet@fsu.edu>
Silvestre Garcia de Jalon <s.garciadejalon@gmail.com>
Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
Dr. Ranjit Kumar Paul <ranjitstat@gmail.com>
Eraldo B. dos Anjos Filho <ebdaf1@de.ufpe.br>
Henrique Jose de Paula Alves <jpahenrique@gmail.com>
Oscar Alberto Quijano Xacur <oscar.quijano@use.startmail.com>
André F. B. Menezes <andrefelipemaringa@gmail.com>
Juan Pablo Acuña González <22253567@uagro.mx>
Taryn B. T. Athey <taryn.athey@gmail.com>
Angel Garcia de la Garza <agarciadlg@gmail.com>
"James P. Howard, II" <jh@jameshoward.us>
Giulio Valentino Dalla Riva <me@gvdallariva.net>
Joerg grosse Schlarmann (aka Produnis) <schlarmann@produnis.de>
Caspar J. Van Lissa <c.j.vanlissa@tilburguniversity.edu>
Olivier M. F. Martin <oliviermfmartin@tutanota.com>
Tharso Augustus Rossiter Araújo Monteiro <tharso.augustus@ufpe.br>
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
Package 'rjags' requires JAGS executabe file (compatiable
version) should be already installed.
The optmatch package, which is useful for running many of the
provided functions, may be downloaded from Github at
<https://github.com/markmfredrickson/optmatch> if not available
on CRAN. The rrelaxiv package, which provides an alternative
solver for the underlying network flow problems, carries an
academic license and is not available on CRAN, but may be
downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
Maintainers are not available to give advice on using a package
they did not author.
This version is a generalisation for (v=wnl) treatments. In the
version 1.0 we used rectangular right angular (m) association
schemes with (v=2nl) and m=4,5 and 7 associated classes. The
Association schemes used in this R-package are : Rectangular
association scheme; Generalized rectangular right angular
association scheme (4); Generalized rectangular right angular
association scheme (5); Generalized rectangular right angular
association scheme (7).
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
One minimum cost flow problem may have several or many solutions
that are equivalent in providing the same minimum total or mean
cost. Minor differences between computers or implementations
may have the minor consequence of altering which equivalent
solution is produced. The optmatch package, which is useful for
running many of the provided functions, may be downloaded from
Github at <https://github.com/markmfredrickson/optmatch> if not
available on CRAN. The rrelaxiv package, which provides an
alternative solver for the underlying network flow problems,
carries an academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002/0004 with the American Institutes for
Research. Mention of trade names, commercial products, or
organizations does not imply endorsement by the U.S.
Government.
Supported by internal research project ING01-1516 from
'Universidad de Playa Ancha'
Ver. 1.0: The author obtained financial resources as part of
financing the doctoral scholarship from the Polish National
Science Centre (DEC-2018/28/T/HS4/00095). Ver. 2.0: The
research was supported by the program ''Excellence Initiative -
Research University (IDUB)'' through the project
BOB-IDUB-622-89/2021 - Nowe Idee POB III IDUB, 01/IDUB/2019/94
at the University of Warsaw, Poland.
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Pakistan.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with the American Institutes for Research.
Mention of trade names, commercial products, or organizations
does not imply endorsement by the U.S. Government.
Research funded by the Polish National Science Centre grant under
the contract number DEC-2015/19/N/HS4/00205.
Few parts are still preliminary and might be changed in the near
future. And more functions will be add as easier tools to
higher efficiency in analyzing.
While reporting issues on github start subject line with
[cran-package]
The authors are grateful to Mikhail Kanevski, Federico Amato and
Luciano Telesca for many fruitful discussions about the use and
the application of Fisher-Shannon method.
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
The package is also based on the discussion
https://stats.stackexchange.com/questions/168964/building-a-regression-tree-with-r-from-scratch/168967#168967
FSAtools replaces and generalizes the former MLPA package from
the same authors.
research product of the RISCOPE project (ANR, project
No.16CE04-0011) <https://perso.math.univ-toulouse.fr/riscope/>.
Department of Genetics and Plant Breeding, University of
Agricultural Sciecnes, Dharwad.
Package 'R2jags' requires JAGS executable file installed
The authors are grateful to Mikhail Kanevski, Michael
Leuenberger, Carmen D. Vega Orozco and Fabian Guignard for many
fruitful discussions about the use of intrinsic dimension in
data mining.
Because IPEDS requirements may change from year to year, having
the most recent version of this package is highly recommended.
Old versions can be found as GitHub branches. The package can
also be used to convert any correctly-prepared data into a
key-value pair format for any survey (IPEDS or non-IPEDS).
Please, cite this package as: Marina Saez (2021). KarsTS: An
Interface For Microclimate Time Series Analysis. R package
version 2.4.1.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan
The originale code was in matlab, see details below.
The developer version of 'keras' should be used, rather than the
CRAN version. The latter will cause tests to fail on an initial
run, but work on subsequent tries. To avoid this, use
devtools::install_github("rstudio/keras"). The user also must
have an installation of 'Python 3'.
a previous version of the library (main version number: 3) was
developed as part of the European Union's Horizon 2020 research
and innovation programme under grant agreement No. 727040
(Virt-EU). The package uses functions from eclat
<https://borgelt.net/eclat.html>, for association rule mining,
Infomap <https://www.mapequation.org>, for the Infomap
community detection method (please refer to their web site for
up-to-date code), and Howard Hinnant's date and time library
<https://github.com/HowardHinnant/date>. The code from these
libraries has been included in our source package.
For convenience, the package includes the necessary header files
for the Eigen distribution. (This is all that is needed to use
that functionality.) You can use an alternative installation of
Eigen on your system or the one we provide. The license for the
Eigen code is very permissive and allows us to distribute it
with this package. See <http://eigen.tuxfamily.org/index.php?
title=Main_Page> and also the License section on that page.
The APT samplers within this package were adapted from MCMC
samplers provided in the NIMBLE package
(https://github.com/nimble-dev/nimble) and developed by the
cited members of the NIMBLE Development Team. The adaptations
for APT included in this package were the work of the package
developer. Many thanks to the NIMBLE Development Team for their
support during the development of this package.
The current version (0.4.2) fixes a numerical issue with
symmetric implementation of the paco_links function.
Package is in active development; please report errors and
suggestions to rchlumsk@uwaterloo.ca.
NOTICE regarding the transfer of data from Retrosheet: The
information used here was obtained free of charge from and is
copyrighted by Retrosheet. Interested parties may contact
Retrosheet at "www.retrosheet.org".
See http://www.json.org/JSON_checker/test.zip for tests. We now
use code from the libjson project
(http://libjson.sourceforge.net).
The development of this package was supported by Carlos
Jaramillo, Center for Tropical Paleoecology and Archaeology at
Smithsonian Tropical Research Institute.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0009/0005 with Sanametrix and Westat. Mention of
trade names, commercial products, or organizations does not
imply endorsement by the U.S. Government.
The GPU computing version can be found soon, on following url
https://sites.google.com/site/mohamedlaibwebpage/Software.
This package is developed as part of ongoing Ph.D (Agricultural
Statistics) thesis research work of first author at ICAR-Indian
Agricultural Statistics Research Institute, New Delhi, India.
The author acknowledges financial support from Sao Paulo Research
Foundation (FAPESP) grant 2016/18599-4
This is experimental software distributed free of charge and
comes with no warranty!  Exercise professional caution.
Package TSE works directly from the data set – no hand
calculations required.  Just upload a properly structured data
set (see TESTNUMB and its documentation), properly input column
names (see examples in the functions documentation), and run
your functions.
'TSEind' is a companion package to 'TSE'. Both calculate TSE for
your surveys, but use 'TSEind' if your surveys and the "gold
standard" survey have independent samples, and use 'TSE' if
your surveys and the "gold standard" survey have paired
samples.
'TSEwgt' is a companion package to 'TSE'. Each package calculates
TSE, but the former for multiple, different surveys, and the
latter for a single survey under multiple, different weighting
schemes.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with American Institutes for Research. Mention
of trade names, commercial products, or organizations does not
imply endorsement by the U.S. government.
2022-11-15 06:15:38 UTC; Kosuke Kashiwabara
2018-03-13 21:10:22 UTC; Steven Harris Ranney
2021-12-04 12:57:38 UTC; Javaria Ahamd Khan
2020-09-22 06:18:07 UTC; Benjamin Schlegel
2023-01-18 00:21:38 UTC; Sidharth Macherla
2023-05-17 21:39:10 UTC; Andreas Kuehnapfel
2021-11-28 13:38:57 UTC; Javaria Ahamd Khan
2022-09-20 14:28:28 UTC; Javaria Ahamd Khan
2023-03-26 20:33:56 UTC; Sebastian Weirich
2023-11-23 12:51:11 UTC; Sebastian Weirich
2020-07-14 07:43:10 UTC; Bernardo Sousa Pinto
2018-10-24 16:05:44 UTC; Ashutosh Srivastava
2023-12-13 11:54:07 UTC; Friedemann von Lampe
2015-08-23 02:08:23 UTC; Fernando de Villar
2023-06-11 18:14:04 UTC; Jonas Christoffer
2022-02-19 01:59:50 UTC; Eleanor Pullenayegum
2023-10-29 17:23:31 UTC; Atanu Bhattacharjee
2020-01-08 09:11:00 UTC; Nadezda Spiridovska
2021-04-16 08:59:10 UTC; Ilias Moutsopoulos
2023-11-22 09:15:18 UTC; ADMIN Romain LEROUX
2021-07-12 03:13:21 UTC; Guillaume Boucher
2018-04-29 13:45:20 UTC; Alberto Vico Moreno
2023-06-20 20:30:41 UTC; Gonzalo Vazquez-Bare
2017-01-03 15:34:03 UTC; Guybrush Threepwood
2020-05-07 11:07:40 UTC; Kanhaiiya Agrawal
2020-06-08 13:17:19 UTC; Kanhaiiya Agrawal
2019-08-02 17:03:10 UTC; Bernardo Sousa Pinto
2017-08-15 05:32:43 UTC; Aurnov Chattopadhyay
2022-01-08 14:42:05 UTC; Sandeep Patakamuri
2016-03-19 23:54:53 UTC; Sophie Baillargeon
Image Magick (for reading non PPM format), dcraw
(for reading RAW images).
ADMIXTOOLS suite of command-line utilities for
population genetics. See
<https://reich.hms.harvard.edu/software> for the most recent
installation instructions and further information.
Alfresco Content Repository (Community or
Enterprise)
Python (>= 3.6.0), (Python) Altair (>= 4.2.0),
vega_datasets (>= 0.9.0). To use image functions for MacOS: X11
None, but greater than 12 GB of RAM highly
recommended.
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) or LyX
(http://www.lyx.org) for saveGIF(); (PDF)LaTeX for saveLatex();
SWF Tools (http://swftools.org) for saveSWF(); FFmpeg
(http://ffmpeg.org) or avconv (https://libav.org/avconv.html)
for saveVideo()
The exact (anti)clustering algorithms require that
either the GNU linear programming kit (GLPK library) is
installed (<http://www.gnu.org/software/glpk/>) or the SYMPHONY
open source MILP solver
(<https://github.com/coin-or/SYMPHONY>). Rendering the vignette
requires pandoc (<https://pandoc.org/>).
libarchive: libarchive-dev (deb), libarchive-devel
(rpm), libarchive (homebrew), libarchive_dev (csw)
C++17; for AWS S3 support on Linux, libcurl and
openssl (optional)
FFmpeg (>= 3.2); with at least libx264 and lame
(mp3) drivers. MacOS Homebrew: ffmpeg. Debian/Ubuntu:
libavfilter-dev. Fedora/CentOS: either ffmpeg-free-devel or
(via https://rpmfusion.org) ffmpeg-devel.
AWS Java SDK (see README.md for more details)
JAGS (>= 4.3.0) (see
http://mcmc-jags.sourceforge.net)
C++11, GDAL (>= 2.2.3): gdal-bin (deb), libgdal-dev
(deb) or gdal-devel (rmp), GEOS (>= 3.4.0), PROJ (>= 4.9.3):
libproj-dev (deb), sqlite3, ImageMagick++: imagemagick (deb),
libmagic-dev (deb), libmagick++-dev (deb) or
ImageMagick-c++-devel (rpm) ImageMagick
(http://imagemagick.org) or GraphicsMagick
(http://www.graphicsmagick.org) or LyX (http://www.lyx.org) for
saveGIF(); (PDF)LaTeX for saveLatex(); SWF Tools
(http://swftools.org) for saveSWF(); FFmpeg (http://ffmpeg.org)
or avconv (https://libav.org/avconv.html) for saveVideo()
Rtools (>= 3.3) for Windows, Xcode (>= 9.0) for Mac
OS X
Preferred genomic operations engine: 'BEDTools',
'BEDOPS' and 'Tabix (>= 1.3)'.
JAGS >=4.3.0 <https://mcmc-jags.sourceforge.io>,
Java JDK >=1.4 <https://www.java.com/en/download/manual.jsp>
Julia (>= 1.5), Bigsimr.jl, Distributions.jl
C++11. Also a few functions from package 'bigsnpr'
wrap existing software such as 'PLINK'
<www.cog-genomics.org/plink2>. Functions are provided to
download these software. Note that these external software
might not work for some operating systems (e.g. 'PLINK' might
not work on Solaris).
A system with a recent-enough C++11 compiler (such
as g++-4.8 or later).
Hugo (<https://gohugo.io>) and Pandoc
(<https://pandoc.org>)
LaTeX(texi2dvi) must be present in the system to
create PDF reports
OpenBUGS (>= 3.2.2), hence Windows or x86 Linux
pandoc (>= 1.12.3, needed for vignettes)
'Microsoft Visual Code', 'Rtools' on 'Windows',
'gdb' or 'lldb' depending on which OS
FFTW (>=3.3.1); possible package: fftw-devel (rpm),
libfftw3-dev (deb) or fftw (brew).
Python and numpyro are needed for Bayesian
inference computations; python (>= 3.8) with header files and
shared library; numpyro (= v0.12.1;
https://https://num.pyro.ai/en/latest/index.html); arviz (=
v0.15.1; https://https://python.arviz.org/en/stable/)
Cargo (Rust's package manager), rustc (>= 1.65)
Google Chrome or other Chromium-based browser.
chromium: chromium (rpm) or chromium-browser (deb)
gsl (with development libraries libgsl-dev)
Cargo (rustc package manager) and GNU make
pandoc (to create a clinical data review report)
pandoc (for export clin DT to a file - inclusion of
list of interactive plots/objects with knitr)
xclip (https://github.com/astrand/xclip) or xsel
(http://www.vergenet.net/~conrad/software/xsel/) for accessing
the X11 clipboard, or wl-clipboard
(https://github.com/bugaevc/wl-clipboard) for systems using
Wayland.
ZeroMQ (libzmq) >= 4.3.0 (optional; otherwise
bundled)
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb), libgmp3: apt-get
install -y libgmp3-dev (deb), libfftw3: apt-get install -y
libfftw3-dev (deb), libtiff5: apt-get install -y libtiff5-dev
(deb)
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb).
ImageMagick++: ImageMagick-c++-devel (rpm) or
libmagick++-dev (deb)
awscli: apt install -y awscli (deb), aws-configure:
aws configure (deb)
pdfcrop (part of TexLive) is required to rebuild
the vignettes.
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/ software/corenlp.shtml> (>= 3.5.2)
GNU make, pandoc (>=1.12.3, needed for vignettes)
pandoc (>= 2.0; https://pandoc.org), git, whoami
none, one or more of JAGS (>= 3.0.0), WinBUGS (>=
1.4), OpenBUGS (>= 3.2.2), Stan
A version of Fortran supporting the LOG_GAMMA
Intrinsic
OpenSSL(>= 1.0.1), NVIDIA CUDA GPU with compute
capability 3.0 or above and NVIDIA CUDA Toolkit 9.0 or above
fontconfig or zlib (only needed for platforms other
than modern OSX and Windows)
Julia (>= 1.6), DifferentialEquations.jl,
ModelingToolkit.jl
Python (>= 2.7.0), TensorFlow
(https://www.tensorflow.org/)
libpq >= 9.0: libpq-dev (deb) or postgresql-devel
(rpm)
LibreOffice (<https://www.libreoffice.org/>)
required to extract data from .doc files or perform .pptx
conversion.
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files, GDAL (>= 3.0.2), GEOS (>= 3.8.0), PROJ (>=
6.2.1)
Requires the OpenMP library for parallel computing.
If the OpenMP library is not available, the code is executed
sequentially and a warning is printed.
The package required the jq command line library.
Please check https://stedolan.github.io/jq/download/ for
download instructions.
Python (>= 2.7 or >= 3.5) and elex package
The function render() requires Pandoc
(http://pandoc.org). To use PGP/GnuPG encryption requires gpg.
PROJ (>= 6, https://proj.org/download.html), GDAL
(>= 3, https://gdal.org/download.html), NetCDF(>= 4,
https://www.unidata.ucar.edu/software/netcdf/).
ProtoBuf libraries and compiler version 3.3.0 or
later; On Debian/Ubuntu these can be installed as
libprotoc-dev, libprotobuf-dev and protobuf-compiler, while on
Fedora/CentOS protobuf-devel and protobuf-compiler are needed.
apt-get-pip: apt-get install -y python3-pip (deb),
python3-pip: python3 -m pip install -U pip (deb), numpy: pip3
install -U numpy (deb), cv2: pip3 install -U opencv-python
(deb), matplotlib: pip3 install -U matplotlib (deb), skimage:
pip3 install -U scikit-image (deb), libarmadillo: apt-get
install -y libarmadillo-dev (deb), libblas: apt-get install -y
libblas-dev (deb), liblapack: apt-get install -y liblapack-dev
(deb), libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb)
Generally, fastText builds on modern Mac OS and
Linux distributions. Since it uses some C++11 features, it
requires a compiler with good C++11 support. These include a
(g++-4.7.2 or newer) or a (clang-3.3 or newer).
fftw3 (libfftw3-dev (deb), or fftw-devel (rpm))
Display resolution >= 1280x800; RAM >= 4GB
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb)
GNU Scientific Library (GSL). Note: users should
have GSL installed.
(license-free) MATLAB Runtime (MCR) V 9.12, Java
(>=8)
Python (>= 2.4), difflib, fuzzywuzzy ( >=0.15.0 ),
python-Levenshtein ( >=0.12.0 ). Detailed installation
instructions for each operating system can be found in the
README file.
Version Requirements to run the wild bootstrap
through Julia - Julia (>= 1.8), WildBootTests.jl (>=0.9). Julia
is downloadable via the official Julia website
(https://julialang.org/downloads/), WildBootTests.jl via
Julia's package manager
(https://docs.julialang.org/en/v1/stdlib/Pkg/) or its github
repository (https://github.com/droodman/WildBootTests.jl)
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
GDAL (>= 2.4.0, built against GEOS), PROJ (>=
4.8.0), sqlite3
libarmadillo: apt-get install -y libarmadillo-dev
(deb)
MongoDB (>= 3.4.0), Python (>= 2.7). Installation
instructions and links can be found in the README file.
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files, C++11, GDAL (>= 2.0.1), GEOS (>= 3.8.0),
PROJ (>= 6.2.1)
libgit2 (>= 1.0): libgit2-devel (rpm) or
libgit2-dev (deb)
Chromium-based browser (Google Chrome, Brave, ...)
By default, git2r uses a system installation of the
libgit2 headers and library. However, if a system installation
is not available, builds and uses a bundled version of the
libgit2 source. zlib headers and library. OpenSSL headers and
library (non-macOS). LibSSH2 (optional on non-Windows) to
enable the SSH transport.
GPGME: libgpgme-dev (deb), gpgme-devel (rpm) gpgme
(brew). On Linux 'haveged' is recommended for generating
entropy when using the GPG key generator.
Python (>= 2.7.0) with header files and shared
library; TensorFlow (v1.14; https://www.tensorflow.org/);
TensorFlow Probability (v0.7.0;
https://www.tensorflow.org/probability/)
Python (>= 3.6) with header files and shared
library; H2O4GPU (https://github.com/h2oai/h2o4gpu)
V8 engine version 6+ is needed for modern JS and
WASM support. On Debian / Ubuntu install either libv8-dev or
libnode-dev, on Fedora use v8-devel.
GNU make, zlib: zlib1g-dev (deb), zlib-devel (rpm)
Apache Hadoop >= 2.7.2
(https://hadoop.apache.org/releases.html#Download); Obsolete:
Hadoop core >= 0.19.1 and <= 1.0.3 or CDH3
(https://www.cloudera.com); standard unix tools (e.g., chmod)
C++17, libpng, cairo, freetype2, fontconfig
LaTeX packages: adjustbox, array, calc, caption,
colortbl, fontspec, graphicx, hhline, hyperref, multirow,
siunitx, tabularx, threeparttable, ulem, wrapfig
Julia (>= 1.0) and Interpretable AI System Image
(>= 1.0.0)
gdal-bin: apt install -y gdal-bin (deb),
libgdal-dev: apt install -y libgdal-dev (deb), libudunits2-dev:
apt install -y libudunits2-dev (deb), libgeos-dev: apt install
-y libgeos-dev (deb), libproj-dev: apt install -y libproj-dev
(deb), unzip: apt install unzip (deb)
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org)
A C library with the fesetround/fegetround
functions.
gmp (optional), libxml2 (optional), glpk (>= 4.57,
optional)
libtiff, libbz2, libdeflate, libjpeg, liblzma,
libwebp, libzstd, zlib
C++17 or C++11 and OpenCV 3 or newer: libopencv-dev
(Debian, Ubuntu) or opencv-devel (Fedora)
Impala driver to support a 'DBI'-compatible R
interface
Package vignettes require pandoc
(https://pandoc.org/). Text mining using n-grams requires
Amazon Corretto (https://aws.amazon.com/corretto/).
pandoc (to export an interactive summary table to a
file)
jupyter, jupyter_kernel_test (Python package for
testing)
libjq: jq-devel (rpm) or libjq-dev (deb)
Optional: libsecret on Linux (libsecret-1-dev on
Debian/Ubuntu, libsecret-devel on Fedora/CentOS)
MOSEK (http://www.mosek.com) and MOSEK license.
Package vignettes based on R Markdown v2 or
reStructuredText require Pandoc (http://pandoc.org). The
function rst2pdf() requires rst2pdf
(https://github.com/rst2pdf/rst2pdf).
procps (linux), ps (macos), wmic (windows)
ImageJ (>=1.48), ij.jar (see
http://imagej.nih.gov/ij/), Java (>=1.6.0)
GNU make (optional), libxml2 (optional), glpk (>=
4.57, optional)
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
At least trial version of LocalSolver to be
downloaded from http://www.localsolver.com/download.html
C++11, A system with zoneinfo data (e.g.
/usr/share/zoneinfo). On Windows the zoneinfo included with R
is used.
Python 3.x with following modules: PyGMO, NumPy,
and cloudpickle
ADMB version 11 <http://admb-project.org/> for
use.admb=TRUE; see readme.txt
On a Unix-alike, one of the C functions
mach_absolute_time (macOS), clock_gettime or gethrtime. If none
of these is found, the obsolescent POSIX function gettimeofday
will be tried.
memory (>= 4GB) Ubuntu: build-essential,
libxml2-dev, libssl-dev, libcurl4-openssl-dev Windows: Rtools
(>= 3.5)
TensorFlow (https://www.tensorflow.org), Keras
(https://keras.io), TensorFlow Probability
(https://www.tensorflow.org/probability)
A C++14 compiler. Version 5 or later of GCC will be
fine.
gdal (optional), geos (optional), proj (optional),
udunits (optional), gsl (optional), gmp (optional), glu
(optional), jags (optional), mpfr (optional), openmpi
(optional)
JAGS (>= 4.0.0) (see
https://mcmc-jags.sourceforge.io)
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb)
A valid account MUST be registered with Microsoft's
Cognitive Services website
<https://www.microsoft.com/cognitive-services/> in order to
obtain a (free) API key. Without an API key, this package will
not work properly.
'libnng' >= 1.5 and 'libmbedtls' >= 2.5, or 'cmake'
to compile NNG and/or Mbed TLS included in package sources
pandoc, jupyter, jupyter nbconvert ("nbconvert"
python package), pywin32 (on windows)
neo4j (>=3 AND <=5) <https://neo4j.com/>
neo4j - http://www.neo4j.com, cypher-shell -
https://github.com/neo4j/cypher-shell/releases
Python (>= 3.0.0), Neptune (https://neptune.ai/)
cmake (>= 3.2.0) which is used only on Linux or
macOS systems when no system build of nlopt (>= 2.7.0) can be
found.
NetLogo (>= 5.3.1), Java (>= 11), pandoc, OpenSSL,
udunits-2, PROJ.4, GEOS, GDAL, libxml2
python3-dev: apt-get install -y python3-dev (deb),
python3-pip: apt-get install -y python3-pip (deb), numpy: pip3
install numpy (deb), scipy: pip3 install scipy (deb), nmslib:
pip3 install --no-binary :all: nmslib (deb)
JAGS (http://mcmc-jags.sourceforge.net/) for
analysis of Bayesian hierarchical models
A chromium based browser, required by "webshot2"
<https://rstudio.github.io/webshot2/>
GNU make, An ODBC3 driver manager and drivers.
GNU make, pandoc (>= 1.12.3), pandoc-citeproc,
C++17
OpenCV 3 or newer: libopencv-dev (Debian, Ubuntu)
or opencv-devel (Fedora). The QR code detector requires at
least libopencv 4.5.2.
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb), libjpeg-dev:
apt-get install -y libjpeg-dev (deb), libpng-dev: apt-get
install -y libpng-dev (deb), libfftw3-dev: apt-get install -y
libfftw3-dev (deb), libtiff5-dev: apt-get install -y
libtiff5-dev (deb)
To use the Localhost of OSRM, you need to build
OSRM
<https://github.com/Project-OSRM/osrm-backend/wiki/Building-OSRM>
locally
Python (>= 3.0.1); bencode-python3 (1.0.2)
pandoc (https://johnmacfarlane.net/pandoc) for
exporting markdown files to other formats.
Rendering the document template requires pandoc (>=
2.0; https://pandoc.org) and for PDFs a TeX distribution, such
as TinyTeX (>= 0.12; https://yihui.org/tinytex/)
For multipoint linkage analysis, 'MERLIN'
(http://csg.sph.umich.edu/abecasis/merlin/index.html).
MERLIN (https://csg.sph.umich.edu/abecasis/merlin/)
for multipoint linkage analysis.
The 'Japanese.Rnw' vignette requires uplatex and
dvipdfmx.
OpenMPI (>= 1.5.4) on Linux, Mac, and FreeBSD.
MS-MPI (Microsoft MPI v7.1 (SDK) and Microsoft HPC Pack 2012 R2
MS-MPI Redistributable Package) on Windows.
'OpenMPI' (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. 'MS-MPI' (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
Linux, Mac OSX, and Windows, or 'ZeroMQ' library >=
4.0.4. Solaris 10 needs 'ZeroMQ' library 4.0.7 and 'OpenCSW'.
Poppler C++ API: libpoppler-cpp-dev (deb) or
poppler-cpp-devel (rpm), and poppler-data (rpm/deb) package.
MERLIN (https://csg.sph.umich.edu/abecasis/merlin/)
for calculations involving multiple linked markers.
OpenSSL library and headers (openssl-dev or
similar)
aria2: apt-get install -y aria2 (deb), gdal-bin:
apt-get install -y gdal-bin (deb), libgdal-dev: apt-get install
-y libgdal-dev (deb)
libssh >= 0.6.0 (the original, not libssh2)
For Windows users, Rtools (see
https://cran.r-project.org/bin/windows/Rtools/).
pandoc (>= 1.12.3) -
http://johnmacfarlane.net/pandoc
JAGS (>= 4.0.0) (see
https://mcmc-jags.sourceforge.io/)
A POSIX system. Currently Linux and OS X are known
to work. GNU make.
proj 4.4.6 or higher (https://proj.org/)
pandoc (http://pandoc.org) for building Vignettes
and output reports as HTML
'python3' needs to built for the same architecture
R is built for (32 or 64 bit).
'QGIS' latest or long-term release, currently
supported according to the 'QGIS' release schedule
(<https://www.qgis.org/en/site/getinvolved/development/roadmap.html>).
Older 'QGIS' releases are not officially supported, but may
work since 'QGIS' 3.16.
Quarto command line tools
(https://github.com/quarto-dev/quarto-cli).
OpenBugs for functions bugs() and openbugs() or
WinBUGS 1.4 for function bugs()
package manual requires pandoc (>= 1.14)
http://pandoc.org
python >= 2.6, by default rasciidoc uses a system
installation of asciidoc. If a system installation of asciidoc
is not available, it downloads the sources from
(<http://gitlab.com/asciidoc>). GNU source-highlight is
recommended.
fftw3 (libfftw3-dev (deb), or fftw-devel (rpm)),
pkg-config
Needs a running BaseX server instance. The testuser
with credentials ('Test'/'testBasex') should have admin rights.
cmake, zlib, GNU make, Solaris: g++ is a
requirement
GNU make, pandoc (>= 1.12.3), pngquant, C++17
A valid Bloomberg installation. The API headers and
dynamic library are downloaded from
<https://github.com/Rblp/blp> during the build step. See
<https://bloomberg.github.io/blpapi-docs/cpp/3.8> as well as
<https://www.bloomberg.com/professional/support/api-library/>
for API documentation. A recent-enough compiler with C++11
support is required.
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek,
GMP (GNU MP bignum library from
<https://gmplib.org/>)
A 64-bit POSIX OS such as Linux or OS X with IANA
time zone data in /usr/share/zoneinfo as well as a
recent-enough C++11 compiler (such as g++-4.9 or later which is
preferred, g++-4.8 works too). On Windows the zoneinfo included
with R is used; and time parsing support is enabled via a
backport of std::get_time from the LLVM libc++ library.
GNU make, pcre2 (>= 10.00), GLib (>= 2.0.0). On
Windows, no prior installations are necessary, as pre-built
(i.e. cross-compiled) binaries of required libraries are
downloaded from a GitHub repository
(<https://github.com/PolMine/libcl>) during installation. On
macOS, static libraries of Glib are downloaded
(<https://github.com/PolMine/libglib>) if Glib is not present.
A POSIX system. Currently Linux and OS X are known
to work.
MeCab 0.996 (or mecab-ko 0.9.2) or higher, GNU make
GNU make, Intel TBB, Windows: cmd.exe and
cscript.exe, Solaris: g++ is required
An available hiredis library (eg via package
libhiredis-dev on Debian/Ubuntu, hiredis-devel on
Fedora/RedHat, or directly from source from
<https://github.com/redis/hiredis>) can be used but version
1.0.2 is also included and built on demand if needed. The
optional 'rredis' package can be installed from the 'ghrr'
'drat' repo for additional illustrations and tests.
Python (>= 3.7), retriever (>= 3.0.0) (version must
be listed to patch to allow parsing)
xclip (https://github.com/astrand/xclip) or xsel
(http://www.vergenet.net/~conrad/software/xsel/) for accessing
the X11 clipboard
GNU GSL for the GSL random-number generators
C++17, HDF5 (>= 1.8.13), little-endian platform
Subversion for install_svn, git for install_git
Presentations written in (R)Markdown require Pandoc
(http://pandoc.org).
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files.
Rust 'cargo'; the crate 'libR-sys' must compile
without error
tk.browse() requires Tcl-tk >= 8.6 (shipped with R
>= 3.4.0 on Windows, may have to be manually updated on other
systems)
Python (>= 3.7), rgf_python, scikit-learn (>=
0.18.0), scipy, numpy. Detailed installation instructions for
each operating system can be found in the README file.
OpenGL and GLU Library (Required for display in R.
See "Installing OpenGL support" in README.md. Not needed if
only browser displays using rglwidget() are wanted.), zlib
(optional), libpng (>=1.2.9, optional), FreeType (optional),
pandoc (>=1.14, needed for vignettes)
GLPK library package (e.g., libglpk-dev on
Debian/Ubuntu)
Maxima (https://sourceforge.net/projects/maxima/,
tested with versions 5.42.0 - 5.46.0), needs to be on PATH
iRODS C++ REST API 0.9.3
(https://github.com/irods/irods_client_rest_cpp)
Java JDK 1.2 or higher (for JRI/REngine JDK 1.4 or
higher), GNU make
C++11, cmake (>= 3.10), clang (optional), CUDA
(optional but recommended)
GNU make, cmake (>= 3.2.0), gcc, gfortran, HDF5
libmariadb-client-lgpl-dev or libmariadb-dev or
libmysqlclient-dev, with libssl-dev (deb),
mariadb-connector-c-devel or mariadb-devel (rpm),
mariadb-connector-c or mysql-connector-c (brew)
notepad.exe, mark.exe (>= 8.0) (or mark32.exe and
mark64.exe) and rel_32.exe (see README.txt)
pandoc (>=2.11.3 with 'commonmark_x' processing and
'sourcepos' extension, needed for 'rmarkdown' formats).
pandoc (>=1.14, needed for the vignette)
gmp (>= 4.2.3), mpfr (>= 3.0.0), pdfcrop (part of
TexLive) is required to rebuild the vignettes.
libmariadb-client-dev | libmariadb-client-lgpl-dev
| libmysqlclient-dev (deb), mariadb-devel (rpm), mariadb |
mysql-connector-c (brew), mysql56_dev (csw)
The tools to run 'R CMD SHLIB' on 'Fortran' code.
The used 'Fortran' compiler must support pointer initialization
which is a feature of the 2008 standard. The compiler from
Oracle Developer Studio 12.6 on Solaris 10 currently does not
meet this requirement.
roger-base (for interface functions only)
GNU Make, GCC Compiler Suite (C and Fortran)
Oracle Instant Client or Oracle Database Client
Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]
C++11, GNU make, Windows: cmd.exe and cscript.exe
ProtoBuf libraries and compiler version 3.3.0 or
later; On Debian/Ubuntu these can be installed as
libprotoc-dev, libprotobuf-dev and protobuf-compiler, while on
Fedora/CentOS protobuf-devel and protobuf-compiler are needed.
A C++17 compiler is required as well.
A user API key (which one can request from the
website at <http://www.pushbullet.com>), and one or more
devices to push messages to which may be any one of an (Android
or iOS) phone, a (Chrome or Firefox, or Opera or Safari)
browser or the (Windows or Mac) desktop application provided
the corresponding Pushbullet 'app' has been installed on any
one of these.
QuantLib library (>= 1.14) from
https://quantlib.org, Boost library from https://www.boost.org
GNU make, pandoc (>= 1.12.3), pandoc-citeproc
GNU make, CMake (>= 3.10), pandoc, libarchive,
libregex, libexpat, liblzma, libzstd, liblz4, libz2, libz,
libbcrypt
"conda (python>=3.6 pytorch torchvision cpuonly
numpy ( >= 1.14.0) matplotlib pandas -c pytorch);
python-minimal, pandoc pandoc-citeproc, qpdf; Python (>=3.6),
pytorch (>=1.4), torchvision, cpuonly, numpy ( >= 1.14.0);
pandoc (>= 2.0), qpdf ( >= 7.0) on Solaris"
ZeroMQ >= 3.0.0: libzmq3-dev (deb) or zeromq-devel
(rpm)
GLPK library, including -dev or -devel part
C++17, zlib headers and libraries, GNU make,
optionally also bzip2 and POSIX-compliant regex functions.
(PDF)LaTeX (https://www.latex-project.org/) with
'pdfpages' package for studentGrowthPlot option in visualizeSGP
to bind together student growth plots into school catalogs
yarn 1.22.17 or higher, Node 12 or higher
Pandoc (http://pandoc.org) needs to installed and
available in the search path.
'SLiM' is a forward simulation software for
population genetics and evolutionary biology. See
<https://messerlab.org/slim/> for installation instructions and
further information. The 'Python' coalescent framework
'msprime' and the 'tskit' module can by installed by following
the instructions at <https://tskit.dev/>.
For playing sounds, a command line system tool for
playing wav-files is required.
C++, arrow package with gzip codec, StarPU
For all functions resp. apps: pandoc, LaTeX. For
the edapy() function resp. Statsomat/EDAPY app: Python (>=3).
LilyPond v2.22.1-2+ (needed for rendering sheet
music or writing MIDI files)
Tcl/Tk (>= 8.5), Tktable (>= 2.9, optional)
Tesseract >= 3.03 (libtesseract-dev /
tesseract-devel) and Leptonica (libleptonica-dev /
leptonica-devel). On Debian you need to install the English
training data separately (tesseract-ocr-eng)
C++11, A modern compiler (>=gcc-4.9), And latexmk
is necessary to compile all output documents with the functions
provided by this package
pandoc (>= 1.12.3) suggested for using wordreg
function; LaTeX packages tikz, booktabs, dcolumn, rotating,
thumbpdf, longtable, paralist for the vignette
TensorFlow >= 1.13.0 (https://www.tensorflow.org/)
and TensorFlow IO >= 0.4.0 (https://github.com/tensorflow/io)
PhantomJS (https://phantomjs.org), pandoc (>=
1.12.3) - https://pandoc.org.
A C++17 compiler is required, and on macOS
compilation for version 10.14 is required. Optionally cmake
(only when TileDB source build selected), curl (only when
TileDB source build selected)), and git (only when TileDB
source build selected); on x86_64 and M1 platforms pre-built
TileDB Embedded libraries are available at GitHub and are used
if no TileDB installation is detected, and no other option to
build or download was specified by the user.
Python (>= 2.7), python-gdal library (For Windows,
gdal installed via OSGeo4W <https://trac.osgeo.org/osgeo4w/>
recommended) clipboard
A system with zoneinfo data (e.g.
/usr/share/zoneinfo) as well as a recent-enough C++11 compiler
(such as g++-4.8 or later). On Windows the zoneinfo included
with R is used.
Tcl/Tk (>= 8.6). To read and write other formats
than PPM/PGM, PNG and GIF it is required the 'tkImg' for
'Tcl/Tk' (https://sourceforge.net/projects/tkimg/), or the
debian 'libtk-img' package (Ubuntu) or the RPM 'tkimg' package
(Fedora or openSUSE).
Rust tool chain w/ cargo, libclang/llvm-config
LibTorch (https://pytorch.org/); Only x86_64
platforms are currently supported except for ARM system running
macOS.
under Linux, fftwtools needs libfftw3-dev
udunits (>= 2) from
https://www.unidata.ucar.edu/software/udunits/
PARI/GP >= 2.3.0 [strongly recommended for
logkda()]
Linux or MacOS Intel chip tensorflow (1.14.0)
V8 engine version 6+ is needed for ES6 and WASM
support. On Linux you can build against libv8-dev (Debian) or
v8-devel (Fedora). We also provide static libv8 binaries for
most platforms, see the README for details.
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb), GNU make.
virtuoso-opensource (Linux). For Mac & Windows,
this package can automate Virtuoso installation.
fftw3, GNU make, sox, Ghostscript, libsndfile, GDAL
PhantomJS for taking screenshots, ImageMagick or
GraphicsMagick and OptiPNG for manipulating images.
Java (>= 5.0); WordNet database files (direct
download:
<https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>; Debian
and Fedora package: wordnet)
Perl (>= 5.0), Unitex (>= 3.0
http://www-igm.univ-mlv.fr/~unitex/)
libxml2: libxml2-dev (deb), libxml2-devel (rpm)
'ghostscript' or 'pdftk' for editing pdf bookmarks
and/or documentation info entries. 'exiftool' for editing pdf
documentation info entries and/or xmp metadata.
libxslt: libxslt1-dev (deb), libxslt-devel (rpm)
Accurate, Adaptable, and Accessible Error Metrics for Predictive
Models
Reliability and Scoring Routines for the Approach-Avoidance Task
Apps Based Activities for Communicating and Understanding
Statistics
Tools for Approximate Bayesian Computation (ABC)
Data Only: Tools for Approximate Bayesian Computation (ABC)
Array Based CpG Region Analysis Pipeline
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computational Model for Estimating P2
Approximate Bayesian Computation via Random Forests
Asymptotically Bias-Corrected Regularized Linear Discriminant
Analysis
Databases Used Routinely by the Brazilian Jurimetrics
Association
Useful Tools for Jurimetrical Analysis Used by the Brazilian
Jurimetrics Association
Measure a Subject's Abnormality with Respect to a Reference
Population
The Abnormal Blood Profile Score to Detect Blood Doping
R Interface to the A/B Street Transport System Simulation
Software
An R-Shiny Application for Creating Visual Abstracts
Survival Analysis using Time Dependent Covariate for Animal
Breeding
High-Dimensional Principal Fitted Components and Abundant
Regression
Inferring Directional Conservative Causal Core Gene Networks
Abrupt Change-Point or Aberration Detection in Point Series
Colour Plots with Palettes from Academic Institutions
Access the Twitter Academic Research Product Track V2 API
Endpoint
A Canonical Correlation Analysis with Inferential Guaranties
Functions for Processing Accelerometer Data
Missing Value Imputation for Accelerometer Data
The Acute COPD Exacerbation Prediction Tool (ACCEPT)
Creation and Evaluation of Acceptance Sampling Plans
Improving the Accessibility of 'rmarkdown' Documents
Accrual Plots and Predictions for Clinical Trials
Natural Abundance Correction of Mass Spectrometer Data
Query the Attentional Control Data Collection
Tools for Autoregressive Conditional Duration Models
Analysis of Correlated High-Dimensional Expression (ACE) Data
Optimal Bayesian Experimental Design using the ACE Algorithm
Analisis Computacional de Eventos de Protesta
ACE and AVAS for Selecting Multiple Regression Transformations
ACE Kin Pair Data Simulations and Model Fitting
Estimating Dynamic Heritability and Twin Model Comparison
Robust Estimation of the ACF from the M-Periodogram
Automated Retrieval of ACLED Conflict Event Data
Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Factorization
Modelling Dependence with Multivariate Archimax (or any
User-Defined Continuous) Copulas
A Tool for Automating the Statistical Disclosure Control of
Research Outputs
A Cost-Minimal Regular Spanning Subgraph with TreeClust
Download, Manipulate, and Present American Community Survey and
Decennial Data from the US Census
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
A Companion Package for the Book "A Course in Statistics with R"
Asymptotic Classification Theory for Cognitive Diagnosis
Extract Circadian Rhythms Metrics from Actigraphy Data
Activity Fragmentation Metrics Extracted from Minute Level
Activity Data
Generate Activity Counts from Raw Accelerometer Data
A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical
Behavior Data
Utilities for Parsing and Plotting Activities
Finding Cancer Driver Proteins with Enriched Mutations in
Post-Translational Modification Sites
Gaussian Process Based Design and Analysis for the Active
Subspace Method
Integrative Pathway Enrichment Analysis of Multivariate Omics
Data
Activity Index Calculation using Raw 'Accelerometry' Data
Advanced Processing and Chart Generation from activPAL Events
Files
Read in Activity Data and Plot Actograms
Actuarial Functions and Heavy Tailed Distributions
Handling Hierarchically Structured Risk Factors using Random
Effects Models
Computation of Actuarial Measures Using Bell G Family
Create Actuarial Experience Studies: Prepare Data, Summarize
Results, and Create Reports
A Package for Displaying Visual Scenes as They May Appear to an
Animal with Lower Acuity
Optimal Out-of-Sample Forecast Evaluation and Testing under
Stationarity
Acute Chronic Workload Ratio Calculation
The R Package Ada for Stochastic Boosting
Applies Multiclass AdaBoost.M1, SAMME and Bagging
Estimator of the Adherer Average Causal Effect
Discrete and Global Optimization Routines
Adaptive Huber Estimation and Regression
Archetypoid Algorithms and Anomaly Detection
Adaptive Nature-Inspired Algorithms for Hybrid Genetic
Optimization
Adaptive Approaches for Signal Detection in Pharmacovigilance
Bayesian Adaptive Designs for Diagnostic Trials
Adaptive Bioequivalence Design for In-Vitro Permeation Tests
Implementation of a Generic Adaptive Monte Carlo Markov Chain
Sampler
Adaptive P-Value Thresholding for Multiple Hypothesis Testing
with Side Information
Automated Deconvolution Augmentation of Profiles for Tissue
Specific Cells
Adaptive Sampling for Positive Unlabeled and Label Noise
Learning
'Arrow' Database Connectivity ('ADBC') Driver Manager
'Arrow' Database Connectivity ('ADBC') 'PostgreSQL' Driver
'Arrow' Database Connectivity ('ADBC') 'SQLite' Driver
'DBI' Compliant Database Access Using 'ADBC'
Binomial and Multinomial Additive Hazard Models
Fit Additive Hazards Models for Survival Analysis
Discover and Install Useful RStudio Addins
'RStudio' Addins for Show Outline of a R Markdown/'LaTeX'
Project
Additive Data Envelopment Analysis Models
Additivity Tests in the Two Way Anova with Single Sub-Class
Numbers
Adds Labeled Center Line and Scale Lines/Regions to Trellis
Plots
Analysis of Accelerated Destructive Degradation Test Data
Analysis of Ecological Data: Exploratory and Euclidean Methods
in Environmental Sciences
Exploratory Analysis of Genetic and Genomic Data
An S4 Lattice-Based Package for the Representation of
Multivariate Data
Analysis of Habitat Selection by Animals
Exploratory Analyses for the Phylogenetic Comparative Method
A 'shiny' Application for the (Audio-)Visualization of Adverse
Event Profiles
Adequacy of Probabilistic Models and General Purpose
Optimization
Estimation in Optimal Adaptive Two-Stage Designs
Import from and Export to Amiga Disk Files
Get Adform Ads Data via the 'Windsor.ai' API
Assess Medication Adherence from Pharmaceutical Claims Data
Adjacency-Constrained Clustering of a Block-Diagonal Similarity
Matrix
Computing Sensitivity at a Fix Value of Specificity and Vice
Versa as Well as Bootstrap Metrics for ROC Curves
Parameter and Adjusted Probability Estimation for Right-Censored
Data
Remove Automated and Repeated Downloads from 'RStudio' 'CRAN'
Download Logs
Confounder-Adjusted Survival Curves and Cumulative Incidence
Functions
Utility Functions and Development Tools for the Admiral Package
Family
Oncology Extension Package for ADaM in 'R' Asset Library
Vaccine Extension Package for ADaM in 'R' Asset Library
Adaptive Mixture of Student-t Distributions
Package Admix for Admixture (aka Contamination) Models
An Interface for Running 'ADMIXTOOLS' Analyses
Algorithms using Alternating Direction Method of Multipliers
Alternating Direction Method of Multipliers to Solve Dense
Dubmatrix Problem
Penalized Precision Matrix Estimation via ADMM
Estimate and Manipulate Age-Depth Models
Ancient Demographic Modelling Using Radiocarbon
No-U-Turn MCMC Sampling for 'ADMB' Models
Adoption Probability, Triers and Users Rate of a New Product
Fast Clustering Using Adaptive Density Peak Detection
Use Least Squares Polynomial Regression and Statistical Testing
to Improve Savitzky-Golay
Design and Analysis of Locally or Globally Efficient Adaptive
Designs
Upper Clopper-Pearson Confidence Limits for Burn-in Studies
under Additional Available Information
Solving 1D Advection Bi-Flux Diffusion Equation
Interactive R Tutorials to Accompany Field (2016), "An Adventure
in Statistics"
Automatic Direct Variable Selection via Interrupted Coefficient
Estimation
Wavelet Analysis of Genomic Data from Admixed Populations
Automated and Early Detection of Disease Outbreaks
Change Point Analysis in ARIMA Forecasting
Automated and Early Detection of Seasonal Epidemic Onset
Statistical Modelling for Asymmetric Exponential Power
Distribution
A Computational Tool for Aerobiological Data
Estimate Aerosol Particle Collection Through Sample Lines
Model-Based Estimation of Confounder-Adjusted Attributable
Fractions
Government Analysis Function Recommended Accessible Colour
Palette
Diagnosis Performance Using Attributable Fraction
Accelerated Functional Failure Time Model with
Error-Contaminated Survival Times
Raster Georeferencing, Grid Affine Transforms, Cell Abstraction
The Attributable Fraction (AF) Described as a Function of
Disease Heritability, Prevalence and Intervention Specific
Factors
Functions for Atomic Force Microscope Force-Distance Curves
Analysis
Tools for Modelling of Animal Flight Performance
Toolkit for Regression Analysis of Kazakhstan Banking Sector
Data
Africa Macroeconomic Monitor Database API
Accelerated Failure Time Model with Generalized Estimating
Equations
Accelerated Failure Time for High Dimensional Data with MCMC
R-Squared Measure under Accelerated Failure Time (AFT) Models
Model Diagnostics for Accelerated Failure Time Models
Toolbox for Downloading and Extracting Copernicus AgERA5 Data
Collection of Functions for Working with Age Intervals
Agnostic Fay-Herriot Model for Small Area Statistics
Aggregate Numeric, Date and Categorical Variables
Utilities for Aggregating Probabilistic Forecasts
Relationship Matrices for Diploid and Autopolyploid Species
Adaptive Gauss Hermite Quadrature for Bayesian Inference
Aggregation Operators and Preordered Sets
Agreement Interval of Two Measurement Methods
Statistical Procedures for Agricultural Research
Visualization of Design of Experiments from the 'agricolae'
Package
Tutorial Analysis of Some Agricultural Experiments
Utilities for Data Analysis in Agriculture
Evapotranspiration and Energy Fluxes Spatial Analysis
Calculate Concentration and Dispersion in Ordered Rating Scales
Experimental Statistics and Graphics for Agricultural Sciences
Regression Analysis Linear and Nonlinear for Agriculture
Stability Analysis for Agricultural Research
Data Analysis of Pesticide Application Technology
Crosswalk Municipality and District Statistics in Germany
Adaptive and Automatic Gradient Boosting Computations
An R Implementation of the 'Align-GVGD' Method
Regularization for Semiparametric Additive Hazards Regression
Additive Heredity Model: Method for the Mixture-of-Mixtures
Experiments
Functions and Data for the Book 'Applied Hierarchical Modeling
in Ecology' Vols 1 and 2
An Implementation of the Artificial Hydrocarbon Networks
Fast Searching for Multiple Keywords in Multiple Texts
Analytic Hierarchy Process for Survey Data
Consistency in the Analytic Hierarchy Process
Hybrid Method for Multiple Criteria Decision-Making (MCDM)
Flexible Parametric Accelerated Hazards Models
Calculates Continuous Time Likelihood Ratio Weights Assuming
Multiplicative Intensity Models and Additive Hazard Models
Testing for Compositional Pathologies in Datasets
Model Selection and Multimodel Inference Based on (Q)AIC(c)
Model Selection of PERMANOVA Models Using AICc
Additive Information & Details of Evidence Synthesis
Experimental Evaluation of Algorithm-Assisted Human
Decision-Making
Irish Weather Observing Stations Hourly Records for 2017
AI Assistant to Write and Understand R Code
Download Mexico City Pollution, Wind, and Temperature Data
Suite of GR Hydrological Models for Precipitation-Runoff
Modelling
Hydro-Meteorological Catchments Datasets for the 'airGR'
Packages
Ensemble-Based Data Assimilation with GR Hydrological Models
'airGR' Integrated Water Resource Management
Teaching Hydrological Modelling with the GR Rainfall-Runoff
Models ('Shiny' Interface Included)
Retrieve 'AirNow' Air Quality Observations and Forecasts
Convenience Tools for Working with Airport Data
Air Quality Measurements in Spain from 2011 to 2018
AIRR Data Representation Reference Library
Evaluation of Algorithm Collections Using Item Response Theory
Additive Isotonic Proportional Hazards Model
Interpolation of Irregularly and Regularly Spaced Data
Immunoglobulin Clonal Lineage and Diversity Analysis
Adaptive Lasso for the Cox Regression with Interval Censored and
Possibly Left Truncated Data
PARAFAC Analysis of Fluorescence Excitation-Emission Matrices
Quantile Regression Using Asymmetric Laplace Distribution
Adjusted Limited Dependent Variable Mixture Models
Interpretable Machine Learning and Statistical Inference with
Accumulated Local Effects (ALE)
Accumulated Local Effects (ALE) Plots and Partial Dependence
(PD) Plots
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS
Proteomics Data
Connectivity to 'Alfresco' Content Management Repositories
Downloading Time Series from ALFRED Database for Various
Vintages
Tools to Standardize Phytoplankton Taxonomic Data and Perform
Functional Group Classifications
Haplotype Two Snips Out of a Paired Group of Patients
Implement an Address Search Auto Completion Menu on 'Shiny' Text
Inputs Using the 'Algolia Places' 'Javascript' Library
Allows you to Easily Interact with the Algorithmia Platform
Converts Human gene symbols to entrez IDs
A Modified DTW Algorithm for Stratigraphic Time Series Alignment
Pre-Processing XY Data from Experimental Methods
Acknowledge all Contributors to a Project
Allele Imputation and Haplotype Reconstruction from Pedigree
Databases
Identifying Unique Multilocus Genotypes where Genotyping Error
and Missing Data may be Present
Allele Retention, Inbreeding, and Demography
Predict and Visualize Population-Level Changes in Allele
Frequencies in Response to Climate Change
Calculating Multiple Performance Metrics of a Prediction Model
Acute Lymphoblastic Leukemia Maintenance Therapy Analysis
Interface for 'All of Us' Researcher Workbench
Removing Allometric Effects of Body Size in Morphological
Analysis
Datasets from the Survival TV Series Alone
Fit GLM's with High-Dimensional k-Way Fixed Effects
Algorithms for High-Throughput Sequencing of Antigen-Specific T
Cells
Confidence Intervals for Coefficient Alpha and Standardized
Alpha
Generalization of the Convex Hull of a Sample of Points in the
Plane
Set Alpha Based on Sample Size Using Bayes Factors
Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Partition/Decomposition of Breeding Values by Paths of
Information
Implementation of the 3D Alpha-Shape for the Reconstruction of
3D Sets from a Point Cloud
Lightweight Interface to the Alpha Vantage API
Adaptive Lasso Quantile Regression with Fixed Effects
Data to Accompany Applied Linear Regression 4th Edition
Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Companion to Applied Linear Statistical Models
Use 'Docsify.js', 'Docute', or 'Mkdocs' to Generate a Package
Documentation
Detection of Altered Protein Quantitative Relationships
Provides an Interface to the NREL Alternate Fuels Locator
Optimal Experimental Designs for Accelerated Life Testing
Alternative Estimators to Adjusted R-Squared
Alternating Manifold Proximal Gradient Method for Sparse PCA
An Interface to the 'AutoNavi Maps' API Geocoding Services
Get Amazon Ads Data via the 'Windsor.ai' API
Get Amazon S3 Data via the 'Windsor.ai' API
Get Amazon Sp Data via the 'Windsor.ai' API
Simulation and Estimation of Ambit Processes
Generate LaTeX Code for Auto-Multiple-Choice (AMC)
Anomaly Detection with Normal Probability Functions
Additive and Multiplicative Effects Models for Networks and
Relational Data
This package includes pricing function for selected American
call options with underlying assets that generate payouts
American Politics-Inspired Color Palette Generator
Checks for Various Computing Environments
Compute the Adjusted Market Inefficiency Measure
Additive Main Effects and Multiplicative Interaction Model
Stability Parameters
Automated Moderated Nonlinear Factor Analysis Using 'M-plus'
Tractable Parametric Odds-Based Regression Models
Statistical Test for the Multivariate Point Null Hypotheses
An Algorithm for Automatic Peak Detection in Noisy Periodic and
Quasi-Periodic Signals
Shiny Apps to Support Capacity Building on Harvest Control Rules
Statistical Analysis of Amplicon Data of the Same Sample to
Identify Artefacts
Analysis of Codon Data under Stationarity using a Bayesian
Framework
Targeted Differential and Global Enrichment Analysis of
Taxonomic Rank by Shared Asvs
Simple and Canonical Correspondence Analysis
Analogue and Weighted Averaging Methods for Palaeoecology
Data Analysis and Automated R Notebook Generation
Assigning Ancestry Based on Population References
Construct Natural-Language Lists with Internationalization
Asynchronous Disk-Based Representation of Massive Data
Make Spectroscopic Data Processing Easier
Simulate a Bus Route Creel Survey of Anglers
Tools for 'ROMS' the Regional Ocean Modeling System
Inferring Dominance Hierarchies and Estimating Uncertainty
Extended Kalman Filters for Animal Movement
Networks Characterising the Physical Configurations of Animal
Habitats
A Web-Based Graphics Device for Animated Visualisations
A Gallery of Animations in Statistics and Utilities to Create
Animations
Visualizing Changes in Performance Measures and Demographic
Affiliations using Animation
Animated Interactive Grammar of Graphics
A Collection of ML Tools for Conservation Research
Animation of Multiple Trajectories with Uncertainty
Statistical Network Analysis of Animal Social Networks
Animate Shiny and R Markdown Content when it Comes into View
Compute High Dimensional Orthant Probabilities
Artificial Neural Networks for Anomaly Detection
Annotated Matrix: Matrices with Persistent Row and Column
Annotations
Annotate the Gene Symbols for Probes in Expression Array
Extract Subsequences from GenBank Annotations
Image Annotation and Polygon Outlining using Free Drawing
A Non-Parametric Statistical Test to Compare Clustering
Structures
Interactive Document for Working with Analysis of Variance
Create Datasets with Identical Summary Statistics
Import, Manipulate and Explore the Results of an 'Antares'
Simulation
Computation of the WHO Child Growth Standards
Computation of the WHO 2007 References for School-Age Children
and Adolescents (5 to 19 Years)
An R Package for the Mean Measure of Divergence (MMD)
Statistical Methods for Anthropometric Data
Antibody Titer Analysis of Vaccinated Patients
Extract Text from Microsoft Word Documents
Sleep Quality and Anxiety in Confinement
Query 'nycflights13'-Like Air Travel Data for Given Years and
Airports
Install and Load Any Package from CRAN, Bioconductor or Github
Anything to 'POSIXct' or 'Date' Converter
Analysis of Overdispersed Data using S3 Methods
Data from the 'Access to Opportunities Project (AOP)'
A-Optimal Block Designs for Comparing Test Treatments with
Controls
Accelerated Oblique Random Survival Forests
Inference in the Balanced One-Way ANOVA Model with Random Factor
Classic, Nonparametric and Bayesian One-Way Analysis of Variance
Panel
Format Outputs of Statistical Tests According to APA Guidelines
Create American Psychological Association (APA) Style Tables
Create R Markdown Text for Results in the Style of the American
Psychological Association (APA)
Resolving Plant Taxon Names Using the Australian Plant Census
Analysis of Unreplicated Orthogonal Experiments using All
Possible Comparisons
A New Age-Period-Cohort Model for Describing and Investigating
Inter-Cohort Differences and Life Course Dynamics
Routines for Descriptive and Model-Based APC Analysis
An Implementation of the Additive Polynomial Design Matrix
Advanced Pathway Enrichment Analysis Representation
Create Interactive Chart with the JavaScript 'ApexCharts'
Library
Multiple Testing Approach using Average Power Function (APF) and
Bayes FDR Robust Estimation
Analysis with Profile Hidden Markov Models
Statistical Inference and Prediction of Annotations in
Phylogenetic Trees
Functions and Data Sets for the Book "A Progressive Introduction
to Linear Models"
Auto-Adaptive Parentage Inference Software Tolerant to Missing
Parents
Interface to 'episensr' for Sensitivity Analysis of
Epidemiological Results
Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic
Regression" (3rd Ed., 2013)
Decorate a 'ggplot' with Associated Information
Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots',
Slider Functions and Others
Augmented and Penalized Minimization Method L0
Automated Population Pharmacokinetic Dataset Assembly
Prioritize and Delete Erroneous Taxa in a Large Phylogenetic
Tree
Tools for Choice Model Estimation and Application
Interface to 'AppEEARS' NASA Web Services
Adjusted Prediction Model Performance Estimation
Get Apple Search Ads Data via the 'Windsor.ai' API
'Apple App Store' and 'iTunes' Data Extraction
A Compilation of Applicability Domain Methods
Functions and Data Sets for 'Applied Predictive Modeling'
Amyloid Propensity Prediction Neural Network
Code and Obtain Customized Planned Comparisons with 'appRiori'
Bayesian Prediction of Complex Computer Codes
Approximately Optimal Fine Balance Matching with Multiple Groups
Get Data from 'Appsflyer' via the 'Windsor.ai' API
Datasets from Draper and Smith "Applied Regression Analysis"
(3rd Ed., 1998)
Amdahl's Profiler, Directed Optimization Made Easy
Inspect, Read, Edit and Run 'APSIM' "Next Generation" and
'APSIM' Classic
Average Positive Predictive Values (AP) for Binary Outcomes and
Censored Event Times
Visualize Population Pyramids Aggregated by Age
Estimation of Rainy Season Calendar and Soil Water Balance for
Agriculture
Integrated Development Toolbox for Aquatic Chemical Model
Generation
Access to Aquo domaintables from R (Dutch)
Another Look at the Acceptance-Rejection Method
Simulate Auto Regressive Data from Precision Matricies
Rigorous Data Reduction and Error Propagation of Ar40 / Ar39
Data
Fitting Univariate Censored Linear Regression Model with
Autoregressive Errors
Bayesian Modeling of Archaeological Chronologies
Post-Processing of the Markov Chain Simulated by 'ChronoModel',
'Oxcal' or 'BCal'
Example Datasets from Archaeological Research
Refitting and Spatial Analysis in Archaeology
Visualisation, Exploration, and Web Communication of
Archaeological Spatial Data
Finds the Archetypal Analysis of a Data Frame
An Archetype for Data Mining and Data Science Projects
Plant Root System Architecture Analysis Using DART and RSML
Files
Multi-Format Archive and Compression Support
Retrieve Archived Web Pages from the 'Internet Archive'
Tools for Storing, Restoring and Searching for R Objects
Tools for Archiving, Managing and Sharing R Objects via GitHub
Autoregressive Cokriging Models for Multifidelity Codes
Process ArcGIS Protocol Buffer FeatureCollections
Processing and Physical Activity Summaries of Minute Level
Activity Data
ARDL, ECM and Bounds-Test for Cointegration
Linear and Nonlinear Autoregressive Distributed Lag Models:
General-to-Specific Approach
Calculate Area of Triangles and Polygons
Harmonise and Integrate Heterogeneous Areal Data
Plot Stacked Areas and Confidence Bands as Filled Polygons
Plots, Summary Statistics and Tools for Arena Simulation Users
Arena for the Exploration and Comparison of any ML Models
Fractional ARIMA (and Other Long Memory) Time Series Modeling
Accurate Estimation of Influenza Epidemics using Google Search
Data
Automatic Regression for Governing Equations (ARGOS)
Command Line Optional and Positional Argument Parser
Random Variate Generator for the Argus Distribution
Adaptable Regularized Hotelling's T^2 Test for High-Dimensional
Data
Efficient Computations of Standard Clustering Comparison
Measures
Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models
Likelihood Based Inference for ARIMA Modeling
Time Series Forecasting using ARIMA-ANN Hybrid Model
Archive and Unarchive Databases Using Flat Files
Data Analysis Using Regression and Multilevel/Hierarchical
Models
A Statistical Methodology to Select Covariates in
High-Dimensional Data under Dependence
Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp'
Covariate-Adjusted Receiver Operating Characteristic Curve
Inference
Analysis of Large Affymetrix Microarray Data Sets
A Probe-Level Data File Format Used by 'aroma.affymetrix'
[deprecated]
Copy-Number Analysis of Large Microarray Data Sets
Core Methods and Classes Used by 'aroma.*' Packages Part of the
Aroma Framework
Geometric Morphometric Methods and Virtual Anthropology Tools
Retrieving and Analyzing Air Quality and Weather Data from ARPA
Lombardia
Censored Mixed-Effects Models with Different Correlation
Structures
Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes
Fast Generators and Iterators for Permutations, Combinations,
Integer Partitions and Compositions
Apply a Function to a Margin of an Array
An Arsenal of 'R' Functions for Large-Scale Statistical
Summaries
Aligned Rank Transform for Nonparametric Factorial Analysis
To Insert Title, Divider, and Block of Comments
Mining Association Rules and Frequent Itemsets
Classification Based on Association Rules
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
Data Sets for "Applied Survival Analysis Using R""
A Collection of Statistical Tools for Biologists
Export R Objects to Several Markup Languages
Create 'Ascii' Screen Casts from R Scripts
Reads Fixed-Width ASCII Data Files (.txt or .dat) that Have
Accompanying Setup Files (.sps or .sas)
Simulate the Spread of Ascochyta Blight in Chickpea
Simulations for Adaptive Seamless Designs
Interface to the Australian Statistical Geography Standard
Methods for Adaptive Shrinkage, using Empirical Bayes
Password Entry Utilities for R, Git, and SSH
Interface to the Algorithm Selection Benchmark Library
Linkage Map Construction using the MSTmap Algorithm
Predicting and Classifying Patient Phenotypes with Multi-Omics
Data
Animal Social Network Inference and Permutations for Ecologists
Functions for Estimating Centrographic Statistics
A General Framework for Multivariate Analysis with Optimal
Scaling
Analysis of Symmetry of Parasitic Infections
Spline Regression with Adaptive Knot Selection
Augments 'ASReml-R' in Fitting Mixed Models and Packages
Generally in Exploring Prediction Differences
Applied Singular Spectrum Analysis (ASSA)
Verbose Assertions for Tabular Data (Data.frames and
Data.tables)
Simple Assertions for Beautiful and Customisable Error Messages
A Lightweight Core of the 'assertive' Package
Assertions to Check Properties of Matrices
Assertions to Check Properties of Models
Assertions to Check Properties of Numbers
Assertive Programming for R Analysis Pipelines
Backtesting Simple Asset Allocation Strategies
Estimating Asset Correlations from Default Data
Optimal Pricing of Assets with Fixed Expiry Date
Population Assignment using Genetic, Non-Genetic or Integrated
Data in a Machine Learning Framework
Infer Geographic Origin from Isotopic Data
A Suite of R Functions Implementing Spline Smoothing Techniques
Adaptive Subgroup Selection in Group Sequential Trials
Measuring Association with Recursive Binning
Implements New and Existing Association Indices for Constructing
Animal Social Networks
Calculate the Assortativity Coefficient of Weighted and Binary
Networks
Translates an R Function to a C++ Function
A Computational Tool for Astrochronology
Astronomy: Time and Position Functions, Misc. Utilities
Applied Statistical Time Series Analysis
Adaptive SURE Thresholding Using Side Information
Stochastic Volatility Models with or without Leverage
Data on Asylum and Resettlement for the UK
Asymmetric Linkage Disequilibrium (ALD) for Polymorphic Genetic
Data
Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)
Multidimensional Scaling of Asymmetric Proximities
Asymmetry Measures for Probability Density Functions
A Simple R Package for Classical Parametric Statistical Tests
and Confidence Intervals in Large Samples
Estimate Asymptomatic Cases via Capture/Recapture Methods
Coroutines: Generators / Yield, Async / Await, and Streams
Regression Analysis of Sparse Asynchronous Longitudinal Data
Create Tables for Reporting Clinical Trials
Automatic Time Series Analysis and Forecasting using the Ata
Method
Bounding Treatment Effects by Limited Information Pooling
Average Treatment Effects with Measurement Error and Variable
Selection for Confounders
Download Air Quality and Meteorological Information of Chile
Tools to Handle and Publish Metadata as 'Atom' XML Format
Wrapper for the 'AT' Protocol Behind 'Bluesky'
Correcting for Attenuation Due to Measurement Error
Threshold Independent Performance Measures for Probabilistic
Classifiers
Standard Conform Pure Tone Audiometry (PTA) Plots
Model Audit - Verification, Validation, and Error Analysis
Automatic Dynamic Regression using Extreme Gradient Boosting
Analysis of Augmented Randomised Complete Block Designs
Data Augmentation for Machine Learning on Tabular Data
Analysis of Data with Mixed Measurement Error and
Misclassification in Covariates
eBird Data Extraction and Processing in R
Area Under Minimum of False Positives and Negatives
Creates a Neighbourhood Using Locality Sensitive Hashing for
Gaussian Projections
TERN AusPlots Australian Ecosystem Monitoring Data
Provides Datasets About Australian Politicians
Approximate Unconditional and Permutation Tests
Automatic Variable Reduction Using Principal Component Analysis
Learning to Rank Bagging Workflows with Metalearning
Automated Covariate Selection Using HDPS Algorithm
An Interface to the 'AutoDesk' 'API' Platform
Automated Stacked Ensemble Classifier for Severe Class Imbalance
Automatic Construction of Forced-Choice Tests
Auto-GO: Reproducible, Robust and High Quality Ontology
Enrichment Visualizations
Semi-Automatic Grading of R and Rmd Scripts
Automated Generation of Data-Informed GLM Models in Task-Based
fMRI Data Analysis
Multiple Heat Maps for Projected Coordinates
Automagically Document and Install Packages Necessary to Run R
Code
Algorithms for Automatically Fitting MFA Models
Auto-Generate Changelog using Conventional Commits
Automated Transcriptome Classifier Pipeline: Comprehensive
Transcriptome Analysis
Automatic Generation of Interactive Visualizations for
Statistical Results
Development of Visualization Tools for Protein Sequence
Automatic Linear and Logistic Regression and Survival Analysis
Minimum Distance Estimation in Autoregressive Model
An Interpretable Machine Learning-Based Automatic Clinical Score
Generator
Fully Automatic Generation of Scorecards
Retrieve Disease-Related Genes from Public Sources
Automatic Transformation of an 'R' Function into a 'shiny' App
Builds Stepwise GLMs via Train and Test Approach
An R Port of the 'ImageJ' Plugin 'Auto Threshold'
A Novel Automatic Shifted Log Transformation
Automatic Model Selection and Prediction for Univariate Time
Series
Check if the Title of a Package is Available, Appropriate and
Interesting
Calculation of Average Population Attributable Fractions and
Confidence Intervals
Access to the List of Birds Species of Peru
A Variable Selection using Genetic Algorithms
A Computational Biologist’s Toolkit To Get Data From 'avidaDB'
Calculate the Inertial Properties of a Flying Bird
Adaptation of Virtual Twins Method from Jared Foster
Convert Dates to Arbitrary Week Definitions
Get Data from 'Awin' via the 'Windsor.ai' API
Amazon 'Kinesis' Consumer Application for Stream Processing
Client for the Amazon Alexa Web Information Services API
Communicating with AWS EC2 and ECS using AWS REST APIs
'AWS Key Management Service' Client Package
Class and Methods Definitions for Packages 'aws', 'adimpro',
'fmri', 'dwi'
Analyzing Zero-Inflated and Zero-Altered Data
Logging in 'R' and Post to 'Azure Log Analytics' Workspace
Include Azure Application Insights in Shiny Apps
Authentication Services for Azure Active Directory
Interface to 'Container Instances', 'Docker Registry' and
'Kubernetes' in 'Azure'
Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service
Interface to 'Kusto'/'Azure Data Explorer'
Interface to the 'Azure Machine Learning' 'SDK'
Interface to the Table Storage Service in 'Azure'
Interface to Azure Computer Vision Services
Interface to Azure Virtual Machine Instance Metadata
Gene Orthologs for Model Organisms in a Tidy Data Format
Use 'nlmixr2' to Interact with Open Source and Commercial
Software
Talking to 'Docker' and 'Singularity' Containers
Bayesian Analysis of Computer Code Output (BACCO)
Bayesian Augmented Control for Clinical Trials
Bayesian Classification and Information Sharing (BaCIS) Tool for
the Design of Multi-Group Phase II Clinical Trials
Reimplementations of Functions Introduced Since R-3.0.0
Learning Causal Cyclic Graphs from Unknown Shift Interventions
Exploring Portfolio-Based Conjectures About Financial
Instruments
Robust Explainable Outlier Detection Based on OutlierTree
A Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)
Filter Covariance and Correlation Matrices with
Bootstrapped-Averaged Hierarchical Ansatz
Bayes Factors for Informative Hypotheses
Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets
Balance Check for Multiple Covariates in Matched Observational
Studies
Balanced and Spatially Balanced Sampling
Bayesian Hierarchical Modeling for Label-Free Proteomics
Statistical Inference and Sure Independence Screening via Ball
Statistics
Expression RNA-Seq Data Analysis Based on Linear Mixed Model
Functions and Datasets for "Bayesian Methods: A Social and
Behavioral Sciences Approach"
High Dimensional Bayesian Mediation Analysis
Bayesian Meta-Analysis of Diagnostic Test Data
Bayesian Additive Models for Location, Scale, and Shape (and
Beyond)
Species Distribution Models as a Function of Biotic, Abiotic and
Movement Factors (BAM)
Analysis and Visualization of Macroevolutionary Dynamics on
Phylogenetic Trees
Bayesian Age-Period-Cohort Modeling and Prediction
Functions for Simple a/B Split Test and Multi-Armed Bandit
Analysis
Compute Upper Prediction Bounds on the FDP in Competition-Based
Setups
Provides Ready to Use Shapefiles for Geographical Map of
Bangladesh
Make Banner Comments with a Consistent Format
Batch Effect Removal and Addon Normalization (in Phenotype
Prediction using Gene Data)
Label Creation for Tracking and Collecting Data from Biological
Samples
Species Identification using DNA Barcodes
Complete Works of William Shakespeare in Tidy Format
Bayesian Additive Regression Trees using Bayesian Model
Averaging
Causal Inference using Bayesian Additive Regression Trees
Bayesian Additive Regression Trees for Confounder Selection
Variable Selection Using Bayesian Additive Regression Trees
Quantifying Habitat-Induced Acoustic Signal Degradation
Bayesian Variable Selection and Model Averaging using Bayesian
Adaptive Sampling
Bayesian Variable Selection with Shrinking and Diffusing Priors
Convert Regression Between Base Function and 'rms' Package
Fast and URL-Safe Base64 Encoder and Decoder
Infrastructure for Computing with Basis Functions
Baseline Models for Classification and Regression
Basic Sequence Processing Tool for Biological Data
Estimation of Seed Germination Base Temperature in Thermal
Modelling
Plot Simple Features with 'base' Sensibilities
Trace Plots, Density Plots and Chain Comparisons for MCMC
Samples
Recovering a Basic Space from Issue Scales
Construct Rich Tables for Output to 'HTML'/'Excel'
Retrieval and Processing of the Spanish National Forest
Inventory
Classification of RNA Sequences using Complex Network Theory
Classification of RNA Sequences using Complex Network and
Information Theory
Bayesian Model to Archaeological Faunal Skeletal Profiles
Analysis and Visualization of Basketball Data
Exact Calculation of Basket Trial Operating Characteristics
Managing Packages Removal and Installation
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical Experiments on Batch Computing Clusters
Downloads and Organizes Financial Data for Multiple Tickers
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Semi-Supervised Bayesian Mixture Models Incorporating Batch
Correction
Convert Categorical Representations of Logicals to Actual
Logicals
Real-Time PCR Data Sets by Batsch et al. (2008)
An R Package for Data Reduction by Battery Reduction
Bayesian Analysis of Clumped Isotope Datasets
Bayesian Estimation and Optimisation of Expected False Discovery
Rate
Bayesian Quantile Elastic Net for Genetic Study
User Friendly Bayesian Data Analysis for Psychology
Bayesian Estimation of the Additive Main Effects and
Multiplicative Interaction Model
Bayesian Inference in the Analysis of Variance via Markov Chain
Monte Carlo in Gaussian Mixture Models
Bayesian Estimation of Bivariate Volatility Model
Bayesian Estimation of Mixtures of Multivariate Bernoulli
Distributions
Miscellaneous Functions for Bioinformatics and Bayesian
Statistics
An Implementation of Rubin's (1981) Bayesian Bootstrap
Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix
Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
Bayesian Analysis of Censored Regression Models Under Scale
Mixture of Skew Normal Distributions
Simulation and Analysis of Adaptive Bayesian Clinical Trials
Two Arm Bayesian Clinical Trial Design with and Without
Historical Control Data
Functions and Datasets for the book "Bayesian Data Analysis"
Methods and Tools for Bayesian Dynamic Conditional Correlation
GARCH(1,1) Model
Bayesian Single-Arm Design with Survival Endpoints
Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Bayesian Decreasingly Informative Priors for Early Termination
Phase II Trials
Statistical Analysis for Task-Based Fmri Data
Implementation of the Bayesian Discount Prior Approach for
Clinical Trials
Computation of Bayes Factors for Common Designs
Bayesian Dynamic Borrowing with Flexible Baseline Hazard
Function
Implementation of Bayesian Neural Networks
Spatial Bayesian Methods for Task Functional MRI Studies
Fit Multivariate Response Generalized Additive Models using
Hamiltonian Monte Carlo
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Bayesian Causal Mediation Analysis using 'Stan'
Bayesian Modeling via Frequentist Goodness-of-Fit
Fast Bayesian Gaussian Process Regression Fitting
Estimate Fish Growth Using MCMC Analysis
Bayesian Grouped Weighted Quantile Sum Regression
Bayesian Estimation of the ETAS Model for Earthquake Occurrences
Bayesian Solutions for the Factor Zoo: We Just Ran Two
Quadrillion Models
Bayesian Gamma Regression: Joint Mean and Shape Modeling
Predict Brain Asymmetry Based on Handedness and Dichotic
Listening
Simulate, Evaluate, and Analyze Dose Finding Trials with
Bayesian MCPMod
Simulate and Analyse Bayesian Platform Trial with Time Trend
Sample Size and Power for Comparing Inequality Constrained
Hypotheses
Plot Positive and Negative Predictive Values for Medical Tests
General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics
Bayesian t Regression for Modeling Mean and Scale Parameters
Bayesian Methods for Image Segmentation using a Potts Model
Efficient Sampling for Gaussian Linear Regression with Arbitrary
Priors
Adjust Longitudinal Regression Models Using Bayesian Methodology
Bayesian Preference Learning with the Mallows Rank Model
Bayesian Data Reconciliation of Separation Processes
Bayesian Random-Effects Meta-Analysis and Meta-Regression
Bayesian Mixture Survival Models using Additive
Mixture-of-Weibull Hazards, with Lasso Shrinkage and
Stratification
A Multistate Life Table (MSLT) Methodology Based on Bayesian
Approach
Non-Parametric Bayesian Analyses of Animal Movement
A Bayesian No-Effect- Concentration (NEC) Algorithm
Bayesian Analysis of Non-Stationary Gaussian Process Models
Bayesian Group Sequential Design for Ordinal Data
Variable Selection in a Hierarchical Bayesian Model for a Hazard
Function
Bayesian Inference for Presence-Only Data
Generate Postestimation Quantities for Bayesian MCMC Estimation
Bayesian Power Prior Design for Survival Data
Fast Projection Direction for Multivariate Changepoint Detection
Bayesian Regression Models with Global-Local Shrinkage Priors
Bayesian Analysis of Replication Studies
Fits the Bayesian Piecewise Linear Log-Hazard Model
Bayesian Robust Generalized Mixed Models for Longitudinal Data
Bayes Factors for Hierarchical Linear Models with Continuous
Predictors
Datasets and Supplemental Functions from Bayes Rules! Book
Bayesian Variable Selection Using Simplified Shotgun Stochastic
Search with Screening (S5)
Bayes Linear Estimators for Finite Population
Different Models of Posterior Distributions of Adjusted Odds
Ratio
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Bayesian Survival Analysis for Right Censored Data
Effect Size Targeted Bayesian Two-Sample t-Tests via Markov
Chain Monte Carlo in Gaussian Mixture Models
Understand and Describe Bayesian Models and Posterior
Distributions
Bayesian Analysis of Item-Level Twin Data
Bayes Factors, Model Choice and Variable Selection in Linear
Models
Visually Learning the Graphical Structure of Bayesian Networks
and Performing MCMC with 'Stan'
Bayesian Change-Point Detection for Process Monitoring with
Fault Detection
R Utilities Accompanying the Software Package BayesX
Distribution of the 'BayesX' C++ Sources
Bayesian Zero-Inflated Bernoulli Regression Model
Global Bayesian Foraminifera Core Top Calibration
Bayesian Shrinkage Estimators for Precision Matrices in Gaussian
Graphical Models
Chronological Bayesian Models Integrating Optically Stimulated
Luminescence and Radiocarbon Age Dating
Bayesian Multidimensional Scaling and Choice of Dimension
Computation of Bayes Factors for Common Biomedical Designs
Bayesian Stability Analysis of Genotype by Environment
Interaction (GEI)
On Bayesian Analysis of Threshold Autoregressive Models
Solving and Optimizing Large-Scale Nonlinear Systems
Identification of Bull and Bear States of the Market
Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Benthic Biotic Indices Calculation from Composition Data
Miscellaneous Helper Functions for B. Bischl
Bayesian Model for Genotyping using RNA-Seq
Tools for General Maximum Likelihood Estimation
Bessel and Beta Regressions via Expectation-Maximization
Algorithm for Continuous Bounded Data
Gene Regulatory Network Inference with Bc3net
Block Coordinate Ascent with One-Step Generalized Rosen
Algorithm
Bias Corrected Bootstrap Confidence Intervals
Bias-Corrected Bayesian Classification with Selected Features
Calculation of Option Prices Based on a Universal Solution
Bayesian Consensus Clustering for Multiple Longitudinal Features
Search and Retrieve Data from the BC Data Catalogue
Business Cycle Dating and Plotting Tools
Bayesian Composition Estimator: Estimating Sample (Taxonomic)
Composition from Biomarker Data
The Bayesian Causal Effect Estimation Algorithm
Semiparametric Bivariate Correlated Frailty Models Fit
Bivariate Correlated Frailty Models with Varied Variances
Bayesian Constrained Generalised Linear Models
Bias-Corrected Estimates for Generalized Linear Models for
Dependent Data
Clinical Trial Calculation Based on BCHM Design
Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
A Dirichlet Process Mixture Model for Clustering Longitudinal
Gene Expression Data
Map Layers and Spatial Utilities for British Columbia
Mixed Effect Model with the Box-Cox Transformation
Bayesian Analysis of Change Point Problems
Behavioral Change Point Analysis of Animal Movement
Bayesian Continual Reassessment Method for Phase I
Dose-Escalation Trials
Bias-Corrected Methods for Estimating the ROC Surface of
Continuous Diagnostic Tests
A Bayesian Semiparametric Factor Analysis Model for Subtype
Identification (Clustering)
Bayesian Context Trees for Discrete Time Series
Cross-Validation for the SVD (Bi-Cross-Validation)
A User-Friendly Biodiversity Data Cleaning App for the
Inexperienced R User
Darwinizer: Darwin Core (DwC) Field Names Standardization
Efficient Determination of Sample Size in Balanced Design of
Experiments
Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC
Transparent and Reproducible Artificial Data Generation
Bayesian Adaptive Designs for Phase II Trials with Binary
Endpoint
Inference and Design for Predictive Values in Diagnostic Tests
Bayesian Detection of Potential Risk Using Inference on Blinded
Safety Data
Remove Weekends and Holidays from ggplot2 Axes
Routines for Block Diagonal Symmetric Matrices
Bayesian Inference for Discrete Weibull Regression
Biometric Exploratory Analysis Creation House
Analysis of Multiplex Cytometric Bead Assays
Fast Bayesian Inference in Large Gaussian Graphical Models
Visualization via Beanplots (Like Boxplot/Stripchart/Violin
Plot)
Bayesian Analysis of Heterogeneous Treatment Effect
Bayesian Estimation of Change-Points in the Slope of
Multivariate Time-Series
Quantifies Effects of Geo/Eco Distance on Genetic
Differentiation
Extract Genotypes from a PLINK .bed File
Genomic Region Processing using Tools Such as 'BEDTools',
'BEDOPS' and 'Tabix'
General Unified Threshold Model of Survival for Bees using
Bayesian Inference
Easily Play Notification Sounds on any Platform
The Bee Swarm Plot, an Alternative to Stripchart
Tools for Behavior Change Researchers and Professionals
Canonical Data Structure for Behavioural Data
Multivariate Conditional Volatility Modelling and Forecasting
Download Historical Data from the Belgrade Stock Exchange
Boltzmann Entropy of a Landscape Gradient
Count Regression Models Based on the Bell Distribution
28 Benchmark Densities from Berlinet/Devroye (1994)
Benchmark and Frontier Analysis Using DEA and SFA
High Precise Measurement of R Expressions Execution Time
Benford Analysis for Data Validation and Forensic Analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bayesian Estimation of Naloxone Kit Number Under-Reporting
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian Estimation for Quantile Regression Mixed Models
Bayesian Exponential Random Graph Models
Bayesian Inference and Model Selection for Stochastic Epidemics
Function Collection Related to Plotting and Hydrology
Best Subset Selection in Linear, Logistic and CoxPH Models
Computations and Approximations for Bessel Functions
Best Subset GLM and Regression Utilities
Generating Bootstrap Estimation Distributions of HR Data
Bayesian Estimation of Intervention Effects
A Comprehensive R Package for Best Subset Selection
Burden Estimate of Common Communicable Diseases in Settlements
of Displaced Populations
Mini Games from Adventures of Beta and Bit
A Family of Beta Mixture Models for Clustering Beta-Valued DNA
Methylation Data
Confidence Intervals for Standardized Regression Coefficients
Functions for Working with Two- And Four-Parameter Beta
Probability Distributions and Psychometric Analysis of
Classifications
Partitioning Beta Diversity into Turnover and Nestedness
Components
Calculate Power and Sample Size with Beta Regression
Taxonomic Uncertainty on Multivariate Analyses of Ecological
Data
Robust Confidence Intervals for Standardized Regression
Coefficients
Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH
Models
Better Statistics for OLS and Binomial Logistic Regression
Bayesian Evaluation of Variant Involvement in Mendelian Disease
Visualize and Parse the Output of 'BeXY'
Compare the Goodness of Fit of Benford's and Blondeau Da Silva's
Digit Distributions to a Given Dataset
Bayesian Spectral Inference for Time Series
Flexible Bayes Factor Testing of Scientific Expectations
Get Data from the Swiss Federal Statistical Office
Plot Maps from Switzerland by Swiss Federal Statistical Office
Bayesian Framework for Computational Modeling
A Suite of Packages for Analysis of Big Genomic Data
Binary Generalized Extreme Value Additive Models
Bell-G and Complementary Bell-G Family of Distributions
BoardGameGeek's Board Game Data Analysis Tools
Bayesian Genomic Linear Models Applied to GE Genome Selection
Bayesian Estimation of Generalized Graded Unfolding Model
Parameters
Example BGM Files for the Atlantis Ecosystem Model
An R Package for Extended Behavior Genetics Analysis
Gaussian Mixture Modeling Algorithms and the Belief-Based
Mixture Modeling
Bayesian Variable Selection for Networks of Binary and/or
Ordinal Variables
Markov Beta and Gamma Processes for Modeling Hazard Rates
Bayesian Group Sparse Multi-Task Regression
Estimate the Burden of Healthcare-Associated Infections
Useful Functions for Box, Hunter and Hunter II
Bayesian Hierarchical Models for Basket Trials
Bayesian Hierarchical Multi-Subject Multiscale Analysis of
Functional MRI (fMRI) Data
Bayesian Hierarchical Poisson Models for Multiple Grouped
Outcomes with Clustering
Structural Bayesian Vector Autoregression Models
Bayesian Multi-Plate High-Throughput Screening of Compounds
Blinding Assessment Indexes for Randomized, Controlled, Clinical
Trials
A GUI to Correct Measurement Bias in DNA Methylation Analyses
R Wrapper for Java Implementation of BiBit
Create Different Types of Bibliometric Networks
Document-Level Matching Between Bibliographic Datasets
Bayesian Inference for the Birnbaum-Saunders Distribution
Binary Classification Using Extensions of Discriminant Analysis
Integrative Inference of De Novo Cis-Regulatory Modules
Bayesian Inference for Directed Acyclic Graphs
Efficient Estimation of Bid-Ask Spreads from Open, High, Low,
and Close Prices
Calculates the Bidimensional Regression Between Two 2D
Configurations
Tools for Accessing the Botanical Information and Ecology
Network Database
Tools for Survey Statistics in Educational Assessment
'BLAS' and 'LAPACK' Routines for Native R Matrices and
'big.matrix' Objects
Utilities for 'big.matrix' Objects from Package 'bigmemory'
Perform Boolean Operations on Large Numbers
Read, Write, Manipulate, Explore Chess PGN Files and R API to
UCI Chess Engines
Flexibly Format Dates and Times to a Given Locale
Distances for Machine Learning and Statistics in the Context of
Big Data
Case-Control Analysis of Multi-Allelic Loci
Scalable Bayesian Disease Mapping Models for High-Dimensional
Data
Biochemically Intuitive Generalized Loewe Model
Extending Lasso Model Fitting to Big Data
Physical and Physiological Ecosystem Properties from Eddy
Covariance Data
Bounded Memory Linear and Generalized Linear Models
Making Optimal Matching Size-Scalable Using Optimal Calipers
Manage Massive Matrices with Shared Memory and Memory-Mapped
Files
A Shared Resource Interface for Bigmemory Project Packages
Arbitrary-Precision Integer and Floating-Point Mathematics
Interface with Google BigQuery with Shiny Compatibility
Big Quadratic Inverse Covariance Estimation
Generalized Linear Models (GLM) for Large Data Sets
An Interface to Google's 'BigQuery' 'API'
Fast Generation of High-Dimensional Random Vectors
Statistical Tools for Filebacked Big Matrices
Big Survival Analysis Using Stochastic Gradient Descent
Table, Apply, and Split Functionality for Matrix and
'big.matrix' Objects
Nonparametric Analysis of Bivariate Gap Time with Competing
Risks
Sparse Estimation of Large Time Series Models
Dimension Reduction Methods for Multivariate Time Series
Co-Clustering Adjusted Rand Index and Bikm1 Procedure for
Contingency and Binary Data-Sets
Contains Data of Billboard Hot 100 Songs
Create Interactive Chart with the JavaScript 'Billboard' Library
Threshold Cut Point of Probability for a Binary Classifier Model
Creates, Merges, and Reads Binary Dosage Files
Variable Selection for Binary Data Using the EM Algorithm
Fit and Predict a Gaussian Process Model with (Time-Series)
Binary Response
Flexible Marginalized Models for Binary Correlated Outcomes
Estimate the Correlation Between Two Irregular Time Series
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Estimates Parameters of Functions Driving Binomial Random
Variables
Get Bing Ads Data via the 'Windsor.ai' API
Evaluation and Experimental Design for Binomial Group Testing
Identification and Estimation using Group Testing
Calculation for Single Arm Group Sequential Test with Binary
Endpoint
Processes Binary Data Obtained from Fragment Analysis (Such as
AFLPs, ISSRs, and RFLPs)
Data Generation with Binary and Continuous Non-Normal Components
Simultaneous Generation of Multivariate Binary and Normal
Variates
Binomial Confidence Intervals for Several Parameterizations
Confidence Intervals for a Binomial Proportion
Binomial Random Forest Feature Selection
Calculates the Statistical Significance of a Trend in a Set of
Measurements
Concurrent Generation of Binary, Ordinal and Continuous Data
Depth-Dependent Binocular Visual Fields Simulation
Cut Numeric Values into Evenly Distributed Groups
Piecewise Smooth Regression by Bootstrapped Binary Segmentation
Efficient Implementation of Binary Segmentation
Exact Binary Sequential Designs and Analysis
Bayesian Inference for Neyman-Scott Point Processes
Data Preprocessing, Binning for Classification and Regression
Bayesian BIN (Bias, Information, Noise) Model of Forecasting
Predict Environmental Conditions from Biological Observations
Analyse Audio Recordings and Automatically Extract Animal
Vocalizations
Summarising Multi Well Plate Cellular Assay
Access Data Dependencies Installed Through 'Biobricks.ai'
Interactive Circular Visualization of Genomic Data using
'htmlwidgets' and 'BioCircos.js'
Access the Bioconductor Project Package Repository
Feature Selection and Classification with the Embedded
Validation Procedures for Biomedical Data Analysis
Create and Manipulate BioCompute Objects
Package for Community Ecology and Suitability Analysis
An R Shiny Application for Biological Dosimetry
Multilevel Modeling of Dendroclimatical Fluctuations
Process Biogas Data and Predict Biogas Production
Mathematical Modelling of (Dynamic) Microbial Inactivation
Create Hyperlinks to Biological Databases and Resources
Biologically Explainable Machine Learning Framework
Find Biomarkers in Two-Class Discrimination Problems
Estimating Aboveground Biomass and Its Uncertainty in Tropical
Forests
Functions to Assist Design and Analysis of Agronomic Experiments
Ensemble Platform for Species Distribution Modeling
Biomarker Prognostic Enrichment Tool for Time-to-Event Trial
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
Biological Analysis and Visualization of Weather Radar Data
From Biological Sequences and Simulations to Correlation
Analysis
From Biological Sequences to Multidimensional Scaling
Biodiversity Index Calculation and Bootstrap Confidence Interval
Estimation
Distributional Data Analysis Techniques for Biosensor Data
A Toolbox for Manipulating Biological Sequences
Tools for Electromyogram Signals (EMG) Analysis
Bioacoustic Basic Operations with Decibels and the Passive Sonar
Equation
Biomarker Selection in Penalized Regression Models
Utility Functions, Datasets and Extended Examples for Survival
Analysis
Calculation of Freshwater Biotic Indices
Tools for Biometry and Applied Statistics in Agricultural
Science
Create Area-Proportional Venn Diagrams from Biological Lists
Visualising Bipartite Networks and Calculating Some (Ecological)
Indices
Partition Bipartite Network into Non-Overlapping Biclusters by
Optimizing Bipartite Modularity
Bayesian Individual Patient Data Meta-Analysis using 'JAGS'
Construct Reactive Calibrated Axes Biplots
Bootstrap on Classical Biplots and Clustering Disjoint Biplot
Ranking Nodes in Bipartite and Weighted Networks
Methods to Analyse Ring Re-Encounter Data
Migration Traffic Rate Calculation Package for 'Birdscan MR1'
Radars
The R Package for "The Basics of Item Response Theory Using R"
Tools and Palettes for Bivariate Thematic Mapping
Download Data from the Bank for International Settlements (BIS)
Tools to find bad commits with git bisect
Toolkit to Identify Candidate Synthetic Lethality
Approximate Bayesian Inference via Sparse Grid Quadrature
Evaluation (BISQuE) for Hierarchical Models
Decomposition of Bulk Expression with Single-Cell Sequencing
Cumulative History Analysis for Bistable Perception Time Series
Classes and Methods for Fast Memory-Efficient Boolean Selections
A S3 Class for Vectors of 64bit Integers
Bayesian Integrative Models of Trait Evolution
Binarization and Trinarization of One-Dimensional Data
Bivariate Alternating Recurrent Event Data Analysis
Tolerance Interval and EIV Regression - Method Comparison
Studies
Bivariate Residual Plots with Simulation Polygons
Generation of Bivariate Uniform Data and Its Relation to
Bivariate Binary Data
Conduct Univariate and Bivariate Wavelet Analyses
Compute the Biweight Mean Vector and Covariance & Correlation
Matrice
Business Days Calculations and Utilities
Bivariate Zero-Inflated Count Models Using Copulas
Parallel Chain Tools for Bayesian Kernel Machine Regression
Black Box Optimization and Exploration of Parameter Space
Processing Raw Black Carbon Concentration
Read and Write FWF Files in the 'Blaise' Format
Blaker's Binomial and Poisson Confidence Limits
Plots (Slightly Extended) Bland-Altman Plots
Bayesian Inference with Laplace Approximations and P-Splines
Native R Implementation of an Efficient BLAST-Like Algorithm
Calculate and Analyze Blau Statuses for Measuring Social
Distance
Black-Litterman and Copula Opinion Pooling Frameworks
Analyze biotic homogenization of landscapes
Joint Analysis of Experiments with Mixtures and Random Effects
Blind Review Using Forward Search Procedures
Record Linkage for Empirically Motivated Priors
Bayesian Functional Linear Regression with Sparse Step Functions
Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
A Simple S3 Class for Representing Vectors of Binary Data
('BLOBS')
Co-Clustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
Estimation of Large Block Covariance Matrices
Spatial and Environmental Blocking for K-Fold and LOO
Cross-Validation
Block Forests: Random Forests for Blocks of Clinical and Omics
Covariate Data
Select an Optimal Block-Length to Bootstrap Dependent Data
(Block Bootstrap)
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Integrating Multi-Source Block-Wise Missing Data in Model
Selection
Generalized and Classical Blockmodeling of Valued Networks
GUI for the Generalised Blockmodeling of Valued Networks
Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Randomization for Block Random Clinical Trials
Nested and Crossed Block Designs for Factorial and Unstructured
Treatment Sets
Block, Assign, and Diagnose Potential Interference in Randomized
Experiments
Estimate and Visualize Voting Blocs' Partisan Contributions
Create Blogs and Websites with R Markdown
Impute and Analyze Data with BLOQ Observations
Tools for Developing Binary Logistic Regression Models
Dose Escalation Design in Phase I Oncology Trial Using Bayesian
Logistic Regression Modeling
Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse
Model
Interactive Document for Working with Binary Logistic Regression
Analysis
Data Viewer for Bureau of Labor Statistics Data
Make Requests from the Bureau of Labor Statistics API
Blyth-Still-Casella Exact Binomial Confidence Intervals
Bayesian Model Averaging for Basket Trials
Multivariate Extremes: Bayesian Estimation of the Spectral
Measure
Bayesian Multivariate Analysis of Summary Statistics
Mediation Analysis with Missing Data Using Bootstrap
Mediation Analysis with Missing Data and Non-Normal Data
Bayesian Multidimensional Generalized Graded Unfolding Model
Miscellaneous Functions for Panel Data, Quantiles, and Printing
Results
Bayesian Estimation for Finite Mixture of Distributions
An Implementation of the Bayesian Markov (Renewal) Mixed Models
Bayesian Multilevel Single Case Models using 'Stan'
Bayesian Modeling of Spatio-Temporal Data with R
Bayesian Approach for MTAR Models with Missing Data
Bayesian Network-Based Clustering of Multi-Omics Data
Access Data from the Brazilian Development Bank (BNDES)
Bounding Omitted Variable Bias Using Auxiliary Data
Bayesian Network Structure Learning, Parameter Learning and
Inference
Bayesian Network Meta-Analysis using 'JAGS'
An Implementation of Sensitivity Analysis in Bayesian Networks
Bagged k-Nearest Neighbors Survival Prediction
Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures
Simulate Genotypes from the BN-PSD Admixture Model
A Bayesian Nonparametric Algorithm for Time Series Clustering
Pathway Enrichment Analysis Based on Bayesian Network
Bayesian Non- And Semi-Parametric Model Fitting
Bayesian Network Structure Learning from Data with Missing
Values
Bayesian Networks Interactive Visualization and Explainable
Artificial Intelligence
Bayesian Output Analysis Program (BOA) for MCMC
Board Games and Tools for Building Board Games
Profiling of Peripheral Blood Mononuclear Cells using CyTOF
Boosting Diversity in Regression Ensembles
Call the Bus Open Data Service ('BODS') API Through R
Percent Body Fat Values Using Anthropometric Prediction
Equations
Interface to the Boilerpipe Java Library
Bayesian Optimal INterval (BOIN) Design for Single-Agent and
Drug- Combination Phase I Clinical Trials
Conduct Simulation Study of Bayesian Optimal Interval Design
with BOIN-ET Family
Model Consistent Lasso Estimation Through the Bootstrap
Functions for Elementary Bayesian Inference
Methods for Fixed-Income Valuation, Risk and Return
Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods and
Various Day Count Conventions
An Improved Multiple Testing Procedure for Controlling False
Discovery Rates
Some Distributions from the 'Boost' Library and More
Authoring Books and Technical Documents with R Markdown
Optimal Estimation of Partially Observed Boolean Dynamical
Systems
Construction, Simulation and Analysis of Boolean Networks
A Boosting Approach to Data Envelopment Analysis
Boosting for Multivariate Longitudinal Responses
Boosted Multivariate Trees for Longitudinal Data
Bootstrap Functions (Originally by Angelo Canty for S)
A Bootstrap-Based Heterogeneity Test for Meta-Analysis
Bootstrapping Estimates of Clustering Stability
Combine Parameter Estimates via Parametric Bootstrap
Bootstrapping the ARDL Tests for Cointegration
Bootstrap Confidence Intervals on Effect Sizes
Simulation and Comparison of Dissolution Profiles
Bootstrap Inference for Multiple Imputation
Bootstrapped Confidence Intervals for (Negative) Likelihood
Ratio Tests
Bootstrap-MRMR Technique for Informative Gene Selection
Bootstrap Methods for Various Network Estimation Routines
Bootstrap Hyperparameter Selection for PLS Models and Extensions
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
A Bootstrap Test for the Probability of Ruin in the Classical
Risk Process
Functions for the Book "An Introduction to the Bootstrap"
Bootstrap Algorithms for Finite Population Inference
Bootstrap cis-QTL Method that Corrects for the Winner's Curse
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Nonparametric Bootstrap Test with Pooled Resampling Card Game
Test Stationarity using Bootstrap Wavelet Packet Tests
Transforming Behavioral Observation Records into Data Matrices
Bayesian Ordination and Regression AnaLysis
Estimate Causal Effects with Borrowing Between Data Sources
Wrapper Algorithm for All Relevant Feature Selection
A Bunch of Structure and Sequence Analysis
Best Orthogonalized Subset Selection (BOSS)
Bound on the Error of the First-Order Edgeworth Expansion
Analytic Solutions for (Ground-Water) Boussinesq Equation
Outlier Detection and Influence Diagnostics for Meta-Analysis
Functions to Create Publication-Quality Plots
Write Reusable, Composable and Modular R Code
Box-Cox-Type Transformations for Linear and Logistic Models with
Random Effects
Clustering Method Based on Boxplot Statistics
Double Box Plot for Two-Axes Correlation
Blood Pressure Device Accuracy Evaluation: Statistical
Considerations
Nonparametric Bounds for the Average Causal Effect Due to Balke
and Pearl and Extensions
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
Bayesian Model Selection Approach for Parsimonious Gaussian
Mixture Models
Bayesian Purity Model to Estimate Tumor Purity
Support for BPMN (Business Process Management Notation) Models
Visualize Process Execution Data on 'BPMN' Diagrams
Bayesian Projected Normal Regression Models for Circular Data
Computations Around Bayesian Predictive Power
Causal Effects in Principal Strata Defined by Antidrug
Antibodies
Bayesian Quantile Regression for Ordinal Models
Bias Reduction Through Analysis of Competing Events (BRACE)
BRACoD: Bayesian Regression Analysis of Compositional Data
Calculate the Revealed Aggregator of Probability Predictions
Visualize Combined Action Response Surfaces and Report BRAID
Analyses
Fitting Dose Response with the BRAID Combined Action Model
Inference the Partial Correlations Based on Time Series Data
Region-Level Connectivity Network Construction via Kernel
Canonical Correlation Analysis
Helper Functions to 'misc3d' and 'rgl' Packages for Brain
Imaging
Efficient Branch and Bound Variable Selection for GLMs using
'RcppArmadillo'
Simulation and Estimation for Branching Processes
Bootstrapped Robustness Assessment for Qualitative Comparative
Analysis
Bayesian Screening and Variable Selection
Download and Processing of Automatic Weather Stations (AWS) Data
of INMET-Brazil
Variable Ranking in Copula Survival Models Affected by General
Censoring Scheme
Fetch Zonal Statistics of Weather Indicators for Brazilian
Municipalities
Analyze Big Files Without Loading Them in Memory
Estimates Degrees of Relatedness (Up to the Second Degree) for
Extreme Low-Coverage Data
Species Richness Estimation and Modeling
Model Agnostic Explainers for Individual Predictions
Methods for Fast Multiple Change-Point Detection and Estimation
An R Package for Multiple Break-Point Detection via the
Cross-Entropy Method
Core Functions to Read and Fit 13c Time Series from Breath Tests
Stan-Based Fit to Gastric Emptying Curves
Brain Cell Type Specific Gene Expression Analysis
Templating Framework for Report Generation
Bias Reduction in Binomial-Response Generalized Linear Models
Bias Reduction in Generalized Linear Models
Interface with the Brickset API for Getting Data About LEGO Sets
An Implementation of the Bridge Distribution with Logit-Link as
in Wang and Louis (2003)
Bridge Hand Generator with Criteria Selector
Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data
Bridge Sampling for Marginal Likelihoods and Bayes Factors
A Tree and Forest Tool for Classification and Regression
Package for Brik, Fabrik and Fdebrik Algorithms to Initialise
Kmeans
Computation of BRINDA Adjusted Micronutrient Biomarkers for
Inflammation
Fast Inference for Large Spatial Datasets using BRISC
Bias Reduction with Missing Binary Response
Bayesian Marginal Effects for 'brms' Models
Bayesian Regularization for Feed-Forward Neural Networks
Broken Adaptive Ridge Regression with Cyclops
Broken Stick Model for Irregular Longitudinal Data
Convert Statistical Objects into Tidy Tibbles
A Compression Format Optimized for the Web
Bayesian Analysis of Quantile Regression Models
Broadly Useful Convenient and Efficient R Functions
Interface to the 'OpenBUGS' MCMC Software
High-Level Modeling Functions with 'torch'
A 'Bootstrap 4' Version of 'shinydashboard'
Safety Assessment in Agricultural Field Trials
Bayesian State-Space Models for Animal Movement
Bayesian Spectral Analysis Models using Gaussian Process Priors
Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Nonparametric Smoothing of the Hazard Function
Bayesian Super Imposition by Translation and Rotation Growth
Curve Analysis
Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'
Bayes Screening and Model Discrimination
Build Decision Trees with Optimal Multivariate Splits
Bayesian Proposal to Fit Spatial Econometric Models
Bayesian Spatial Blind Source Separation
Bayesian Nonparametric Spectral Density Estimation Using
B-Spline Priors
Adds Functionality to the R Markdown + Shiny Bootstrap Framework
Bayesian Semiparametric Models for Meta-Analysis
Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Matrix Estimates
Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
Blind Source Separation for Multivariate Spatial Data using
Eigen Analysis
Whitening Data as Preparation for Blind Source Separation
Create HTML Content with Bootstrap 5 Classes and Layouts
Submitter and Monitor of the 'LSF Cluster'
Bayesian Estimation of Structural Vector Autoregressive Models
Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil
(BSW) Algorithm
Beyond the Border - Kernel Density Estimation for Urban
Geography
Bradley-Terry Model with Exponential Time Decayed Log-Likelihood
and Adaptive Lasso
Temporal Exponential Random Graph Models by Bootstrapped
Pseudolikelihood
Modelling Heterogeneity in Paired Comparison Data
Estimate the Number of Factors in Large Nonstationary Datasets
Implementing BTYD Models with the Log Sum Exp Patch
Probabilistic Models for Assessing and Predicting your Customer
Base
Produces 'bubbleHeatmap' Plots for Visualising Metabolomics Data
Beautiful Bubbles for 'shiny' and 'rmarkdown' Backgrounds
Bucky's Archive for Data Analysis in the Social Sciences
Bias and Uncertainty Corrected Sample Size
A Deep Learning for Statistical Classification and Regression
Analysis with Random Effects
Stepwise Elimination and Term Reordering for Mixed-Effects
Regression
Organize & Run Build Scripts Comfortably
System for Building and Debugging C/C++ Dynamic Libraries
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Interactive Shiny App for Bulk Sequencing Data
The Ultimate Tool for Reading Data in Bulk
Automatic Groove Identification via Bayesian Changepoint
Detection
Bullish Trading Strategies Through Graphs
Tools for Modeling Bumblebee Colony Growth and Decline
Quantify Disease Transmission Within and Between Population
Groups
Analyze Bunching in a Kink or Notch Setting
Serialize Model Objects with a Consistent Interface
R Wrapper for Bursa Municipality Open Data Portal
Markov Model for Bursty Behavior in Streams
Calculates Business Duration Between Two Dates
Simple Modelling Tools for Business Plans
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
Bayesian Inference of Vector Autoregressive and Error Correction
Models
Bayesian Vector Heterogeneous Autoregressive Modeling
The Stark-Parker algorithm for bounded-variable least squares
Bayesian Variable Selection in High Dimensional Settings using
Nonlocal Priors
Backward Procedure for Change-Point Detection
Describe Image Patterns in Natural Structures
Bandwidth Selectors for Local Linear Quantile Regression
Baumgartner Weiss Schindler Test of Equal Distributions
Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs
Statistics About Bytes Contained in a File as a Circle Plot
Bivariate Zero-Inflated Negative Binomial Model Estimator
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Dataset of Regional COVID-19 Deaths per 100,000 Pop (NUTS)
Methods for Detecting Safety Signals in Clinical Trials Using
Body-Systems (System Organ Classes)
Compare Two Classifications or Clustering Solutions of Varying
Structure
Inferring Large-Scale Gene Networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Project Specific Workspace Organization Templates
Bootstrap Confidence Regions for Simple and Multiple
Correspondence Analysis
Conjunctive Analysis of Case Configurations
Cross-Species Analysis of Cell Identities, Markers and
Regulations
Classification Accuracy and Consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
A Package to Perform Covariate Augmented Dickey-Fuller Unit Root
Tests
Provides a GUI to Several Statistical Methods
Simulation of Plasma Caffeine Concentrations by Using Population
Pharmacokinetic Model
Conditional Akaike Information Criterion for 'lme4' and 'nlme'
Graphical Aid in Correspondence Analysis Interpretation and
Significance Testings
R Graphics Device using Cairo Graphics Library for Creating
High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG,
PostScript) and Display (X11 and Win32) Output
Comparison of Algorithms with Iterative Sample Size Estimation
Calculations for All Common Subsequences
Comparative Analysis with Annotation-Based Genomic Components
High Dimensional Numerical and Symbolic Calculus
Simple Wrapper for Computationally Expensive Functions
Create, Read, Write, and Work with 'iCalander' Files, Calendars
and Scheduling Data
Ready to Print Monthly and Yearly Calendars Made with 'ggplot2'
'calendR' Fork with Additional Features (Backwards Compatible)
Multiple Imputation Using MICE and Random Forest
Calibration Plots for the Transition Probabilities from
Multistate Models
Automated Parameter Estimation for Complex (Ecological) Models
Calibration of Scatterplot and Biplot Axes
Calibration for Computer Experiments with Binary Responses
Weighting and Estimation for Panel Data with Non-Response
Bayesian Calibration of Complex Computer Codes
Mapping ML Scores to Calibrated Predictions
Assesses the Quality of Estimates Made by Complex Sample Designs
Recording Synchronisation, Call Detection and Assignment, Audio
Analysis
Improved Allele-Specific Copy Number of SNP Microarrays for
Downstream Segmentation
Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
'CamelUp' Board Game as a Teaching Aid for Introductory
Statistics
Datasets from "Microeconometrics: Methods and Applications" by
Cameron and Trivedi
Cell-Typing using Variance Adjusted Mahalanobis Distances with
Multi-Labeling
Connect to Campaign Manager via the 'Windsor.ai' API
Generic PK/PD Simulation Platform CAMPSIS
Maps of the Political and Administrative Divisions of Canada
Effortlessly Create Stunning Canadian Maps
Categorical Analysis of Neo- And Paleo-Endemism
Access, Retrieve, and Work with Canadian Census Data and
Geography
Publication Quality Phylogenetic Tree Plots
Streamline Access to Cancer Screening Data
Estimation of Temporal Ordering of Cancer Abnormalities
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Batch Correction of Single Cell Transcriptome Data
Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Chemical Metrics of Differentially Expressed Proteins
Accessing Statistics Canada Data Table and Vectors
Directly Extracts Complete CANSIM Data Tables
Visualization Package for CanvasXpress in R
Covariate Assisted Principal (CAP) Regression for Covariance
Matrix Outcomes
Combined Analysis of Pleiotropy and Epistasis for Diversity
Outbred Mice
Comparative Analyses of Phylogenetics and Evolution in R
Capital Budgeting Analysis, Annuity Loan Calculations and
Amortization Schedules
Compute and Visualize CAPL-2 Scores and Interpretations
Correspondence Analysis with Geometric Frequency Interpretation
A Virtual Patient Simulator in the Context of Calcium and
Phosphate Homeostasis
CAlibrating Penalities Using Slope HEuristics
Automatic Calibration by Evolutionary Multi Objective Algorithm
Covariate-Adaptive Randomization for Clinical Trials
Spatial Generalised Linear Mixed Models for Areal Unit Data
Data Used in the Vignettes Accompanying the CARBayes and
CARBayesST Packages
Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
Data
Estimation of the Number of Fatalities from Carcass Searches
Cardiovascular and Autonomic Research Design
Companion to Applied Regression Data Sets
Identification of Cardinal Dates in Ecological Time Series
High-Dimensional Regression and CAR Score Variable Selection
Carbon-Related Assessment of Silvicultural Concepts
Government of Canada Vehicle Recalls Database API Wrapper
Procedures for Computing Indices of Careless Responding
Correspondence Analysis Plot and Associations Visualisation
Conformal Time Series Forecasting Using State of Art Machine
Learning Algorithms
Continuous-Time Fractionally Integrated ARMA Process for
Irregularly Spaced Long-Memory Time Series Data
Estimation of Caribou Abundance Based on Radio Telemetry Data
Functions for Statistics Classes at Carleton College
Carlson Elliptic Integrals and Incomplete Elliptic Integrals
Covariate-Adjusted Adaptive Randomization via
Mahalanobis-Distance
Continuous Biomarker Evaluation with Adjustment of Covariates
A Collection of Database, Data Structure, Visualization, and
Utility Functions for R
Build Common Tables of Summary Statistics for Reports
Predicting Categorical and Continuous Outcomes Using One in Ten
Rule
Correlation-Adjusted Regression Survival (CARS) Scores
Selection, Reverse-Engineering and Prediction in Cascade
Networks
Experimental Data of Cascade Experiments in Genomics
Covariate Assisted Spectral Clustering on Ratios of Eigenvectors
Casualty Actuarial Society Individual Claim Simulator
Fitting Flexible Smooth-in-Time Hazards and Risk Functions via
Logistic and Multinomial Regression
Identify Similar Cases for Qualitative Case Studies
Stratified Evaluation of Subgroup Classification Accuracy
Detection of Statistically Significant Combinations of SNPs in
Association Mapping
Finds Missing Links and Metric Confidence Intervals in
Ecological Bipartite Networks
Compute Scagnostics on Pairs of Numeric Variables in a Data Set
'caret' Applications for Spatial-Temporal Models
Analysis and Imputation of Categorical-Variable Datasets with
Missing Values
Handling an Inconsistently Coded Categorical Variable in a
Longitudinal Dataset
Analysis of Check-All-that-Apply (CATA) Data
Access the 'Spark Catalog' API via 'sparklyr'
Interface to the API 'Sede Electronica Del Catastro'
Covariate-Adjusted Tensor Classification in High-Dimensions
Test, Identify, Select and Mutate Categorical or Continuous
Values
Categorical Data Analysis Program Package
Datasets for Categorical Data Analysis by Agresti
Fishery Stock Assessment by Catch Dynamics Models
High Dimensional Factor Analysis and Confounder Adjusted Testing
and Estimation
Community Assembly by Traits: Individuals and Beyond
Simulate IRT-Based Computerized Adaptive Tests
Formal Psychological Models of Categorization and Learning
Case-Control and TDT Meta-Analysis Package
Heatmap for Categorical Data using 'plotly'
Tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc
Optimal Categorisation of Continuous Variables in Prediction
Models
Generation of IRT Response Patterns under Computerized Adaptive
Testing
Solution Paths for Linear and Logistic Regression Models with
Categorical Predictors, with SCOPE Penalty
Create Catseye Plots Illustrating the Normal Distribution of the
Means
Binary and Categorical Image Similarity Index
Computerized Adaptive Testing for Survey Research
Computation of the p-Value for the Exact Conditional
Cochran-Armitage Trend Test
Powerful Test for Survival Data under Non-Proportional Hazards
Robust Principal Component Analysis Using the Cauchy
Distribution
Trait Evolution on Phylogenies Using the Cauchy Process
Fast, Easy, and Visual Bayesian Inference
Nonparametric and Cox-Based Estimation of Average Treatment
Effects in Competing Risks
Example Data Sets for Causal Inference Textbooks
Tools for Causal Discovery on Observational Data
Estimating Causal Dose Response Functions
Deriving Expressions of Joint Interventional Distributions and
Transport Formulas in Causal Models
Estimation of Causal Effects with Generalized Additive Models
Matching on Generalized Propensity Scores with Continuous
Exposures
Inferring Causal Effects using Bayesian Structural Time-Series
Models
MBSTS Models for Causal Inference and Forecasting
Causal Inference Modeling for Estimation of Causal Effects
An Interface to Specify Causal Graphs and Compute Bounds on
Causal Effects
Optimal Transport Weights for Causal Inference
Causal Effect for Population Attributable Fractions (PAF)
Make, Update, and Query Binary Causal Models
Selection Bias Approach to Sensitivity Analysis for Causal
Effects
Estimation Methods for Causal Inference Based on Inverse
Probability Weighting
Calculate Correlations and Estimate Causality
Control Charts with Guaranteed In-Control Performance and
Cautious Parameters Learning
Cluster Analysis via Random Partition Distributions
CRISPR Pooled Screen Analysis using Beta-Binomial Test
Classification -- Bayesian Adaptive Smoothing Splines
Compare Baseline Characteristics Between Groups
Choice-Based Conjoint Experiment Design Generation and Power
Evaluation in R
Continuous Analog of a Binomial Distribution
Browse and Query Clinical and Genomic Data from cBioPortal
Causal Discovery under a Confounder Blanket
Statistics Netherlands (CBS) Open Data API Client
Fits Cubic Bezier Spline Functions to Intertemporal and Risky
Choice Data
(Robust) Canonical Correlation Analysis via Projection Pursuit
Concordance Correlation Coefficient for Repeated (and
Non-Repeated) Measures
Combined Cluster and Discriminant Analysis
Cox Model for Case-Cohort Data with Stratified
Subcohort-Selection
Transforming and Harmonizing CCHS Variables
Cross-Covariance Isolate Detect: a New Change-Point Method for
Estimating Dynamic Functional Connectivity
Convex Clustering Methods and Clustering Indexes
Consensus Clustering for Different Sample Coverage Data
Minimization of the Convex Clustering Loss Function
Implementation of Case-Control Optimal Matching
Estimation of Mean Costs in Censored Data
Significance Tests for Canonical Correlation Analysis (CCA)
Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing
Data
Correct and Cluster Response Style Biased Data
Tools for the Continuous Convolution Trick in Nonparametric
Estimation
Consensus Analysis, Model-Based Clustering, and Cultural
Consensus Theory Applications
Perform Weighted Linear Regression for Calibration Curve
Cognitive Diagnostic Computerized Adaptive Testing
Retrieve Flu Season Data from the United States Centers for
Disease Control and Prevention ('CDC') 'FluView' Portal
Conditional Distance Correlation Based Feature Screening and
Conditional Independence Inference
Interface to the California Data Exchange Center (CDEC)
Quantile Regression for Random Variables on the Unit Interval
Downscaling and Bias Correction via Non-Parametric CDF-Transform
Causal Decomposition of Group Disparities
Cause-Deleted Life Expectancy Improvement Procedure
Tools to Download and Work with USDA Cropscape Data
Useful Tools for Cognitive Diagnosis Modeling
Components of Canadian Monetary and Credit Aggregates
Constrained Dual Scaling for Detecting Response Styles
'Copernicus Data Space Ecosystem' API Wrapper
Sampling from Conditional C- and D-Vine Copulas
CRISPR Screen and Gene Expression Differential Analysis
CEEMDAN Decomposition Based Hybrid Machine Learning Models
Collection of Common Astronomical Conversion Routines and
Functions
Consistent Perturbation of Statistical Frequency- And Magnitude
Tables
Finds RNASeq Source Tissues Using In Situ Hybridisation Data
Cell Type Pipes for Single-Cell RNA Sequencing Data
Translate Spreadsheet Cell Ranges to Rows and Columns
Functions to Fit Cell Volume Distributions and Thereby Estimate
Cell Growth Rates and Division Times
Broken Adaptive Ridge AFT Model with Censored Data
Consistent Estimation of the Number of Communities via
Regularized Network Embedding
Estimating Time-Dependent ROC Curve and AUC for Censored Data
Linear Regression with a Randomly Censored Covariate
Finite Mixture of Multivariate Censored/Missing Data
Base de Datos de Facil Acceso del Censo 2017 de Chile (2017
Chilean Census Easy Access Database)
Download Data from Brazil's Population Census
Show the Strength of Relationships Between Centre and Peripheral
Items
Non-Trivial Balance of Centrifuge Rotors
Datasets of the CEO (Centre d'Estudis d'Opinio) for Opinion
Polls in Catalonia
Cross-Entropy R Package for Optimization
Context Driven Exploratory Projection Pursuit
Censored Quantile Regression & Monotonicity-Respecting Restoring
Causal Effect Random Forest of Interaction Tress
Outlier Detection Using the Iterated RMCD Method of Cerioli
(2010)
Identification and Analysis of ceRNA Regulation
Utilities for Certara's Nonlinear Mixed-Effects Modeling Engine
Easily Install Pharmacometric Packages and Shiny Applications
Developed by Certara
Access the Canadian Election Study Datasets
Colony Formation Assay: Taking into Account Cellular Cooperation
Access College Football Play by Play Data
Cause-Specific Framework for Competing-Risk Analysis
Counterfactual Decomposition: MC Integration of the G-Formula
Simple Similarity for User-Based Collaborative Filtering Systems
Generate Citation File Format ('cff') Metadata for R Packages
Identification of Counterfactual Queries in Causal Models
Collaborative Filtering by Reference Classes
Cystic Fibrosis Survival Prediction Model Based on Stanojevic
Model
Estimate Disease Severity and Case Ascertainment
Using CF-Compliant Calendars with Climate Projection Data
Compare Groups, Analytically and Graphically
Constrained Groupwise Additive Index Models
Calculate AUC-type measure when gold standard is continuous and
the corresponding optimal linear combination of variables with
respect to it
Conditional Graphical LASSO for Gaussian Graphical Models with
Censored and Missing Values
Fits Conditional Generalized Linear Models
Clean and Analyze Continuous Glucose Monitor Data
Continuous Glucose Monitoring Data Analyzer
Analyzing Glucose and Glucose Variability
Powell Miscellaneous Functions for Teaching and Learning
Statistics
Continuous Time Generalized Rapid Response CUSUM
Statistical Methods and Models for Claims Reserving in General
Insurance
Automatic Colors for Multi-Dimensional Data
Geometrically Inspired Multivariate Changepoint Detection
Package to Calculate the Influence of the Data on a Changepoint
Segmentation
Methods for Nonparametric Changepoint Detection
A Collection of Change-Point Detection Methods
Change-Points Detections for Changes in Variance
Change Point Estimation for Clustered Signals
Change Point Tests for Joint Distributions and Copulas
Change Metrics for Species Geographic Ranges
Markov Model for Online Multi-Channel Attribution
Shiny Web Application for the Multichannel Attribution Problem
Loglikelihood Adjustments for Econometric Models
Simplified Language for Plots and Charts
Chat-Based Interactive Artificial Intelligence for R
Check the Basic Validity of a (Function) Argument
Check-Fields and Check-Boxes for 'rmarkdown'
Checks 'R' Configuration Set Up Correctly Before Class
Checks if a Number is Valid Using the Luhn Algorithm
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility
Intuitive Unit Testing Tools for Data Manipulation
Analysis and Visualisation of Ecological Communities
Interactively Explore Local Explanations with the Radial Tour
Tools for Working with Data During Statistical Analysis
Collection of 3 Chemical Databases from Public Sources
Chemical Metrics of Community Reference Proteomes
Calibration Functions for Analytical Chemistry
Analysis of Chemical Degradation Kinetic Data
Causal Inference with High-Dimensional Error-Prone Covariates
and Misclassified Treatments
A Cheminformatics Modeling Laboratory for Fitting and Assessing
Machine Learning Models
Analysing Chemodiversity of Phytochemical Data
Multivariate Statistical Analysis in Chemometrics
Exploratory Chemometrics for 2D Spectroscopy
Functions Supporting Packages ChemoSpec and ChemoSpec2D
Multiple Testing Methods for Exploratory Research
Read, Write, Create and Explore Chess Games
Create Network Connections Based on Chess Moves
Change-in-Estimate Approach to Assess Confounding Effects
Closest History Flow Field Forecasting for Bivariate Time Series
Data and Methods Around Reference Values in Pediatrics
Mapas de las Divisiones Politicas y Administrativas de Chile
(Maps of the Political and Administrative Divisions of Chile)
Processing Chill and Heat Models for Temperate Fruit Trees
Statistical Methods for Phenology Analysis in Temperate Fruit
Trees
Miscellaneous Tools for Chinese Text Mining and More
Toolkit of Helper Functions to Pre-Process Amplification Data
Nonparametric Methods for Identifying Differential Enrichment
Regions with ChIP-Seq Data
A Post Hoc Analysis for Pearson's Chi-Squared Test for Count
Data
Chi-Square and G-Square Test of Independence, Power and Residual
Analysis, Measures of Categorical Association
Convert Antipsychotic Doses to Chlorpromazine Equivalents
Estimation and Hypothesis Testing for Threshold Regression
Thermodynamic Calculations and Diagrams for Geochemistry
Amend, Augment and Aid Analysis of John Snow's Cholera Map
Cholesky Decomposition of the Wishart Distribution
Choose the Number of Principal Components via Recistruction
Error
Permutation Test for Some Positive and Many Zero Responses
Simplify the Creation of Choropleth Maps in R
Contains an Administrative-Level-1 Map of the World
Contains Maps Used by the 'choroplethr' Package
Generation of Different Animated Christmas Cards
Interface to Chromosome Counts Database API
Interactive Genomic Visualization of Biological Data
Chronological Objects which Can Handle Dates and Times
Chunkwise Text-File Processing for 'dplyr'
Functions for Conditional Hypergeometric Distributions
Cluster Independent Algorithm for Rare Cell Types Identification
Calibration of Confidence Intervals to Support Intervals
Causal Inference in Case-Control and Case-Population Studies
Causal Inference with Continuous (Multiple Time Point)
Interventions
Estimating and Testing Direct Effects in Directed Acyclic Graphs
using Estimating Equations
Estimate the Confidence Intervals for Predictive Values
Toolbox for Connectivity Informatics Technology Initiative
('CIFTI') Files
Tools for Reading, Writing, Viewing and Manipulating CIFTI Files
Causal Inference for Multiple Treatments with a Binary Outcome
A Computational Pipeline for Bulk 'ATAC-Seq' Profiles
Ready-to-Use Curated Gene Sets for 'cinaR'
Classification International Normalized of Education
Deciphering Central Informative Nodes in Network Analysis
Computationally-Efficient Confidence Intervals for Mean Shift
from Permutation Methods
Encryption and Decryption with Text Ciphers
Centered Isotonic Regression and Dose-Response Utilities
Algorithm for Computation of the Intersection Areas of N Circles
Curve Text Elements in 'Shiny' Using 'CircleType.js'
Maximum Likelihood Analysis of Circular Data
Statistical Analysis of Circular Data using Nonnegative
Trigonometric Sums (NNTS) Models
Multivariate Circular Data using MNNTS Models
Detection of Outliers in Circular-Circular Regression
Spatial and Spatio-Temporal Bayesian Model for Circular Data
Circular Statistics, from "Topics in Circular Statistics" (2001)
Extreme Value Analysis for Circular Data
Fast Silhouette on Circular or Linear Data Clusters
Analysis and Visualization of Circular Data
Covariates, Interaction, and Selection for DGLM
Perform Forward and Backwards Chasing in Evidence Syntheses
Clinical Trials with Intercurrent Events Simulator
Confidence or Prediction Intervals, Quantiles, and Probabilities
for Statistical Models
Customer Intelligence Tool for Rapid Understandable Segmentation
Kabaila and Giri (2009) Confidence Interval
Copula-Based Joint Analysis of Multiple Phenotypes
R Client for 'Customer Journey Analytics' ('CJA') API
Heterogeneous Effects Analysis of Conjoint Experiments
Canonical Joint and Individual Variation Explained (CJIVE)
Client for the Comprehensive Knowledge Archive Network ('CKAN')
API
Composite Kernel Association Test for Pharmacogenetics Studies
Composite Kernel Machine Regression Based on Likelihood Ratio
Test
Optimal, Fast, and Reproducible Univariate Clustering
Forward-in-Time Simulation and Tallying of Pairwise
Relationships
Measuring Morphological Diversity and Evolutionary Tempo
C++ Implementations of Phylogenetic Cladogenesis Calculations
Classical Age-Depth Modelling of Cores from Deposits
Time Series Modeling for Climate Change Proxies
Interior Point Conic Optimization Solver
Simulation-Based Inference for Regression Models
Classes and Methods for "Class Comparison" Problems on
Microarrays
Classes and Methods for "Class Discovery" with Microarrays or
Proteomics
Construct Graphs of S4 Class Hierarchies
Generates a Visualization of Classifier Performance as a Grid of
Diagnostic Plots
Explore Classification Models in High Dimensions
Exact Confidence Limits after a Sequential Trial
To Inspect and Manipulate Data; and to Keep Track of This
Process
Cleaning Geometries from Spatial Objects
A Tidy Data Model for Natural Language Processing
Clean Class-Less 'R Markdown' HTML Documents
Testbench for Univariate Time Series Cleaning
Composite Likelihood Estimation for Spatial Data
Clustering and Link Prediction Evaluation in R
Helpers for Developing Command Line Interfaces
Web Data Analysis by Bayesian Mixture of Markov Models
Model-Based Clustering of Categorical Sequences
Mixtures of Continuous Time Markov Models
A Simple HTTP Database Interface to 'ClickHouse'
Semi-Automatic Preprocessing of Messy Data with Change Tracking
for Dataset Cleaning
Analyzes Clickstreams Based on Markov Chains
Monthly Climate Data for Germany, Usable for Heating and Cooling
Calculations
Execute Command Line Programs Interactively
Easily Download and Visualise Climate Data from CliFlo
Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent
for Irregularly Sampled Time Series
Censoring Data and Likelihood-Based Correlation Estimation
Estimating Climate Stability from Climate Model Data
Climate Tools (Series Homogenization and Derived Products)
Climate Variability Indices for Ecological Modelling
Climate Classification According to Several Indices
Generating Rainfall Rasters from IMD NetCDF Data
Constrained L1-Minimization for Inverse (Covariance) Matrix
Estimation
Download, Extract and Visualise Climate and Elevation Data
Set of Tools to Compute Various Climate Indices
Simulation and Analysis Tools for Clinical Dose Response
Modeling
Clinical Trial Design and Data Analysis Functions
A Toolbox for Clinical Significance Analyses in Intervention
Studies
Summary Measures for Clinical Trials with Survival Outcomes
Recalibration Methods for Improved Clinical Utility of Risk
Scores
Hemodynamic Calculations from Clinical Monitoring
Clinical Natural Language Processing using 'spaCy', 'scispaCy',
and 'medspaCy'
General Utility Functions for Analysis of Clinical Data
Calculating Likelihoods by Pedigree Paring
Read and Write from the System Clipboard
Constrained Inference for Linear Mixed Effects Models
Fitting Exact Conditional Logistic Regression with Lasso and
Elastic Net Penalties
Sparse Conditional Logistic Regression for Matched Studies
Estimate Growth Rates from Phylogenetic Trees
Seeking and Finding Clones in Copy Number and Sequencing Data
Composite Likelihood Inference and Diagnostics for Replicated
Spatial Ordinal Data
Streamlined Interface to Interact with Cloud Storage Platforms
Interface to the Google Cloud Machine Learning Platform
Constrained Estimation of Linear Probability Model
Compute Price of Production and Labor Values
Curve Linear Regression via Dimension Reduction
Clinical and Laboratory Standards Institute (CLSI) EP15-A3
Calculations
Central Limit Theorem Experiments (Theoretical and Simulation)
Classification Using Binary Procrustes Rotation
Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections
Multidimensional Cluster Generation Using Support Lines
Bootstrap a Clustering Solution to Establish the Stability of
the Clusters
Simultaneous Semi-Parametric Estimation of Clustering and
Regression
ROC Analysis in Three-Class Classification Problems for
Clustered Data
Simultaneous Detection of Clusters and Cluster-Specific Genes in
High-Throughput Transcriptome Data
Statistical Methods for Analyzing Clustered Matched Pair Data
Clustering Dose-Response Curves and Fitting Appropriate Models
to Them
Clusters of Effects Curves in Quantile Regression Models
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et
al.
Performs Tests for Cluster Tendency of a Data Set
Analyze Clustered Data with Generalized Linear Models using the
Cluster Bootstrap
Consensus Clustering using Multiple Algorithms and Parameters
Causal Effects from Observational Studies with Clustered
Interference
Location and Visualization of Clustered Somatic Mutations
Random Cluster Generation (with Specified Degree of Separation)
Tools for Clustering High-Dimensional Data
Optimal Distance-Based Clustering for Multidimensional Data with
Sequential Constraint
Unbiased Single-Cell Transcriptomic Data Cell Type
Identification
Evaluate Function Calls on HPC Schedulers (LSF, SGE, SLURM,
PBS/Torque)
Integrative Clustering for Heterogeneous Biomedical Datasets
Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids
and Affinity Propagation Clustering
Reproducibility of Gene Expression Clusters
Calculate Cluster-Robust p-Values and Confidence Intervals
Searching for Optimal Clustering Procedure for a Data Set
Assessment of Stability of Individual Objects or Clusters in
Partitioning Solutions
Hierarchical Clustering with Spatial Constraints
K-Means Clustering with Build-in Missing Data Imputation
Learn Clustering Techniques Through Examples and Code
k-Prototypes Clustering for Mixed Variable-Type Data
Prediction and Clustering on the Torus by Conformal Prediction
Monitor Changes in Cluster Solutions of Dynamic Datasets
Methods for Joint Dimension Reduction and Clustering
Visualise Clusterings at Different Resolutions
Interactive Document for Working with Cluster Analysis
Clustering of Variables Around Latent Variables
Variable Selection for Gaussian Model-Based Clustering
Gaussian-Based Visualization of Gaussian and Non-Gaussian
Model-Based Clustering
Tools for Customer Lifetime Value Estimation
Covariance Matrix Adapting Evolutionary Strategy
Covariance Matrix Adaptation Evolution Strategy
Commercial Modular Aero-Propulsion System Simulation Data Set
Representation Tool For Output Of Connectivity Map (CMap)
Analysis
Implementation of the Conic Multivariate Adaptive Regression
Splines in R
Conditional Mixture Modeling and Model-Based Clustering
Computer Model Calibration for Deterministic and Stochastic
Simulators
An Implementation of the 'Congruent Matching Cells' Method
Framework for Building Interfaces to Shell Commands
Bi-Level Selection of Conditional Main Effects
Collective Matrix Factorization for Recommender Systems
Calibrated Model Fusion Approach to Combine Surrogate Markers
Access, Retrieve, and Work with CMHC Data
Computational Methods for Numerical Analysis
Subdistribution Analysis of Competing Risks
Use IPW to Estimate Treatment Effect under Competing Risks
Analysis of Competing Risks Using Quantile Regressions
R Implementation of Congruent Matching Profile Segments Method
Analysis of Cardiac Magnetic Resonance Images
Misc Functions of the Center for Mathematical Research
Interactive Document for Working with Confusion Matrix
Statistical Methods for Composite Material Data
The Complex Multivariate Gaussian Distribution
Causal Modeling with Coincidence Analysis
Common Network Asset Indices Methodology (CNAIM)
Optimizing Consistency and Coverage in Configurational Causal
Modeling
Canonical Non-Symmetrical Correspondence Analysis in R
Get Basic Information from Chinese ID Number
Complex-Valued Wavelet Lifting for Signal Denoising
Complex-Valued Wavelet Lifting for Univariate and Bivariate Time
Series Analysis
Pre-Process DNA Copy Number (CN) Data for Detection of CN Events
Dirichlet Multinomial Modeling of Relative Abundance Data
A Versatile Toolkit for Copy Number Variation Relationship Data
Analysis and Visualization
Bayesian "Now-Cast" Estimation of Event Probabilities in
Multi-Party Democracies
Calculate the Coastline Fractal Dimension
Conditional Method Agreement Trees (COAT)
Biclustering via Latent Block Model Adapted to Overdispersed
Count Data
Constrained B-Splines (Sparse Matrix Based)
Convolution-Closed Models for Count Time Series
COmbat CO-Normalization Using conTrols (COCONUT)
CoCoSo - Combined Compromise Solution Method for MCDA
Dependence Condition Test Using Ranked Correlation Coefficients
Statistical Comparisons of Two or more Alpha Coefficients
Output Analysis and Diagnostics for MCMC
A Basic Set of Functions for Compositional Data Analysis
Compositional Data Analysis for Microbiome Studies
Learning Sparse Log-Ratios for Compositional Data
Transformation-Free Linear Regression for Compositional Outcomes
and Predictors
Compositional Data Linear Models with Composition Redistribution
Automated C Code Generation for 'deSolve', 'bvpSolve'
Automatic Codebooks from Metadata Encoded in Dataset Attributes
Counting Codes in a Text and Preparing Data for Analysis
Analysis of R Code for Reproducible Research and Code
Comprehension
Generate Code Lists for the OMOP Common Data Model
Generate 'CodeMeta' Metadata for R Packages
Generation of Code Names for Organizations, People, Projects,
and Whatever Else
Deterministic Categorization of Items Based on External Code
Data
The CODEX (Comorbidity, Obstruction, Dyspnea, and Previous
Severe Exacerbations) Index: Short and Medium-Term Prognosis in
Patients Hospitalized for Chronic Obstructive Pulmonary Disease
(COPD) Exacerbations
Seq2Seq Encoder-Decoder Model for Time-Feature Analysis Based on
Tensorflow
Alternative Factor Coding Matrices for Linear Model Formulae
Meta Analysis of Factor Analysis Based on CO-Occurrence Matrices
Robust Coefficient Alpha and Omega with Missing and Non-Normal
Data
Chronological Ordering for Fossils and Environmental Events
Cognitive Mapping Tools Based on Coding of Textual Sources
Authentication for 'Shiny' Apps with 'Amazon Cognito'
Cohen's D_p Computation with Confidence Intervals
Use of Interval Algebra to Create New Cohort(s) from Existing
Cohorts
Explorer of Profiles of Patients in a Cohort
Simulation of Cohort Platform Trials for Combination Treatments
Estimate Survival from Common Data Model Cohorts
Contextualization and Evaluation of COI-5P Barcode Data
Conditional Inference Procedures in a Permutation Test Framework
Get 'Cryptocurrencies' Market Cap Prices from Coin Market Cap
Composite Indicator Construction and Analysis
Consistent Monitoring of Stationarity and Cointegrating
Relationships
Parameter Estimation and Inference in a Cointegrating Regression
Constrained Optimization on Linear Function
Collapses Levels, Computes Information Value and WoE
Interactive Collapsible Tree Diagrams using 'D3.js'
Functions Related to the Collatz/Syracuse/3n+1 Problem
Check for Namespace Collisions Across Packages and Functions on
CRAN
Visualization the Effects of Collinearity in Distributed Lag
Models and Other Linear Models
Collocation Inference for Dynamic Systems
Collapsed Latent Position Cluster Model for Social Networks
Auxiliary Package for Package 'CollapsABEL'
Dengue Cases and Climate Variables in Colombo Sri Lanka
Colocalisation Tests of Two Genetic Traits
Conduct Co-Localization Analysis of Fluorescence Microscopy
Images
Check Color Palettes for Problems with Color Vision Deficiency
Distance Metrics for Image Color Similarity
Extract Colors from Windows BMP, JPEG, PNG, TIFF, and SVG Format
Images
Coloring Wildlife Distributions in Space-Time
Colorize Old Images Using the 'DeOldify' Image Colorization API
Color Palettes using Colormaps Node Module
Optimized Rendering of Fold Changes and Confidence Values
Basic S4 Classes and Methods for Mapping Between Numeric Values
and Colors
Color Palettes for EPL, MLB, NBA, NHL, and NFL Teams
A Toolbox for Manipulating and Assessing Colors and Palettes
Color Calculations with Emphasis on Spectral Data
A Colour Picker Tool for Shiny and for Selecting Colours in
Plots
Causal Discovery for Categorical Data with Label Permutation
Creates Co-Occurrence Matrices of Spatial Data
Analysis of Concept Maps and Concept Landscapes
A Combined Association Test for Genes using Summary Statistics
Fit and Apply ComBat Harmonization for ENIGMA
Introduction to Some Combinatorial Relations
Calculate Scores and Marks for Track and Field Combined Events
Estimation of Optimal Portfolio Weights by Combining Simple
Portfolio Strategies
A Combined Interaction Test for Unreplicated Two-Way Tables
Construction Methods of some Series of PBIB Designs
Selection and Ranking of Omics Biomarkers Combinations Made Easy
Correcting Misclassified Binary Outcomes in Association Studies
Exact Test from the Combinations of Mutually Exclusive
Alterations (CoMEt) Algorithm
Models and Equations for Human Comfort Research
Commonly Used Functions for R Shiny Applications
Computational Methods for Immune Cell-Type Subsets
Coarsened Mixtures of Hierarchical Skew Kernels
Call Functions Without Commas Between Arguments
Network-Based Communities and Kernel Machine Methods
Useful Libraries for Building a Java Based GUI under R
High Performance CommonMark and Github Markdown Rendering in R
JAR Files of the Apache Commons Mathematics Library
Feature Extraction and Model Estimation for Audio of Human
Speech
Comparison Functions for Clustering and Record Linkage
Compare Two Correlated C Indices with Right-Censored Survival
Outcome
Interface to Diverse Estimation Methods of Causal Networks
Statistical Functions for the Design of Studies with Composite
Endpoints
Do a Git Style Diff of the Rows Between Two Dataframes with
Similar Structure
Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines
Finding the Best Model Using Eight Metrics Values
Correct for Verification Bias in Diagnostic Accuracy & Agreement
Multivariate Likelihood Ratio Calculation and Evaluation
Comparison of Survival Curves Between Two Groups
Conformational Manipulations of Protein Atomic Structures
A Graphical User Interface for Antitrust and Trade Practitioners
Computation of Relative Weights of Variables and Composite Index
Values Based on PCA
Calculate the Proportion of Permutations in Line with an
Informative Hypothesis
Create Complex UpSet Plots Using 'ggplot2' Components
Robust Linear Regression with Compositional Data as Covariates
Determine the Composite Reliability of a Naturalistic,
Unbalanced Dataset
Univariate Feature Selection and Compound Covariate for
Predicting Survival
Discrete Composite Distributions with Pareto Tails
Distribution Function of Quadratic Forms in Normal Variables
Analysis of COMRADES (Cross-Linking Matched RNA and Deep
Sequencing) Data
Flexible Framework for Simulating Community Assembly
Calculate the AQI from Pollutant Concentration
Calculate the Dutch Air Quality Index (LKI)
Human-Friendly Text from Unknown Strings
Connected Components of an Undirected Graph
Optimisation with Continuous Convex Piecewise (Linear and
Quadratic) Functions
An 'Rcpp' Implementation of Lin's Concordance Correlation
Coefficient (CCC)
An Alternative to the Kruskal-Wallis Based on the Kendall Tau
Distance
Continuous-Time Competing Risks Estimation using Targeted
Minimum Loss-Based Estimation (TMLE)
Market Structure, Concentration and Inequality Measures
Copy Number Profile Curve-Based Association Test
Approximate Conditional Inference for Logistic and Loglinear
Models
Estimation and Inference for Conditional Copula Models
Parameter Estimation in Conditional GEE for Recurrent Event Gap
Times
Computation of P Values and Bayes Factors for Conditioning Data
Censored Data Imputation for Direct Modeling
Control How Many Times Conditions are Thrown
Conditional Multivariate t Distribution, Expectation
Maximization Algorithm, and Its Stochastic Variants
Estimation of the Conditional Survival Function for Ordered
Multivariate Failure Time Data
Conditional Truncated Multivariate Normal Distribution
Create Tours in 'Shiny' Apps Using 'Shepherd.js'
Query-Driven Pipeline Execution and Query Templates
Conditional Visualization for Statistical Models
Interactive Conditional Visualization for Supervised and
Unsupervised Models in Shiny
Primal or Dual Cone Projections with Routines for Constrained
Regression
Visualization and Analysis of Statistical Measures of Confidence
Confidence Estimation of Environmental State Classifications
Manage Environment Specific Configuration Values
Package to Parse an INI File, Including Variable Interpolation
An Implementation of Parsing and Writing Configuration File
(JSON/INI/YAML/TOML)
Descriptive Interpretations of Confidence Intervals
Confidence Interval for the Univariate Population Variance
without Normality Assumption
An Alternative Conflict Resolution Strategy
Jackknife(+) Predictive Intervals for Bayesian Models
Transductive and Inductive Conformal Predictions for
Classification Problems
Tools for Conformal Inference for Regression in Multivariate
Functional Setting
Conformal Inference Tools for Regression with Multivariate
Response
Configural Frequencies Analysis Using Log-Linear Modeling
Estimates and Bounds for the False Discovery Proportion, by
Permutation
Confidence Envelopes for Model Selection Criteria Based on
Minimum ZIC
Distance Metrics for Trees Generated by Congreve and Lamsdell
Correlation Guided Network Integration (CoNI)
Algorithms for Fitting Circles, Ellipses and Conics Based on the
Work by Prof. Nikolai Chernov
An Implementation of Conjoint Analysis Method
A Parametric Method for Generating Synthetic Data
Estimations using Conley Standard Errors
Contingency Measure-Based Networks for Binary Time Series
Utilities for Interacting with the 'Posit Connect' Server API
Integrates with the 'RStudio' Connections Pane and 'pins'
Organize and Curate Your Content Within 'Posit Connect'
Tools for Working with Connectivity Data
Conover-Iman Test of Multiple Comparisons Using Rank Sums
Client for the Microsoft's 'Cognitive Services Text to Speech
REST' API
Identifying Conservation Prioritization Methods Based on Data
Availability
An Easy and Quick Way to Loop a Character Vector as a Menu in
the Console
Partial Linear Least-Squares Regression using Constrained
Splines
Compute the Median Ranking(s) According to the Kemeny's
Axiomatic Approach
Classification and Clustering of Preference Rankings
Fits Regression & ARMA Models Subject to Constraints to the
Coefficient
Reference on Constants, Units and Uncertainty
Identify Event Sequences Using Time Series Joins
Constrained, Covariance-Matching Constrained and Universal Point
or Block Kriging
Models Spatially Continuous and Discrete Population Genetic
Structure
Display Idiomatic Code to Construct Most R Objects
Social Contact Matrices for 177 Countries
Clustering and Classification with the Contaminated Normal
An Interface for Content-Based Identifiers
'a la Carte' on Text (ConText) Embedding Regression
Continued Fraction Generators and Evaluators
Statistical Analysis of Contingency Tables
Contouring of Non-Regular Three-Dimensional Data
Create Contour Plots from Data or a Function
Plots x,y,z Co-Ordinates in a Contour Map
Predictive Probability for a Continuous Response with an ANOVA
Structure
A Tiny Contribution Table Generator Based on 'ggplot2'
Control Function Methods with Possibly Invalid Instrumental
Variables
Quantile Comparison for Two-Sample Right-Censored Survival Data
Visualize the Effect of a Continuous Variable on a Time-to-Event
Outcome
Use the Given Parameters to Calculate the European Option Value
Convert Gene IDs Between Each Other and Fetch Annotations from
Biomart
Estimating IRT Parameters via Machine Learning Algorithms
Income Concentration Analysis with Complex Survey Samples
Probabilistic Species Co-Occurrence Analysis in R
Your Friendly Solution to Managing Browser Cookies
Practical Tools for Scientific Computations and Visualizations
Co-Operation: Fast Covariance, Correlation, and Cosine
Similarity Operations
Important Concepts of Cooperative Game Theory
Cooperative Aspects of Linear Production Programming Problems
Automated Cleaning of Occurrence Records from Biological
Collections
Coordinated Networks Detection on Social Media
General Bivariate Copula Theory and Many Utility Functions
Fitting the copCAR Regression Model for Discrete Areal Data
Correlates of Protection and Correlates of Risk Functions
Copula-Based Semiparametric Analysis for Time Series Data with
Detection Limits
Coverage Probability Excursion (CoPE) Sets
Estimating Copula Entropy and Transfer Entropy
Search Download and Handle Data from Copernicus Marine Service
Information
Adaptation of the Coloc Method for PheWAS
Tools for Nonparametric Martingale Posterior Sampling
Copula-Based Sensitivity Analysis for Observational Causal
Inference
Copula-Based Estimation and Statistical Process Control for
Serially Correlated Time Series
Copula Markov Model with Dependent Censoring
Analysis of Bivariate Survival Data Based on Copulas
Fitting Additive Copula Regression Models for Binary Outcome
Regression
Copula-Based Regression Models for Multivariate Censored Data
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test
Accuracy Studies
Estimation of Distribution Algorithms Based on Copulas
Estimation and Goodness-of-Fit of Copula-Based Models with
Arbitrary Distributions
Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic
Test Accuracy Studies
Virtual Patient Simulation by Copula Invariance Property
Assembling Long Gene Copies from Short Read Data
The COR for Optimal Subset Selection in Distributed Estimation
Apply 'colorffy' Color Gradients Within 'shiny' Elements
Collection of Rudimentary Bioinformatics Tools
Generate High-Dimensional Binary Data with Correlation
Structures
Community Estimation in G-Models via CORD
Programmatic Analysis of Sediment Cores Using Computed
Tomography Imaging
Classification, Regression and Feature Evaluation
R Binding for the 'Certifiably Optimal RulE ListS (Corels)'
Learner
Core Functionality for Simulating Quantities of Interest from
Generalised Linear Models
Hidden Markov Models of Character Evolution
Record Linkage, Incorporating Imputation for Missing Agreement
Patterns, and Modeling Correlation Patterns Between Fields
Correct Mass Isotopologue Distribution Vectors
Count Regression for Correlated Observations with the
Beta-Binomial
Collection of Scripts for Interface Between 'Cornerstone' and
'R'
Confounding Robust Independent Component Analysis for Noisy and
Grouped Data
The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
Efficient Estimation of Covariance and (Partial) Correlation
Statistics and Data Sets for Corpus Frequency Data
A 'Shiny' App for Exploration of Text Collections
Power Calculations for Assessing Correlates of Risk in Clinical
Efficacy Trials
Managing, Querying and Analyzing Tokenized Text
Implementation of 2D Correlation Analysis in R
Correlation Arrays and 2-Sample Correlation Matrices
Nonparametrics with Clustered Binary and Multinomial Data
Correcting the Coverage of Credible Sets from Bayesian Genetic
Fine Mapping
Finding Associations in Position-Wise Aligned DNA Sequence
Dataset
Correct Overloaded Peaks from GC-APCI-MS Data
Corrected Test Statistics for Comparing Machine Learning Models
on Correlated Samples
Functions and Methods for Correspondence Regression
Speed Up Exploratory Data Analysis (EDA) with the Correlation
Funnel
A Collection of Functions for Graphing Correlation Matrices
Creating Correspondence Tables Between Two Statistical
Classifications
Explore Correlations Between Variables in a Machine Learning
Model
Estimate Correlations Between Repeatedly Measured Endpoints
(E.g., Reliability) Based on Linear Mixed-Effects Models
Software for Summarising and Evaluating STRUCTURE Output
Creates and Saves Out a Correlation Table with Significance
Levels Indicated
Modeling Correlational Magnitude Transformations in
Discretization Contexts
Arbitrary Bounding of Series and Time Series Objects
Significance Testing of Rank Cross-Correlations under SRD
Bound Constrained Optimal Sample Size Allocation
Tools for Estimating and Predicting the Cosinor Model
Extended Tools for Cosinor Analysis of Rhythms
Mutational Signatures from COSMIC (Catalogue of Somatic
Mutations in Cancer)
Functions for Cosmological Distances, Times, Luminosities, Etc
Fit Regularized Nonparametric Regression Models Using COSSO
Penalty
Copula-Based Semiparametric Models for Spatio-Temporal Data
Locates and Counts Pixels Within Color Range(s) in Images
A Countdown Timer for HTML Presentations, Documents, and Web
Apps
Estimation and Inference Methods for Counterfactual Analysis
Comprehensive Automatized Evaluation of Distribution Models for
Count Data
Discrete Response Regression for High-Dimensional Data
Penalized Estimation of Flexible Hidden Markov Models for Time
Series of Counts
Analysis of Biological Count Data, Especially from Single-Cell
RNA-Seq
Calculate Model-Based Metrics of Proportionality on Count-Based
Compositional Data
Flexible Univariate Count Models Based on Renewal Processes
Mean and Variance Modeling of Count Data
Splitting a Count Matrix into Independent Folds
Convert Counts to Fragments per Kilobase of Transcript per
Million (FPKM)
Transform Counts in RNA-Seq Data Analysis
Thomson Sampling for Zero-Inflated Count Outcomes
Packages and Functions for 'CourseKata' Courses
Local Polynomial Regression of State Dependent Covariates in
State-Space Models
Tests on Properties of Space-Time Covariance Functions
Brownian Motion Processes for 'nlme'-Models
Combine Partial Covariance / Relationship Matrices
Produce Forest Plots to Visualize Covariate Effects
Test for Equality of Covariance Matrices
Process Digital Cover Photography Images of Tree Crowns
Load and Analyze Live Data from the COVID-19 Pandemic
Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data in
R Format
Pulling Clean Data from Covid19india.org
The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset
The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in Sri
Lanka
COVID-19 Cases in Switzerland and Principality of Liechtenstein
Prepare, Analyze, and Visualize Covid-19 Wastewater Data
Client for Delphi's 'COVIDcast Epidata' API
Downloading, Reading and Analyzing PNAD COVID19 Microdata
Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease
2019)
Descarga y analiza datos de COVID-19 en México
Estimate the Unit-Wide Probability of COVID-19
Covariance Estimation for Matrix Data with the Kronecker-Core
Decomposition
Covariance Regression with Random Forests
Robust Covariance Estimation via Nearest Neighbor Cleaning
Tests for Determining if the Covariance Structure of
2-Dimensional Data is Separable
VITA, IG and PLSIM Simulation for Given Covariance and Marginals
Statistical Tools for Covariance Analysis
Performs Segmented Linear Regression on Two Independent
Variables
Streamlined Plot Theme and Plot Annotations for 'ggplot2'
Messages, Warnings, Strings with Ascii Animals
Doubly Robust Inference for Cox Marginal Structural Model with
Informative Censoring
Bias-Corrected Sandwich Variance Estimators for Marginal Cox
Analysis of Cluster Randomized Trials
Duration-Based Quantities of Interest for the Cox Proportional
Hazards Model
Cox Regression with Dependent Error in Covariates
Variable Selection for Cox's Model with Interval-Censored Data
Cox Regression with Firth's Penalized Likelihood
Sparse Estimation of Cox Proportional Hazards Models via
Approximated Information Criterion
Stochastic Gradient Descent log-Likelihood Estimation in Cox
Proportional Hazards Model
Cox Regression (Proportional Hazards Model) with Multiple Causes
and Mixed Effects
R-Squared Measure Based on Partial LR Statistic, for the Cox PH
Regression Model
Fit Robustly Proportional Hazards Regression Model
Cox Proportional Hazards Regression for Right-Truncated Data
Confirmatory Path Analysis Through 'd-sep' Tests
Bayesian Meta Analysis for Studying Cross-Phenotype Genetic
Associations
Efficient and Customized Preconditioned Conjugate Gradient
Method for Solving System of Linear Equations
Concordance Probability Estimates in Survival Analysis
Classification with Parallel Factor Analysis
Association Between Methylation and a Phenotype of Interest
CpG Filtering Method Based on Intra-Class Correlation
Coefficients
Sequential and Batch Change Detection Using Parametric and
Nonparametric Methods
Fitting Semiparametric Cumulative Probability Models for Big
Data
Correction of the P-Value after Multiple Coding in Generalized
Linear Models
Calculation of Log2 Counts per Million Cutoff from ERCC Controls
Conceptual Properties Norming Studies as Parameter Estimation
Detection of Multiple Changes in Slope in Univariate Time-Series
Composition of Probabilistic Preferences (CPP)
Check 'C' and 'C++' Files using 'Cppcheck'
Fast Relative Comparisons of Floating Point Numbers in 'C++'
Algorithms for Routing and Solving the Traffic Assignment
Problem
Conditional Probit Model for Analysing Continuous Outcomes
Functions for Working with Danish CPR Numbers
Constructs a Correlated Pseudo-Marginal Sampler
Load CPS Microdata into R Using the 'Census Bureau Data' API
Change-Point Detection by Sample-Splitting Methods
Nonparametric Change Point Estimation for Survival Data
Simulating Survival Data from Change-Point Hazard Distributions
A Toolbox for Using the CPS’s Voting and Registration Supplement
Nonparametric Change Point Detection for Multivariate Time
Series
Quantile, Composite Quantile Regression and Regularized Versions
Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data
Compute Cluster Robust Standard Errors with Degrees of Freedom
Adjustments
Congruent Rate Analyses in Birth-Death Scenarios
Multivariate Nonparametric Cramer-Test for the
Two-Sample-Problem
Network Analysis of Dependencies of CRAN Packages
Download Logs from the 'RStudio' 'CRAN' Mirror
Package Directives and Collaboration Networks in CRAN
RStudio Addin for Searching Packages in CRAN Database Based on
Keywords
Conditional Random Sampling Sparse Matrices
Fit Continuous-Time Correlated Random Walk Models to Animal
Movement Data
Cyclic Redundancy Check with CPU-Specific Acceleration
Censored Regression with Conditional Heteroscedasticity
Univariate Analysis of Cluster Trials with Multiple Outcomes
Interpretable Discovery and Inference of Heterogeneous Treatment
Effects
Clustering of Genomic Regions Analysis Method
Create Logical Pairwise Comparison Matrix for the Analytic
Hierarchy Process
Tools for Managing SSH and Git Credentials
Toolkit for Credit Modeling, Analysis and Visualization
Calibrated Ratio Estimator under Double Sampling Design
Simple Conjoint Tidying, Analysis, and Visualization
Crew Launcher Plugins for Traditional High-Performance Computing
Clusters
Conditional Random Fields for Labelling Sequential Data in
Natural Language Processing
Analyze Cricketers and Cricket Teams Based on ESPN Cricinfo
Statsguru
Group-Based Modelling of Longitudinal Data
Access Crime Data from the Open Crime Database
A Comprehensive Set of Functions to Clean, Analyze, and Present
Crime Data
Fits a Model that Partitions the Covariate Space into Blocks in
a Data- Adaptive Way
Modeling Fissile Material Operations in Nuclear Facilities
An Implementation of the Critical Path Method
Setting the Critical Path in Project Management
Continual Reassessment Method (CRM) for Phase I Clinical Trials
Cell Ranger Output Filtering and Metrics Visualization
CCMN and Other Normalization Methods for Metabolomics Data
Object-Oriented Implementation of CRM Designs
Cellwise Robust M-Regression and SPADIMO
Implementation Helper for '[' and '[<-' of Custom Matrix-Like
Types
A Dashboard to Visualize Scientific Workflows in 'Cromwell'
Create an HTML Vertical Timeline from a Data Frame in
'rmarkdown' and 'shiny'
Schedule R Scripts and Processes with the 'cron' Job Scheduler
Open Data of Agricultural Production of Crops of Peru
Crop Growing Degree Days and Agrometeorological Calculations
Changepoints for a Range of Penalties (CROPS)
Access Cropland Data Layer Data via the 'CropScape' Web Service
Climate Crop Zoning Based in Air Temperature for Brazil
Analysis of Data from a Crossover Design with GEE
Local Haplotype Clustering and Visualization
Apply Functions to All Combinations of List Elements
Cross-Design & Cross-Format Network Meta-Analysis and Regression
Analysis and Search of Crossover Designs
Joint Distribution of Number of Crossings and Longest Run
Cross Tabulation and Loglinear Analyses of Categorical Data
Inter-Widget Interactivity for HTML Widgets
Analysis of Graph-Structured Data with a Focus on
Protein-Protein Interaction Networks
Cross-Fitting for Doubly Robust Evaluation of High-Dimensional
Surrogate Markers
Verbal Autopsy Data Transformation for InSilicoVA and InterVA5
Algorithms
Classes and Methods for Cross Validation of "Class Prediction"
Algorithms
Rename and Encode Data Frames Using External Crosswalk Files
Constrained Regression Model for an Ordinal Response and Ordinal
Predictors
Unidimensional and Multidimensional Methods for Recurrence
Quantification Analysis
Bias-Corrected Variance for Competing Risks Regression with
Clustered Data
Competing Risks Regression for Stratified and Clustered Data
Stepwise Covariate Selection for the Fine & Gray Competing Risks
Regression Model
A Robust and Powerful Test of Abnormal Stock Returns in
Long-Horizon Event Studies
Nonlinear Regression Parameters Estimation by 'CRS4HC' and
'CRS4HCe'
Tidying and Analyzing 'Coursera' Research Export Data
Obtain Suggested Coordinate Reference System Information for
Spatial Data
Conditional Randomization Testing (CRT) Approach for Conjoint
Analysis
Doubly Robust Inverse Probability Weighted Augmented GEE
Estimator
Sample Size Estimation Functions for Cluster Randomized Trials
Workflow for Cluster Randomised Trials with Spillover
Interface to Climatic Research Unit Time-Series Version 3.21
Data
Cluster Robust Wild Bootstrap Meta Regression
Changing the Reference Group without Re-Running the Model
Download Crypto Currency Data from 'CoinMarketCap' without 'API'
A Streamlined Access to OHLC-v Market Data and Sentiment
Indicators
Visualization and Analytics for the Cryptoverse
Statistical Tests for Cryptographic Randomness
Calculate the Relative Crystallinity of Starch by XRD and FTIR
A Cross-Scale Analysis Tool for Model-Observation Visualization
and Integration
Alerts from Public Health Surveillance Data
Functions for Conditional Simulation in Regression-Scale Models
Fitting and Tuning Regularized Cause-Specific Cox Models with
Elastic-Net Penalty
Frequent Contiguous Sequential Pattern Mining of Text
CRISPR-Based Salmonella Enterica Serotype Analyzer
Collecting Counter Strike Global Offensive Data
Climate Services' Indicators Based on Sub-Seasonal to Decadal
Predictions
Preformatted Maps of Norway that Don't Need Geolibraries
Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain
Cost Conditions
Biomarker Confirmation, Selection, Modelling, Prediction, and
Validation
Complete Discrete Fourier Transform (DFT) and Periodogram
A Tool for the Correlates of State Policy Project Data
Data Only: The Correlates of State Policy Project Dataset
Selection of Number of Clusters via Normalized Clustering
Instability
Covariate Specific Treatment Effect (CSTE) Curve
Helpful Functions for Cleaning Surveillance Data
Date and Time Functions for Public Health Purposes
Assessing Skill of Climate Forecasts on Seasonal-to-Decadal
Timescales
Common Base-R Problems Relating to Lists
Read and Write CSV Files with Selected Conventions
Read and Write CSV on the Web (CSVW) Tables and Metadata
Import and Export CSV Data with a YAML Metadata Header
Contingency Table Analysis Based on ML Fitting of MPH Models
Interactive Application for Working with Contingency Tables
A Procedure to Clean, Decompose and Aggregate Timeseries
A Method for 'Connecting The Dots' in Weighted Graphs
Closed Testing with Globaltest for Pathway Analysis
A Text Mining Toolkit for Chinese Document
Estimating the Parameters of a Continuous-Time Markov Chain from
Discrete-Time Data
Modeling Animal Movement with Continuous-Time Discrete-Space
Markov Chains
Collaborative Targeted Maximum Likelihood Estimation
Censored and Truncated Quantile Regression
Retrieve and Analyze Clinical Trials in Public Registers
Query Data from U.S. National Library of Medicine's Clinical
Trials Database
Assess the Stability of Candidate Housekeeping Genes
Continuous Time Structural Equation Modelling
Continuous Time SEM - 'OpenMx' Based Functions
Interactive Document for Working with Classification Tree
Analysis
A Class of Mixture Models for Ordinal Data
Adaptive Multivariate Integration over Hypercubes
A Vector Spatio-Temporal Data Structure for Data Analysis
Computation of a Cubic B-Spline Basis and Its Derivatives
Rule- And Instance-Based Regression Modeling
R Interface for the RAPIDS cuML Suite of Libraries
Charles's Utility Function using Formula
Tools, Measures and Statistical Tests for Cultural Evolution
Analyze Multivariate Phenotypes by Combining Univariate Results
Functions Centered Around Microsoft Excel Cumprinc Function
Change Point Detection in Genomic Sequences
Cumulative Percent Decay Curve Generator
Dependent Censoring Regression Models with Cure Fraction
Client for the 'currencyapi.com' Currency Conversion API
Apply Mapping Functions in Frequent Saving
Partial Function Application with %<%, %-<%, and %><%
Confidence Intervals for the Current Status Model
Finds Binary Outcome Designs Using Stochastic Curtailment
Multiple Curve Comparisons Using Parametric Bootstrap
Tukey Curve Depth and Distance in the Space of Curves
Cusp-Catastrophe Model Fitting Using Maximum Likelihood
Custom Made Gauss Quadrature Nodes and Weights
Pricing Various Types of Custom Derivatives
Evaluation Metrics for Customer Scoring Models Depending on
Binary Classifiers
Customized Training for Lasso and Elastic-Net Regularized
Generalized Linear Models
Costs Allocation for the Installation of an Elevator
Cumulative Sum (CUSUM) Charts for Monitoring of Hospital
Performance
Compute Decision Interval and Average Run Length for CUSUM
Charts
Determine and Evaluate Optimal Cutpoints in Binary
Classification Tasks
Optimal Equal-HR Method to Find Two Cutpoints for U-Shaped
Relationships in Cox Model
Compute Expected Shortfall and Value at Risk for Continuous
Distributions
Cross-Validated Area Under the ROC Curve Confidence Intervals
Efficient Design and Analysis of Cluster Randomized Trials
Tests for the Equality of Coefficients of Variation from
Multiple Groups
Lasso Penalized Precision Matrix Estimation
Cross-Validation Model Averaging for Partial Linear Functional
Additive Models
Cramer von Mises Tests for Discrete or Grouped Distributions
Compute Risk Scores for Cardiovascular Diseases
SEM Model Comparison with K-Fold Cross-Validation
Fast Cross-Validation via Sequential Testing
Cross-validation tools for regression models
Tools to Create, Modify and Manage 'CWB' Corpora
Cauchy Weighted Joint Test for Pharmacogenetics Analysis
A Toolbox for Modelling Species Coexistence in R
Extend 'cxxfunction' by Saving the Dynamic Shared Objects
Cycle Routing and Data for Cycling Advocacy
Tools for Calculating Hypocycloids, Epicycloids, Hypotrochoids,
and Epitrochoids
Survival Modeling with a Periodic Hazard Function
Cyclic Coordinate Descent for Logistic, Poisson and Survival
Analysis
Circular-Linear Copulas with Angular Symmetry for Movement Data
Query 'Team Cymru' 'IP' Address, Autonomous System Number
('ASN'), Border Gateway Protocol ('BGP'), Bogon and 'Malware'
Hash Data Services
Agent-Based Model for Taenia_solium Transmission and Control
Plot Fan Plots for Cytometry Data using 'ggplot2'
Automated Cytometry Gating and Annotation
Optimal Transport for Gating Transfer in Cytometry Data with
Domain Adaptation
Simplex Visualization of Cell Fate Similarity in Single-Cell
Data
Use Open Data from the Czech Statistical Office in R
Descriptive 3D Multidimensional Item Response Theory Modeling
Tools for creating D3 JavaScript network, tree, dendrogram, and
Sankey graphs from R
Interactive Charts of Nested and Hierarchical Data with 'D3.js'
Fast and Beautiful Interactive Visualization for 'Markdown' and
'Shiny'
Create Interactive Collapsible Trees with the JavaScript 'D3'
Library
Interface to 'D4Science' 'StorageHub' API
Discriminant Analysis for Evolutionary Inference
Data Analysis and Graphics Data and Functions
Data Sets and Functions, for Demonstrations with Expression
Arrays and Gene Sequences
Damped Anderson Acceleration with Epsilon Monotonicity for
Accelerating EM-Like Monotone Algorithms
Data Analysis using Bootstrap-Coupled Estimation
Detection and Attribution Analysis of Climate Change
Data Analysis with Ceiling and/or Floor Data
Three-Way / Multigroup Data Analysis Through Densities
Functions Useful in the Design and ANOVA of Experiments
Design and Analysis of Experiments with R
Download, Wrangle, and Analyse Vessel Monitoring System Data
Spatial Vector Data for Danmarks Administrative Geografiske
Inddeling DAGI
Graphical Analysis of Structural Causal Models
Directed Acyclic Graphs: Analysis and Data Simulation
DAGs with Omitted Objects Displayed (DAGWOOD)
Data Quality Reporting for Temporal Datasets
Interface with Google Cloud Document AI API
Dynamical Assembly of Islands by Speciation, Immigration and
Extinction
Extracts Phylogenetic Island Community Data from Phylogenetic
Trees
moDel Agnostic Language for Exploration and eXplanation
Nonparametric Double Additive Location-Scale Model (DALSM)
Leveraging Experiment Lines to Data Analytics
The DALY Calculator - Graphical User Interface for Probabilistic
DALY Calculation in R
Dynamic Assembly Model of Colonization, Local Extinction and
Speciation
Interface to Drosophila Activity Monitor System Result Files
Dams in the United States from the National Inventory of Dams
(NID)
Dandelion Plot for R-Mode Exploratory Factor Analysis
Design and Analysis of Non-Inferiority Trials
Functions for Wayne W. Daniel's Biostatistics, Tenth Edition
Discriminant Adaptive Nearest Neighbor Classification
R Client for the Statistics Denmark Databank API
Data Quality Assessment for Process-Oriented Data
Differential Analysis with Random Reference Genes
Estimating Dark Diversity and Site-Specific Species Pools
Importing and Analysing 'SNP' and 'Silicodart' Data Generated by
Genome-Wide Restriction Fragment Analysis
Analysing 'SNP' and 'Silicodart' Data - Basic Functions
Analysing 'SNP' Data to Support Captive Breeding
Auxiliary Data Package for Our Main Package 'dartR'
Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide
Restriction Fragment Analysis
Applying Landscape Genomic Methods on 'SNP' and 'Silicodart'
Data
Install and Load the 'dartRverse' Suits of Packages
Customise the Appearance of 'shinydashboard' Applications using
Themes
Tools for Reading, Processing and Writing 'DSSAT' Files
General Purpose Hierarchical Data Structure
Wrapper for 'TCdata360' and 'Govdata360' API
Connecting to Various Database Platforms
JAR Dependencies for the 'DatabaseConnector' Package
Swarm Intelligence for Self-Organized Clustering
Easily Create Structured Lists or Data Frames with Input
Validation
Interactive and Reproducible Data Cleaning
Tools for Easily Combining and Cleaning Data Sets
Compare Two Data Frames and Summarise the Difference
An Interactive Editor for Viewing, Entering, Filtering & Editing
Data
Generate Fake Data for Relational Databases
Familiarity with Dataframes Before Data Manipulation
Estimating Finite State Machine Models from Data
Call Real Time APIs from Data Gov Singapore
A Suite of Checks for Identification of Potential Errors in a
Data Frame as Part of the Data Screening Process
Create and Append a Data Dictionary for an R Dataset
Modules to Import and Manipulate Data in 'Shiny'
Data and Functions to Accompany Analisis De Datos Con R
Easily Source Publicly Available Data on Derivatives
A Flexible Container to Transport and Manipulate Data and
Associated Resources
Efficient and Flexible Data Preprocessing Tools
Data Quality in Epidemiological Research
Reproducible Data Screening Checks and Report of Possible Errors
C3S Quality Control Tools for Historical Climate Data
Retrieval Functions for USGS and EPA Hydrology and Water Quality
Data
Data Bank for Statistical Analysis and Visualization
Row by Row Data Processing Tool, Using 'DataSailr' Script
Create Data Frames that are Easier to Exchange and Reuse
Graphical Interface for Loading Datasets
Datasets from the Book "An Introduction to Clustering with R"
Create Lightweight Schema.org Descriptions of Data
An Implementation of a SAS-Style Data Step
Provides a Link Between the 'Refinitiv Datastream' System and R
The Research Data Warehouse of Miguel de Carvalho
A Lightweight, Flexible, and Fast Data Validation Package that
Can Handle All Sizes of Data
Data Visualisation Using an HTML Page and 'D3.js'
Easy Data Wrangling and Statistical Transformations
Simplify Access to Data from the Amazon Region
Collect and Install R Packages on a Specified Date with
Dependencies
Standardize Dates in Different Formats or with Missing Data
Scientific Data on Time of Lineage Divergence for Your Taxa
Datetimes with Optional UTC Offsets and/or Heterogeneous Time
Zones
A Datetime Range Picker Widget for Usage in 'Shiny' Applications
Modify Dates to ISO Standard ("International Organization for
Standardization")
Traduce al Español Varios Conjuntos de Datos de Práctica
Preparation of Object Dating Ranges for Density Plots (Aoristic
Analysis)
Compact Standard for Australian Phone Numbers
Discriminant Analysis with Additional Information
Calculating Days' Supply and Daily Dose of Prescriptions
Autocovariance Estimation via Difference-Based Methods
Discrete Bayesian Additive Regression Trees Sampler
Detecting Breakpoints and Estimating Segments in Trend
A Double Bootstrap Method for Analyzing Linear Models with
Autoregressive Errors
Read and Write Files to SQLite Databases
Generalised Linear Models by Subsampling and One-Step Polishing
Sequence Clustering with Discrete-Output HMMs
'DBHYDRO' Hydrologic and Water Quality Data
Compute the NPMLE of Distribution Function from Doubly Censored
Data, Plus the Empirical Likelihood Ratio for F(T)
Deterministic Blockmodeling of Signed, One-Mode and Two-Mode
Networks
Confidence Interval for Matrix Completion via De-Biased
Estimator
Distance-Based Measures of Spatial Structures
Dynamic Bayesian Network Structure Learning, Parameter Learning
and Forecasting
Rank Selection for Non-Negative Matrix Factorization
Dynamic Bayesian Network Learning and Inference
Simplifies Plotting Data Inside Databases
Finite Sample Power Calculations for Detection Boundary Tests
Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) and Related Algorithms
Produce R Functions to Create HTML Forms Based on SQL Meta Data
Inferring Condition-Specific Networks via Differential Network
Inference
Detrended Fluctuation and Detrended Cross-Correlation Analysis
Analyzing Dichotomous Choice Contingent Valuation Data
DCC Models with GARCH-MIDAS Specifications in the Univariate
Step
Fast Computation of Distance Correlations
Clustering Big Data using Expectation Maximization Star (EM*)
Algorithm
Efficient and Accessible Discrete Choice Experiments
Data Cloud Geometry (DCG): Using Random Walks to Find Community
Structure in Social Network Analysis
Genetic Relatedness Between Polyclonal Infections
Claims Reserving under the Double Chain Ladder Model
Distance Based Cell Lineage Reconstruction
Data Cloning and MCMC Tools for Maximum Likelihood Methods
Functions for the Detection of Spatial Clusters of Diseases
Model-Based Detection of Disease Clusters
Calculating the M2 Model Fit Statistic for Diagnostic
Classification Models
Hierarchical Models Made Easy with Data Cloning
Modify Data Using Externally Defined Modification Rules
List Linear n-Peptide Constraints for Overlapping Protein
Regions
Providing Fast and Flexible Functions for Distance Correlation
Analysis
Variable Selection Algorithms Using the Distance Correlation
A Fast Implementation of Distance Covariance
Distance Covariance and Correlation for Time Series Analysis
Nonparametric Regression and Bandwidth Selection for Spatial
Models
Decision Curve Analysis for Model Evaluation
Depth-Based Classification and Calculation of Data Depth
Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)
The Data Defect Index for Samples that May not be IID
Distance & Density-Based Outlier Detection
Diagonally Dominant Principal Component Analysis
Analysis and Visualization of Droplet Digital PCR in R and on
the Web
Data Sets for Discrete Probability Models
Disease-Drived Differential Proteins Co-Expression Network
Analysis
Data-Driven Sparse Partial Least Squares
Latent Class Analysis with Dirichlet Diffusion Tree Process
Prior
Learning Bayesian Networks with Mixed Variables
Conventional and Fuzzy Data Envelopment Analysis
Duration Estimation for Biomarker Enrichment Studies and Trials
Efficient Inference on High-Dimensional Linear Model with
Missing Outcomes
Bifurcation Analysis of Ordinary Differential Equation Systems
Bayesian Inference for Differential Equations
Analysis of Non-Decimal Currencies and Double-Entry Bookkeeping
Box-Plots and Outlier Detection for Probability Density
Functions
Debug Tool to Watch Objects/Expressions While Running an R
Script
Detecting Changes in Autocorrelated and Fluctuating Signals
DEComposition of Indirect and Direct Effects
Bindings for 'Mapbox' Ear Cutting Triangulation Library
Quantitative Support of Decision Making under Uncertainty
Differential Co-Expression and Differential Expression Analysis
Decode Coded Variables to Plain Text and the Other Way Around
Decomposition Based Deep Learning Models for Time Series
Forecasting
Time Series Prediction with PSF and Decomposition Methods (EMD
and EEMD)
Empirical Mode Decomposition for Cyclostratigraphy
Deconvolution Estimation in Measurement Error Models
Identifies Signatures Present in a Tumor Sample
Extend the Behaviour of a Function without Explicitly Modifying
it
Deductive Correction, Deductive Imputation, and Deterministic
Correction
Data Correction and Imputation Using Deductive Methods
Visualise and Explore the Deep Dependencies of R Packages
Bayesian Deep Gaussian Processes using MCMC
Interface to the 'DeepL' Translation API
Clustering of Short Texts by Mixture of Unigrams and Its Deep
Extensions
Seamless Language Translation in 'RStudio' using 'DeepL' API and
'Rstudioapi'
Plotting Tools for Anyone Working in Deep Time
Fitting Deep Conditional Transformation Models
Density Estimation by Extreme Value Distributions
Change the Default Arguments in R Functions
Fitting Differential Equations to Time Series Data
Deferred List - A Read-Only List-Like Object with Deferred
Access
Estimation and Simulation of Multi-Binary Response Models
Classify RGB Images into Forest or Non-Forest
Spatial Deformation and Dimension Expansion Gaussian Processes
Integration of One-Dimensional Functions with Double Exponential
Formulas
Inferring Differentially Expressed Genes using Generalized
Linear Mixed Models
Models for Skewed Count Distributions Relevant to Networks
Density Estimation from GROuped Summary Statistics
Multiple Imputation for Recurrent Events
Statistical Functions for the Delaporte Distribution
A Framework for Parallelizing Dependent Tasks
Sample Size and Power Calculations using the APPLE, SEPPLE,
APPLE+ and SEPPLE+ Methods
Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
Quantify Rhythmic Gene Co-Expression Relative to a Reference
Delta Measurement of Agreement for Nominal Data
Identification of Dichotomous Differential Item Functioning
(DIF) using Angoff's Delta Plot Method
Kernel Density Estimation using Lifetime Distributions
The Distributed EM Algorithms in Multivariate Gaussian Mixture
Models
Decision Making Trial and Evaluation Laboratory Technique in R
Dynamic Estimator of Microbial Communities
Deming, Theil-Sen, Passing-Bablock and Total Least Squares
Regression
Simulating Differential Equations with Data
Analysis of Age-Structured Demographic Models
Extrapolate Gender, Age and Nationality of a Name
Providing Demographic Table with the P-Value, Standardized Mean
Difference Value
Forecasting Mortality, Fertility, Migration and Population Data
Kernel Density Estimation for Demonstration Purposes
Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps
DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using
Test Set
Optimal Design Emulators via Point Processes
Extending 'dendrogram' Functionality in R
A Tool for Processing and Analyzing Dendrometer Data
Extra Functions to Cut, Label and Colour Dendrogram Clusters
Forest Estimations and Dendrometric Computations
A Set of Tools for Calculating Spatial Synchrony Between
Tree-Ring Chronologies
Linear and Nonlinear Methods for Analyzing Daily and Monthly
Dendroclimatological Data
Dendrogram Seriation: Ordering for Visualisation
Modular Leaf Ordering Methods for Dendrogram Nodes
A Tidy Format Datasets of Dengue by Country
Differential Expression Analysis Using a Bottom-Up Model
Statistical Analyses of De Novo Genetic Variants
Functional Linear Mixed Models for Densely Sampled Data
Density Estimation via Bayesian Inference Engines
Analysing Density Profiles from Resistance Drilling of Trees
Polygons of Bivariate Density Distributions
Clustering by Fast Search and Find of Density Peaks
Dens-Based Method for Partial Correlation Estimation in Large
Scale Brain Networks
Density Strips and Other Methods for Compactly Illustrating
Distributions
Simple Dengue Test and Vaccinate Cost Thresholds
Global Optimization by Differential Evolution
Differential Evolution Optimization in Pure R
Cache R Expressions, Taking Their Dependencies into Account
Statistical Methods for the Analysis of Dependently Truncated
Data
A Utility Package to Help you Deal with "Pignas"
Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
Read, Plot and Analyse Output from the DEPONS Model
Dependency Management with 'roxygen'-Style Comments
Statistical Depth Functions for Multivariate Analysis
Functions and R Code to Accompany Derivatives Markets
Seasonal Adjustment by Frequency Analysis
An Interface to the 'DescribeDisplay' 'GGobi' Plugin
Describe Data in R Using Common Descriptive Statistics
Descriptive Representation Calculator: Characterizing Observed
and Expected Representation
Publication Quality Descriptive Statistics Tables
Produce Descriptive and Comparative Tables Easily
Interactive Functions to be Used as Shortcuts in 'RStudio'
Data-Driven Locally Weighted Regression for Trend and
Seasonality in TS
Design Clinical Trials with Potential Biomarker Effect
Matched Samples that are Balanced and Representative by Design
Sample Size Calculation of Various Study Designs
Desirability Functions for Ranking, Selecting, and Integrating
Data
Function Optimization and Ranking via Desirability Functions
Desirability Functions for Multiparameter Optimization
Desparsified Lasso Inference for Time Series
Solvers for Initial Value Problems of Differential Equations
('ODE', 'DAE', 'DDE')
Plotting Field Plans for Agricultural Experiments
Create Details HTML Tag for Markdown and Package Documentation
Analyzing Wildlife Data with Detection Error
Detect Nonnormality in Meta-Analysis without Raw Data
Detect Data Containing Personally Identifiable Information
Detect Runs of Homozygosity and Runs of Heterozygosity in
Diploid Genomes
Detect and Check for Separation and Infinite Maximum Likelihood
Estimates
Life Insurance Premium and Reserves Valuation
Implementation of the DetMCD Algorithm (Robust and Deterministic
Estimation of Location and Scatter)
Density Estimation and Random Number Generation with
Distribution Element Trees
Suite of Deterministic and Robust Algorithms for Linear
Regression
A Graphical User Interface (GUI) to Visualize and Analyze
Dendrochronological Data
Clear and Condense Argument Check for User-Defined Functions
Data sets from Devore's "Prob and Stat for Eng (7th ed)"
Quantify the Relationship Between Development Rate and
Temperature in Ectotherms
Tools to Make Developing R Packages Easier
Develop Treatment Rules with Observational Data
CML and Bayesian Calibration of Multistage Tests
Linear Discriminant Function and Canonical Correlation Analysis
Degrees of Freedom Adjustment for Robust Standard Errors
Phase I/II Adaptive Dose-Finding Design for Combination Studies
Compare Two Dataframes and Return Adds, Changes, and Deletes
Dose-Finding by the Continual Reassessment Method
Extract Drugs from Differential Expression Data from LINCS
Database
Automatic Differentiation of Simple Functions
Automatically Expand Delimited Column Values into Multiple
Binary Columns with 'dfexpand'
Differential Functioning of Items and Tests
Meta-Analysis of Phase I Dose-Finding Early Clinical Trials
Phase I/II Adaptive Dose-Finding Design for MTA
Detection and Analysis of Insect Defoliation Signals in Tree
Rings
Extrapolation and Bridging of Adult Information in Early Phase
Dose-Finding Paediatrics Studies
Phase I Control Charts (with Emphasis on Distribution-Free
Methods)
Diewert and Fox's Method of Value Added Growth Decomposition
Capture-Recapture Estimation using Bayesian Model Averaging
Genetic Algorithm with Incomplete Dominance for Feature
Selection
Differential Gene Expression Analysis with R
Differential Gene Expression (DGE) Analysis Results Data Object
Differential Gene Expression (DGE) Analysis Utility Toolkit
Differential Geometric Least Angle Regression
Double Generalized Linear Models Extending Poisson Regression
Interface to 'dgpsi' for Deep and Linked Gaussian Process
Emulations
The Gumbel Distribution Functions and Gradients
Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
Regression Models
Double Hierarchical Generalized Linear Models
National Library of Norway Quantitative Text Data API Tools
Reproductive Age Female Data of Various Demographic Health
Surveys
Independence Testing via Hilbert Schmidt Independence Criterion
Diagnostic Plot and Multivariate Summary Statistics of Weighted
Samples from Importance Sampling
Routines for Fit, Inference and Diagnostics in Linear L1 Models
Meta-Analysis of Diagnostic Accuracy Studies with Several
Cutpoints
Block Diagonal Extraction or Replacement
Functions for Visualising Simple Graphs (Networks), Plotting
Flow Diagrams
Export DiagrammeR Graphviz Graphs as SVG
Parse, Format, and Validate International Phone Numbers
Required 'libphonenumber' jars for the 'dialr' Package
Tools for Creating Tuning Parameter Values
Calculate Ecological Information and Diatom Based Indices
Calculate probabilities of various dice-rolling events
Construction and Evaluation of Metamodels
Kriging Methods for Computer Experiments
Kriging-Based Optimization for Computer Experiments
Methods for Visualization of Computer Experiments Design and
Surrogate
Finding Patterns of Monotonicity and Convexity in Data
R6 Based Key-Value Dictionary Implementation
Retrieve the Dictionary Definitions of English Words
Treatment Effects with Multiple Periods and Groups
Two-Stage Difference-in-Differences Following Gardner (2021)
A Simple Implementation and Demonstration of Gradient Boosting
A Big Data Implementation of Difference-in-Differences
Estimation with Staggered Treatment
Imputation Estimator from Borusyak, Jaravel, and Spiess (2021)
Compute Recency Frequency Monetary Scores for your Customer Data
Diagnostic Index Expectation Maximisation in R
R Interface to the ZEIT ONLINE Content API
Detection of Differential Item Functioning (DIF) in Rasch Models
by Boosting Techniques
Fisher's z-Tests Concerning Difference of Correlations
Analyzing and Visualizing Differential Correlation Networks in
Biological Data
Compute the Difference Between Data Frames
Fast and Scalable Learning of Sparse Changes in High-Dimensional
Gaussian Graphical Model Structure
Given a List of Gene Symbols, Performs Differential Enrichment
Analysis
Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
Metrics of Difference for Comparing Pairs of Maps or Pairs of
Variables
Computation of TES-Based Cell Differentiation Trees
Diffusion IRT Models for Response and Response Time Data
Identifying Significant Node Scores using Network Diffusion
Algorithm
Display Differences Between Two Files using Codediff Library
Pattern Analysis Across Contingency Tables
A Penalty Approach to Differential Item Functioning in Rasch
Models
DIF and DDF Detection by Non-Linear Regression Models
Multilevel Mantel-Haenszel Statistics for Differential Item
Functioning Detection
Collection of Methods to Detect Dichotomous Differential Item
Functioning (DIF)
Differential Item Functioning via Shiny Application
Item Focussed Trees for the Identification of Items in
Differential Item Functioning
Create Compact Hash Digests of R Objects
Deconvolution of Bulk RNA-Seq Data Based on Deep Learning
Tests for Detecting Irregular Digit Patterns
Determination of Intervals Using Georeferenced Survey Simulation
Differential Identification using Mixture Ensemble
Gathering Bibliographic Records from 'Digital Science
Dimensions' Using 'DSL' API
A Framework for Dimensionality Reduction
DiNAMIC A Method To Analyze Recurrent DNA Copy Number
Aberrations in Tumors
Finding Recurrent DNA Copy Number Alterations and Differences
A Toolkit for Year-Quarter, Year-Month and Year-Isoweek Dates
Differential Pattern Analysis via Linear Modeling
Robust Tests for Differential Dispersion and Differential
Expression in RNA-Sequencing Data
Depth Importance in Precision Medicine (DIPM) Method
Conduct Direction-Projection-Permutation Tests and Display Plots
Directional Penalties for Optimal Matching in Observational
Studies
A Dipping Sauce for Data Analysis and Visualizations
Hartigan's Dip Test Statistic for Unimodality - Corrected
Debiased Inverse Propensity Score Weighting
Linear Regressions with a Latent Outcome Variable
Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior
Estimating Controlled Direct Effects for Explaining Causal
Findings
A Collection of Functions for Directional Data Analysis
Creates an Interactive Tree Structure of a Directory
Direction Analysis for Pathways and Kinases
Adjusted Means and Proportions by Direct Standardisation
Build Dirichlet Process Objects for Bayesian Modelling
Nonparametric Methods for Directional Data
Distributional Regression for Time to Event Data
Two-Steps Benchmarks for Time Series Disaggregation
Bayesian Multi-State Modelling of Chronic Disease Burden Data
Linear and Non-Linear AUC for Discounting Data
A User-Friendly Pipeline for Biomarker Discovery in Single-Cell
Transcriptomics
Cox Models for Time-to-Event Data with Nonparametric Discrete
Group-Specific Frailties
Discrete Laplace Mixture Inference using the EM Algorithm
Mixtures of Discrete Laplace Distributions using Numerical
Optimisation
Text Conversion from Nexis Uni PDFs to R Data Frames
Test for Discretized Normality in Ordinal Data
Functions for Discordant Kinship Modeling
Analyze Group Patterns using Graph Theory in Educational
Settings
Checks Title, Abstract and Keywords to Optimise Discoverability
Coordinate-Descent Algorithm for Learning Sparse Discrete
Bayesian Networks
Multiple Testing Procedures with Adaptation for Discrete Tests
Simulated Goodness-of-Fit Tests for Discrete Distributions
Improved q-Values for Discrete Uniform and Homogeneous Tests
Create and Manipulate Discrete Random Variables
Discrete Weibull Distributions (Type 1 and 3)
Data Preprocessing, Discretization for Classification
Model Wrappers for Discriminant Analysis
A Companion Package for the Book "Discrete Choice Analysis with
'R'"
Measuring Discursive Sophistication in Open-Ended Survey
Responses
Data for Comparing Species Distribution Modeling Methods
Modelling Spatial Variation in Disease Risk for Areal Data
Feature Stores for the 'diseasy' Framework
Estimate the Gene Expression Levels and Component Proportions of
the Normal, Stroma (Immune) and Tumor Components of Bulk Tumor
Samples
Calculate Dissimilarity Matrix for Dataset with Mixed Attributes
Calculates Disproportionate Impact When Binary Success Data are
Disaggregated by Subgroups
Larger-than-RAM Disk-Based Data Manipulation Framework
Disparity Filter Algorithm for Weighted Networks
Simulation of Demic Diffusion with Environmental Constraints
Create Disposable R Packages for Testing
Analysis of Disposition Effect on Financial Portfolios
Multiple Assemblage Dissimilarity for Orders q = 0-N
Spatial Downscaling using the Dissever Algorithm
Fitting Sample Selection Models for Discrete Response Variables
Distance Sampling Detection Function and Abundance Estimation
Assessing Dissimilarity Between Multivariate Time Series
DiSTATIS Three Way Metric Multidimensional Scaling
Computations over Distributed Data without Aggregation
Distributional Method for the Dichotomisation of Continuous
Outcomes
'R Markdown' Format for Scientific and Technical Writing
Method Functions for Confidence Intervals and to Distill
Information from an Object
Model Distillation and Interpretability Methods for Machine
Learning Models
Unified Interface to Distance, Dissimilarity, Similarity
Matrices
A Graphical User Interface for Plotting Common Univariate
Distributions
Object Oriented Implementation of Distributions
Documentation for 'distr' Family of R Packages
Framework for the Visualization of Distributional Regression
Models
S4 Classes for Elliptically Contoured Distributions
Four Essential Functions for Statistical Distributions Analysis:
A New Functional Approach
Powerful Goodness-of-Fit Tests Based on the Likelihood Ratio
Object Oriented Implementation of Probability Models
Estimate and Manage Empirical Distributions
Distribution Classes for Distributions from Rmetrics
Simulation Classes Based on Package 'distr'
Extensions of Package 'distr' for Teaching
Stochastics/Statistics in Secondary School
Estimation and Testing Classes Based on Package 'distr'
A Collection of Full Defined Distribution Tails
A Test Environment for Database Requests
Report on Diversity and Inclusion in a Corporate Setting
Diversity Dynamics using Fossil Sampling Data
Easily Install and Load Interactive Data Visualization Tools
Evolutionary Trait Divergence Between Sister Species and Other
Paired Lineages
Econometric Tools to Measure Portfolio Diversification
Comparative 'Phylogenetic' Analyses of Diversification
Innovative Complex Split Procedures in Random Forests Through
Candidate Split Sampling
Building Diversity Models from Multiple Species Occupancy Models
Tools for Analysis of Diversity and Similarity in Biological
Systems
Diversity Metrics Calculations for Rasterized Data
Calculate Diversity and Segregation Indices
Spatial Subsampling of Biodiversity Occurrence Data
Nearest Neighbour Contingency Table Analysis
Dixon's Ratio Test for Outlier Detection
Record Linkage and Epidemiological Case Definitions in 'R'
Lightweight Utilities for 'DIZ' R Package Development
Utilities for 'DIZ' R Package Development
Distance Measure Based Judgment and Learning
Time Series Regression Models with Distributed Lag Models
Implementation of Adaptive or Non-Adaptive Differentiable Lasso
and SCAD Penalties in Linear Models
Use Dirichlet Laplace Prior to Solve Linear Regression Problem
and Do Variable Selection
Bayesian and Likelihood Analysis of Dynamic Linear Models
Distributed-Lag Linear Structural Equation Models
Model Based Clustering of Binary Dissimilarity Measurements
Diffusion Model of Conflict (DMC) in Reaction Time Tasks
Computational Tools for Meta-Analysis of Diagnostic Accuracy
Test
Daily Based Morgan-Morgan-Finney (DMMF) Soil Erosion Model
Dynamic Modeling and Parameter Estimation in ODE Models
DiST - Diffusion Direction Smoothing and Tracking
DMR Detection by Non-Homogeneous Hidden Markov Model from
Methylation Array Data
Delete or Merge Regressors Algorithms for Linear and Logistic
Model Selection and High-Dimensional Data
Tools for Applying Distribution Mapping Based Transfer Learning
Functions and Data for the Second Edition of "Data Mining with
R"
Statistical Inference for Mixed Traces of DNA (Lite-Version)
Differential Network Analysis using Gene Pathways
Tools for Analysing Forensic Genetic DNA Data
Dungeons & Dragons Functions for Players and Dungeon Masters
Integrative Analysis of Omics Data in Terms of Network,
Evolution and Ontology
Estimating Differential Networks under Semiparametric Gaussian
Graphical Models
Differential Network Local Consistency Analysis
Simulate Dynamic Networks using Exponential Random Graph Models
(ERGM) Family
Analysis of Discretely Observed Linear
Birth-and-Death(-and-Immigration) Markov Chains
Dimension Reduction for Outlier Detection
Data from the GLM Book by Dobson and Barnett
Groupwise Statistics, LSmeans, Linear Estimates, Utilities
Measuring Concreteness in Natural Language
Distributed Representations of Sentences, Documents and Topics
Using the Docker Container to Create R Workers on Local or Cloud
Platform
Use Docker Images to Process Rmarkdown Blocks
Provides Docstring Capabilities to R Functions
Generate Tests from Examples Using 'roxygen' and 'testthat'
Run 'roxygen2' on (Chunks of) Single Code Files
Extract Data Tables and Comments from 'Microsoft' 'Word'
Documents
Acceptance Sampling Ideas Originated by H.F. Dodge
Full Factorials, Orthogonal Arrays and Base Utilities for DoE
Packages
Creation of Arrays by Mixed Integer Programming
Wrapper Package for Design of Experiments Functionality
Analysis of Design of Experiments for Biological Research
The Distributed Online Expectation Maximization Algorithms to
Solve Parameters of Poisson Mixture Models
Rare Variant Association Test Based on Position Density
Estimation
Use Foreach to Parallelize via the Future Framework
R Console Bindings for the 'Domino Command-Line Client'
Tools to Support Relative Importance Analysis
Foreach Parallel Adaptor for the Rmpi Package
Nearest Neighbour Search with Variables on a Torus
Exploration of Dental Surface Topography
Foreach Parallel Adaptor for the 'parallel' Package
'Foreach' Parallel Adapter Using the 'Redis' Database
Dynamics of Return to Equilibrium During Multiple Inputs
Generic Reproducible Parallel Backend for 'foreach' Loops
Design of Observational Studies, Companion to the Second Edition
Causal Effect Identification from Multiple Incomplete Data
Sources
Interactive Designing of Dose Finding Studies
Planning and Analyzing Dose Finding Experiments
Extract Drug Dosages from Free-Text Prescriptions
Foreach Parallel Adaptor for the 'snow' Package
Enhanced Foreign Function Interface Supporting Long Vectors
Gene-Set Analysis via Decorrelation by Orthogonal Transformation
Create and Manage Configuration Profiles
Dot-and-Whisker Plots of Regression Results
Survivor Function Estimation for Doubly Interval-Censored
Failure Time Data
Test Against Parametric Regression Function
Differential Exon Usage Test for RNA-Seq Data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Enable Operators Containing the '?' Symbol
Create Elegant Table 1 in HTML for Bio-Statistics
Kernel Hazard Estimation with Best One-Sided and Double
One-Sided Cross-Validation
Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's
Book Measuring Market Risk
Syntax Highlighting and Automatic Linking
Implement Download Buttons in 'rmarkdown'
A Tool to Downsize Large Analysis Projects for Testing
Dynamic Programming for Convex Clustering
Automated Analysis of Multiplex Digital PCR Data
RStudio Addin for Teaching and Learning Data Manipulation Using
'dplyr'
Inference of Parameters of Normal Distributions from a Mixture
of Normals
Differentially Private Statistical Analysis and Machine Learning
Determinantal Point Process Mixture Models
Density, Probability, Quantile ('DPQ') Computations
DPQ (Density, Probability, Quantile) Distribution Computations
using MPFR
Computation of Some Important Distributional Properties
Piecewise Linear Segmentation by Dynamic Programming
Graphical User Interface for Data Quality Assessment
Core Functions for Data Quality Assessment
A BSA Software for Detecting All Types of QTLs in BC, DH, RIL
and F2
Methods for Dimension Reduction for Regression
Joint Dimension Reduction and Spatial Clustering
Dose Response Data Analysis using the 4 Parameter Logistic (4pl)
Model
Deep Time Redox Analysis of the Geobiology Ontology Network
Statistical Tools to Identify Dragon Kings
Drag and Drop Elements in 'Shiny' using 'Dragula Javascript
Library'
A Pipeline Toolkit for Reproducible Computation at Scale
Wrapper Functions for Producing Graphics
Draw Samples with the Desired Properties from a Data Set
Computation of Rayleigh Densities of Arbitrary Dimension
Data Representation: Bayesian Approach That's Sparse
Improving Estimation Efficiency in CAR with Imperfect Compliance
Utilities for Data Analyses in Seed Germination/Emergence Assays
Statistical Approaches for Time-to-Event Data in Agriculture
Drug Response Prediction from Differential Multi-Omics Networks
A Nonparametric Doubly Robust Test for Continuous Treatment
Effect
Dose Response Models for Bayesian Model Averaging
Regularized Estimation of Dynamic Linear Regression in the
Presence of Autocorrelated Residuals (DREGAR)
Doubly Robust Generalized Estimating Equations
Differential Risk Hotspots in a Linear Network
Calculates the Test-Statistic for the Drift Burst Hypothesis
Concept Drift and Concept Shift Detection for Predictive Models
Estimation, Simulation and Reliability of Drifting Markov Models
Imputing Dropout Events in Single-Cell RNA-Sequencing Data
Discontinuous Regression and Image Processing
Prioritizing Cancer Driver Genes Using Genomics Data
Handling Incomplete Responses in Survey Data Analysis
Doubly-Robust Estimators for Ordinal Outcomes
Statistical Modeling and Assessment of Drought
Efficient and Doubly Robust Population Size Estimation
Quality Measurements for Dimensionality Reduction
Doubly-Robust Nonparametric Estimation and Inference
Utility-Based Optimal Phase II/III Drug Development Planning
Diagnostics for OMOP Common Data Model Drug Records
Prepare Electronic Prescription Record Data to Estimate Drug
Exposure
Predict Drug Functional Similarity to Drug Repurposing
Summarise Patient-Level Drug Utilisation in Data Mapped to the
OMOP Common Data Model
Drug Repurposing in Cancer via a Subpathway Crosstalk Network
Seasonal Adjustment of Daily Time Series
Dynamical Systems Approach to Infectious Disease Epidemiology
(Ecology/Evolution)
Dynamical Systems Approach to Immune Response Modeling
Data Splitting Algorithms for Model Developments
Discretization-Based Direct Random Sample Generation
Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)
Bayesian Subgroup Analysis in Clinical Trials
Density Estimation with Semidefinite Programming
Dynamic Systems Estimation (Time Series Package)
What Skills and Qualifications are Required for Data Science
Related Jobs?
'DataSHIELD' Implementation on Local Datasets
Density Surface Modelling of Distance Sampling Data
Data Science Box of Pandora Miscellaneous
Estimation and Simulation of Drifting Semi-Markov Models
'DataSHIELD' Client for 'MOLGENIS Armadillo'
Tests and Confidence Intervals on Directly Standardized Rates
for Several Methods
A Comprehensive R Interface for the DSSAT Cropping Systems Model
Implementation of the Direct Sampling Spatial Prior
The Discrete Stable Distribution Functions
Analyze Two Choice Reaction Time Data with the D*M Method
Conditional Sensitivity Analysis for Matched Observational
Studies
Demonstrated Insensitivity to Bias in 2x2xK Contingency Tables
Multiple Testing of Local Extrema for Detection of Change Points
A Wrapper of the JavaScript Library 'DataTables'
Cell Type Deconvolution from Gene Expressions
Creates Plots Accompanying Bayesian Diagnostic Test Accuracy
Meta-Analyses
Diagnostic Test Assessment in the Absence of Gold Standard
Multi-Way Spherical Clustering via Degree-Corrected Tensor Block
Models
Statistical Combination of Diagnostic Tests
Comparison of Binary Diagnostic Tests in a Paired Study Design
Doubly Truncated Data Analysis, Cumulative Incidence Functions
Doubly Truncated Data Analysis, Non Iterative
Discrete Transmuted Generalized Inverse Weibull Distribution
Analysis of Diffusion Weighted Imaging (DWI) Data
Suite of Functions Related to Discrete-Time Discrete-State
Markov Chains
Dose Transition Pathways for Continual Reassessment Method
Estimate and Make Inference About Optimal Treatment Regimes via
Smoothed Methods
Statistical Learning Methods for Optimizing Dynamic Treatment
Regimes
DTR Estimation and Inference via G-Estimation, Dynamic WOLS,
Q-Learning, and Dynamic Weighted Survival Modeling (DWSurv)
Dynamic Treatment Regimes for Survival Analysis
A Class for Working with Time Series Data Based on 'data.table'
and 'R6' with Largely Optional Reference Semantics
Manipulate Date, POSIXct and hms Vectors
Imputation of Time Series Based on Dynamic Time Warping
Time Series Clustering Along with Optimizations for the Dynamic
Time Warping Distance
Simplified Data Analysis with Wrapper Functions for the
'Data.Table' Package
Imputation of Multivariate Time Series Based on Dynamic Time
Warping
Automatic Differentiation with Dual Numbers
Dual Sparse Partial Least Squares Regression
Decimated and Undecimated 2D Complex Dual-Tree Wavelet Transform
Securely Wrangle Dataset According to Data Usage Agreement
Unpacking Assignment for Lists via Pattern Matching
DBI Package for the DuckDB Database Management System
High Performance Remote File System Access Using 'duckdb'
Simple Client for the DuckDuckGo Instant Answer API
Creating a Color-Blind Friendly Duke Color Package
Displaying Changes Between Two Points Using Dumbbell Plots
Automatic Creation of Dummies with Support for Predictive
Modeling
Preprocessing Tools for Clinical Trial Data
Dunn's Test of Multiple Comparisons Using Rank Sums
Effect Size Estimation and Visualisation
Analyze Dose-Volume Histograms and Check Constraints
Convenience Functions, Moving Window Statistics, and Graphics
Dynamic VAR - Based Control Charts for Batch Process Monitoring
Edit and Validate Darwin Core Taxon Data
Fast Algorithms for Large Scale Generalized Distance Weighted
Discrimination
Read Binary Radar Files from 'DWD' (German Weather Service)
Gene Expression Deconvolution Using Dampened Weighted Least
Squares
Parametric Regression for Discrete Response
Detection of Chaotic and Regular Intervals in the Data
Interface to 'Dygraphs' Interactive Time Series Charting Library
Quadratic Programming using Cyclic Projections
Dynamic Mode Decomposition for Multivariate Time Feature
Prediction
Dynamic Logging for R Inspired by Configuration Driven
Development
Dynamic Simulation and Testing for Single-Equation ARDL Models
A Unified Mechanistic Model for the Population Dynamics of
Invasive Aedes Mosquitoes
Modelling and Analysis of Dynamic Computer Experiments
Species Distribution and Abundance Modelling at High
Spatio-Temporal Resolution
Methods for Detection of Clusters in Hierarchical Clustering
Dendrograms
Bayesian Modeling and Causal Inference for Multivariate
Longitudinal Data
Bringing the Power of 'Dynare' to 'R', 'R Markdown', and
'Quarto'
An Implementation of Dynamic TOPMODEL Hydrological Model in R
Algorithms for Helping Build Dynamic TOPMODEL Implementations
from Spatial Data
Full Interactive GUI for Dynamic Biplot in R
Denoising and Clustering for Dynamical Image Sequence (2D or
3D)+t
Complexity of Short and Coarse-Grained Time Series
Dynamic Models for Confidence and Response Time Distributions
Dimensionality Reduction Methods in a Common Format
Resisting Neighbor Label Attack in a Dynamic Network
Random Forest with Multivariate Longitudinal Predictors
A Multi-Modal Simulator for Spearheading Single-Cell Omics
Analyses
Visualising Statistical Models using Dynamic Nomograms
Companion Package to "Dynamic Prediction in Clinical Survival
Analysis"
Dynamic Simulations of Autoregressive Relationships
Dynamic Stem Profile Models, AKA Tree Taper Equations
Methods for Estimating Optimal Dynamic Treatment Regimes
Common Functionality for the 'dynverse' Packages
Representing and Inferring Single-Cell Trajectories
Background and Pathway Data Used in 'DysPIA'
Misc Functions of the Department of Statistics, Probability
Theory Group (Formerly: E1071), TU Wien
Management and Processing Tools for Data Produced by the
Empatica E4
Fitting Dose-Response Models Using an Evolutionary Algorithm
Plots of the Empirical Attainment Function
Multiple Locus Association Mapping on a Genome-Wide Scale
Estimator Augmentation and Simulation-Based Inference
Dynamic Web-Based Analytics for the Energy Industry
Properties of Bayesian Early Gating Designs
Estimation of Transmissibility in the Early Stages of a Disease
Outbreak
Early Warning Signals for Critical Transitions in Time Series
Parallel Implementation of 'ETERNA 3.40' for Prediction and
Analysis of Earth Tides
Derive a Color Palette from a Particular Location on Earth
Simulating Explicit Population Genetics Models
Frequently Used Functions for Easy R Programming
Efficient Approximate Bayesian Computation Sampling Schemes
Generate Alluvial Plots with a Single Line of Code
Analysis of Variance and Other Important Complementary Analyses
Extracting and Visualizing Bayesian Graphical Models
Quickly Find, Extract, and Marginalize U.S. Census Tables
Easy Access to High-Resolution Daily Climate Data for Europe
Easily Connect to Common Types of Databases
A Convenient Way of Descriptive Statistics
Easily Performs Differential Coexpression Analysis
Interactive Scatter Plot and Volcano Plot Labels
Easy Model-Builder Functions for 'OpenMx'
Tools to Easily Read/Write NetCDF Files into/from
Multidimensional R Arrays
Sample Size Estimation for Experimental Designs
Reading, Formatting, and Organizing the Panel Study of Income
Dynamics (PSID)
Search and Retrieve Scientific Publication Records from PubMed
Helpful Functions from Oliver Wyman Actuarial Consulting
Easy Interface to the Statistical Disclosure Control Package
'sdcTable' Extended with Own Implementation of
'GaussSuppression'
Framework for Easy Statistical Modeling, Visualization, and
Reporting
Ensemble Forecast Verification for Large Data Sets
Create Constraints for Small Test Assembly Problems
Spreadsheet Interface for Relational Databases
Data Management of Large Hierarchical Data
Exponentially Weighted Moving Average with Adjustments to
Measurement Error
Educational Assessment Tools for Replication Methods
Miscellaneous Functions for the Analysis of Educational
Assessments
Entropy Reweighting to Create Balanced Samples
Empirical Bayes Thresholding and Related Methods
An Empirical Bayes Method for Chi-Squared Data
Robust Empirical Bayes Confidence Intervals
Empirical Bayes Estimation of Dynamic Bayesian Networks
Genotyping and SNP Detection using Next Generation Sequencing
Data
Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Linear Models
Access and Analyze eBird Status and Trends Data Products
Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs
Sampling or EM-Algorithms
Ensemble-Based Methods for Class Imbalance Problem
Solve the Empirical Bayes Normal Means Problem
Derive Polygenic Risk Score Based on Emprical Bayes Theory
Implementation of the Empirical Bayes Method
"Empirical Bayes Smoothing Splines with Correlated Errors"
Empirical Bayesian Tobit Matrix Estimation
Working with netCDF for Essential Biodiversity Variables
An Automated Way to Estimate EC50 for Stratified Datasets
Programmatic Access to the European Central Bank's Statistical
Data Warehouse
Translate English Words into Chinese, or Translate Chinese Words
into English
Simulation from Endpoint-Conditioned Continuous Time Markov
Chains
Functions for Spatial Point Pattern Analysis
A Goodness-of-Fit Test for Elliptical Distributions with
Diagnostic Capabilities
Downloading and Processing Microdata from ECH-INE (Uruguay)
Embedding Interactive Charts Generated with ECharts Library into
Shiny Applications
Create Interactive Graphs with 'Echarts JavaScript' Version 5
Minimal R/Shiny Interface to JavaScript Library 'ECharts'
The Echelon Analysis and the Detection of Spatial Clusters using
Echelon Scan Method
Finding Rhythms Using Extended Circadian Harmonic Oscillators
(ECHO)
Efficient Calculation of Fine Structure Isotope Patterns via
Fourier Transforms of Simplex-Based Elemental Models
Ensemble Correlation-Based Low-Rank Matrix Completion
Environment Based Clustering for Interpretable Predictive Models
in High Dimensional Data
Interface to 'ECMWF' and 'CDS' Data Web Services
Calculating Optimum Sampling Effort in Community Ecology
Integrating Ecosystem Remote Sensing Products to Derive EBV
Indicators
Tools to Create, Use, and Convert ecocomDP Data
Graphical Modelling and Ordination using Copulas
Estimating a Diet Matrix from Biotracer and Stomach Content Data
Dissimilarity-Based Functions for Ecological Analysis
A General Framework for Combining Ecosystem Models
Complexity Measures for Supervised Problems
"A Practical Guide to Ecological Modelling - Using R as a
Simulation Platform"
Coalescent-Based Simulation of Ecological Communities
Ecological Inference of RxC Tables by Latent Structure
Approaches
General Analysis of Various Economics Demand Systems
Estimation of Parameter-Dependent Network Centrality Measures
Simulate and Sample from Ecological Interaction Networks
Computing Key Indicators of the Spatial Distribution of Economic
Activities
Computational Methods for Economic Complexity
Null Model Analysis for Ecological Networks
Power Estimates and Equivalence Testing for Multivariate Data
Ecological Regression using Aggregate and Individual Data
Conducts Analyses Informing Ecosystem Restoration Decisions
Economic Statistics System of the Bank of Korea
Simulating Community Assembly and Ecological Diversification
Using Ecospace Frameworks
Code and Data Accompanying the Eco-Stats Text (Warton 2022)
Statistical Scaling Functions for Ecological Systems
Characterising and Locating Ecotones and Communities
Download and Extract Data from US EPA's ECOTOX Database
An Implementation of the EcoTroph Ecosystem Modelling Approach
Procedures for Ecological Assessment of Surface Waters
Non-Parametric Multiple Change-Point Analysis of Multivariate
Data
Flexible Co-Data Learning for High-Dimensional Prediction
Quantitative Estimates of Small Ectotherm Temperature Regulation
Effectiveness
Cointegration Based Support Vector Regression Model
Cointegration Based Timedelay Neural Network Model
Equality of 2 (or k) Continuous Univariate and Multivariate
Distributions
Enhanced Coefficient of Variation and IDR Extensions for
Reproducibility Assessment
Estimate ED50 and Its Confidence Interval
Experimental Design and Analysis for Tree Improvement
Download School District Geospatial Data, Perform Spatial
Analysis, and Create Formatted Exportable Maps
Ecological Data Collection and Processing Package
Exploratory and Descriptive Event-Based Data Analysis
Read Data from European Data Format (EDF and EDF+) Files
Goodness of Fit Based on Empirical Distribution Function
Tool for the U.S. SEC EDGAR Retrieval and Parsing of Corporate
Filings
Algorithms for Bundling Edges in Networks and Visualizing Flow
and Metro Maps
Datasets that Support the EDGE Server DIY Logic
Encapsulating Elements of Experimental Design
Modeling Urban Agriculture at City Scale
Bayesian Estimation of an Exploratory Deterministic Input, Noisy
and Gate Model
Network Reconstruction and Changepoint Detection
'DT' Extension for CRUD (Create, Read, Update, Delete)
Applications in 'shiny'
'RStudio' Addin for Editing a 'data.frame'
Model Transport of Environmental DNA in River Networks
Parsing, Applying, and Manipulating Data Cleaning Rules
An API Client for the Environmental Data Initiative Repository
Toolbox for Error-Driven Learning Simulations with Two-Layer
Networks
R Integration for Edlib, the C/C++ Library for Exact Pairwise
Sequence Alignment using Edit (Levenshtein) Distance
Dynamic Model Averaging with Grid Search
Euclidean Distance Matrix Completion Tools
Implements the EDNE-Test for Equivalence
Empirical Distribution Ordering Inference Framework (EDOIF)
Analysis of NCES Education Survey and Assessment Data
Retrieve Records from the Urban Institute's Education Data
Portal API
Compute and Decompose Inequality in Education
Handle Air Quality Data from the European Environment Agency
Data Portal
Robust Analytical Methods for Evaluating Educational
Interventions using Randomised Controlled Trials Designs
Read and Preprocess Fluorescence Excitation-Emission Matrix
(EEM) Data
EEMD Based Auto Regressive Integrated Moving Average Model
Ensemble Empirical Mode Decomposition and Its Variant Based ELM
Model
EEMD Based LSTM Model for Time Series Forecasting
Ensemble Empirical Mode Decomposition and Its Variant Based
Support Vector Regression Model
EEMD and Its Variant Based Time Delay Neural Network Model
Tools for Pre-Processing Emission-Excitation-Matrix (EEM)
Fluorescence Data
Convenience Functions for Education Data
Simulate and Evaluate Time Series for Environmental Epidemiology
Eigenvectors from Eigenvalues Sparse Principal Component
Analysis (EESPCA)
Modelling Framework for the Estimation of Salmonid Abundance
Exploratory Factor Analysis Functions for Assessing
Dimensionality
Dimensionality Assessment Using Minimum Rank Factor Analysis
Fast and Flexible Implementations of Exploratory Factor Analysis
Tools
Utility Functions for Exploratory Factor Analysis
Simulate Forest Resources with the European Forestry Dynamics
Model
Wavelet-Based Enhanced FDR for Detecting Signals from Complete
or Incomplete Spatially Aggregated Data
Efficient Least Squares for Total Causal Effects
Effect Displays for Linear, Generalized Linear, and Other Models
Visualization of Categorical Response Models
Efficient Initials for Computing the Maximal Eigenpair
The Causal Effects for a Causal Loglinear Model
Fetch Data from the Federal Reserve Economic Database
Exploratory Graph Analysis – a Framework for Estimating the
Number of Dimensions in Multivariate Data using Network
Psychometrics
Extensions for 'ggplot2': Custom Geom, Custom Themes, Plot
Alignment, Labelled Panels, Symmetric Scales, and Fixed Panel
Size
Hierarchical Modelling of Faecal Egg Counts
Extended Generalised Linear Hidden Markov Models
Simple Presentation of Estimated Exponential Semi-Variograms
Import and Analyse Ego-Centered Network Data
Exploration and Graphics for RivEr Trends
Exploration and Graphics for RivEr Trends Confidence Intervals
Ensemble Gene Regulatory Network Inference
Leveraging eQTLs to Identify Individual-Level Tissue of Interest
for a Complex Trait
Quality Control and Semantic Enrichment of Datasets
Enhanced Help to Enable "Docstring"-Comments in Users Functions
Electronic Health Record (EHR) Data Processing and Analysis Tool
Delineating Temporal Dataset Shifts in Electronic Health Records
Real Datasets for Assessing Ecological Inference Algorithms
API Wrapper for 'US Energy Information Administration' Open Data
Compares Different Ecological Inference Methods
Utilities for Expanding Functionality of 'eiCompare'
Semiparametric Factor and Regression Models for Symmetric
Relational Data
Efficiently Impute Large Scale Incomplete Matrix
Effective Information and Causal Emergence
Access to RFR (Risk-Free Rate) Curves Produced by the EIOPA
Ecological Inference and Higher-Dimension Data Management
Explanatory Item Response Modeling for Dichotomous and
Polytomous Items
An Algorithm for Reducing Errors-in-Variable Bias in Simple and
Multiple Linear Regressions
MCMC Procedures for Estimating Enzyme Kinetics Constants
Empirical Likelihood (EL) for Comparing Two Survival Functions
A 'shiny' Application for Exploring Laboratory Data
Elastic Analysis of Sparse, Dense and Irregular Curves
Enhanced Least Absolute Shrinkage and Selection Operator
Regression Model
Elastic Full Procrustes Means for Sparse and Irregular Planar
Curves
General Purpose Interface to 'Elasticsearch'
Elastic-Net for Sparse Estimation and Sparse PCA
The Empirical Likelihood-Based Consistent Information Criterion
Collection of Functions for Statistical Election Audits
Estimation of Life Expectancies Using Multi-State Models
Ranked Voting Election Audits with Dirichlet-Trees
R Functions to Download and Clean Brazilian Electoral Data
Allocating Seats Methods and Party System Scores
Load Associated Press Election Results with Elex
Kumaraswamy Complementary Weibull Geometric (Kw-CWG) Probability
Distribution
Ecological Limit Function Model Generation and Analysis Toolkit
Sampling from a Empirical Likelihood Bayesian Posterior of
Parameters Using Hamiltonian Monte Carlo
ELISA Data Analysis with Batch Correction
Equipment for Logarithmic and Linear Time Stepwise Multiple
Hypothesis Testing
Linear, Logistic and Generalized Linear Models Regressions for
the EnvWAS/EWAS Approach
Functions for Drawing Ellipses and Ellipse-Like Confidence
Regions
Inference of Elliptical Distributions and Copulas
Weierstrass and Jacobi Elliptic Functions
Implementation of the Efficient Large-Scale Online Display
Advertising Algorithm
Model-Based Gene Selection for Paired Data
Ranking Teams by Elo Rating and Comparable Methods
Preference Rating for Visual Stimuli Based on Elo Ratings
Animal Dominance Hierarchies by Elo Rating
Bayesian Dominance Hierarchy Steepness via Elo Rating and
David's Scores
Entropy-Based Local Indicator of Spatial Association
Utilise Catastrophe Model Event Loss Table Outputs
Empirical Likelihood Analysis for the Cox Model and
Yang-Prentice (2005) Model
EM Algorithm for Maximum Likelihood Estimation by Non-Precise
Information
Client for the 'emailalvalidation.io' E-Mail Validation API
Data Management Tools for Real-Time Monitoring/Ecological
Momentary Assessment Data
Robust Bayesian Variable Selection via Expectation-Maximization
Fast Embedding Guided by Self-Organizing Map
Tools to Analyze the Thermal Reaction Norm of Embryo Growth
EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
Empirical Mode Decomposition and Hilbert Spectral Analysis
Empirical Mode Decomposition Based Artificial Neural Network
Model
Support Functions and Data for "Ecological Models and Data"
Estimating and Mapping Disaggregated Indicators
Empirical Mode Decomposition Based Support Vector Regression
Model
Exponentially Modified Gaussian (EMG) Distribution
EM Algorithms for Estimating Item Response Theory Models
Tools for Create Emissions for Air Quality Models
Emissions and Statistics in R for Wastewater and Pollutants in
Combined Sewer Systems
Read and Write Ecological Metadata Language Files
Computationally Efficient Maximum Likelihood Identification of
Linear Dynamical Systems
Estimated Marginal Means, aka Least-Squares Means
A Mixture Model-Based Approach to the Clustering of Microarray
Expression Data
Mixture Models with Component-Wise Factor Analyzers
Semi-Supervised Gaussian Mixture Model with a Missing-Data
Mechanism
A Maximum Likelihood Approach to the Analysis of Modularity
Fitting Mixed Models with Known Covariance Structures
Tools for Environmental and Ecological Survey Design
Eye Movement Analysis Package for Fixation and Saccade Detection
Expected Maximum Profit Classification Performance Measure
Conducting the Peak Fitting Based on the EM Algorithm
Evaluates the Empirical Characteristic Function for Multivariate
Samples
Routines for Performing Empirical Calibration of Observational
Study Estimates
Empirical Likelihood Ratio for Censored/Truncated Data
Empirical Likelihood Ratio Test for Two Samples with Censored
Data
Empirical Likelihood Ratio Test/Confidence Interval for AUC or
pAUC
Epimed Solutions Collection for Data Editing, Analysis, and
Benchmark of Health Units
Some EM-Type Estimation Methods for the Heckman Selection Model
Tools for Fast Computing and Visualizing Euclidean Minimum
Spanning Trees
Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate Data
Main Package of the EMU Speech Database Management System
Portable Tools for 'UTF-8' Character Data
Encoding of Nucleotide Sequences into Numeric Feature Vectors
Ensemble Methods for Multiple Change-Point Detection
Represent Ordered Lists and Pairs as Strings
Easily Encrypt and Decrypt Data Frame/Tibble Columns or Files
using RSA Public/Private Keys
Recursive Two-Stage Models to Address Endogeneity
Bayesian Measurement Models for Analyzing Endorsement
Experiments
Transmissions and Receptions in an End to End Network
E-Statistics: Multivariate Inference via the Energy of Data
Distribution Free Multivariate Control Chart Based on Energy
Test
Data Published by the United States Federal Energy Regulatory
Commission
Robust and Sparse Methods for High Dimensional Linear and Binary
and Multinomial Regression
Data sets from "Introductory Statistics for Engineering
Experimentation"
Automated Tuning and Evaluations of Ecological Niche Models
Ecological Niche Modeling using Presence-Absence Data
Species Distribution Modeling and Ecological Niche Modeling
Ensemble Partial Least Squares Regression
Enrichment Analysis and Intersecting Sankey Diagram
Methods to Enrich R Objects with Extra Components
Extensible Package for Parallel, Batch Training of Base Learners
for Ensemble Modeling
Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Extensible Package for Cross-Validation-Based Integration of
Base Learners
Extensible Package for Principal-Component-Regression-Based
Heterogeneous Ensemble Meta-Learning
Extensible Classes and Methods for Penalized-Regression-Based
Integration of Base Learners
Ensemble Taxonomic Assignments of Amplicon Sequencing Data
Entropy Partitioning to Measure Diversity
Estimation of Entropy, Mutual Information and Related Quantities
Estimation of Entropy and Related Quantities
MCMC Simulation and Convergence Evaluation using Entropy and
Kullback-Leibler Divergence Estimation
Miscellaneous Functions for Environmental Analyses
Detection of Structural Changes in Climate and Environment Time
Series
Various Utilities for Microbial Genomics and Metagenomics
Environmental Interpolation using Spatial Kernel Density
Estimation
GC/LC-MS Data Analysis for Environmental Science
Isotope Pattern, Profile and Centroid Calculation for Mass
Spectrometry
Environmental Probabilistic Risk Assessment Tools
Keep Track of User-Defined Environment Names
Whole Genome Phylogenies Using Sequence Environments
Methods for Identification of Outliers in Environmental Data
Configurable Reporting on your External Compute Environment
Package for Environmental Statistics, Including US EPA Guidance
Make Working with Environment Variables Easier and More
Consistent
Wildlife Mortality Estimator for Low Fatality Rates and
Imperfect Detection
Official 'eodhd' API R Financial Library
Export or Graph and Tables to 'Microsoft' Office and Import
Figures and Tables
Data Management Package (Exposure and Occurrence Data in R)
Statistical Functions for the Censored and Uncensored
Epanechnikov Distribution
Call 'EPANET' Functions to Simulate Pipe Networks
Ensemble Penalized Cox Regression for Survival Prediction
EPE's 4MD Model to Forecast the Adoption of Distributed
Generation
Argentina's Permanent Household Survey Data and Manipulation
Utilities
Elementary Epidemiological Functions for Epidemiology and
Biostatistics
Ewnet: An Ensemble Wavelet Neural Network for Forecasting and
Epicasting
A Tidy Solution for Epidemiological Data
Handling, Visualisation and Analysis of Epidemiological Contacts
Epidemiological Tool for Contact Tracing
Tools to Retrieve Economic Policy Institute Data Library
Extracts
Estimate Time Varying Reproduction Numbers from Epidemic Curves
Analysis and Simulation of Plant Disease Progress Curves
Predicting Disease Spread from Flow Data
Code Sharing at the Department of Epidemiological Research at
Statens Serum Institut
Interface Package for the 'EpiGraphDB' Platform
Spatial and Network Based Individual Level Models for Epidemics
Continuous Time Distance-Based and Network-Based Individual
Level Models for Epidemics
Miscellaneous Helper Tools for Epidemiologists
The 'epilogi' Variable Selection Algorithm for Continuous Data
A Fast and Flexible Bayesian Tool for Estimating Epidemiological
Parameters
Functions and Data for "Epidemics: Models and Data in R"
Functions and Data for "Epidemics: Models and Data in R (2nd
Edition)"
Mathematical Modeling of Infectious Disease Dynamics
Epistatic Network Modelling with Forward-Time Simulation
Estimate Real-Time Case Counts and Time-Varying Epidemiological
Parameters
Analysis of Omics Data in Observational Studies
Tools for the Analysis of Epidemiological Data
Ensemble Based Machine Learning Approach for Predicting
Methylation States
Basic Sensitivity Analysis of Epidemiological Results
Constructing an Epistemic Model for the Games with Two Players
Flexible Contingency Tables for Epidemiology
Test for Gene x Gene Interactions in Bi-Parental Populations
Small Helpers and Tricks for Epidemics Analysis
Early Detection of Public Health Threats from 'Twitter' Data
Partial Linear Single Index Models for Environmental Mixture
Analysis
A Toolkit for Using Whole Building Simulation Program
'EnergyPlus'
Robust Estimation of Probit Models with Endogeneity
Endogenous Perturbation Analysis of Cancer
Clinical Coding of Patients with Kidney Disease
String Interpolation for Documents, Reports and Apps
Exact Post Selection Inference with Applications to the Lasso
Ensemble Patch Transform, Visualization and Decomposition
Create Future 'EnergyPlus' Weather Files using 'CMIP6' Data
Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' Index
Scores
Testing the Equality of Two Covariance Matrices
Testing the Equality of a High Dimensional Set of Densities
Examine Measurement Invariance via Equivalence Testing and
Projection Method
Calculate Urinary Saturation with the EQUIL2 Algorithm
Modeling, Confidence Intervals and Equivalence of Survival
Curves
Provides Tests and Graphics for Assessing Tests of Equivalence
Equivalence Test for the Means of Two Normal Distributions
Testing for Equivalence and Noninferiority
Uniformly Most Powerful Invariant Tests of Equivalence
Automated Spectral Deconvolution, Alignment, and Metabolite
Identification in GC/MS-Based Untargeted Metabolomics
Fitting Tails by the Empirical Residual Coefficient of Variation
Fits Expectile Regression for Panel Fixed Effect Model
Fit, Simulate and Diagnose Exponential-Family Models for
Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Networks with Count Edges
Fit, Simulate and Diagnose Exponential-Family Random Graph
Models to Egocentrically Sampled Network Data
Fit, Simulate and Diagnose Exponential-Family Models for
Multiple or Multilayer Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Rank-Order Relational Data
Process Analysis for Exponential Random Graph Models
Tools for Modeling ERGM Generating Processes
Local Health Department network data set
Exponential Random Graph Models for Small Networks
Check Arguments and Generate Readable Error Messages
Significance Analysis of Event-Related Potentials Data
Event-Related Potential (ERP) Data Exploration Made Easy
Exploratory Reduced Reparameterized Unified Model Estimation
Empirically Informed Random Trajectory Generation in 3-D
Estimate Number of Latent Factors and Factor Matrix for Factor
Analysis
Extended Empirical Saddlepoint Density Approximations
Indicators of Electoral Systems and Party Systems
Effect Size Computation for Meta Analysis
Modular Approach to Dose Finding Clinical Trials
Experiment-Selector CV-TMLE for Integration of Observational and
RCT Data
Adaptive Enrichment Designs with Sample Size Re-Estimation
Ensemble Tool for Predictions from Species Distribution Models
Adaptive Clinical Trial Designs with Endpoint Selection and
Sample Size Reassessment
Exploratory Structural Equation Modeling ESEM
Data Preparation During and After the Use of the Experience
Sampling Methodology (ESM)
Preprocessing Experience Sampling Method (ESM) Data
Easy Study of Patient DICOM Data in Oncology
Explore and Visualize Your Data Interactively
Joint Quantile and Expected Shortfall Regression
Efficient Stepwise Selection in Decomposable Models
Essential Functions not Included in Base R
Download Data from the European Social Survey on the Fly
Fundamentos De Estadistica Descriptiva e Inferencial
Calibrating Parameters for the Samejima's Continuous IRT Model
Efficient Sequential Testing with Evidence Ratios
Tools for Assessing Estimability of Linear Predictions
Perform the Joint Graphical Lasso and Selects Tuning Parameters
Maximum Likelihood Estimation for Probability Functions from
Data Sets
Fast Estimators for Design-Based Inference
Diagnostic Tools and Unit Tests for Statistical Estimators
Estimation of Four-Fold Table Cell Frequencies (Raw Data) from
Effect Size Measures
Estimating the Sample Mean and Standard Deviation from Commonly
Reported Quantiles in Meta-Analysis
Visualization and Estimation of Effect Sizes
Forest Yield Tables for Northwest Germany and their Application
Modeling Earthquake Data Using 'ETAS' Model
Bootstrap Confidence Interval Estimation for 'ETAS' Model
Parameters
Mixed FLP and ML Estimation of ETAS Space-Time Point Processes
for Earthquake Description
Ethnicity Annotation from Whole-Exome and Targeted Sequencing
Data
Extract-Transform-Load Framework for Medium Data
Utility Functions to Execute Standard Extract/Transform/Load
Operations (using Package 'ff') on Large Data
Classification and Regression with Structured and Mixed-Type
Data
Computes Moments of Univariate Truncated t Distribution
Creates Safety Results Summary in XML to Upload to EudraCT, or
ClinicalTrials.gov
Analyse 'EuFMDiS' Output Files via a Shiny App
eulerian: A package to find eulerian paths from graphs
Area-Proportional Euler and Venn Diagrams with Ellipses
Efficient Univariate Maximum Mean Discrepancy
Makes it Easier to Work with Daily 'netCDF' from EURO-CORDEX
RCMs
Fast and Easy Eurostat Data Import and Search
Interact with Metadata Records and Media on the Europeana
Repository
R Interface to the Europe PubMed Central RESTful Web Service
Historical Populations of European Cities, 1500-1800
Bayesian Single Arm Sample Size Calculation Software
Extreme Value Analysis with Goodness-of-Fit Testing
Evaluation Clustering Methods for Disease Subtypes Diagnosis
Parsing and Evaluation Tools that Provide More Details than the
Default
Collection of Model Evaluation Measure Functions
Sensitivity Analyses for Unmeasured Confounding and Other Biases
in Observational Studies and Meta-Analyses
Modelling Actual, Potential and Reference Crop
Evapotranspiration
Computes and Plot the Optimal Charging Strategy for Electric
Vehicles
Functions for Extreme Value Distributions
Bayesian Analysis in Extreme Value Theory
Graphical User Interface for the 'EventDetectR' Package
Regression Models for Event History Outcomes
Streaming Events and their Early Classification
Estimation and Visualization of Linear Panel Event Studies
Event Prediction for Time-to-Event Endpoints
Event-Specific Win Ratios for Terminal and Non-Terminal Events
Generalised Additive Extreme Value Models
Epidemic Volatility Index as an Early-Warning Tool
Evidential Analysis of Genetic Association Data
Analysis of Scientific Evidence Using Bayesian and Likelihood
Methods
Reporting Tools for Sensitivity Analysis of Evidence Factors in
Observational Studies
Synthesizing Causal Evidence in a Distributed Research Network
Evidence Factors in Observational Studies
A Seamless Integration of 'EViews' and R
Test Dice Sets for Intransitive Properties
Inference with Extreme Value Inflated Count Data
Extreme Value Mixture Modelling, Threshold Estimation and
Boundary Corrected Kernel Density Estimation
Comparative and Population Genetic Analyses
Important Concepts of Evolutionary Game Theory
Evolutionary Parameter Estimation for 'Repast Simphony' Models
Pre- And Postprocessing of Morphological Data from Relaxed Clock
Bayesian Phylogenetics
Mean of Order P, Peaks over Random Threshold Hill and High
Quantile Estimates
Extreme Value Theory for Open Set Classification - GPD and GEV
Classifiers
Evolutionary Learning of Globally Optimal Trees
Goodness-of-Fit Tests for the Exponential and Two-Parameter
Weibull Distributions
Forecasting Tipping Points at the Community Level
Exact Samples Sizes and Inference for Clinical Trials with
Binary Endpoint
Exact Tests and Confidence Intervals for 2x2 Tables
Exact P-Values and Matching Confidence Intervals for Simple
Discrete Parametric Cases
Inductive Confidence Intervals for the Difference Between Two
Proportions
Fast Extraction from Raster Datasets using Polygons
An Exact Method for Life Table Response Experiment (LTRE)
Analysis
Exact Mediation Analysis for Binary Outcomes
Exact Distributions for Rank and Permutation Tests
Help for Writing Unit Tests Based on Function Examples
Question Generation in the 'MyLearn' XML Format
Interface for 'exams' Exercises in 'learnr' Tutorials
Automatic Generation of Exams in R for 'Sakai'
Confidence/Credible Regions for Exceedance Sets and Contour
Lines
Confidence Intervals for Exceedance Probability
Convenient Data Exchange with Microsoft Excel
Streamline Data Import, Cleaning and Recoding from 'Excel'
A Wrapper of the 'JavaScript' Library 'jExcel'
Checks for Exclusion Criteria in Online Data
Creating Tables of Excluded Observations
Excursion Sets and Contour Credibility Regions for Random Fields
Quantile Maximization Likelihood Estimation and Bayesian
Ex-Gaussian Estimation
A Fast and Scalable Exhaustive Feature Selection Framework
A k Nearest Neibour Ensemble Based on Extended Neighbourhood
Rule
Calls Copy Number Variants from Targeted Sequence Data
Pacote Para Analise De Experimentos Com Graficos De Superficie
Resposta
Fitting of Exponential Autoregressive Moving Average (EXPARMA)
Model
Modular Differential Evolution for Experimenting with Operators
R Package for Designing and Analyzing Randomized Experiments
Datasets Used in Social Science Experiments: A Hands-on
Introduction
Design of Discrete Choice and Conjoint Analysis
Incorporate Expert Opinion with Parametric Survival Models
Non-Additive Expression Analysis of Hybrid Offspring
Tool For Analysis of Images in Experiments
Exponential Integral and Incomplete Gamma Function
Explanation of Predictions for Classification and Regression
Models
Calculate Exploded Coordinates Based on Original Node
Coordinates and Node Clustering Membership
Interactive Interfaces for Results Exploration
A Tool for Large-Scale Exploratory Analyses
Streamlined Export of Graphs and Data Tables
Exploratory Analysis with the Singular Value Decomposition
Models Topographic Exposure to Hurricane Winds
Data Mining of Plant Diversity and Distribution for R
Computing Expectations and Marginal Likelihoods for Permutations
An Exponential Stochastic Block Model for Interaction Lengths
Data Sets from "Forecasting with Exponential Smoothing"
Tables, Labels and Some Useful Functions from Spreadsheets and
'SPSS' Statistics
Experience Study Tools for Analytics and Communications
Fast, Reliable and Elegant Reproducible Research
Extending the Range of Functions for Probability Distributions
Additional Families for Generalized Linear Models
Constructs the Exterior Match from Two Matched Control Groups
Maximum Penalized Likelihood Estimation with Extended Lasso
Penalty
An Extended Mallows Model and Its Hierarchical Version for
Ranked Data Aggregation
Additional Univariate and Multivariate Distributions
Estimation and Additional Tools for Alternative Shared Frailty
Models
Extra Binary Relational and Logical Operators
Astrobiology Equations Estimating Extraterrestrial Life
Summary Statistics for Geospatial Features
Extended RC Models for Contingency Tables
Estimation of Extreme Conditional Quantiles and Probabilities
Forecast Verification for Extreme Events
Extreme Value Statistics and Quantile Estimation
Estimation of Extreme Value Dependence for Time Series Data
Spatial Dependencies and Indices for Extremes
Econometric Analysis of Explosive Time Series
Value Added in Exports and Other Input-Output Table Analysis
Tools
Open Source Ophthalmic Data Sets Curated for R
Import ASC Files from EyeLink Eye Trackers
Import Gaze Data for EyeLink Eye Tracker
Prepare/Analyse Eye Tracking Data for Reading
Organising and Analysing Eye-Tracking Data
Easy Analysis and Visualization of Factorial Experiments
Easy Interface to Effective Concentration Calculations
Task Oriented Interface for Exploratory Data Analysis
Easy-to-Interpret Gaussian Process Models for Computer
Experiments
Avoid the Typical Working Directory Pain When Using 'knitr'
Easy Use of R via Shiny App for Basic Analyses of Experimental
Data
Tunes AdaBoost, Elastic Net, Support Vector Machines, and
Gradient Boosting Machines
Change Point Detection in High-Dimensional Time Series Networks
'ATAforecasting' Modelling Interface for 'fable' Framework
Core Tools for Packages in the 'fable' Framework
Overfitting Bayesian Mixtures of Factor Analyzers with
Parsimonious Covariance and Unknown Number of Components
Wrapper Functions Collection Used in Data Pipelines
Create Easily Canvas in 'shiny' and 'RMarkdown' Documents
Fast Covariance Estimation for Sparse Functional Data
Access to Facebook Ads via the 'Windsor.ai' API
Get Facebook Leads Ads Data via the 'Windsor.ai' API
Get Data from 'Facebook Organic' via the 'Windsor.ai' API
Time Series Factor Models for Asset Returns
Instrumental Variables Estimation for 2^k Factorial Experiments
Extract and Visualize the Results of Multivariate Data Analyses
Automatic Description of Factorial Analysis
Multivariate Exploratory Data Analysis and Data Mining
Factorial Optimal Designs for Two-Colour cDNA Microarray
Experiments
Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
Use Raw Vectors to Minimize Memory Consumption of Factors
Set of Assumptions for Factor and Principal Component Analysis
Factor, Bi-Factor, Second-Order and Factor Tree Copula Models
Design and Analysis of a 2x2 Factorial Trial
Factor Model Estimation Using Proxy Variables
Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models
Perform Factorial Analysis from 'FactoMineR' with a Shiny
Application
Multi-Environment Genomic Prediction with Penalized Factorial
Regression
Variable Selection for Supervised Classification in High
Dimension
Distributions that are Sometimes Used in Hydrology
Functional Data Clustering Using Adaptive Density Peak Detection
FDA Adverse Event Reporting System Quarterly Data Extracting
Tool
Data from the Book "Multivariate Statistical Modelling Based on
Generalized Linear Models", First Edition, by Ludwig Fahrmeir
and Gerhard Tutz
Fair Data Adaptation with Quantile Preservation
Clustering for Data with Sensitive Attribute
Flexible Tool for Bias Detection, Visualization, and Mitigation
Flexible Data Simulation Using the Multivariate Normal
Distribution
Generate Fake Datasets for Prototyping and Teaching
Fuzzy Adaptive Resonance Theory K-Means Clustering Technique
Finding Allele-Specific Copy Number in Next-Generation
Sequencing Data
Finding Allele-Specific Copy Number in Whole-Exome Sequencing
Data
Family Age-at-Onset Data Simulation and Penetrance Estimation
Statistical Tests of Familial Hypotheses
End-to-End Automated Machine Learning and Model Evaluation
Probabilities for Pedigrees Given DNA Data
Algorithm for Ranking Predictors Using Graphical Domain
Knowledge
Maximum Likelihood and Bayesian Estimation of Univariate
Probability Distributions
A Flexible Algorithm for Model Selection
Family Sequence Kernel Association Test for Rare and Common
Variants
Factor Analysis for Multiple Testing (FAMT) : Simultaneous Tests
under Dependence in High-Dimensional Data
Penalized Likelihood Factor Analysis via Nonconvex Penalty
Feature Allocation Neighborhood Greedy Search Algorithm
Building Kriging Models from FANOVA Graphs
Visualisation of Sequential Probability Distributions Using Fan
Charts
ANSI Control Sequence Aware String Functions
Translate Words or Sentences via Online Translators
Evapotranspiration Based on FAO Penman-Monteith Equation
Influential Case Detection Methods for Factor Analysis and
Structural Equation Models
Modelization for Functional AutoRegressive Processes
Functions and Datasets for Books by Julian Faraway
Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte
from Expression Profiles using Least Trimmed Squares
Data and Code for Financial Accounting Research
Interactive Interface to Data Envelopment Analysis Modeling
High Performance Colour Space Manipulation
Factor-Augmented Sparse Regression Tuning-Free Testing
Fasano-Franceschini Test: A Multidimensional Kolmogorov-Smirnov
Two-Sample Test
Joint Segmentation of Correlated Time Series
A Fast and Scalable Joint Estimator for Learning Multiple
Related Sparse Gaussian Graphical Models
Rmetrics - Analysing and Modelling Financial Assets
Analyze, Summarize, and Visualize Daily Streamflow Data
Analyze and Visualize FAST-Generated Data
Self-Tuning Data Adaptive Matrix Imputation
Fast Regression for the Accelerated Failure Time (AFT) Model
Fast Estimation of a Covariance Matrix by Banding the Cholesky
Factor
Fast Estimation of Time-Varying Infectious Disease Transmission
Rates
Fast Hierarchical Clustering Routines for R and 'Python'
Significant Interval Discovery with Categorical Covariates
Lasso and Elastic-Net Penalized Cox's Regression in High
Dimensions Models using the Cocktail Algorithm
Fast Change Point Detection via Sequential Gradient Descent
Fast EM and Best-Subset Selection for CUB Models for Rating Data
Fast, Low Memory-Footprint Digests of R Objects
Fast Creation of Dummy (Binary) Columns and Rows from
Categorical Variables
Fast Functional Mixed Models using Fast Univariate Inference
Fast and Exact Computation of Gaussian Stochastic Process
Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature
Fast and Stable Fitting of Generalized Linear Models using
'RcppEigen'
Efficiently Using Gaussian Processes with Rcpp and RcppEigen
Fast Drawing and Shading of Graphs of Statistical Distributions
FastICA Algorithms to Perform ICA and Projection Pursuit
Learn from Training Data then Quickly Fill in Missing Data
Semi-Parametric Joint Modeling of Longitudinal and Survival Data
A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA,
FastCLARANS
A Fast Laplace Method for Spatial Generalized Linear Mixed Model
Fast Probabilistic Record Linkage with Missing Data
A Fast Way to Calculate the p-Value of One or Multiple
Log-Rank-Tests
Fast Computation of Multivariate M-Estimators
High-Performance Matrix Market File Operations
Fast Computation of some Matrices Useful in Statistics
Fast Mutual Information Based Independence Test
Extremely Fast Implementation of a Naive Bayes Classifier
FastPCS Robust Fit of Multivariate Location and Scatter
Finds the Critical Sequential Point of Stability for a Pearson
Correlation
Faster Generation of Quantile Quantile Plots with Large Samples
Foundations and Applications of Statistics Using R (2nd Edition)
Fits the FastRCS Robust Multivariable Linear Regression Model
Time-Saving Package for Creating Reports
Sample Generalized Random Dot Product Graphs in Linear Time
Functions to Access Premier Hockey Federation and National
Hockey League Play by Play Data
Fast Interactive Framework for Web Scripting Using R
A Fast Algorithm for Sparse Precision Matrix Estimation
Efficient Learning of Word Representations and Sentence
Classification
Fast Utility Function for Time Parsing and Conversion
Fast Algorithms for Fitting Topic Models and Non-Negative Matrix
Factorizations to Count Data
A Suite of High-Performance Packages for Statistics and Data
Manipulation
Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À
Trous Discrete Wavelet Transform
Two-Locus Family-Based Association Test with Polytomous Outcome
Quantification of Fate Bias in Multipotent Progenitors
Kiener Distributions and Fat Tails in Finance
Fault Trees for Risk and Reliability Analysis
Find the URL to the 'Favicon' for a Website
Functions and Datasets for "Forest Analytics with R"
An Extensible Approach to Flux Balance Analysis
Gene by Environment Interaction and Conditional Gene Tests for
Nuclear Families
Phase I Optimal Dose Assignment using the FBCRM and MFBCRM
Methods
Algorithm for Searching the Space of Gaussian Directed Acyclic
Graph Models Through Moment Fractional Bayes Factors
Flexible Bayesian Model Selection and Model Averaging
FISH Based Normalization and Copy Number Inference of SNP
Microarray Data
Analyzing and Managing Facebook Ads from R
Fast Algorithms to Bootstrap Receiver Operating Characteristics
Curves
The Full Bayesian Evidence Test, Full Bayesian Significance Test
and the e-Value
Standard Evaluation-Based Multivariate Function Composition
Floating Catchment Area (FCA) Methods to Calculate Spatial
Accessibility
Inference of Fuzzy Cognitive Maps (FCMs)
Flexible Cutoffs for Model Fit Evaluation in Covariance-Based
Structural Models
Rmetrics - Bivariate Dependence Structures with Copulae
Functional Concurrent Regression for Sparse Data
A Method to Search for Differentially Expressed Genes and to
Detect Recurrent Chromosomal Copy Number Aberrations
A Collection of Fluorescence Fluctuation Spectroscopy Analysis
Methods
Estimation and Inference for the Fractionally Cointegrated VAR
Measuring Functional Diversity (FD) from Multiple Traits, and
Other Tools for Functional Ecology
Functional Data Analysis and Utilities for Statistical Computing
Autocorrelation Function for Functional Time Series
Joint Clustering and Alignment of Functional Data
Concurrent Regression and History Index Models for Functional
Data
Functional Data Analysis for Density Functions by Transformation
to a Hilbert Space
Functional Data Analysis in a Mixed Model Framework
Model-Based Clustering for Functional Data with Covariates
Analysis of Variance for Univariate and Multivariate Functional
Data
Outlier Detection Tools for Functional Data Analysis
Functional Data Analysis and Empirical Dynamics
Physics-Informed Spatial and Functional Data Analysis
Partially Observed Integrated Functional Depth
Interval Testing Procedure for Functional Data
Fast Implementation of the Diffusion Decision Model
Data Mining and R Programming for Beginners
Dynamic Model Averaging and Dynamic Model Selection for
Continuous Outcomes
Permutation-Based FDR Point and Confidence Interval Estimation
False Discovery Rate Procedures Under Discrete and Heterogeneous
Null Distributions
Estimate, Plot, and Summarize False Discovery Rates
Computing Power and Sample Size for the False Discovery Rate in
Multiple Applications
Compute Sample Size that Meets Requirements for Average Power
and FDR
Estimation of (Local) False Discovery Rates and Higher Criticism
Frequency Distribution Tables, Histograms and Polygons
False Discovery Exceedance Controlling Multiple Testing
Procedures
Feature Extraction and Statistics for Time Series
Local Inferential Feature Significance for Multivariate Kernel
Density Estimation
Measurement Level Independent Feature Correlation Matrix
Turn Features On and Off using Feature Flags
Creates a Model Matrix via Feature Hashing with a Formula
Interface
Feature Importance for Partitional Clustering
Feature Extraction for Discretely-Sampled Functional Data
Feature Selection Engine to Remove Features with Minimal
Predictive Power
Some Helper Functions that Help Create Features from Data
Data Package for the 2016 United States Federal Elections
Fechnerian Scaling of Discrete Object Sets
Functions to Automate Downloading Geospatial Data Available from
Several Federated Data Sources
Client Package for the U.S. Federal Register API
Fast, Flexible, and User-Friendly Record Linkage Methods
Text Analysis of the US Code of Federal Regulations
Unofficial API for Fedstat (Rosstat EMISS System) for Automatic
and Efficient Data Queries
An Easier Access to Financial Accounts of the United States(Z.1)
Estimating Fixed Effects Individual Slope Models
Functional Help for Functions, Objects, and Packages
Fixed Effects Nonlinear Maximum Likelihood Models
Fixed Effects Logistic Model with High-Dimensional Parameters
Estimation and Fit Assessment of Frailty Exponential Random
Graph Models
Facilitate Exploration of touRR optimisatioN
Fertilizer Response Curve Analysis by Bootstrapping Residuals
Process Data from the Social Networks and Fertility Survey
Get Data from Google Analytics via the 'Windsor.ai' API
Get Data from Salesforce via the 'Windsor.ai' API
Rmetrics - Modelling Extreme Events in Finance
Memory-Efficient Storage of Large Data on Disk and Fast Access
Functions
Download Data from Kenneth French's Website
Fully Flexible Probabilities for Stress Testing and Portfolio
Construction
API Client for Fantasy Football League Platforms
Forgetting Factor Methods for Change Detection in Streaming Data
Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees
Fast FFT and DCT Based on the FFTW Library
Wrapper for 'FFTW3' Includes: One-Dimensional, Two-Dimensional,
Three-Dimensional, and Multivariate Transforms
Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Utilities for Fundamental Geo-Spatial Data
Access Small ForestGEO Datasets For Examples
Using 'FastGit' to Accelerate the Access to 'GitHub'
Truncated Functional Generalized Linear Models
A Feasible Generalized Least Squares Estimator for Regression
Analysis of Outcomes with Network Dependence
Partial Separability and Functional Gaussian Graphical Models
Fractional Hot Deck and Fully Efficient Fractional Imputation
Fitting Hidden Markov Models to Financial Data
Tests for Right and Interval-Censored Survival Data Based on the
Fleming-Harrington Class
Provide functions for forest inventory calculations
Focused Information Criteria for Model Comparison
Classical, Reloaded and Adaptive FastICA Algorithms
Bayesian Multinomial Logistic Normal Regression
A Shiny App for Design of Experiments in Life Sciences
Simulation of Plot Errors and Phenotypes in Plant Breeding Field
Trials
A Lightweight and Flexible Web Framework
Forest Inventory Estimation and Analysis
Utility Functions for Forest Inventory Estimation and Analysis
Check Validity of FIGI, CUSIP, ISIN, SEDOL
Easily Arrange External Figures with Patchwork Alongside
'ggplot2' Figures
Support for a Variety of Figure Production Tasks
Transform Files to 'microtable' Object with 'microeco' Package
File-Backed Array for Out-of-Memory Computation
Wrapper for the Filebin File Sharing API
File-Backed Matrix Class with Convenient Read and Write Access
Effective File Navigation from the R Console
Matrix Completion, Imputation, and Inpainting Methods
Filter Raw JSATS Acoustic Telemetry Files from Lotek, ATS,
Teknologic
Rmetrics - Importing Economic and Financial Data
Quickly Create Elegant Regression Results Tables and Plots when
Modelling
Financial Analysis and Regression Diagnostic Analysis
Financial Instrument Model Infrastructure and Meta-Data
Classifies implicit trading activity from market quotes and
computes the probability of informed trading
Interface for the 'Finnish Biodiversity Information Facility'
API
Learning Bipartite Graphs: Heavy Tails and Multiple Components
Time Value of Money, Time Series Analysis and Computational
Finance
Covariance Matrix Estimation and Regularization for Finance
Find Pattern in Files of All Branches of a 'git' Repository
Functions to Find an Acceptable Python Binary
Find Code Snippets, R Scripts, R Markdown, PDF and Text Files
with Pattern Matching
Calculate Social Vulnerability Index for Communities
A View Generator for Multidimensional Data
Fine-Scale Population Analysis (Rewrite for
Gene-Trait-Environment Interaction Analysis)
Functions to Operate on Binary Fingerprint Data
Computation of the Probability of Ruin Within a Finite Time
Horizon
Test for Finiteness of Moments in a Distribution
Quickly Build and Manipulate Financial Networks
Microsoft Finance Time Series Forecasting Framework
Financial Data from U.S. Securities and Exchange Commission
Companion to Tsay (2005) Analysis of Financial Time Series
Lightweight Federal Information Processing System (FIPS) Code
Information Retrieval
Induced Priors in Bayesian Mixture Models
Integrates 'Google Firebase' Authentication Storage, and
'Analytics' with 'Shiny'
Prediction of Wildland Fire Behavior and Hazard
Factor Importance Ranking and Selection using Total Indices
A Small Collection of Fish Population Datasets
The FisherEM Algorithm to Simultaneously Cluster and Visualize
High-Dimensional Data
Transform Base Maps Using Log-Azimuthal Projection
Dataset of Measurements of Fish Species at Kirkkojarvi Lake,
Finland
Fits Poisson-Sum-of-Gammas GLMs, Tweedie GLMs, and Delta
Log-Normal Models
Phylogenies for a List of Finned-Ray Fishes
Analytical Tool for Aquatic Respirometry
Fuzzy Inference System Design and Optimization
Transcriptomic Dynamics Models in Field Conditions
Help to Fit of a Parametric Distribution to Non-Censored or
Censored Data
Mean and Covariance Matrix Estimation under Heavy Tails
Fit Vector Fields and Potential Landscapes from Intensive
Longitudinal Data
Finite Mixture Model Fitting of Lifespan Datasets
Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD
Tools for Ordinary Differential Equations Model Fitting
Genotype Calling for Bi-Allelic Marker Assays
Classes for Fitness Landscapes and Seascapes
FITS (Flexible Image Transport System) Utilities
Fit Hundreds of Theoretical Distributions to Empirical Data
Fit Univariate Mixed and Usual Distributions
Data and Code Behind the Stories and Interactives at
'FiveThirtyEight'
Fixed Income Models, Calculations, Data Structures and
Instruments
Algorithms for Finding Fixed Point Vectors of Functions
Determine and Test the Fixed-Point Property in Binary Mixture
Data
The Distribution of Distances Between Discrete Events in Fixed
Time
An R Client for the "Fixer.io" Currency API
Fixing Data Made Easy for Statistical Analysis
Fixed Sequence Multiple Testing Procedures
Available Light Within the Water Column and Seafloor of Arctic
Fjords
Fast Kalman Filtering Through Sequential Processing
Feature-Based Landscape Analysis of Continuous and Constrained
Optimization Problems
Fits Piecewise Constant Models with Data-Adaptive Knots
Interpretable Matching for Causal Inference
Functional Latent Data Models for Clustering Heterogeneous
Curves ('FLaMingos')
Implementation of optimal hierarchical clustering
Shed Light on Black Box Machine Learning Models
Create Flashcards of Terms and Definitions
Transforms Contingency Tables to Data Frames, and Analyses Them
Obtaining a Flat Table from Pivot Tables
Tools for Working with XML Files as R Dataframes
Smoother Change Tracking and Versioning for R Packages
Flexible, Ensemble-Based Variable Selection with Potentially
Missing Data
R Markdown Format for Flexible Dashboards
Tools to Work with the Flexible Dirichlet Distribution
Generalized Additive Models with Flexible Response Functions
Flexible Latent Trait Metrics using the Filtered Monotonic
Polynomial Item Response Model
Finite Mixture Modeling of Generalized Nonlinear Models
A General Framework for Flexible Multi-State Survival Modelling
Regression Models for Bounded Continuous and Discrete Responses
Breaks Single Page Applications from 'flexdashboard' in Multiple
Files
Flexible Parametric Survival and Multi-State Models
Estimate Joint Models with Subject-Specific Variance
Download Flight and Airport Data from Brazil
Simple and Flexible Tests of Sample Exchangeability
Parses Code, Creates Partial Code Builds, Delivers Code Movie
Implement a Countdown in 'RMarkdown' Documents and 'shiny'
Applications
A Wrapper of JavaScript Library 'flipdown.js'
Flexible Inference via Permutations in R
Robust Score Testing in GLMs, by Sign-Flip Contributions
Convert Files to and from Binary Objects (BLOBs)
Process Synchronization Using File Locks
Statistical Methods for the (Regional) Analysis of Flood
Frequency
Flood Probability Plotting and Graphical Frequency Analysis
Tools for Interacting with the Brazilian Flora 2020
Fit Log-Ratio Lasso Regression for Compositional Data
View and Browse Code Using Flow Diagrams
A Backend for a 'nextflow' Pipeline that Performs
Machine-Learning-Based Modeling of Biomedical Data
Streamlining Design and Deployment of Complex Workflows
The Environmental Costs of Flow Regulation
Daily Streamflow Trend and Change Point Screening
Tracing Information Flow for Inter-Software Comparisons in Mass
Spectrometry-Based Bottom-Up Proteomics
Path Algorithm for the General Fused Lasso Signal Approximator
Mining Rigs for Problems in the Subset Sum Family
Influenza-Attributable Mortality with Distributed-Lag Models
Estimate Insect Survival from Fluorescence Data
Flux Rate Calculation from Dynamic Closed Chamber Measurements
Data Sets from "Forecasting: Methods and Applications" by
Makridakis, Wheelwright & Hyndman (1998)
Factorial Experiments with Minimum Level Changes
Efficient Estimation of Clustered Current Status Data
A Flexible Modelling Environment for Inverse Modelling,
Sensitivity, Identifiability and Monte Carlo Analysis
Model-Agnostic Interpretations with Forward Marginal Effects
Tools of Heterogeneity Pursuit via Finite Mixture Effects Model
Triangle Meshes and Related Geometry Tools
Fast Class Noise Detector with Multi-Factor-Based Learning
Rhythmic Patterns Modeling by FMM Models
Filtered Monotonic Polynomial IRT Models
R Access to the 'FMP Cloud' and 'Financial Modeling Prep' API
Functional MRI Quality Assurance Routines
Scrubbing and Other Data Cleaning Routines for fMRI
Routines for Common fMRI Processing Tasks
Functions for Medical Statistics Book with some Demographic Data
Regression Models with Finite Mixtures of Skew Heavy-Tailed
Errors
Variance Estimation of FMT Method (Fully Moderated T-Statistic)
Rmetrics - Modeling of Multivariate Financial Return
Distributions
Factor-Adjusted Network Estimation and Forecasting for
High-Dimensional Time Series
Fast Nearest Neighbor Search Algorithms and Applications
Rmetrics - Nonlinear and Chaotic Time Series Modelling
Feature Ordering by Conditional Independence
Summarize CRAN Check Results in the Terminal
Datasets for Teaching Archaeology and Paleontology
Add Extra 'Fomantic UI' Components to 'shiny.semantic'
Fonts with 'Bitstream Vera Fonts' License
Food Network Inference and Visualization
Food Quotient and Nutrient Analysis for HSFFQ
Enhanced Wrapper to Show Which Functions Call What
Penalties in the German Men's Football Bundesliga
Fitting Bayesian and MLE Football Models
Field Observation Quick Analysis Toolkit
Organization Measures for Visual Foraging
Foraminifera and Community Ecology Analyses
Tools for Working with Categorical Variables (Factors)
Forecasting Functions for Time Series and Linear Models
Convenient Functions for Ensemble Time Series Forecasts
Forecasting Routines for Locally Stationary Wavelet Processes
Time Series Forecasting with Machine Learning Methods
Forecasting for Stationary and Non-Stationary Time Series
Test Bench for the Comparison of Forecast Methods
Size-Power Tradeoff Visualization for Equal Predictive Ability
of Two Forecasts
Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata',
'Systat', 'Weka', 'dBase', ...
Information Theory Tools for Forensic Analysis
Systems Insights from Generation of Hydroclimatic Timeseries
Forest Carbon Sequestration and Potential Productivity
Calculation
Approximate False Positive Rate Control in Selection Frequency
for Random Forest
Fitting and Assessing Neighborhood Models of the Effect of
Interspecific Competition on the Growth of Trees
A Unified Framework for Random Forest Prediction Error
Estimation
Statistical Modelling for Plant Size Distributions
Forest Health Evaluation System at the Forest Stand Level in
China
Design-Based Global and Small-Area Estimations for Multiphase
Forest Inventories
Advanced Forest Plot Using 'grid' Graphics
Ecosystem and Canopy Structural Complexity Metrics from LiDAR
Implements the Forest-R.K. Algorithm for Classification Problems
Tools for Analyzing Remote Sensing Forest Data
Projecting Customer Retention Based on Fader and Hardie
Probability Models
Foraminiferal Image Analysis and Test Measurement
Functions to Estimate Tree Volume and Phytomass in the Italian
Forest Inventory 2005
Formatting Tools for 'rmarkdown' Documents
A Simple Connection Between the 'FormShare App' and 'R' for
Advanced Analytics
Programmatic Utilities for Manipulating Formulas, Expressions,
Calls, Assignments and Other R Objects
Dynamic Generation and Quality Checks of Formula Objects
Forensic Pedigree Analysis and Relatedness Inference
Diagnostic Analysis Using Forward Search Procedure for Various
Models
String Manipulation Package for Those Familiar with 'Microsoft
Excel'
Automatic Processing of Terrestrial-Based Technologies Point
Cloud Data for Forestry Purposes
Palaeoecological and Palaeogeographical Analysis Tools
Automated Cleaning of Fossil Occurrence Data
'Palantir Foundry' Software Development Kit
Bayesian 4 Parameter Item Response Model
A Game for Human vs. Human or Human vs. AI
Parsimonious Hidden Markov Models for Four-Way Data
Power and Sample Size for Projection Test under Repeated
Measures
Three Dimensional Functional Component Analysis
Predictive Confidence Bands for Functional Time Series
Forecasting
Reliable Comparison of Floating Point Numbers
Automatic Distribution Graphs Using Formulas
Segmentation using Optimal Partitioning and Function Pruning
Weighted Segmentation using Functional Pruning and Optimal
Partioning
Rmetrics - Portfolio Selection and Optimization
Computing the noncentrality parameter of the noncentral F
distribution
Data for "Forecasting: principles and practice"
Data for "Forecasting: Principles and Practice" (2nd Edition)
Data for "Forecasting: Principles and Practice" (3rd Edition)
Approximation of First-Passage-Time Densities for Diffusion
Processes
Testing Hypotheses via Fuzzy P-Value in Fuzzy Environment
Calculate Floristic Quality Assessment Metrics
Contains Regional Floristic Quality Assessment Databases
Floristic Quality Assessment Tools for R
Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models
Numerical CDFs for Fractional Unit Root and Cointegration Tests
Fraction Proliferation via a Quiescent Growth Model
Simulation and Parameter Estimation of Randomized Sierpinski
Carpets using the p-p-p-q-Model
Performs Fractal Analysis and Fractal Regression
Estimation of Fractal Dimension of a Black Area in 2D and 3D
(Slices) Images
Assessing and Visualizing Fragility of Clinical Results with
Binary Outcomes
Fitting Frailty Models with the EM Algorithm
Frailty Models via Hierarchical Likelihood
Efficient Algorithm for High-Dimensional Frailty Model
Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
General Semiparametric Shared Frailty Model
Financial Risk Modelling and Portfolio Optimisation with R
Automatic Data Processing and Visualization for FRAP
Fuzzy Rule-Based Systems for Classification and Regression Tasks
Fast Regularized Canonical Correlation Analysis
Statistical Analysis for Random Objects and Non-Euclidean Data
Estimation and Inference for High Dimensional Mediation and
Surrogate Analysis
Client for the 'freecurrencyapi.com' Currency Conversion API
Read and Write 'FreeSurfer' Neuroimaging File Formats
Tree Method for High Dimensional Longitudinal Data
Rmetrics - Regression Based Decision and Prediction
Generate Open or Closed Interpolating Curves
Download Data Sets from Kenneth's French Finance Data Library
Site
FREQ: Estimate population size from capture frequencies
Automated Cumulative Frequency Plots for Grouped Distribution
Frequency Domain Based Analysis: Dynamic PCA
Functional Time Series: Dynamic Functional Principal Components
Estimates Allele Frequency on qPCR DeltaDeltaCq from Bulk
Samples
Frequency Profiles Computing and Plotting
Make Quick Descriptive Tables for Categorical Variables
Spectral Decomposition of Connectedness Measures
Implementation of Frequent-Directions Algorithm for Efficient
Matrix Sketching
Screened Selection Design with Survival Endpoints
Feature Selection Algorithms for Computer Aided Diagnosis
Create Custom 'Bootstrap' Themes to Use in 'Shiny'
Fast Robust Estimation of Signals in Heterogeneous Data
Fractional Factorial Designs with 2-Level Factors
Catalogues of Resolution IV 128 Run 2-Level Fractional
Factorials Up to 33 Factors that Do Have 5-Letter Words
Relative Importance of Main and Interaction Effects
Read and Write Frictionless Data Packages
The Entire Transcript from Friends in Tidy Format
Utilities for the Forest Research Institute of the State
Baden-Wuerttemberg
Emulate a 'Forth' Programming Environment
Functions for Calculating Fit-Robustness of CNA-Solutions
Evaluation Tools for Assessing Climate Adaptation of Fruit Tree
Species
Cross-Platform File System Operations Based on 'libuv'
Simple Fisheries Stock Assessment Methods
Data to Support Fish Stock Assessment ('FSA') Package
Fragment Analysis and Capillary Sequencing Tool Kit
Managing and Visualizing Brain Surface Data
Automated Feature Selection from 'caret'
Forward Stepwise Deep Autoencoder-Based Monotone NLDR
Robust Data Analysis Through Monitoring and Dynamic
Visualization
'Rcpp' Implementation of 'FSelector' Entropy-Based Feature
Selection Algorithms with a Sparse Matrix Support
Estimation, Variable Selection and Prediction for Functional
Semiparametric Models
Import and Analysis of OMR Data from FormScanner
An Interface Between the 'FSKX' Standard and 'R'
Wrapper Functions for 'FSL' ('FMRIB' Software Library) from
Functional MRI of the Brain ('FMRIB')
Feature Selection for Multivariate Time Series
Imputation of Time Series Based on Fuzzy Logic
Rosenthal's Fail Safe Number and Related Functions
Estimating the Number of Factors in EFA with Out-of-Sample
Prediction Errors
Social Relations Analyses with Roles ("Family SRM")
Fused Sparse Structural Equation Models to Jointly Infer Gene
Regulatory Network
Generate Fully-Sequential Space-Filling Designs Inside a Unit
Hypercube
Lightning Fast Serialization of Data Frames
Genetic Distance Segmentation for Population Genetics
Unified Sequence-Based Association Tests Allowing for Multiple
Functional Annotation Scores
Fault Tree Analysis Based on Proxel Simulation
A Wrapper for the API of the Danish Parliament
Rmetrics - Trading and Rebalancing Financial Instruments
Feature Extraction from Biological Sequences
Spectral Density Estimation and Comparison for Functional Time
Series
Framework for Unified Estimation in Lognormal Models
Sensitivity Analysis Optimized for Matched Sets of Varied Sizes
Plot Full ROC Curves using Eyewitness Lineup Data
Clean and Visualize Over Expression Results from
'ConsensusPathDB'
Functional Cheng and Church Bi-Clustering
Convenience Functions by Michael Chirico
Model-Free Functional Chi-Squared and Exact Tests
Functional Analysis (with Interactions) for Dense SNP Data
Easy Computation of Functional Diversity Indices
Clustering in the Discriminative Functional Subspace
Psychometric Functions from the Waller Lab
Gaussian Process Models for Scalar and Functional Inputs
Rmetrics - Modelling Trends and Unit Roots
Functional Data Analysis for Multiplexed Cell Images
Generating Funky Heatmaps for Data Frames
Model-Based Co-Clustering of Functional Data
Functional Mediation for a Distal Outcome
Time-Based Joins to Analyze Sequences of Events
Funnel Plots for Comparing Institutional Performance
Simple Utility Providing Terminal Access to all R Functions
Functional Regression for Irregularly Timed Data
Creating and Representing Functional Trait Spaces
Apply Mapping Functions in Parallel using Futures
Implementation of Fused MGM to Infer 2-Class Networks
Fused Lasso for High-Dimensional Regression over Groups
Embedding 'FusionCharts Javascript' Library in R
Clustering and Feature Screening using L1 Fusion Penalty
Interim Analysis of Operational Futility in Randomized Trials
with Time-to-Event Endpoints and Fixed Follow-Up
Unified Parallel and Distributed Processing in R for Everyone
Apply Function to Elements in Parallel using Futures
A Future API for Parallel and Distributed Processing using
'batchtools'
A Future API for Parallel Processing using 'callr'
Find, Characterize, and Explore Extreme Events in Climate
Projections
Join Tables Together on Inexact Matching
Apply Two Fuzzy Numbers on a Monotone Function
Fuzzy Quantification of Common and Rare Species
Fuzzy Rank Tests and Confidence Intervals
Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers
Fuzzy Similarity in Species Distributions
Simulation and Resampling Methods for Epistemic Fuzzy Data
Fuzzy Stationary Probabilities from a Sequence of Observations
of an Unknown Markov Chain
Statistical Methods for Trapezoidal Fuzzy Numbers
Package 'FuzzyStatTra' in Encapsulated Object Oriented
Programming
Selecting Variables in Regression Models
Fast Wild Cluster Bootstrap Inference for Linear Models
Scatter Plot After Residualizing Using 'fixest' Package
Fresh Weight Determination from Visual Image of the Plant
'fxl' Single Case Design Charting Package
Generalized Ridge Regression for Linear Models
Grain-Size Statistics and Description of Sediment
Interactively Visualize Genetic Mutation Data using a
Lollipop-Diagram
Abbreviating Items Measures using Genetic Algorithms
Facilitation of Data Preparation and Plotting Procedures for RDA
and PCA Analyses
Genetic Similarity in User-Based Collaborative Filtering
GAD: Analysis of variance from general principles
A Genetic Algorithm for Learning Directed Acyclic Graphs
Gadget is the Globally-Applicable Area Disaggregated General
Ecosystem Toolbox
Globally-Applicable Area Disaggregated General Ecosystem Toolbox
V3
Global Adaptive Generative Adjustment Algorithm for Generalized
Linear Models
Genetic Algorithm Assisted Genomic Best Liner Unbiased
Prediction
Genetic and Isotopic Assignment Accounting for Habitat
Suitability
Machine Learning-Based Analysis of Potential Power Gain from
Passive Device Installation on Wind Turbine Generators
Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
Biodiversity Data from the GBIF Node Network
Generalized Additive Latent and Mixed Models
Collection of Packages for Internet Marketing
Genomic Annotation in Livestock for Positional Candidate LOci
Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)
Estimation
Interface to Galvanize 'Highbond' Internal Audit Software
Fit the Gambin Model to Species Abundance Distributions
One-Dimensional Random Walks Through Simulation of the Gambler's
Ruin Problem
Functions and Data for a Course on Modern Regression and
Classification
Generalized Additive Models for Bivariate Conditional Dependence
Structures and Vine Copulas
Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
Genetic Algorithm for Sequential Symmetric Games
Tools for Calculating Allocations in Game Theory
Generalised Additive Models for Location Scale and Shape
Extra Additive Terms for Generalized Additive Models for
Location Scale and Shape
Fitting an Interval Response Variable Using `gamlss.family'
Distributions
Generating and Fitting K-Inflated 'discrete gamlss.family'
Distributions
Data for Generalised Additive Models for Location Scale and
Shape
Distributions for Generalized Additive Models for Location Scale
and Shape
Parallel Computations for Distributional Regression
Fitting Mixed (Inflated and Adjusted) Distributions
Extra Lasso-Type Additive Terms for GAMLSS
Fitting Mixture Distributions with GAMLSS
Spatial Terms in Generalized Additive Models for Location Scale
and Shape Models
Generating and Fitting Truncated `gamlss.family' Distributions
Bimodal Skew Symmetric Normal Distribution
Generalized Additive Mixed Models using 'mgcv' and 'lme4'
Classic Gamma Regression: Joint Modeling of Mean and Shape
Parameters
Generalized Additive Mixed Model Analysis via Slice Sampling
Robust and Sparse Regression via Gamma-Divergence
Fit Regularization Path for Generalized Additive Models
Bayesian Generalized Additive Model Selection
Build a Metric Subspaces Data Model for a Data Source
Generate Generative Data for a Data Source
Gene Association Network-Based Pathway Analysis (GANPA)
Searching Parsimony Models with Genetic Algorithms
Drawing Gapped Cluster Heatmap with 'ggplot2'
Estimating a GARCHSK Model and GJRSK Model
Gene and Region Counting of Mutations ("GARCOM")
Fitting and Forecasting Gegenbauer ARMA Time Series Models
Generalized Array Arithmetic for Ragged Arrays with Named
Margins
Import, Recompute and Analyze Data from Portable Gas Analyzers
Genetic Algorithm (GA) for Variable Selection from
High-Dimensional Data
Greenhouse Gas Flux Calculation from Chamber Measurements
Train and Apply a Gaussian Stochastic Process Model
Analyzing Gastric Emptying from MRI or Scintigraphy
Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models
Easily Gate or Select Points on a Scatter Plot
Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation
Geographic and Taxonomic Occurrence R-Based Scrubbing
Lotka-Volterra Models for Gause's 'Struggle for Existence'
The Gaussian Covariate Method for Variable Selection
Difference measures for multivariate Gaussian probability
density functions
The Greatest Mathematician Since Antiquity
Inference, Goodness-of-Fit and Forecast for Univariate Gaussian
Hidden Markov Models
Collection of Functions for Gaussian Quadrature
Tabular Data Suppression using Gaussian Elimination
Multi-Trait Dissimilarity with more Uniform Contributions
Generalize Lambda Distribution and Generalized Bootstrapping
Generalized Beta Distribution of the Second Kind: Properties,
Likelihood, Estimation
Estimation of the Generalised Beta Distribution of the Second
Kind from Grouped Data
Gradient Boosting Algorithm for Predicting Methylation States
Generalized Beta and Beta Prime Distributions
Generalized Berk-Jones Test for Set-Based Inference in Genetic
Association Studies
Automated Boosted Regression Tree Modelling and Mapping Suite
Utilities for processing Rd objects and files
Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data
Hyperparameter Search for Gradient Boosted Trees
Utilities for Simulation, Plots, Quantile Functions and
Programming
A Data Manipulation and Analysis Package for Calcium Indicator
Data
Simple Peak Alignment for Gas-Chromatography Data
Graph-Based Two-Sample Tests for Categorical Data
'GPU'/CPU Benchmarking in Debian-Based Systems
GCC Estimation of the Multilevel Factor Model
The (Adaptive) LASSO and Elastic Net Penalized Least Squares,
Logistic Regression, Hybrid Huberized Support Vector Machines,
Squared Hinge Loss Support Vector Machines and Expectile
Regression using a Fast Generalized Coordinate Descent
Algorithm
Analysis of Geostatistical Count Data using Gaussian Copulas
Bayesian Meta-Analysis of Pleiotropic Effects Using Group
Structure
Manipulate and Analyze Growth Curve Data
Implements Generic Composite Similarity Measure
Geographical Detectors for Assessing Spatial Factors
Datasets for the Book Graphical Data Analysis with R
GDAL Classes Wrapper for Reading and Writing Raster Blocks
Earth Observation Data Cubes from Satellite Image Collections
Bindings to the 'Geospatial Data Abstraction Library' Raster API
Wrappers for 'GDAL' Utilities Executables
Various R Programming Tools for Data Manipulation
Download, Slice, and Normalize GDELT V1 Event and Sentiment API
Data
Volumetric Analysis using Graphic Double Integration
Estimate Graph Dimension using Cross-Validated Eigenvalues
Distances and Routes on Geographical Grids
Gauss - Dantzig Selector: Aggregation over Random Models
Utilities for Graphical Rendering and Fonts Management
IO for GAMS GDX Files using 'data.table'
Geographical Ecology and Conservation Knowledge Online
Adaptive Association Tests for Multiple Phenotypes using
Generalized Estimating Equations (GEE)
Solve Generalized Estimating Equations for Clustered Data
Bias-Corrected GEE for Cluster Randomized Trials
Marginal Proportional Hazards Mixture Cure Models with
Generalized Estimating Equations
Mediation Analysis for Generalized Linear Models Using the
Difference Method
Modified Variance Estimators for Generalized Estimating
Equations
Modified Generalized Estimating Equations for Binary Outcome
Calculate Generalized Eigenvalues, the Generalized Schur
Decomposition and the Generalized Singular Value Decomposition
of a Matrix Pair with Lapack
Gene-Environment Interaction Analysis Incorporating Prior
Information
File Conversion for 'Gem Infrasound Logger'
Network Meta-Analysis Using Bayesian Methods
Generalized Two-Stage Designs for Phase II Single-Arm Studies
General Engine for Eco-Evolutionary Simulations
Genetic Algorithm and Particle Swarm Optimization
Classes and Methods to Use Genetic Algorithms for Feature
Selection
Analysis and Visualization Tools for Genetic Barcode Data
Clopper-Pearson Confidence Interval and Generalized Binomial
Distribution
Interacting with Roberts and Utych's (2019) Gendered Language
Dictionary
Predict Gender from Names Using Historical Data
Predict Gender from Brazilian First Names
This is a Collection of Functions to Analyse Gender Differences
Quantitative Analysis Tools for Gender Studies
Segmentation and Classification of Accelerometer Data
Visualisation of Raw or Segmented Accelerometer Data
Identification of Periodically Expressed Genes
Easily Downloads a Gene Expression Dataset from a GEO Code and
Retrieves the Gene Symbols of Its Probesets
Gene Haplotype Statistics, Phenotype Association and
Visualization
A Pipeline to Define Gene Families in Legumes and Beyond
Genetic Epidemiology Design and Inference
Population Genetic Data Analysis Using Genepop
Generalized Correlations, Causal Paths and Portfolio Selection
Goodness of Fit Tests for Logistic Regression Models
Test for Conditional Independence Based on the Generalized
Covariance Measure (GCM)
Credible Visualization for Two-Dimensional Projections of Data
Fully Parameterized Generalized Wendland Covariance Function
Blinder-Oaxaca Decomposition for Generalized Linear Model
Generate Data Containing Fake Personally Identifiable
Information
Common S3 Generics not Provided by Base R Methods Related to
Model Fitting
Estimate Gender from Names in Spanish and Portuguese
Simulation of Single Cell RNA-Seq Data with Complex Structure
'GeneSelectR' - Comprehensive Feature Selection Workflow for
Bulk RNAseq Datasets
Get Gene Sets for Gene Enrichment Analysis
Genome-Wide Association Study with SLOPE
Statistical Tools for the Analysis of Multi Environment
Agronomic Trials
Identify Favorable Subsets of Germplasm Collections
Spatial Graph-Theoretic Genetic Gravity Modelling
Fast, Robust, and Outlier Resistant Hierarchical Clustering
Project GENIE BioPharma Collaborative Data Processing Pipeline
Fast and Robust Hierarchical Clustering with Noise Points
Detection
Path Algorithm for Generalized Lasso Problems
Implements Generalized Meta-Analysis Using Iterated Reweighted
Least Squares Algorithm
Finding the DNA and Protein Sequences of Any Genomic or
Proteomic Loci
Methods for Analysing Forensic Ancestry Informative Markers
Models Dealing with Spatial Dependency in Genomic Data
Circular Genomic Permutation using Genome Wide Association
p-Values
Plot Publication-Grade Gene and Genome Maps
Simulation of Discrete Random Variables with Given Correlation
Matrix and Marginal Distributions
A Genome-Wide Scan Statistic Framework for Whole-Genome Sequence
Data Analysis
Genotyping Triploids (or Diploids) from Luminescence Data
Pathmox Approach Segmentation Tree Analysis
Power Calculations Under Genetic Model Misspecification
Generalized Ridge Trace Plots for Ridge Regression
R Functions for Generalized Simulated Annealing
Predict Epidemic Curves with Generalized SEIR Modeling
Generates Data Sets for Class Demonstrations
Resampling Based Yield Stability Analyses
A Generalized Multiclass Support Vector Machine
R Shiny App for Creating Simplified Trial Summary (TS) Domain
Generalized Two Arms Clinical Trial Sample Size Calculation
Spline Based Window Boundaries for Genomic Analyses
Accounting for Random Displacements of True GPS Coordinates of
Data
Analysis of Geostatistical Data using Bayes and Empirical Bayes
Methods
Download Official Spatial Data Sets of Brazil
Causal Inference with Spatio-Temporal Data
Implementing Methods for Spatial Fuzzy Unsupervised
Classification
Fast, Dependency-Free Geodesic Distance Calculations
Methods for Calculating Gradient Surface Metrics
3D-Visualization of Geoelectric Resistivity Measurement Profiles
'ggplot2' Faceting Utilities for Geographical Data
Spatial Prediction for Function Value Data
Kriging Method for Spatial Functional Data
Ordinary Functional Kriging Using Fourier Smoothing and Gaussian
Quadrature
Select Sparse Geoadditive Models for Spatial Prediction
Generator from American Community Survey Geodatabases
Turn Geospatial Polygons into Regular or Hexagonal Grids
Convert Data from and to 'GeoJSON' or 'TopoJSON'
Web-Processing of Large Gridded Datasets
Geographic Tools for Studying Gerrymandering
Data for Topographic and Geologic Mapping
Easily Visualize Geographic Data of Morocco
Tools for Reading and Writing ISO/OGC Geographic Metadata
Convert Between R Objects and Geometric Structures
Mesh Generation and Surface Tessellation
A Computer Program for Geotechnical Investigations
Procedures for Gaussian and Non Gaussian Geostatistical (Large)
Data Analysis
Geometric Morphometric Analyses of 2D and 3D Landmark Data
Multidimensional Queries Enriched with Geographic Data
Interface to the "Geonames" Spatial Query Web Service
Calculating Geographic Range from Occurrence Data
Open Source Geometry Engine ('GEOS') R API
Smallest Enclosing Disc for Latitude and Longitude Points
Geostatistical Analysis and Design of Optimal Spatial Sampling
Networks
Spatio-Temporal Radial Basis Functions with Distance-Based
Methods (Optimization, Prediction and Cross Validation)
An Introduction to Statistics for Geoscientists
Geostatistical Modelling with Likelihood and Bayes
An R Plug-in for the Distributed Hydrological Model GEOtop
Methods for Handling and Analyzing Time Series of Satellite
Images
User-Friendly Interface for Geographically-Weighted Models
Google Encoded Polyline Algorithm Format
Generalized Efficient Regression-Based Imputation with Latent
Processes
Indices and Graphics for Assess Seed Germination Process
Seed Germination Indices and Curve Fitting
Reproducible Data Retrieval from the GESIS Data Archive
Hierarchical GxE Interactions in a Regularized Regression Model
Tools for Making and Combining Functions
Generalised Survival Trial Assessment Tool Environment
General Purpose G-Estimation for End of Study or Time-Varying
Outcomes
Fetching Tabular Data "Onload" in Compiled R Markdown HTML
Documents
Imports Datasets from BCB (Central Bank of Brazil) using Its
Official API
Generalized Extended Triangular Designs ('GETdesigns')
Reading Annual Financial Reports from Bovespa's DFP, FRE and FCA
System
Reading Annual and Quarterly Financial Reports from B3
Translating Coding Statements using get() and eval() for
Improved Run-Time Coding Efficiency
Reading FRE Corporate Data of Public Traded Companies from B3
Import and Process Data from the 'Lattes' Curriculum Platform
Reading Bibliometric Data from Lattes Platform
Quantifying Systematic Heterogeneity in Meta-Analysis
Parsing Command-Line Arguments and Simple Variable Interpolation
Fast and Cached Import of Data from 'Quandl' Using the 'json
API'
General-to-Specific (GETS) Modelling and Indicator Saturation
Methods
General-to-Specific Modelling of Panel Data
SPRE Statistics for Exploring Heterogeneity in Meta-Analysis
Get Data for Brazilian Bonds (Tesouro Direto)
Joint Test of Gene and GxE Interactions via Varying Coefficients
GEV Conditional Density Estimation Network
Tools for Working with Hansen et al. Global Forest Change
Dataset
General Hypothesis Testing Problems for Multivariate
Coefficients of Variation
Tests for Survival Data in General Factorial Designs
Gross Flows Estimation under Complex Surveys
Generalized Fiducial Inference for Extremes
Generalized Fiducial Inference for Normal Linear Mixed Models
Generalized Fiducial Inference for Binary Logistic Regression
Models
Generalized Fisher's Combination Tests Under Dependence
Generalized Fiducial Inference for Ultrahigh-Dimensional
Regression
Offline 'Google' Fonts for 'Markdown' and 'Shiny'
G-Formula for Causal Inference via Multiple Imputation
Graph-Constrained Functional Pruning Optimal Partitioning
Visualizing Sequence Alignment by Generating Publication-Ready
Plots
Extra Coordinate Systems, 'Geoms', Statistical Transformations,
Scales and Fonts for 'ggplot2'
Flexible Segment Geoms with Arrows for 'ggplot2'
Create Maps from a Column of Place Names
Categorical Scatter (Violin Point) Plots
A Grammar of Graphics Implementation of Biplots
Blending and Compositing Algebra for 'ggplot2'
Create Images of Volumetric Brain Data in NIfTI Format Using
'ggplot2' Syntax
Mini Bubble Plots for Comparison of Discrete Data with 'ggplot2'
Combines Changepoint Analysis with 'ggplot2'
Shorten the Distance from Data Visualization Idea to Actual Plot
Gabriel Graph Based Large-Margin Classifiers
Implementation of Plots from Cleveland's Visualizing Data Book
Visualization of a Correlation Matrix using 'ggplot2'
Analyze and Create Elegant Directed Acyclic Graphs
'ggplot2' Extension for Seasonal and Trading Day Adjustment with
'RJDemetra'
Create Dendrograms and Tree Diagrams Using 'ggplot2'
Interpretable Bivariate Density Visualization with 'ggplot2'
Visualizations of Distributions and Uncertainty
Modern Graphs for Design of Experiments with 'ggplot2'
Interactive 'ggplot2' Layer and Theme Aesthetic Editor
Create Tidy Data Frames of Marginal Effects for 'ggplot' from
Model Outputs
Exploratory Symbolic Data Analysis with 'ggplot2'
Visualisation of High-Throughput Behavioural (i.e. Ethomics)
Data
Add Marginal Histograms to 'ggplot2', and More 'ggplot2'
Enhancements
Visualise and Calculate Life History Parameters for Fisheries
Science using 'ggplot2'
Dedicated 'ggplot2' Methods for 'fixest' Objects
Scales that Focus Specific Levels in your ggplot()
Formula Interface to the Grammar of Graphics
Data Visualization Tools for Statistical Analysis Results
Streamlined Creation of Segments on the Y-Axis of 'ggplot2'
Plots
Capture the Spirit of Your 'ggplot2' Calls
Create a Half Normal Plot Using 'ggplot2'
Compose Half-Half Plots Using Your Favourite Geoms
Visualisation of Highest Density Regions in 'ggplot2'
HDX Theme, Scales, and Other Conveniences for 'ggplot2'
A Fast 'ggplot2'-Based Implementation of Hilbert Curves
Graphics Layers for Plotting Image Data with 'ggplot2'
Visualise the Results of Inferential Statistics using 'ggplot2'
Explore the Innards of 'ggplot2' Objects
Data Visualization for IP Addresses and Networks
Make Interactive 'ggplot2'. Extension to 'ggplot2' and 'ggiraph'
Wearable Accelerometer Data File Readers
'ggplot2' Friendly Isotonic or Monotonic Regression Curves
Group Lasso Penalized Learning Using a Unified BMD Algorithm
Grammar of Graphics for Linear Model Diagnostic Plots
Plotting Lorenz Curve with the Blessing of 'ggplot2'
Graphical Markov Models with Mixed Graphs
Plot Columns of Two Matrices Against Each Other Using 'ggplot2'
Tools for Analyzing MCMC Simulations from Bayesian Inference
Visualizations for 'mice' with 'ggplot2'
Variable Selection in Linear Mixed Models for SNP Data
Gaussian Graphical Models with Nonconvex Regularization
Gaussian Graphical Models Using Ridge Penalty Followed by
Thresholding and Reestimation
Create Muller Plots of Evolutionary Dynamics
Geometries to Plot Networks with 'ggplot2'
Multiple Fill and Colour Scales in 'ggplot2'
A 'ggplot2' Extension to Make Normal Violin Plots
Plot Data on Oceanographic Maps using 'ggplot2'
'Okabe-Ito' Scales for 'ggplot2' and 'ggraph'
Make 'ggplot2' Versions of Vegan's Ordiplots
Identify Individuals with Unusual Geo-Genetic Patterns
Package Plot Layers for Easier Portability and Modularization
Variations of Parallel Coordinate Plots for Categorical Data
'ggplot' Visualizations for the 'partykit' Package
Robust Image Rendering Support for 'ggplot2'
Parallel Coordinate Plots in the 'ggplot2' Framework
Easy Plotting of Periodic Data with 'ggplot2'
Make 'PICRUSt2' Output Analysis and Visualization Easier
Create Layout Plots of Biological Culture Plates and Microplates
Multiple Summary Statistics for Binned Stats/Geometries
Create Elegant Data Visualisations Using the Grammar of Graphics
'RStudio' Addin for Teaching and Learning 'ggplot2'
Create Ggplots via a Graphical User Interface
Convert Plot to 'grob' or 'ggplot' Object
Extra Convenience Functions for 'Plotly'
'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME
Models
A Cross Between a 2D Density Plot and a Scatter Plot
A Small Collection of Geometries, and Stats for 'ggplot2'
Visualizing Social Science Data with 'ggplot2'
Dots and Their Connections in Polar Coordinate System
Polygons with Holes for the Grammar of Graphics
A 'ggplot2' Extension Inspired by 'GraphPad Prism'
Annotate Statistical Tests for 'ggplot2'
Compare Big Datasets to the Uniform Distribution
Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary
Tables
An Implementation of Grammar of Graphics for Graphs and Networks
Allows Interactive Visualization of Data Through a Web Browser
GUI
Gaussian-Based Genome Representative Selector with
Prioritization
Draw Histograms and Restricted Cubic Splines (RCS)
Scales, Geometries, and Extensions of 'ggplot2' for Election
Mapping
Automatically Position Non-Overlapping Text Labels with
'ggplot2'
Panels and Interactive Versions of Diagnostic Plots using
'ggplot2'
Add Some Van Gogh Colors and Overlay Colors on Your 'ggplot()'
Scatter Plot Combined with Ridgelines in 'ggplot2'
Scientific Journal and Sci-Fi Themed Color Palettes for
'ggplot2'
'stats' for Seasonal Adjustment on the Fly with 'ggplot2'
Tri-Surface Mesh Plots for Brain Atlases
A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM
New Data Visualisations for SOMs Networks
Extensions to 'ggplot2' for Radiation Spectra
Multiple Geometric Shape Point Layer for 'ggplot2'
Extension to 'ggplot2' for Plotting Stats
'ggplot2' Based Plots with Statistical Details
Continuous Confidence Interval Plots using t-Distribution
Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects
An Extension to 'ggplot2', for the Creation of Ternary Diagrams
Improved Text Rendering Support for 'ggplot2'
Extra Themes, Scales and Geoms for 'ggplot2'
A 'ggplot' Theme for University of Ljubljana
Post-Process 'ggplot2' Plots with 'TikZ' Code Using Plot
Coordinates
Time Series Visualisations Using the Grammar of Graphics
Trace and Highlight Groups of Data Points
Add Trendline and Confidence Interval to 'ggplot'
Create Sector and Other Charts Easily Using Grammar of Graphics
Combination Matrix Axis for 'ggplot2' to Create 'UpSet' Plots
A Natural Language Interface to 'ggplot2'
Quantifying Evolution and Selection on Complex Traits
Functional Conditional Independence Testing with the GHCM
Missing Data Segments Imputation in Multivariate Streams
The Knockoff Inference Using Summary Statistics
Graphical Horseshoe MCMC Sampler Using Data Augmented Block
Gibbs Sampler
Generalized Hyperbolic Distribution and Its Special Cases
Fit and Simulate Generalised Hypergeometric Ensembles of Graphs
Interface to the Computer Algebra System 'Giac'
An Alternative 'Rcpp' Wrapper of 'MeCab'
A General Iterative Clustering Algorithm
Multivariate Analysis with Optimal Scaling
Access to the Global Inventory of Floras and Traits (GIFT)
Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information
GIFT Questions Format Generator from Dataframes
Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with
Grouping Structure
Random Variate Generator for the GIG Distribution
Gillespie's Stochastic Simulation Algorithm (SSA)
Gillespie's Stochastic Simulation Algorithm for Impatient People
Group Iterative Multiple Model Estimation
Graphical Utilities for Visualizing and Exploring Data
Supplemental Tools for the 'gimme' R Package
A New Gini Correlation Between Quantitative and Qualitative
Variables
Gini Indices, Variances and Confidence Intervals for Finite and
Infinite Populations
Computing the Gini-Based Coefficients for Weighted and Negative
Attributes
Generalized Inverse Normal Distribution Density and Generation
Generalized Iterative Proportional Fitting for Relational Models
Gaussian Model Invariant by Permutation Symmetry
Download Map Data from GISCO API - Eurostat
Network Analysis on the Norwegian Road Network
Store and Retrieve Data.frames in a Git Repository
Turn Your Git Commit Messages into a HTML Book
'Rstudio' Addin for Version Control and Assignment Management
using Git
Automated Git Commit Messages using the 'OpenAI' 'GPT' Model
A Helpful Way to Install R Packages Hosted on GitHub
Create Useful .gitignore Files for your Project
Add 'Git' Links to Your Web Based Assets
Data Version Control for the Targets Package
A Generalized Joint Location and Scale Framework for Association
Testing
g-and-k and g-and-h Distribution Functions
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Accessing the Official 'Google Knowledge Graph' API
Generalized Kernel Regularized Least Squares
Penalization in Large Scale Generalized Linear Array Models
Generate tables and plots to get summaries of data
Generalized Linear Autoregressive Moving Average Models
Solve the Graphical Lasso Problem with 'Armadillo'
Variable Selection in Sparse GLARMA Models
Graphical Lasso: Estimation of Gaussian Graphical Models
General Linear Ballistic Accumulator Models
An R Package for Multiple-Group Latent Class Analysis
Calculate Textures from Grey-Level Co-Occurrence Matrices
(GLCMs)
Estimation and Use of the Generalised (Tukey) Lambda
Distribution
Fitting Single and Mixture of Generalised Lambda Distributions
Fit GLD Regression/Quantile/AFT Model to Data
Global and Individual Tests for Direct Effects
Learning Interactions via Hierarchical Group-Lasso
Regularization
Phylogenetic Comparative Methods with Uncertainty Estimates
Generalized Linear Latent Variable Models
'C' and 'Java' Source Code Generator for Fitted Glm Objects
Predicted Values and Discrete Changes for GLM
An Adaptive Test on High Dimensional Parameters in Generalized
Linear Models
All Hierarchical or Graphical Models for Generalized Linear
Model
Fitting Generalized Linear Models Subject to Constraints
Generalized Linear Models for Categorical Responses
GLRT P-Values in Generalized Linear Models
Fit GLM with LEP-Based Penalized Maximum Likelihood
Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Hierarchical Partitioning of Marginal R2 for Generalized
Mixed-Effect Models
Generalized Linear Mixed Models using Adaptive Gaussian
Quadrature
Fit a Cosinor Model Using a Generalised Mixed Modelling
Framework
Generalized Linear Mixed Model Analysis via Expectation
Propagation
Generalized Linear Mixed Models with Robust Random Fields for
Spatiotemporal Modeling
Variable Selection for Generalized Linear Mixed Models by
L1-Penalized Estimation
Generalized Linear Models with Clustering
High Dimensional Penalized Generalized Linear Mixed Models
(pGLMM)
Generalized Linear Mixed Model (GLMM) for Binary Randomized
Response Data
Bayesian Model Selection for Generalized Linear Mixed Models
General Linear Mixed Models for Gene-Level Differential
Expression
Generalized Linear Mixed Models using Template Model Builder
Lasso and Elastic-Net Regularized Generalized Linear Models
Fit a Penalized Constrained Continuation Ratio Model for
Predicting an Ordinal Response
Nested Cross Validation for the Relaxed Lasso and Other Machine
Learning Models
Add Nonparametric Bootstrap SE to 'glmnet' for Selected
Coefficients (No Shrinkage)
Data and Code to Accompany Generalized Linear Models, 2nd
Edition
L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
Fit a Penalized Continuation Ratio Model for Predicting an
Ordinal Response
Dimension Reduction of Non-Normally Distributed Data
Creates Ideal Data for Generalized Linear Models
Generalized Linear Models with Truncated Lasso Penalty
Set of Tools to Data Analysis using Generalized Linear Models
Transfer Learning under Regularized Generalized Linear Models
Model Selection and Multimodel Inference Made Easy
Variable Selection Deviation Measures and Instability Tests for
High-Dimensional Generalized Linear Models
A Collection of Graphic Tools for GLM Diagnostics and some
Extensions
Inhomogeneous K- And Pair Correlation Functions Using Global
Estimators
Generate Functions to Get or Set Global Options
Objective functions for benchmarking the performance of global
optimization algorithms
Identify Global Objects in R Expressions
Download and Measure Global Trends Through Google Search Volumes
Plot 2D and 3D Views of the Earth, Including Major Coastline
Estimating Functions with Multivariate B-Splines
Fitting and Testing Generalized Logistic Distributions
Glossaries for Markdown and Quarto Documents
Language Mapping and Geospatial Analysis of Linguistic and
Cultural Data
Genome-Wide Association and Linkage Analysis under Heterogeneity
Generalized Least Squares with Measurement Error
Glucose Variability Measures from Continuous Glucose Monitoring
Data
Methods to Assess Generalized Latent Variable Model Fit
Generate Music Easily and Show Them Anywhere
Genomic Mediation Analysis with Adaptive Confounding Adjustment
Distance and Travel Time Between Two Points from Google Maps
Fast Estimation of Gaussian Mixture Copula Models
Graph Based Multiple Comparison Procedures
Lightweight Graph Based Multiple Comparison Procedures
Accessing a Geometallurgical Database with R
Short Term Forecasting via GMDH-Type Neural Network Algorithms
Binary Classification via GMDH-Type Neural Network Algorithms
Geometric Median, k-Medians Clustering and Robust Median PCA
Inference and Clustering of Functional Data
Gaussian Mixture Graphical Model Learning and Inference
Descriptive Statistics, Transition Plots, and More
Unsupervised Gaussian Mixture and Minkowski and Spherical
K-Means with Constraints
Generalized Method of Moments and Generalized Empirical
Likelihood
Generalized Linear Mixed Model Association Tests
Likelihood-Based Boosting for Generalized Mixed Models
Multinomial Logit Models with Random Parameters
Various R Programming Tools for Model Fitting
Tools for 2D and 3D Plots of Single and Multi-Objective
Linear/Integer Programming Models
Multivariate Polynomials with Rational Coefficients
Generalised Management Strategy Evaluation Simulator
Generalized Mass Spectrum Missing Peaks Abundance Imputation
Interface Between GMT Map-Making Software and R
Estimate Gaussian and Student's t Mixture Vector Autoregressive
Models
Joint Models of Survival and Multivariate Longitudinal Data
Estimate Functional and Stochastic Parameters of Linear Models
with Correlated Residuals
Methods for Fitting Network Time Series Models
Computation of Generalized Nash Equilibria
Goodness of Fit Test for Continuous Distribution Functions
Estimate Natural Mortality for Different Life Stages
Various Non-Additive Models for Genetic Associations
Access the 'Geographic Name Resolution Service'
Read Data from Files Readable by 'gnumeric'
Weighted and Lexicographic Goal Programming Interface
Comprehensive GO Terms Comparison Between Species
Functions for Community Data and Ordinations
Fast DocumentTermMatrix and TermDocumentMatrix Creation
Goodness-of-Fit Measures for Categorical Response Models
Goodness-of-Fit Methods for Right-Censored Data
Goodness of Fit Tests Based on Empirical Distribution Functions
Goodness-of-Fit Tests for Functional Data
Goodness-of-Fit Tests for the Gamma Distribution
Testing Goodness-of-Fit with the Kernel Density Estimator
Khmaladze Martingale Transformation Goodness-of-Fit Test
Interactive Document for Working with Goodness of Fit Analysis
Tests of Fit for some Probability Distributions
Classical Goodness-of-Fit Tests for Univariate Distributions
Generalized Orthogonal GARCH (GO-GARCH) Models
Statistical Network Models for Dynamic Network Data
Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints
A Framework for Robust Shiny Applications
Animate Text using the 'Animate.css' Library
Conducting Maximum Likelihood Estimation with Truncated
Mortality Data
Detection and Reconstruction of Muscle Fibers from diceCT Image
Data
Association Analysis for Categorical Variables
Access to 'Google Ads' via the 'Windsor.ai' API
R Scripts in the Google Cloud via Cloud Run, Cloud Build and
Cloud Scheduler
Access to the 'Google Cloud Vision' API for Image Recognition,
OCR and Labeling
Send Error Reports to the Google Error Reporting Service API
Create Array Data from 2D Image Thumbnails via Google Image
Search
Retrieve Information from 'Google Knowledge Graph' API
Call Google's 'Natural Language' API, 'Cloud Translation' API,
'Cloud Speech' API and 'Cloud Text-to-Speech' API
An Interface to Google's Cloud Natural Language API
Encoding Coordinates into 'Google' Polylines
Working with Google's 'Public Data Explorer' DSPL Metadata Files
R Interface for Google 'Cloud Pub/Sub' REST API
Access Google Sheets using the Sheets API V4
Access the 'Google Tag Manager' API using R
Accesses Google Maps APIs to Retrieve Data and Plot Maps
Visualization of Functional Analysis Data
Algorithms for the Subject Graphs and Network Optimization
Fit Generalized Odds Rate Mixture Cure Model with Interval
Censored Data
Evaluation of Inequality Constrained Hypotheses Using GORICA
Tools for Data Analysis in Experimental Agriculture
Scroll Back to Top Icon in 'rmarkdown' and 'shiny'
functions to get public survey data in Japan
Structural Exploration of the Gene Ontology (GO) Knowledge Base
Maximum Likelihood Estimation of the Generalized Poisson
Distribution
Gaussian Processes for Pareto Front Estimation and Optimization
Derivative Free Gradient Projection Factor Rotation
Tools for Gaussian Process Modeling in Uncertainty
Quantification
Combining Tree-Boosting with Gaussian Process and Mixed Effects
Models
Comprehensive Statistical Analysis of Plant Breeding Experiments
Gaussian Processes for Estimating Causal Exposure Response
Curves
Differential Item Functioning in Generalized Partial Credit
Models
Gaussian Process for Functional Data Analysis
Solving Complex Game Problems using Gaussian Processes
Fast Gaussian Process Computation Using Vecchia's Approximation
Quantifying Group Performance in Individual Competitions
Utilities for the Open Geospatial Consortium 'GeoPackage' Format
Convert Chip ID of the GPL2015 into GeneBank Accession and
ENTREZID
General Purpose Gaussian Process Modelling
Generalized Partial Linear Models (GPLM)
Various R Programming Tools for Plotting Data
Generalized Partially Linear Tree-Based Regression Model
Gaussian Process Modeling of Multi-Response and Possibly Noisy
Datasets
Analysing and Plotting Genotype-Phenotype Maps
Gaussian Process Regression for Mortality Rates
Generalized Propensity Score Cumulative Distribution Function
Sequential Clustering Algorithm for Location Data
Gaussian Processes on Graphs and Lattices in 'Stan'
Use Large Language Models Directly in your Development
Environment
Identify Text Written by Large Language Models using 'GPTZero'
Maximum Likelihood Estimation of Multiple Membership Mixed
Models Used in Value-Added Modeling
Process GPX Files into R Data Structures
Prediction of G Quadruplexes and Other Non-B DNA Motifs
Probability of Detection for Grab Sample Selection
Granularity-Based Spatially Variable Genes Identifications
Graph-Constrained Estimation and Hypothesis Tests
Helps Grade Assignment Submissions that are R Scripts
Stochastic Quasi-Gradient Differential Evolution Optimization
Interactive Color Gradient Picker Using 'htmlwidgets' and the
Modified JS Script 'jquery-gradient-picker'
Easy Graphs for Data Visualisation and Linear Models for ANOVA
Supervised Machine Learning for Textual Data Using Transformers
and 'Quanteda'
Landscape Connectivity, Habitat, and Protected Area Networks
Guidelines for Reporting About Network Data
Comprehensive Analysis of Nucleotide Conversion Sequencing Data
Inference on Granger-Causality in the Frequency Domain
Graphical Analysis of Variance Using ggplot2
Gene-Ranking Analysis of Pathway Expression
Collection of Shiny Apps for Agricultural Research Data Analysis
A Wrapper of the JavaScript Library 'vis-graph3d'
Build Graphs for Landscape Genetics Analysis
Hierarchical Graph Clustering for a Collection of Networks
A Multi-Platform GUI for Drawing Customizable Graphs in R
An R Interface to the 'GraphHopper' Directions API
Statistical Methodology for Graphical Extreme Value Models
Graphical VAR for Experience Sampling Data
Additional Layout Algorithms for Network Visualizations
A Collection of Graphon Estimation Methods
Estimating and Displaying Population Attributable Fractions
Simulate Expression Data from 'igraph' Networks
Graceful 'ggplot'-Based Graphics and Other Functions for GAMs
Fitted Using 'mgcv'
Generating Time Series with Diverse and Controllable
Characteristics
Inflators for Australian Policy Analysis
Bayes Nets: 'RHugin' Emulation with 'gRain'
Explore Probability Distributions for Bivariate Temporal
Granularities
Gravitational and Magnetic Attraction of 3-D Vertical
Rectangular Prisms
Inference in Graphical Gaussian Models with Edge and Vertex
Symmetries
Parameter Inference and Optimal Designs for Grouped and/or
Right-Censored Count Data
Modified Poisson Regression of Grouped and Right-Censored Counts
Gene Registration from Expression and Time-Courses in R
Gradient-Based Recognition of Spatial Patterns in Environmental
Data
Clustering and Model Selection with the Integrated
Classification Likelihood
Routines for Writing Greek Letters and Mathematical Symbols on
the 'RStudio' and 'RGui'
Sensitivities of Prices of Financial Options and Implied
Volatilities
Combine Categories Using Greenacre's Method
Run 'Stan' Models to Interpret Green Crab Monitoring Assessments
Geospatial Regression Equation for European Nutrient Losses
(GREEN)
Graph-Based k-Sample Comparisons and Relevance Analysis in High
Dimensions
Mixed-Effects REML Incorporating Generalized Inverses
Simple and Scalable Statistical Modelling in R
Modelling Structured Dynamical Systems in 'greta'
Generalized Path Analysis for Social Networks
A Seamless Integration of 'Gretl' and 'R'
Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data
Toolbox for Model Building and Forecasting
Bivariate Copula Functions Based on Regular Grid
Miscellaneous Functions for "Grid" Graphics
Redraw Base Graphics Using 'grid' Graphics
Cluster-Preserving Multivariate Joint Grid Discretization
Approximate Optimal Transport Between Two-Dimensional Grids
A Simulation Tool to Determine the Required Sample Size for
Repertory Grid Studies
Improved Text Rendering Support for 'Grid' Graphics
Calculate Optical Parameters from Spindle Stage Measurements
Bayesian Inference for A|B and Bandit Marketing Tests
Genetic-Relationship-Matrix Structural Equation Modelling
(GRMSEM)
General Regression Neural Networks Package
Creating Flexible, Reproducible 'PDF' Reports
Generalized Regression on Orthogonal Components
Gromov-Hausdorff Type Distances for Labeled Metric Spaces
Version-Control for CRAN, GitHub, and GitLab Packages
Inferring Group Bayesian Networks using Hierarchical Feature
Clustering
Efficient Estimation of Grouped Survival Models Using the Exact
Likelihood Function
'Grouphmap' is an Automated One-Step Common Analysis of Batch
Expression Profile
Independent Component Analysis for Grouped Data
Group Sequential Design Probabilities - With Graphical User
Interface
Multiple Testing Procedure for Grouped Hypotheses
Simulating and Modeling Group (Pooled) Testing Data
Grouped Weighted Quantile Sum Regression
Wavelet Functional ANOVA Through Markov Groves
Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data
Implementation of the Vegetation Model ModVege
Multivariate Normal and Elliptically-Contoured Repeated
Measurements Models
Data Cleaner for Anthropometric Measurements
Simple Metrics to Summarize Growth Curves
Functional Analysis of Phenotypic Growth Data to Smooth and
Extract Traits
Bayesian reconstruction of growth velocity
Estimate Growth Rates from Experimental Data
Penalized Cox Model for High-Dimensional Data with Grouped
Predictors
Graphviz Pipeline Plot Based on Grids (grPipe: Graphviz
Pipeline)
Fitting User-Specified Models with Group Lasso Penalty
Standardization for Group Lasso Models with Categorical
Predictors
Regularization Paths for Regression Models with Grouped
Covariates
Group Sequential Analysis of Clinical Trials
Patterns and Statistical Differences Between Two Groups of
Strings
Alternative Implementations of Base R Functions
GRS Test for Portfolio Efficiency, Its Statistical Power
Analysis, and Optimal Significance Level Calculation
Testing Gene-Environment Interactions Through Genetic Risk
Scores
Tools for the Analysis of Gutenberg-Richter Distributions of
Earthquake Magnitudes
River Hydrograph Separation and Analysis
Gene Set Analysis with Fisher Combined Method
Two Functions for Generalized SARIMA Time Series Simulation
Estimate Parameters in the Generalized SBM
Generalized Structure Component Analysis- Latent Class Analysis
& Latent Class Regression
Group Sequential Designs with Negative Binomial Outcomes
Density-Based Gene Set Specificity Evaluation
Group Sequential Design with Non-Constant Effect
Robust Estimation in the Presence of Cellwise and Casewise
Contamination and Missing Data
Implement for 'GSEA' Enrichment Visualization
Download Google Sheets Using Just the URL
High Efficient Functions to Decode NFL Player IDs
Global Sea Level and Oxygen Isotope Data
Group Sequential Designs of Multi-Arm Multi-Stage Trials
Global Surface Summary of the Day ('GSOD') Weather Data Client
Base Class and Methods for 'gson' Format
Generalized Supervised Principal Component Regression
Group Sequential Refined Secondary Boundary
Gene Structure Survival using Topological Data Analysis
Spatial and Spatio-Temporal Geostatistical Modelling, Prediction
and Simulation
Graph-Based Sequential Change-Point Detection for Streaming Data
Utilities for Strings and Function Arguments
Estimation of the Generalized Symmetry Point, an Optimal
Cutpoint in Continuous Diagnostic Tests
Easily Create Presentation-Ready Display Tables
Generalizability Theory for Information Retrieval Evaluation
The Generalized Time-Dependent Logistic Family
Extending 'gt' for Beautiful HTML Tables
Estimating Public Transport Emissions from General Transit Feed
Specification (GTFS) Data
Converting Transport Data from GTFS Format to GPS-Like Records
Read and Write General Transit Feed Specification (GTFS) Files
Routing with 'GTFS' (General Transit Feed Specification) Data
General Transit Feed Specification (GTFS) Editing and Analysing
Tools
Perform and Display Google Trends Queries
Presentation-Ready Data Summary and Analytic Result Tables
Genome and Transcriptome Wide Association Study
Nombres Inscritos en Chile (1920 - 2021)
Access 'The Guardian' Newspaper Open Data API
Maps, Data and Methods Related to Guerry (1833) "Moral
Statistics of France"
Adjust Estimates of Learning for Guessing
Supervised Dimensional Reduction by Guided Partial Least Squares
Implementation of Sampling Formulas for the Unified Neutral
Model of Biodiversity and Biogeography, with or without Guild
Structure
Bootstrap Analyses of Sampling Uncertainty in Goodness-of-Fit
Statistics
Generalized UniFrac Distances, Distance-Based Multivariate
Methods and Feature-Based Univariate Methods for Microbiome
Data Analysis
Statistical Analysis of Monthly Background Checks of Gun
Purchases
A User-Oriented Statistical Toolkit for Analytical Variance
Estimation
Download and Process Public Domain Works from Project Gutenberg
Fast Calculation of the Likelihood of a Stochastic Survival
Model
Perform Global Vector Autoregression Estimation and Inference
Global Validation of Linear Models Assumptions
Interactive Exploratory Data Analysis Tool
Manhattan, Q-Q, and PCA Plots using 'ggplot2'
Identifying Significant SNPs in Genome Wide Association Studies
(GWAS) via Clustering
Exact Hardy-Weinburg testing for Genome Wide Association Studies
Non-Local Prior Based Iterative Variable Selection Tool for
Genome-Wide Association Studies
Comprehensive and Easy to Use Quality Control of GWAS Results
'REST' 'API' Client for the 'NHGRI'-'EBI' 'GWAS' Catalog
Multi-Site Stochastic Models for Daily Precipitation and
Temperature
Count and Continuous Generalized Variability Indexes
Rewrite of gWidgets API for Simplified GUI Construction
Toolkit Implementation of gWidgets2 for tcltk
Geographically Weighted Logistic Elastic Net Regression
Geographically Weighted Non-Negative Principal Components
Analysis
Geographically Weighted Partial Correlation Coefficient
Geographically Weighted Partial Correlation Mapper
Generalized Weighted Quantile Sum Regression
A Hybrid Model for Spatial Prediction Through Local Regression
Generalized Waring Regression Model for Count Data
Fits Geographically Weighted Regression Models with Diagnostic
Tools
GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)
Estimating Genome-Wide Significance for Whole Genome Sequencing
Studies, Either Single SNP Tests or Region-Based Tests
Genotype-by-Environment Interaction in Polygenic Score Models
Run GWAS/GWEIS Scans Using Binary Dosage Files
A Robust Bayesian Meta-Analysis for Estimating the Hubble
Constant via Time Delay Cosmography
R Interface for the 'H2O' Scalable Machine Learning Platform
Machine Learning Model Evaluation for 'h2o' Package
Sample Size Calculation in Hierarchical 2x2 Factorial Trials
Exposes the 'Uber' 'H3' Library to R Packages
Software to Perform Haar Fisz Transforms
Detecting Spatial Clustering Based on Connection Cost Between
Grids
Estimation, Simulation and Visualization of Hierarchical
Archimedean Copulae (HAC)
Wrapper for the 'Official Hacker News' API
Additional Tools for Splitting and Cleaning Data
A Tidy Framework to Hack Gene Expression Signatures
Iterative Extrapolation of Species' Haplotype Accumulation
Curves for Genetic Diversity Assessment
Analysis and Visualisation of Hydrogen/Deuterium Exchange Mass
Spectrometry Data
Incomplete Block Designs using Hadamard Matrix (HadIBDs)
Analysis of Plant Pathogen Pathotype Complexities, Distributions
and Diversity
Haplotype-Aware Hidden Markov Model for RNA
Authenticated HTTP Request Client for the 'Hakai' API
Highly Adaptive Lasso Conditional Density Estimation
Methods to Create Hierarchical Age Length Keys for Age
Assignment
Hierarchical Optimal Matching and Machine Learning Toolbox
Useful Functions for Handfully Manipulating and Analyzing Data
with Data.frame Format
Provides Easy Downloading Capabilities for the UK Parliament API
Package for Easy Interpretation of Statistical Methods
Inference of Trait Associations with SNP Haplotypes and Other
Attributes using the EM Algorithm
Inference of Chromosome-Length Haplotypes Using Genomic Data of
Single Gamete Cells
Analyzing Case-Parent Triad and/or Case-Control Data with SNP
Haplotypes
Estimate Haplotype Relative Risks in Case-Control Data
Statistical Analysis of Haplotypes with Traits and Covariates
when Linkage Phase is Ambiguous
Tool for Clustering Genotypes in Haplotypes
Manipulating DNA Sequences and Estimating Unambiguous Haplotype
Network with Statistical Parsimony
An Unified Time Series Event Detection Framework
Statistical Tests and Graphics for Hardy-Weinberg Equilibrium
Harmonic Mean p-Values and Model Averaging by Mean Maximum
Likelihood
Fast, Sensitive, and Accurate Integration of Single Cell Data
Wrangle Phylogenetic Distance Matrices and Other Utilities
Palettes Generated from All "Harry Potter" Movies
Full Featured Implementation of Hash Tables/Associative
Arrays/Dictionaries
Generate Short Unique YouTube-Like IDs (Hashes) from Integers
A Test for Comparing the Predictive Accuracy of Two Sets of
Forecasts
Import and Export 'SPSS', 'Stata' and 'SAS' Files
Hawkes process simulation and calibration toolkit
Estimation of Hawkes Processes from Binned Observations
Conditional Treatment Effect for Competing Risks
Identifying Foggy and Cloudy Images by Quantifying Haziness
Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'
Hierarchical Bayesian Modeling of Decision-Making Tasks
Hierarchical Bayesian Benefit-Risk Assessment Using Discrete
Choice Experiment
Hill/Bliss Independence Model for Combination Vaccines
Hierarchical Bayesian Analysis of Recognition Memory
Hierarchical Bayesian Small Area Estimation
Hierarchical Bayesian Space-Time Models for Gaussian Space-Time
Data
Design and Analysis of Hierarchical Composite Endpoints
Hierarchical Clustering of Univariate (1d) Data
Regression with a Large Number of Potential Explanatory
Variables
Causal Discovery from Discrete Data using Hidden Compact
Representation
Calculates Significance Criteria and Power for a Single Arm
Trial
Group Sequential Design for Historical Control Trial with
Survival Outcome
Using Historical Controls for Designing Phase II Clinical Trials
Change-Point Analysis of High-Dimensional Time Series via Binary
Segmentation
Bayesian Mediation Analysis with High-Dimensional Data
High Dimensional Bayesian Ridge Regression without MCMC
Estimation of Conditional Average Treatment Effects with
High-Dimensional Data
High Dimensional Confidence Interval Based on Lasso and
Bootstrap
High Dimensional Supervised Classification and Clustering
Clustering High Dimensional Data with Hidden Markov Model on
Variable Blocks
Detect Change Points in Means of High Dimensional Data
Easy Manipulation of Out of Memory Data Sets
Sample Size Calculation for High Dimensional Classification
Study
Use Known Groups in High-Dimensional Data to Derive Scores for
Plots
Interface to the 'HDF5' Binary Data Format
Tests for High Dimensional Generalized Linear Models
A Batch Process for High Dimensional Imputation
Statistical Inference for Noisy Vector Autoregression
Distribution-Free Exact High Dimensional Low Sample Size
k-Sample Tests
ADMM for High-Dimensional Mediation Models
High-Dimensional Regression with Measurement Error
Methods for Mediation Analysis with High-Dimensional Mediators
A Multiple Testing Procedure for High-Dimensional Mediation
Hypotheses
Benchmarking and Visualization Toolkit for Penalized Cox Models
High-Dimensional Orthogonal Maximin Distance Designs
Leland Wilkinson's Algorithm for Detecting Multidimensional
Outliers
Principal Component Analysis in High-Dimensional Data
Hierarchical Dirichlet Process Generalized Linear Models
Highest Density Regions and Conditional Density Estimation
Data for the 'Health Data Science in R' Online Textbook
Multivariate and Functional Spatial Scan Statistics
High Dimensional Stimulation Immune Mapping ('HDStIM')
High Dimensional Time Series Analysis Tools
The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD
Algorithm
Compose Sentences to Describe Comparisons
Financial Projections and Planning for Health Care Practices
The Machine Learning and AI Modeling Companion to 'healthyR'
The Time Series Modeling Companion to 'healthyR'
Easily Install and Load the 'healthyverse'
Easily Download Heaps of PDF and CSV Files
Heat exchange calculations during physical activity
Fit Statistic for Binary Dependent Variable Models
Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2'
Fit Normal or Student-t Heckman Selection Models
Markov Models for Health Economic Evaluations
Decode and Validate HEIMS Data from Department of Education,
Australia
Providing 'stringsAsFactors=FALSE' Variants of 'data.frame()'
and 'as.data.frame()'
Minimal Examples of Using Rust Code in R
Access London Natural History Museum Host-Helminth Record
Database
Tools for Environmental Analyses, Ecotoxicology and Various R
Functions
Hierarchical Ensemble Methods for Directed Acyclic Graphs
Visualizing Hypothesis Tests in Multivariate Linear Models
'sf'-Based Interface to the 'HERE' REST APIs
Hierarchical Exponential-Family Random Graph Models
Marker-Based Estimation of Heritability Using Individual Plant
or Plot Data
Heritability of Gene Expression for Next-Generation Sequencing
Efficient Sequential and Batch Estimation of Univariate and
Bivariate Probability Density Functions and Cumulative
Distribution Functions along with Quantiles (Univariate) and
Nonparametric Correlation (Bivariate)
Health Economic Simulation Modeling and Decision Analysis
Test of No Main and/or Interaction Effects in Functional Data
Spatial Functions for Heterogeneity and Climate Variability
Gaussian Graphical Model-Based Heterogeneity Analysis
Copula Graphical Models for Heterogeneous Mixed Data
Heteroskedastic Gaussian Process Modeling and Design under
Replication
MLE and Bayesian Estimation of Heteroskedastic Ordered Probit
(HETOP) Model
Assessing Heterogeneity in the Utility of a Surrogate Marker
Testing for a Treatment Effect Using a Heterogeneous Surrogate
Marker
Heterogeneous Treatment Effects in Regression Analysis
Fisherian and Neymanian Methods for Detecting and Measuring
Treatment Effect Variation
Structural Handling of Finnish Personal Identity Codes
Heuristic Capacitated Vehicle Routing Problem Solver
Heuristics Including Take the Best and Unit-Weight Linear
Discovery of Process Models with the Heuristics Miner
Integrated Tool for Hairping Extraction of RNA Sequences
Estimate Hierarchical Feature Regression Models
Holonomic Gradient Method and Gradient Descent
Heterogeneous Graphical Model for Non-Negative Data
Download and Import the HUGO Gene Nomenclature Committee
('HGNC') Data Set into R
Identify and Correct Invalid HGNC Human Gene Symbols and MGI
Mouse Gene Symbols
Hierarchical and Geographically Weighted Regression
Statistical Analysis and Data Display: Heiberger and Holland
Heller-Heller-Gorfine Tests of Independence and Equality of
Distributions
Age-Structured Spatio-Temporal Models for Infectious Disease
Counts
Calculate and Visualize the Herfindahl-Hirschman Index
Hierarchical Heterogeneity Analysis via Penalization
Hidden Hybrid Markov/Semi-Markov Model Fitting
The Hilbert-Huang Transform: Tools and Methods
Individual-Level, Summary-Level and Single-Step Bayesian
Regression Model
The All-Configurations, Maximum-Interaction F-Test for Hidden
Additivity
Create HIDECAN Plots for Visualising Genome-Wide Association
Studies and Differential Expression Results
Set Data Visualization Using Hierarchies
Estimation and Tests of Hierarchical F-Statistics
Hierarchical Clustering-Based Portfolio Allocation Strategies
Testing Two-Sample Mean in High Dimension
Implementation of Voting Rules Electing the Candidate with
Highest Median Grade
Code Syntax Highlighting using the 'Prism.js' Library
Two-Sample Tests for High-Dimensional Mean Vectors
Feature Selection for High Dimensional Survival Data
Design of High-Order Portfolios Including Skewness and Kurtosis
Determining and Evaluating High-Risk Zones
High-Throughput Screening for Plate Based Essays
Simultaneous Critical Values for t-Tests in Very High Dimensions
Statistical Inference for Online Learning and Stochastic
Approximation via HiGrad
Hilbert Similarity Index for High Dimensional Data
Integral Analysis of Diversity Based on Hill Numbers
Create Hillshade Relief Maps Using Ray-Tracing
High Mach Finds Routes for Supersonic Aircraft
Simulating the Development of h-Index Values
H-Index Calculator using Data from a Web of Science (WoS)
Citation Report
Tools for Hypothesis Testing Based on Hypergeometric
Intersection Distributions
Parentage Assignment using Bi-Allelic Genetic Markers
Non-Targeted Fluxomics on High-Resolution Mass-Spectrometry Data
Hierarchical Item Response Theory Models
Summary Statistics for Histogram/Count Data
Data Sets from the History of Statistics and Data Visualization
Construction of Regular and Irregular Histograms with Different
Options for Automatic Choice of Bins
Non-Longitudinal Bayesian Historical Borrowing Models
Longitudinal Bayesian Historical Borrowing Models
Enhanced Command History Tracking for R Sessions and Dynamic
Documents
"Hit and Run" and "Shake and Bake" for Sampling Uniformly from
Convex Shapes
Six-Year Chronological Data of HIV and ART Cases in Pakistan
Hierarchical Joint Analysis of Marginal Summary Statistics
Conversion Tools for HK80 Geographical Coordinate System
Spatial Extreme Value Analysis with the Hierarchical Model of
Reich and Shaby (2012)
Apps and Data for the Book "Introduction to Statistics"
Diagnostic Tools for Hierarchical (Multilevel) Linear Models
Hidden Markov Cognitive Diagnosis Models for Learning
Fit Statistical Models Using Hamiltonian Monte Carlo
Read Human Mortality Database and Human Fertility Database Data
from the Web
The H-Measure and Other Scalar Classification Performance
Metrics
Hidden Markov Models with Discrete Non-Parametric Observation
Distributions
Markov Regime Switching Copula Models Estimation and Goodness of
Fit
A Fast Solver for Henderson Mixed Model Equation via Row
Operations
Determine the Number of States in Hidden Markov Models via
Marginal Likelihood
Analysing Accelerometer Data Using Hidden Markov Models
"Mixture and Hidden Markov Models with R" Datasets and Example
Code
Fit Hidden Markov Models using Template Model Builder
Hypothesis Testing and Power Calculations for Comparing
Metagenomic Samples from HMP
Statistical Object Oriented Data Analysis of RDP-Based Taxonomic
Trees from Human Microbiome Data
Flux Estimation with Static Chamber Data
Hierarchical Model of Species Communities
Group Association Test using a Hidden Markov Model
Half-Normal Plots with Simulation Envelopes
Collect and Retrieve Annotation Data for Various Genomic Data
Using Different Webservices
Higher Order Assortativity for Complex Networks
Download and Visualize Essential Climate Change Data
Statistics for Holland's Theory of Vocational Choice
A Tidy Interface for Simulating Multivariate Data
An User-Friendly R 'shiny' Application for Multi-Omics Data
Integration and Analysis
Methods for Closed Testing with Simes Inequality, in Particular
Hommel's Method
Quick Access to Homologene and Gene Annotation Updates
BFV, BGV, CKKS Schema for Fully Homomorphic Encryption
Access Men's Basketball Play by Play Data
Transmissions and Receptions in a Hop by Hop Network
Hierarchical Ordered Probit Models with Application to Reporting
Heterogeneity
Calculate Hopkins Statistic for Clustering
Supplemental Functions and Datasets for "Handbook of Regression
Methods"
Horn's (1965) Test to Determine the Number of Components/Factors
Nonlinear Regression using Horseshoe Prior
Building Networks of Hospitals Through Patients Transfers
Hotelling T-Square and Confidence Ellipse
CSS Animations for 'shiny' Button Elements
A lower bound for the number of correct rejections
Calculate How many Imputations are Needed for Multiple
Imputation
High Performance Cluster Models Based on Kiefer-Wolfowitz
Recursion
The One- And Two-Sided Hodrick-Prescott Filter
High-Probability Lower Bounds for the Total Variance Distance
Superefficient Estimation of Future Conditional Hazards Based on
Marker Information
Miscellaneous Convenience Functions and Dataset
Regularization Paths for Lasso or Elastic-Net Penalized Huber
Loss Regression and Quantile Regression
Toolkit for Data Analytics in Human Resources
Additional Themes, Theme Components and Utilities for 'ggplot2'
Nonparametric Assessment Between Competing Risks Hazard Ratios
Group Variable Selection for Quantile and Robust Mean Regression
Datasets, Functions and Scripts for Semiparametric Regression
Supporting Harezlak, Ruppert & Wand (2018)
Homogenous Segmentation for Spatial Lines Data
A Handbook of Statistical Analyses Using R (1st Edition)
A Handbook of Statistical Analyses Using R (2nd Edition)
A Handbook of Statistical Analyses Using R (3rd Edition)
Calculation of Covariance Between Markers for Half-Sib Families
Homogeneity and Sparsity Detection Incorporating Prior
Constraint Information
Hierarchical Bayesian Species Distribution Models
Sets of Numbers Implemented with Hash Tables
Phasing, Pedigree Reconstruction, Sire Imputation and
Recombination Events Identification of Half-sib Families Using
SNP Data
Hidden Smooth Polynomial Regression for Rupture Detection
Estimation of Recombination Rate and Maternal LD in Half-Sibs
Hierarchical Shrinkage Stan Models for Biomarker Selection
Reweighted Marginal Hypothesis Tests for Clustered Data
Causal Inference with Tree-Based Machine Learning Algorithms
Bayesian Logistic Regression with Heavy-Tailed Priors
Convert 'HTML' to 'R' with a 'Shiny' App
Simple Scraping and Tidy Webpage Summaries
Facilitates Automated HTML Report Creation
Haplotype Trend Regression with eXtra Flexibility (HTRX)
Clustering High-Throughput Transcriptome Sequencing (HTS) Data
High Throughput Sequencing of Stable Isotope Probing Data
Analysis
Report Errors in Web Applications with 'Problem Details' (RFC
7807)
Perform HTTP Requests and Process the Responses
Get Data from the French National Database on Water 'Hub'Eau'
Providing Data from the US Department of Housing and Urban
Development
Distinct Colour Palettes Based on 'iwanthue'
Compute HUM Value and Visualize ROC Curves
Calculate Water Vapor Measures from Temperature and Dew Point
High-Performance Stemmer, Tokenizer, and Spell Checker
Zero-Inflated and Hurdle Modelling Using Bayesian Inference
Models Hurricane Wind Speed, Wind Direction, and Wind Damage
Explore and Map County-Level Hurricane Exposure in the United
States
Easily Create and Style Tables for LaTeX, HTML and Other Formats
Constructing Hierarchical Voronoi Tessellations and Overlay
Heatmaps for Data Analysis
Models and Tests for Departure from Hardy-Weinberg Equilibrium
and Independence Between Loci
Objective Bayesian Testing for the Hardy-Weinberg Equilibrium
Problem
HTML Writer - Outputs R Objects in HTML Format
Hybrid Design for Phase I Dose-Finding Studies
Analysis of Host-Associated Microbiomes from Hybrid Organisms
An R Package for the Stochastic Simulation of Disease Spreading
in Dynamic Networks
Function that Creates a Heat Map from Hybridization Data
Hybrid Time Series Forecasting Using Error Remodeling Approach
1d Water Level Interpolation along the Rivers Elbe and Rhine
Flood Extents and Durations along the Rivers Elbe and Rhine
Extract Event Statistics in Hydrologic Time Series
Groundwater Data Presentation and Interpretation
Estimating Water Retention and Infiltration Model Parameters
using Experimental Data
Detect and Characterize Sub-Daily Flow Fluctuations
Hydrologic Indices for Daily Time Series Data
Hydrological Tools for Handling Hydro-Meteorological Data
Records
Hydrological Tools for Handling Hydro-Meteorological Data from
Argentina and Chile
Hydrologic Model Evaluation and Time-Series Tools
Machine Learning of Hyperrectangular Gating Strategies for
High-Dimensional Cytometry
Hypergeometric Function of a Matrix Argument
Overlap Detection in n-Dimensional Space
Attribute Sampling Plan with Exact Hypergeometric Probabilities
using Chebyshev Polynomials
Work with Hyperspectral Data, i.e. Spectra + Meta Information
(Spatial, Time, Concentration, ...)
High Dimensional Geometry, Set Operations, Projection, and
Inference Using Kernel Density Estimation, Support Vector
Machines, and Convex Hulls
Tools for Processing and Analyzing Files from the Hydrological
Catchment Model HYPE
Wrapper for the 'Hypothes.is' Web Annotation Service
A Tool Used to Conduct Hypsometric Analysis of a Watershed
Multicriteria Risk Management using Zero-Sum Games with
Vector-Valued Payoffs that are Probability Distributions
Fit the Hysteretic Threshold Autoregressive Model
Tools for Modeling Rate-Dependent Hysteretic Processes and
Ellipses
Get Pedestrian Frequency Data from the 'Hystreet' Project
Internationalization Data from the 'Unicode CLDR' in Tabular
Form
Functions to Work with 'incidence2' Objects
Data and Functions for "An Intro. to Accept. Samp. & SPC/R"
Two-Stage Adaptive Dose-Finding Clinical Trial Design
Analysis of Intra Annual Density Fluctuations
Interaction Difference Test for Prediction Models
Formulations of the International Association for the Properties
of Water and Steam
Thermophysical Properties of Water and Steam
Model Selection for Index of Asymmetry Distribution
Cleaning and Visualizing Implicit Association Test (IAT) Data
Compute Effect Sizes and Reliability for Implicit Association
Test (IAT) Data
Scoring Algorithm for the Implicit Association Test (IAT)
Implicit Association Test Scores Using Robust Statistics
Iterative Bayesian Additive Regression Trees Descriptor
Selection Method
Functions and Datasets for the Data Science Course at IBAW
R Wrapper for Istanbul Municipality Open Data Portal
Item Based Collaborative Filtering for Multi-Trait and
Multi-Environment Data
Regression Methods for IBD Linkage with Covariates
Simulation of Chromosomal Regions Shared by Family Members
Initial Basic Feasible Solution for Transportation Problem
Toolkits to Develop Individual-Based Models in Infectious
Disease
Individual Based Model Population Simulation
Generate Personality Insights Sunburst Diagrams
Model Agnostic Instance Level Variable Attributions
R API to Interactive Brokers Trader Workstation
Integrating Biomarker-Based Assessments and Radarchart Creation
Inequality Constrained Inference in Linear Normal Situations
A Copy Number and Expression-Based Classifier for Breast Tumours
Infer Community Assembly Mechanisms by Phylogenetic-Bin-Based
Null Model Analysis
In-Depth Characterization and Analysis of Mutational Signatures
('ICAMS')
Optimal Designs for Nonlinear Statistical Models by Imperialist
Competitive Algorithm (ICA)
Subsetting using Focused Identification of the Germplasm
Strategy (FIGS)
Integrative Conditional Autoregressive Horseshoe Model
Calibrates and Reweights Units in Samples
Data-Driven Design of Targeted Gene Panels for Estimating
Immunotherapy Biomarkers
Facilitating Estimation of the Intraclass Correlation
Coefficient
Calculation of Sample Size and Power for ICC
Cox Model with Interval-Censored Starting Time of a Covariate
Multilevel Model Intraclass Correlation for Slope Heterogeneity
Facilitates Clustered Binary Data Generation, and Estimation of
Intracluster Correlation Coefficient (ICC) for Binary Data
Intraclass Correlation Coefficient for Count Data
Computation of the Double-Entry Intraclass Correlation
An Ensemble Method for Interval-Censored Survival Data
Estimates the Intraclass Correlation Coefficient for Trajectory
Data
International Classifcation of Diseases (ICD) Data
Metadata Processing for the German Modification of the ICD-10
Coding System
EM by the Method of Weights for Incomplete Categorical Data in
Generlized Linear Models
'ICD' Programs for Injury Categorization in R
Identification of Cancer Dysfunctional Subpathway with Omics
Data
Individual Conditional Expectation Plot Toolbox
Incremental Cost-Effectiveness Inference using Two Unbiased
Samples
Analyzing High-Throughput Single Cell Sequencing Data
Regression Models for Interval Censored Data
Accompanion to the Book on Interval Censoring by Bogaerts,
Komarek, and Lesaffre
Study Design and Data Analysis in the Presence of Error-Prone
Diagnostic Tests and Self-Reported Outcomes
Provides User Tokens for Access to ICES Web Services
DATRAS Trawl Survey Database Web Services
Functions to Interact with the ICES Data Submission Utility
(DATSU)
Run Quality Checks on Data Prior to Submission to ICES
Stock Assessment Graphs Database Web Services
Functions to Support the ICES Transparent Assessment Framework
Link to the ICES Vessel Monitoring System and Logbook Database
Web Services
Estimation of Number of Clusters and Identification of Atypical
Units
Information Criteria for Generalized Linear Regression
Fit Generalized Odds Rate Hazards Model with Interval Censored
Data
Visualization and Tools for Ichimoku Kinko Hyo Strategies
A Button-Based GUI for Financial and Economic Data Analysis
Empirical Bayes Variable Selection via ICM/M Algorithm
Data Analysis for ODS and Case-Cohort Designs with
Interval-Censoring
Global Triangular and Penta-Hexagonal Grids Based on Tessellated
Icosahedra
Reproducible Data Retrieval from the ICPSR Archive
Simultaneous Confidence Intervals for Ranks
A Modified Random Survival Forest Algorithm
Tools for Exploring Multivariate Data via ICS/ICA
Tandem Clustering with Invariant Coordinate Selection
Interval-Censored Sequence Kernel Association Test
Outlier Detection Using Invariant Coordinate Selection
Data Smoothing by Interpolating Cubic Splines
Semiparametric Regression Analysis of Interval-Censored Data
Estimating and Testing the Number of Interesting Components in
Linear Dimension Reduction
Indirect Cross-Validation (ICV) for Kernel Density Estimation
Vector Fields from Spatial Time Series of Population Abundance
Datasets and Functions for the Class "Modelling and Data
Analysis for Pharmaceutical Sciences"
R Interface to the US Census Bureau International Data Base API
Update Chinese ID Card Number to Eighteen Digits
Interactive Application for Analyzing Representativeness and
Nonresponse Bias
Agnostic, Idiomatic Data Filter Module for Shiny
Easy Manipulation of IDEAM's Climatological Data
Individual and Group Farm Sustainability Assessments using the
IDEA4 Method
Efficient Designs for Discrete Choice Experiments
Inference in Randomized Controlled Trials with Death and
Missingness
Various Methods for Estimating Intrinsic Dimension
Isolate-Detect Methodology for Multiple Change-Point Detection
Integrative Differential Network Analysis in Genomics
Shiny App. Idiograms with Marks and Karyotype Indices
Interpreting Differences Between Mean ACT Scores
Assessment of Individual Identity in Animal Signals
Identification of Dysregulated MiRNAs Based on MiRNA-MiRNA
Interaction Network
Infectious Disease Model Library and Utilities
A Network Tool to Dissect Spatial Community Ecology
Durability of Vaccine Efficacy Against SARS-CoV-2 Infection
'Utilities of Institute of Data Analyses and Process Design
(www.zhaw.ch/idp)'
Imprecise Dirichlet Process for Survival Analysis
An Interactive Detector for Spatial Associations
Composite Spectra Analysis (CSA) for High-Resolution Mass
Spectrometry Analyses
Intrinsic Peak Analysis (IPA) for HRMS Data
Parser for mzML, mzXML, and netCDF Files (Mass Spectrometry
Data)
United Formula Annotation (UFA) for HRMS Data Processing
Exhaustive Chemical Enumeration for United Formula Annotation
Convert Files to and from IDX Format to Vectors, Matrices and
Arrays
Irucka Embry's Miscellaneous USGS Functions
Irucka Embry's Miscellaneous USGS Data Collection
Functions to set and get the IEEE rounding mode
Irucka Embry's Miscellaneous Data Collection
Irucka Embry's Miscellaneous Text Collection
Toolkit for Item Factor Analysis with 'OpenMx'
Isolation-Forest Based 'CNV' Detection from 'NGS' Data
Italian Fiscal Code ('Codice Fiscale') Utilities
Intuitionistic Fuzzy Multi-Criteria Decision Making Methods
Machine Learning Time Series Forecasting
Instagram 2014 Vancouver Top Colour Dataset
Score Taxon-Level IgA Binding in IgA-Seq Experiments
Statistical Tests and Utilities for Genetic Association
Guided Analytics for Testing Manufacturing Parameters
Simulate Impact of Different Urban Policies Through a General
Equilibrium Model
Computational Tools for the IG and IGL Copula Families
Interpreting Glucose Data from Continuous Glucose Monitors
A Shiny Interface for Simple Data Management
A Collection of Network Data Sets for the 'igraph' Package
Convert iGraph graps to SoNIA .son files
Integrative Gene Set Enrichment Analysis Approaches
For Writing Geneva Graduate Institute Documents
Download Data from the International House Price Database
Computing Individual Components of the Gini and the Theil
Indices
Analyzing Data Through of Percentage of Importance Indice
(Production Unknown) and Its Derivations
Modeling Within Individual Variability as Predictor
Comprehensive TIFF I/O with Full Support for 'ImageJ' TIFF Files
Linear Regression Based on 'ILSE' for Missing Data
Incremental Mixture Approximate Bayesian Computation (IMABC)
Binarize Images for Enhancing Optical Character Recognition
Implementation of the Canny Edge Detector for Images
Implementation of the Unsupervised Smooth Contour Line Detection
for Images
Find Corners in Digital Images with FAST-9
Implementation of the Harris Corner Detection for Images
Image Processing Functionality using the 'dlib' Package
Convolutional Neural Network for Face Detection
Aids in Processing and Plotting Data from a Lemna-Tec
Scananalyzer
Image Statistics Based on Processing Fluency
Image Processing Library Based on 'CImg'
Extra Image Processing Library Based on 'imager'
Deep Learning Models for Image Segmentation
Simple 'htmlwidgets' Image Viewer with WebGL Brightness/Contrast
Simulate General Insurance Policies and Losses
IMAGing engINEs, Tools for Application of Image Filters to Data
Matrices
Preprocessing Algorithms for Imbalanced Datasets
Relocated Data Oversampling for Imbalanced Data Classification
Mass Cytometry S4 Class Structure Pipeline for Images
Efficiency of Cluster Sampling for Crop Surveys
Index of Multiple Deprivation Data for the UK
Get Movie, Television Data from the 'imdb' Database
Likelihood Methods for Mediation Analysis
An Interface to IMF (International Monetary Fund) Data JSON API
Non-Linear Height Diameter Models for Forestry
Artificial Intelligence Based Machine Learning Algorithms for
Height Diameter Relationships of Conifer Trees
Infinite Mixtures of Infinite Factor Analysers and Related
Models
Gaussian Mixture Model for Multi-Omics Data Integration
Poly-Pharmacology Toolkit for Traditional Chinese Medicine
Research
Item Response Models for Multiple Ratings
Iterative Max-Min Entropy Margin-Maximization with Interaction
Terms for Feature Selection
Machine Learning Algorithms Fitting and Validation for Forestry
Tunable Simulation of B- And T-Cell Receptor Repertoires
Neural Networks for Predicting Volume of Forest Trees
CSS Layouts (Grid and Flexbox) Implementation for R/Shiny
Calculation and Visualization of the Impact Effect Size Measure
Quantification of Population-Level Impact of Vaccination
Mechanical Loading Prediction Through Accelerometer Data
Imprecise Imputation for Statistical Matching
Array Operations for Arrays of Mismatching Sizes
Compute Scores for Different Implicit Measures
Convert Between Bookmaker Odds and Probabilities
Analyzing Data Through of Percentage of Importance Indice and
Its Derivations
Enables Importing/Loading of Packages or Functions While
Creating an Alias for Them
Download and Manage Open Data from INEGI
Calculate Birth Year-Specific Probabilities of Immune Imprinting
to Influenza
Improved Shrinkage Estimations for Multiple Linear Regression
Classification Trees with Imprecise Probabilities
Imputation of Financial Time Series with Missing Values and/or
Outliers
Ease the Implementation of Imputation Methods
A Collection of Methods for Left-Censored Missing Data
Imputation
Longitudinal Imputation of Categorical Variables via a Joint
Transition Model
Imputation Methods for Multivariate Multinomial Data
A General Multivariate Imputation Framework
Impute Missing Rare Earth Element Data in Zircon
Robust Multiple Imputation with Generalized Additive Models for
Location Scale and Shape
Test Bench for the Comparison of Imputation Methods
Imputing the Last Largest Censored Observation(s) Under Weighted
Least Squares
Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and
Other Metrics)
Immune Cell Gene Signatures for Profiling the Microenvironment
of Solid Tumours
Information Matrix Test for Generalized Partial Credit Models
Volume Prediction of Trees Using Linear and Nonlinear Allometric
Equations
Initialization Algorithms for Partitioning Cluster Analysis
Analysing Inbreeding Based on Genetic Markers
Pipe-Friendly Vector Replacement with Case Statements
Internal Control Analysis of Translatome Studies by Microarrays
Incremental Calculation of Dynamic Time Warping
Incremental Graphlet Counting for Network Optimisation
Compute, Handle, Plot and Model Incidence of Dated Events
Compute, Handle and Plot Incidence of Dated Events
Estimate Incidence and Prevalence using the OMOP Common Data
Model
Implements Empirical Bayes Incidence Curves
Generates Incidence Matrices and Bipartite Graphs
Comparison of Means for the Incomplete Paired Data
Parametric Time-to-Event Analysis with Variable Incubation
Phases
Insertion Deletion Analysis While Accounting for Possible
Missing Data
Estimates Weights for Confounding Control for Continuous-Valued
Exposures
Nonparametric Independence Tests Based on Entropy Estimation
Constructing and Analyzing Complex Selection Indices
Tests of Independence Between Innovations of Generalized Error
Models
Influence Diagnostics in Statistical Models
Relationship Between Species and Groups of Sites
Elicitation of Independent Conditional Means Priors for
Generalised Linear Models
Obtaining and Estimating Unidimensional and Multidimensional IRT
Dual Models
Tests of Independence and Analysis of Dependence Between Point
Processes in Time
Data, Functions and Support Materials from the Book "industRial
Data Science"
Integrate INEGI’s (Mexican Stats Office) API with R
Measuring Inequality, Concentration, and Poverty
Two-Dimensional Decomposition of the Theil Index and the Squared
Coefficient of Variation
Performing Inference on Networks with Regularization
Interpolation and Extrapolation for Species Diversity
Inferring Cell-Specific Gene Regulatory Network
Inference About the Standardized Mortality Ratio when Evaluating
the Effect of a Screening Program on Survival
Methods for Causal Inference with Interference
Estimate and Account for Tumor Purity in Cancer Methylation Data
Analysis
Melt Curve Fitting and Melt Shift Analysis
Tools for Detecting Influential Data in Mixed Effects Models
Case Influence in Structural Equation Models
Identify Influential Observations in Binary Classification
Software Tools to Quantify Structural Importance of Nodes in a
Network
Identification and Classification of the Most Influential Nodes
Information Decomposition of Two-Phase Experiments
Data Exploration with Information Theory (Weight-of-Evidence and
Information Value)
Multiple Imputation for Informative Censoring
Sensitivity Analysis Informed by a Test for Bias
Calculates the Probability of Informed Trading (PIN)
Effects and Importances of Model Ingredients
Translate ICD-9 into Injury Severity Score
A Toolkit for Sports Injury Data Analysis
Bayesian Latent Gaussian Modelling using INLA and Extensions
Multivariate Joint Modeling for Longitudinal and Time-to-Event
Outcomes with 'INLA'
Spatial and Spatio-Temporal Models using 'INLA'
Color Schemes for the USGS Idaho National Laboratory Project
Office
Functions to Inline C, C++, Fortran Function Calls from R
Convert Inline Comments to Documentation
Infix Operators for Detection, Subsetting and Replacement
Analyse Text Documents Using Ecological Tools
Quality Control of Climatological Daily Time Series
Ecological Database of the World's Insect Pathogens
Tools to Easily Download Data from INSEE BDM Database
Easy Access to Model Information for Various Model Objects
Probabilistic Verbal Autopsy Coding with 'InSilicoVA' Algorithm
High-Dimensional Changepoint Estimation via Sparse Projection
Inspection, Comparison and Visualisation of Data Frames
Validation of Arguments and Objects in User-Defined Functions
IN-vivo reSPonsE Classification of Tumours
Inferring Shared Modules from Multiple Gene Expression Datasets
with Partially Overlapping Gene Sets
Access to Instagram Ads via the 'Windsor.ai' API
Using R to Install Stuff on Windows OS (Such As: R, 'Rtools',
'RStudio', 'Git', and More!)
Pre-Compiled 'CmdStan' Models in R Packages
A Collection of Insurance Datasets Useful in Risk Classification
in Non-life Insurance
Interactive Add-on Functionality for 'intamap'
Semiparametric Competing Risks Regression under Interval
Censoring
AUC Estimation of Interval Censored Survival Data
Integrating Multiple Modalities of High Throughput Assays Using
Item Response Theory
An Implementation of Interaction Graphs of Aleks Jakulin
Joint Modeling of the Gene-Expression and Bioassay Data, Taking
Care of the Effect Due to a Fingerprint Feature
Integrated Prediction using Uni-Variate and Multivariate Random
Forests
A Group of Fun Datasets of Various Sizes and Differing Levels of
Quality
Intensity Analysis of Spatial Point Patterns on Complex Networks
Power Analyses for Interaction Effects in Cross-Sectional
Regressions
Calculate Estimates in Models with Interaction
Comprehensive, User-Friendly Toolkit for Probing Interactions
Calculates Critical Test Statistics to Control False Discovery
Rates in Marginal Effects Plots
Compute Chi-Square Measures with Corrections
Multiple Correspondence Analysis Based on Interpretive
Coordinates
Interaction Analysis of Repeated Measure Data
GUI to Analyse qPCR Results after PMA Treatment or not
Multiplicative Interaction Models Diagnostics and Visualization
Interference Color Charts for Polarized Light Microscopy
Coercion Routines for Network Data Objects
Scheduling Interim Analyses in Clinical Trials
App for Scheduling Interim Analyses in Clinical Trials
Converts Tabular Data to Interleaved Vectors
Importing Interlinearized Corpora and Dictionaries as Produced
by Descriptive Linguistics Software
Plot the Effects of Variables in Interaction Terms
Fit Interpretable Machine Learning Models
Estimate the Confidence Interval and Interpret Step by Step
Interpreting High Resolution Mass Spectra
Binary Classifier and Regression Model Interpretation Functions
Flexible Tools for Estimating Interactions
Simulation of Inter-Related Genomic Datasets
Weighted Logrank Tests and NPMLE for Interval Censored Data
Time-Weighted Averaging for Interval Data
Tools to Deal with Interval-Valued Responses in Questionnaires
Tools for Working with Points and Intervals
Hypothesis Testing Using the Overlapping Interval Estimates
Tools and Statistical Procedures in Plant Science
A Numerical Implementation of Interval-Valued Kriging
Mediation Analysis using Interventional Effects
Integrative Clustering of Multiple Genomic Dataset
Modeling and Forecasting Financial Intraday Signals
Quantify and Control Reproducibility in High-Throughput
Experiments
An R Package for Factor Model Asset Pricing
Sample Size Planning Based on Intrinsic Kappa Value
Analysis of Time-Ordered Event Data with Missed Observations
Reproducible Integrated Species Distribution Models Across
Norway using 'INLA'
Interval and Enum-Type Representation of Vectors
Analyse Biological Invasion Costs with the 'InvaCost' Database
Inventory Analytics, Pricing and Markdowns
Reverse Engineers Regular Expression Patterns for R Objects
Obtains the Log Likelihood for an Inverse Gamma Stochastic
Volatility Model
Threshold Regression that Fits the (Randomized Drift) Inverse
Gaussian Distribution to Survival Data
Inverse Stable Prior for Widely-Used Exponential Models
Tools for Exploring Multiple Response Data
Graphical Tools for Exploring Data with 'iNZight'
Tools for Exploring Regression Models with 'iNZight'
A Unified Framework for Input-Output Operations in R
Iterated Ordinary Least Squares Regression
Calling Copy Number Alterations in Amplicon Sequencing Data
Functions for Smoothing with Infinite Order Flat-Top Kernels
Reproducible Input-Output Economics Analysis, Economic and
Environmental Impact Assessment with Empirical Data
Iota Inter Coder Reliability for Content Analysis
Data Analysis for IP Addresses and Networks
Iterative Pruning Population Admixture Inference Framework
Client for the 'ipbase.com' IP Geolocation API
Tools for Message Passing Between Processes
Iterative Pruning to Capture Population Structure
Kendall's Tau Partial Corr. for Survival Trait and Biomarkers
Inverse Probability of Censoring Weights to Deal with Treatment
Switch in Randomized Clinical Trials
Basic Functions to Check Readability, Consistency, and Content
of an Individual Participant Data File
Map Digitized Survival Curves Back to Individual Patient Data
Spatial Interpolation by Inverse Path Distance Weighting
Data from the Integrated Post-Secondary Education Data System
Transforms Institutional Data into Text Files for IPEDS
Automated Import/Upload
Fast Implementation of the Iterative Proportional Fitting
Procedure in C
List Balancing for Reweighting and Population Synthesis
Install R Packages or Download File from GitHub via the Proxy
Site
Identification of Parental Lines via Genomic Prediction
Client for the 'iplookupapi.com' IP Lookup API
Functions and Data for the Book 'Integrated Population Models'
Individual Growth Curve Parameter Calculation using Polynomial
Functions
Interfaces to Phylogenetic Software in R
Spatially Explicit Capture-Recapture by Inverse Prediction
Intuitionistic, Pythagorean, and Spherical Fuzzy Similarity
Measure