Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.
Self-Organizing Map (with application in gene clustering).
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
An R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results), can 
             handle (and impute) missing values and is delivered with a 
             graphical user interface based on 'shiny'.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the mixed-model-equations-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available in sommer.
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Provides basic tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.
The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name 'sorcering' is an acronym for 'Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'.
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
Enables drag-and-drop behaviour in Shiny apps, by exposing the 
    functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> 
    JavaScript library as an 'htmlwidget'. 
    You can use this in Shiny apps and widgets, 'learnr' tutorials as well as 
    R Markdown. In addition, provides a custom 'learnr' question type - 
    'question_rank()' - that allows ranking questions with drag-and-drop.
Implements the estimation and inference methods for sorted causal effects and 
    classification analysis as in Chernozhukov, Fernandez-Val and Luo (2018) <doi:10.3982/ECTA14415>.
Misc support functions for rOpenGov and open data downloads.
Search contributed R packages, sort by package.
Access statistical information on welfare and health in Finland 
    from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.
The President of the United States is constitutionally obligated to provide
  a report known as the 'State of the Union'. The report summarizes the current challenges
  facing the country and the president's upcoming legislative agenda. While historically
  the State of the Union was often a written document, in recent decades it has always
  taken the form of an oral address to a joint session of the United States Congress.
  This package provides the raw text from every such address with the intention of
  being used for meaningful examples of text analysis in R. The corpus is well suited
  to the task as it is historically important, includes material intended to be read
  and material intended to be spoken, and it falls in the public domain. As the corpus
  spans over two centuries it is also a good test of how well various methods hold up
  to the idiosyncrasies of historical texts. Associated data about each address, such
  as the year, president, party, and format, are also included.
Basic functions for dealing with wav files and sound samples.
Provides an all-in-one solution for automatic classification of 
    sound events using convolutional neural networks (CNN). The main purpose 
    is to provide a sound classification workflow, from annotating sound events
    in recordings to training and automating model usage in real-life
    situations. Using the package requires a pre-compiled collection of 
    recordings with sound events of interest and it can be employed for: 
    1) Annotation: create a database of annotated recordings, 
    2) Training: prepare train data from annotated recordings and fit CNN models, 
    3) Classification: automate the use of the fitted model for classifying 
    new recordings. By using automatic feature selection and a user-friendly GUI
    for managing data and training/deploying models, this package is intended 
    to be used by a broad audience as it does not require specific expertise in 
    statistics, programming or sound analysis. Please refer to the vignette for
    further information.
    Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101>
    Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995>
    Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103>
    LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.
A set of tools that can be used in computer-aided analysis of
    sound correspondences between languages, plus several helper functions.
    Analytic functions range from purely qualitative analysis, through
    statistic methods yielding qualitative results, to an entirely
    quantitative approach.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Performs parametric synthesis of sounds with harmonic and noise 
    components such as animal vocalizations or human voice. Also offers tools 
    for audio manipulation and acoustic analysis, including pitch tracking, 
    spectral analysis, audio segmentation, pitch and formant shifting, etc. 
    Includes four interactive web apps for synthesizing and annotating audio, 
    manually correcting pitch contours, and measuring formant frequencies. 
    Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.
Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.  Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
A set of tools for state-dependent 
  empirical analysis through both VAR- and local projection-based 
  state-dependent forecasts, impulse response functions, 
  historical decompositions, and forecast error variance decompositions.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). BLUP function formats data for genome-wide prediction and association analysis.
Data sets used by 'Krause et al. (2022)'  <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc.
A programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying 'Species 2000' API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.
Social network analysis is becoming commonplace in many social 
    science disciplines, but access to useful network data, especially 
    among marginalized populations, still remains a formidable challenge. 
    This package mitigates that problem by providing tools to simulate spatial 
    Bernoulli networks as proposed in Carter T. Butts 
    (2002, ISBN:978-0-493-72676-2), "Spatial models of large-scale interpersonal            
    networks." Using this package, network analysts can simulate a spatial point 
    process or sequence with a given number of nodes inside a geographical 
    boundary and estimate the probability of a tie formation between all node 
    pairs. When simulating a network, an analyst can choose between five spatial
    interaction functions. The package also enables quick comparison of summary             
    statistics for simulated networks and provides simple to use plotting 
    methods for its classes that return plots which can be further refined with 
    the 'ggplot2' package.
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.
Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others.  User-defined RGB spaces are also possible.  There is partial support for ACES Color workflows.
Functions for converting among CIE XYZ, xyY, Lab, and Luv.
      Calculate Correlated Color Temperature (CCT) and the Planckian and daylight loci.  
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Three standard color difference metrics are included.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <http://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
    Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
    Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Fits multivariate Bayesian spatial regression models for large datasets using Spatial Multivariate Trees (SpamTrees). 
    The methods in this package are detailed in Peruzzi and Dunson (2020) <arXiv:2012.00943>. 
    Funded by ERC grant 856506 and NIH grant R01ES028804.
Character vector to numerical translation in Euros from Spanish
    spelled monetary quantities. Reverse translation from integer to Spanish.
    Upper limit is up to the millions range. Geocoding via Cadastral web site.
Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data. The package includes methods for spectral fitting: Wilson
    (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018)
    <DOI:10.1002/mrm.27605>.
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.
A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.
'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general
    engine for big data processing, see <https://spark.apache.org/>. This
    package supports connecting to local and remote Apache Spark clusters,
    provides a 'dplyr' compatible back-end, and provides an interface to
    Spark's built-in machine learning algorithms.
This sparklyr extension makes 'Flint' time series
    library functionalities (<https://github.com/twosigma/flint>) easily
    accessible through R.
A 'sparklyr' extension adding the capability to work easily with nested data.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an
  optimized distributed gradient boosting library.
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>.  Details about kernel density estimation 
        can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.  More 
        information about relative risk functions using kernel density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.
'C++' implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) 
  <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides the header file
  'SparseChol.h' that specifies the 'SparseChol' class to implement sparse LDL decomposition in 'Rcpp' functions. A limited 
  set of 'R' functions that implement the method are also included.
Implements the algorithm described in 
    Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
    clustering algorithm for tracing cell type changes via single-cell
    RNA-sequencing data", Nucleic Acids Research, gkx1113,
    <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
    different populations, links the clusters across the conditions and
    identifies marker genes for these changes. The package was designed for
    scRNA-Seq data but is also applicable to many other data types, just
    replace cells with samples and genes with variables. The package also
    contains functions for estimating the parameters for SparseDC as outlined
    in the paper. We recommend that users further select their marker genes
    using the magnitude of the cluster centers.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier
    from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
    those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
    al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
    <doi:10.1093/bioinformatics/btr690>.
Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties <doi:10.1007/s11222-013-9424-2>. Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) <doi:10.1080/01621459.2020.1765784>. B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided.
Efficient implementation of sparse group lasso with optional
    bound constraints on the coefficients. It supports the use of a sparse
    design matrix as well as returning coefficient estimates in a sparse
    matrix. Furthermore, it correctly calculates the degrees of freedom to
    allow for information criteria rather than cross-validation with very
    large data. Finally, the interface to compiled code avoids unnecessary
    copies and allows for the use of long integers.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
Computation of sparse portfolios for financial index tracking, i.e., joint
    selection of a subset of the assets that compose the index and computation
    of their relative weights (capital allocation). The level of sparsity of the
    portfolios, i.e., the number of selected assets, is controlled through a
    regularization parameter. Different tracking measures are available, namely,
    the empirical tracking error (ETE), downside risk (DR), Huber empirical
    tracking error (HETE), and Huber downside risk (HDR). See vignette for a
    detailed documentation and comparison, with several illustrative examples.
    The package is based on the paper:
    K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional
    Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1,
    pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.
Creates a wrapper for the 'SuiteSparse' routines 
    that execute the Takahashi equations. These equations compute the
    elements of the inverse of a sparse matrix at locations where the
    its Cholesky factor is structurally non-zero. The resulting matrix is known as a 
	sparse inverse subset. Some helper functions are also implemented. 
	Support for spam matrices is currently limited and will be implemented 
	in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> 
	and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the 
	application of these equations to statistics.
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.
Provides an S4 class for representing and
    interacting with sparse plus rank matrices. At the moment the
    implementation is quite spare, but the plan is eventually subclass
    Matrix objects.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.
Implements the algorithm described in
    Barron, M., and Li, J. (Not yet published). This algorithm clusters
    samples from multiple ordered populations, links the clusters across the
    conditions and identifies marker genes for these changes. The package was
    designed for scRNA-Seq data but is also applicable to many other data
    types, just replace cells with samples and genes with variables. The
    package also contains functions for estimating the parameters for SparseMDC
    as outlined in the paper. We recommend that users further select their
    marker genes using the magnitude of the cluster centers.
Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions).
Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common.  It deals correctly with parameters whose estimated values can be considered as being negative infinity.  It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
An implementation of ranked sparsity methods, including
    penalized regression methods such as the sparsity-ranked lasso, its
    non-convex alternatives, and elastic net, as well as the sparsity-ranked
    Bayesian Information Criterion. As described in Peterson and
    Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
    sparsity is a philosophy with methods primarily useful for
    variable selection in the presence of prior informational
    asymmetry, which occurs in the context of trying to perform variable
    selection in the presence of interactions and/or polynomials. Ultimately,
    this package attempts to facilitate dealing with cumbersome interactions
    and polynomials while not avoiding them entirely. Typically, models
    selected under ranked sparsity principles will also be more transparent,
    having fewer falsely selected interactions and polynomials than other
    methods.
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.
Computes sparse vector autoregressive 				coefficients and precision matrices for time series 		chain graphical models. Fentaw Abegaz and Ernst Wit (2013)	<doi:10.1093/biostatistics/kxt005>.
A wrapper for sparse VAR/VECM time series models estimation
             using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped 
             Absolute Deviation) and MCP (Minimax Concave Penalty). 
             Based on the work of Sumanta Basu and George Michailidis 
             <doi:10.1214/15-AOS1315>.
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.
The sparse vector field consensus 
		(SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector 
		field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.
Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
Fast Multiplication and Marginalization of Sparse Tables.
Statistical pattern recognition and dating using archaeological artefacts assemblages.
    Package of statistical tools for archaeology.
    hclustcompro(perioclust): Bellanger Lise, Coulon Arthur, Husi Philippe (2020, ISBN:978-3-030-60103-4).
    mapclust: Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>.
    seriograph: Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>.
The Stratified-Petersen Analysis System (SPAS) is designed
    to estimate abundance in two-sample capture-recapture experiments 
    where the capture and recaptures are stratified. This is a generalization
    of the simple Lincoln-Petersen estimator.
    Strata may be defined in time or in space or both, 
    and the s strata in which marking takes place 
    may differ from the t strata in which recoveries take place.
    When s=t, SPAS reduces to the method described by 
    Darroch (1961) <https://www.jstor.org/stable/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) 
    <https://www.jstor.org/stable/2533994>.
    Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe
    the use of SPAS in estimating return of salmon stratified by
    time and geography. 
    A related package, BTSPAS, deals with temporal stratification where 
    a spline is used to model the distribution of the population 
    over time as it passes the second capture location.
    This is the R-version of the (now obsolete) standalone Windows 
    program available at <http://www.cs.umanitoba.ca/~popan/spas/spas_home.html>.
Sample size estimation and blinded sample size reestimation in Adaptive Study Design.
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds' contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards' entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 2.3-0). The speed of most functions has also been substantially increased.
Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.
The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results.
Graphs (or networks) and graph component
        calculations for spatial locations in 1D, 2D, 3D etc.
A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) <doi:10.1109/TNNLS.2018.2884792>."
Functions for kriging and point pattern analysis.
Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.
Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
    and modelling in ecological applications. Functions include models for species 
	population density, spatial smoothing, multivariate separability, point process 
	model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and 
	analysis, auto-logistic modeling, sampling models, cluster optimization, statistical 
	exploratory tools and raster-based metrics.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Finite element modeling (FEM) uses meshes of triangles to define surfaces.  
  A surface within a triangle may be either linear or quadratic. 
  In the order one case each node in the mesh is associated with a basis 
  function and the basis is called the order one finite element basis.
  In the order two case each edge mid-point is also associated with a basis
  function.  Functions are provided for smoothing, density function estimation
  point evaluation and plotting results.  Two papers illustrating the 
  finite element data analysis are Sangalli, L.M., Ramsay, J.O., 
  Ramsay, T.O. (2013)<http://www.mox.polimi.it/~sangalli> and
  Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L. 
  (2018)<http://www.mox.polimi.it/~sangalli>. Modelling 
  spatial anisotropy via regression with partial differential 
  regularization Journal of Multivariate Analysis, 167, 15-30.
Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2021) <doi:10.1016/j.csda.2021.107240>. Model inference is done using either 'Stan' <https://mc-stan.org/> or 'INLA' <https://www.r-inla.org/>.
Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2021) <arXiv:2110.07051>.
Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().
Calculate Kernel Density Estimation (KDE) for spatial data. 
  The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
  Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, 
  Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>,
  Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.
Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
             Allows for a geographically weighted random forest regression 
             including a function to find the optical bandwidth. (Georganos 
             and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).
Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.
Tools to assess the association between two spatial processes. Currently,
  several methodologies are implemented: A modified t-test to perform hypothesis testing
  about the independence between the processes, a suitable nonparametric correlation
  coefficient, the codispersion coefficient, and an F test for assessing the multiple
  correlation between one spatial process and several others. Functions for image
  processing and computing the spatial association between images are also provided.
  Functions contained in the package are intended to accompany Vallejos, R., Osorio, F.,
  Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables:
  With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.
A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.  This spatially generated data can be used to test different issues related to the statistical analysis of spatial data.
 This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.
Computes spatial position models: the potential model as defined
    by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as
    defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.
Bayesian Estimation of Spatial Probit and Tobit Models.
A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'.
A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a 
     circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine 
     the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See 
     Church (1974) <doi:10.1007/BF01942293> for a description of the problem.
Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
Functions and classes for spatial resampling to use with the
    'rsample' package, such as spatial cross-validation (Brenning, 2012)
    <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and
    'spatialsample' is to provide the basic building blocks for creating
    and analyzing resamples of a spatial data set, but neither package
    includes functions for modeling or computing statistics. The resampled
    spatial data sets created by 'spatialsample' do not contain much
    overhead in memory.
Visualization and analysis  of Vectra Immunoflourescent
    data. Options for calculating both the univariate and bivariate Ripley's K
    are included. Calculations are performed using a permutation-based 
    approach presented by Wilson et al.  <doi:10.1101/2021.04.27.21256104>.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field.  Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets>
    for interactive plotting of spatial data.
    This package provides functions for converting R objects, such as simple features, 
    into structures suitable for use in 'htmlwidgets' mapping libraries.
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. To do so, it relies on and extends a number of facilities that the 'pomp' package provides for inference on time series data using partially-observed Markov process (POMP) models. Implemented methods include filtering and inference methods in Park and Ionides (2020) <doi:10.1007/s11222-020-09957-3>, Rebeschini and van Handel (2015) <doi:10.1214/14-AAP1061>, Evensen and van Leeuwen (1996) <doi:10.1029/94JC00572> and Ionides et al. (2021) <doi:10.1080/01621459.2021.1974867>. Pre-print statistical software article: Asfaw et al. (2021) <arXiv:2101.01157>.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
Detects spatial and temporal groups in GPS relocations 
    (Robitaille et al. (2020) <doi:10.1111/2041-210X.13215>). 
    It can be used to convert GPS relocations to 
    gambit-of-the-group format to build proximity-based social networks 
    In addition, the randomizations function provides data-stream 
    randomization methods suitable for GPS data.
Contains all the datasets for the 'spatstat' family of packages.
Functionality for exploratory data analysis and nonparametric analysis of
	     spatial data, mainly spatial point patterns,
	     in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Defines spatial data types and supports geometrical operations
	     on them. Data types include point patterns, windows (domains),
	     pixel images, line segment patterns, tessellations and hyperframes.
	     Capabilities include creation and manipulation of data
	     (using command line or graphical interaction),
	     plotting, geometrical operations (rotation, shift, rescale,
	     affine transformation), convex hull, discretisation and
	     pixellation, Dirichlet tessellation, Delaunay triangulation,
	     pairwise distances, nearest-neighbour distances,
	     distance transform, morphological operations
	     (erosion, dilation, closing, opening), quadrat counting,
	     geometrical measurement, geometrical covariance,
	     colour maps, calculus on spatial domains,
	     Gaussian blur, level sets of images, transects of images,
	     intersections between objects, minimum distance matching.
	     (Excludes spatial data on a network, which are supported by
	     the package 'spatstat.linnet'.)
Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.
Extension to the 'spatstat' family of packages, for analysing
	     large datasets of spatial points on a network. The geometrically-
	     corrected K function is computed using a memory-efficient
	     tree-based algorithm described by Rakshit, Baddeley and Nair (2019).
Defines types of spatial data on a linear network
	     and provides functionality for geometrical operations,
	     data analysis and modelling of data on a linear network,
	     in the 'spatstat' family of packages.
	     Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
	     Data types defined on a network include point patterns, pixel images, functions, and tessellations.
	     Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
	Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
	Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
	Random point patterns on a network can be generated using a variety of models.
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Functionality for random generation of spatial data in the 'spatstat' family of packages.
	     Generates random spatial patterns of points according to many simple rules (complete spatial randomness,
	     Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns
	     (thinning, random shift, jittering),  simulated realisations of random point processes
	     (simple sequential inhibition, Matern inhibition models, Matern cluster process,
	     Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes)
	     and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm,
	     alternating Gibbs sampler). Also generates random spatial patterns of line segments,
	     random tessellations, and random images (random noise, random mosaics).
	     Excludes random generation on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.
Defines sparse three-dimensional arrays
	     and supports standard operations on them.
	     The package also includes utility functions for
	     matrix calculations that are common in
	     statistics, such as quadratic forms.
Contains utility functions for the 'spatstat' family of packages
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.
Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes support for simple features,  round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation
    that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Implements a spatial Bayesian non-parametric factor analysis model 
    with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). 
    Spatial correlation is introduced in the columns of the factor loadings 
    matrix using a Bayesian non-parametric prior, the probit stick-breaking 
    process. Areal spatial data is modeled using a conditional autoregressive 
    (CAR) prior and point-referenced spatial data is treated using a Gaussian 
    process. The response variable can be modeled as Gaussian, probit, Tobit, or
    Binomial (using Polya-Gamma augmentation). Temporal correlation is 
    introduced for the latent factors 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 "Bayesian Non-Parametric Factor 
    Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), 
    <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean or variance of normally
        distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added.
        Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.
Provides statistical process control tools for stochastic 
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.
Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means. See Walvoort et al. (2010) 
        <doi:10.1016/j.cageo.2010.04.005> for details.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper on arXiv
    by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <arXiv:1811.11038>.
The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. 
    It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep').
    Unlike other spatial data packages such as 'rnaturalearth' and 'maps', 
    it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. 
    Some of the datasets are designed to illustrate specific analysis techniques.
    cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.
Inspired by space-time regressions often performed to assess
    the expansion of the Neolithic from the Near East to Europe (Pinhasi et
    al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant
    correlations between the (earliest) radiocarbon dates of archaeological
    sites and their respective distances from a hypothetical center of origin.
    Both ordinary least squares (OLS) and reduced major axis (RMA) methods
    are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>).
    It is also possible to iterate over many sites to identify the most likely
    origin.
A collection of functions to create spatial weights matrix
  objects from polygon 'contiguities', from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial 'autocorrelation',
  including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord'
  (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel'
  general cross product statistic, Empirical Bayes estimates and
  'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992)
  <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured
  join count statistics, 'APLE' ('Li 'et al.' )
  <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' 
  ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and
  'Getis/Ord' G ('Ord' and 'Getis' 1995)
  <doi:10.1111/j.1538-4632.1995.tb00912.x>,
  'saddlepoint' approximations ('Tiefelsdorf' 2002)
  <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests
  for global and local 'Moran's I' ('Bivand et al.' 2009)
  <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators
  of spatial heteroscedasticity ('Ord' and 'Getis')
  <doi:10.1007/s00168-011-0492-y>. The implementation of most of
  the measures is described in 'Bivand' and 'Wong' (2018)
  <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022)
  <doi:10.1111/gean.12319>.
  From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions
  previously present in this package are defunct in 'spdep' and may be found
  in 'spatialreg'.
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic 
    verbs that modify data attributes, remove or re-arrange rows are supported
    and provide complete 'Spatial' analogues of the input data. The group by
    and summarize work flow returns a non-topological spatial union. There is 
    limited support for joins, with left and inner to copy attributes from 
    another table.
An implementation of split-population duration regression models. 
    Unlike regular duration models, split-population duration models are
    mixture models that accommodate the presence of a sub-population that is 
    not at risk for failure, e.g. cancer patients who have been cured by 
    treatment. This package implements Weibull and Loglogistic forms for the 
    duration component, and focuses on data with time-varying covariates. 
    These models were originally formulated in Boag (1949) 
    <http://www.jstor.org/stable/2983694> and Berkson and Gage (1952) 
    <http://www.jstor.org/stable/2281318>, and extended in Schmidt and Witte 
    (1989) <doi:10.1016/0304-4076(89)90034-1>.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.
It allows running 'Praat' scripts from R and it provides some
    wrappers for basic plotting. It also adds support for literate markdown
    tangling. The package is designed to bring reproducible phonetic research
    into R.
Makes Nuclear Magnetic Resonance spectroscopy (NMR spectroscopy) data analysis as easy as possible by only requiring a small set of functions to perform an entire analysis. 'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More details can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018>.
Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <10.1080/10920277.2012.10597644>).
Creates a data specification that describes the columns of a 
 table (data.frame). Provides methods to read, write, and update the 
 specification. Checks whether a table matches its specification. See
 specification.data.frame(),read.spec(), write.spec(), as.csv.spec(),
 respecify.character(), and %matches%.data.frame().
Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
Implementation of various methods in estimation of species richness or diversity in Wang (2011)<doi:10.18637/jss.v040.i09>.
A simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) <doi:10.1002/ece3.8830> for further details.
Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <arXiv:0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2015, <doi: 10.2139/ssrn.2694998>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.
Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in
    Smeekes and Wijler (2020) <arXiv:1809.08889>. SPECS provides a fully automated estimation procedure for large and potentially
    (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or
    by means of the functions included in this package, and various specialised forms of penalized regression can be applied to
    the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty
    weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic
    configurations to further simplify the model building process.
A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
Provides interface to the 'Spectator Earth' API <https://api.spectator.earth/>, 
    mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, 
    Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can 
    also be obtained for a much larger set of satellites. It is also possible to search 
    the archive for available images over the area of interest for a given (past) period, 
    get the URL links to download the whole image tiles, or alternatively to download the 
    image for just the area of interest based on selected spectral bands.
Provides a consistent interface to use various methods to calculate
  the periodogram and estimate the period of a rhythmic time-course. Methods
  include Lomb-Scargle, fast Fourier transform, and three versions of the
  chi-square periodogram. See Tackenberg and Hughey (2021)
  <doi:10.1371/journal.pcbi.1008567>.
On discrete data spectral analysis is performed by Fourier and Hilbert
    transforms as well as with model based analysis called Lomb-Scargle method.
    Fragmented and irregularly spaced data can be processed in almost all methods. Both,
    FFT as well as LOMB methods take multivariate data and return standardized PSD. 
    For didactic reasons an analytical approach for deconvolution of noise spectra and 
    sampling function is provided.
    A user friendly interface helps to interpret the results.
Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the (pharma-)chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc.
    Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. 
    Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions.
    The functionality to read in spc-files was adapted from the 'hyperSpec' package.
Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
In the era of big data and hyperconnectivity, learning
    high-dimensional structures such as graphs from data has become a prominent
    task in machine learning and has found applications in many fields such as
    finance, health care, and networks. 'spectralGraphTopology' is an open source,
    documented, and well-tested R package for learning graphs from data. It
    provides implementations of state of the art algorithms such as Combinatorial
    Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation
    based on Majorization-Minimization (GLE-MM), and Graph Estimation based on
    Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph
    learning has been widely employed for clustering, where specific algorithms
    are available in the literature. To this end, we provide an implementation of
    the Constrained Laplacian Rank (CLR) algorithm.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service (<https://earthengine.google.com/>) and used as a source of spectral data.
Predict regional community composition at a fine spatial 
    resolution using only sparse biological and environmental data. The package 
    is based on the DynamicFOAM algorithm described 
    in Mokany et al. (2011) <doi:10.1111/j.1461-0248.2011.01675.x>.
Spectra viewer, organizer, data preparation and property blocks
        from within R or stand-alone. Binary (application) part is 
        installed separately using spnInstallApp() from spectrino package.
Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class 'spectra' that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Converts the floor speeches of Uruguayan legislators, extracted from the 
    parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.
Scrap speech text and speaker informations of speeches of House of 
          Representatives of Brazil, and transform in a cleaned tibble.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
Label, recode, rename, and convert datasets and ASCII files more efficiently. 'speedycode' automates the code necessary for labeling variables with the 'labelled' package, recoding and renaming variables with 'dplyr' syntax, and converting ASCII files with the 'readroper' package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.
Functions for fitting semiparametric regression models for
        panel count survival data. An overview of the package can be found 
        in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and
	Chiou et al. (2018) <doi:10.1111/insr.12271>.
Performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Corrects the spelling of a given word in English 
    using a modification of Peter Norvig's spell correct 
    algorithm (see <http://norvig.com/spell-correct.html>) 
    which handles up to three edits. The algorithm tries to 
    find the spelling with maximum probability of intended
    correction out of all possible candidate corrections from
    the original word.
Spell checking common document formats including latex, markdown, manual pages,
    and description files. Includes utilities to automate checking of documentation and 
    vignettes as a unit test during 'R CMD check'. Both British and American English are 
    supported out of the box and other languages can be added. In addition, packages may
    define a 'wordlist' to allow custom terminology without having to abuse punctuation.
This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and 
    can be applied on spatial data. It can handle both gridded or un-gridded datasets.
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
Implements spatial error estimation and
    permutation-based variable importance measures for predictive models
    using spatial cross-validation and spatial block bootstrap.
Performs non-parametric tests of parametric specifications.
    Five tests are available. 
    Specific bandwidth and kernel methods can be chosen along with many other options. 
    Allows parallel computing to quickly compute p-values based on the bootstrap. 
    Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>,
    J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>,
    P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>,
    P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>,
    P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>,
    J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>,
    C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>,
    J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002>
    and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.
Functions to produce a fully fledged 'geo-spatial' object extent as a
    'SpatialPolygonsDataFrame'. Also included are functions to generate polygons
    from raster data using 'quadmesh' techniques, a round number buffered extent, and
    general spatial-extent and 'raster-like' extent helpers missing from the originating
    packages. Some latitude-based tools for polar maps are included.
Implements a group-bridge penalized function-on-scalar regression
    model proposed by Wang et al. (2020) <arXiv:2006.10163>, to simultaneously
    estimate functional coefficient and recover the local sparsity.
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
Efficient estimation of spatial econometric models of origin-destination flows, which may exhibit spatial autocorrelation in the dependent variable, the explanatory variables or both.
    The model is the one proposed by LeSage and Pace (2008) <doi:10.1111/j.1467-9787.2008.00573.x>, who develop a matrix formulation that exploits the relational structure of flow data.
    The estimation procedures follow most closely those outlined by Dargel (2021) (preprint available at <https://www.tse-fr.eu/fr/publications/revisiting-estimation-methods-spatial-econometric-interaction-models>).
An implementation of feature selection, weighting and ranking via simultaneous perturbation
    stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of
    features that yield the best predictive performance using some error measures such as mean 
    squared error (for regression problems) and accuracy rate (for classification problems).
Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.
A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <arXiv:1609.00711>: simulation of spatial ARCH-type processes (spARCH, exponential spARCH, complex spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Fitting dimension reduction methods to data lying on two-dimensional sphere.
    This package provides principal geodesic analysis, principal circle, principal curves 
    proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally   
    defined principal geodesics which is underway. The detailed procedures 
    are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. 
    Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.
Implementation of uniformity tests on the circle and
    (hyper)sphere. The main function of the package is unif_test(), which
    conveniently collects more than 30 tests for assessing uniformity on
    S^{p-1}={x in R^p : ||x||=1}, p >= 2. The test statistics are implemented
    in the unif_stat() function, which allows computing several statistics to
    several samples within a single call, thus facilitating Monte Carlo
    experiments. Furthermore, the unif_stat_MC() function allows
    parallelizing them in a simple way. The asymptotic null distributions of
    the statistics are available through the function unif_stat_distr(). The
    core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package.
    The package also provides several novel datasets and gives the
    reproducibility for the data application in García-Portugués,
    Navarro-Esteban and Cuesta-Albertos (2020) <arXiv:2008.09897>.
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
The 'Robots Exclusion Protocol' <https://www.robotstxt.org/orig.html> documents
    a set of standards for allowing or excluding robot/spider crawling of different areas of
    site content. Tools are provided which wrap The 'rep-cpp' <https://github.com/seomoz/rep-cpp>
    C++ library for processing these 'robots.txt' files.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time Cook & Buja (1997) 
  <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand 
  (random walk) and guided (optimizing some objective function) are available 
  in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed in Spyrison & Cook (2020) 
  <doi:10.32614/RJ-2020-027>.
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
In 2015, The 17 United Nations' Sustainable Development Goals were adopted. 'spiR' is a wrapper of several open datasets published by the Social Progress Imperative (<https://www.socialprogress.org/>), including the Social Progress Index (a synthetic measure of human development across the world). 'spiR''s goal is to provide data to help policymakers and researchers prioritize actions that accelerate social progress across the world in the context of the Sustainable Development Goals. Please cite: Warin, Th. (2019) "spiR: An R Package for the Social Progress Index", <doi:10.6084/m9.figshare.11421573.v2>.
It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. 
    It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high 
    resolution. 2. It is efficient for time series data to reveal periodic patterns.
Contains an R Markdown template for a clinical trial 
    protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol 
    Items for Interventional Trials) statement outlines recommendations for a 
    minimum set of elements to be addressed in a  clinical trial protocol. 
    Also contains functions to create a xml document from the template and 
    upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for 
    trial registration.
Import, process, summarize and visualize raw data from 
    metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
This program calculates bioclimatic indices and fluxes (radiation, 
    evapotranspiration, soil moisture) for use in studies of ecosystem function, 
    species distribution, and vegetation dynamics under changing climate 
    scenarios. Predictions are based on a minimum of required inputs: latitude, 
    precipitation, air temperature, and cloudiness. 
    Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.
The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects.
An extension to the individual claim simulator called 'SynthETIC'
    (on CRAN), to simulate the evolution of case estimates of incurred losses
    through the lifetime of an insurance claim. The transactional simulation
    output now comprises key dates, and both claim payments and revisions of
    estimated incurred losses. An initial set of test parameters, designed to
    mirror the experience of a real insurance portfolio, were set up and applied
    by default to generate a realistic test data set of incurred histories (see
    vignette). However, the distributional assumptions used to generate this
    data set can be easily modified by users to match their experiences.
    Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss
    and Incurred Cost Experience Simulator" <arXiv:2109.04058>.
Constructs basis matrix of B-splines, M-splines,
    I-splines, convex splines (C-splines), periodic M-splines,
    natural cubic splines, generalized Bernstein polynomials,
    and their integrals (except C-splines) and derivatives
    of given order by close-form recursive formulas.
    It also contains a C++ head-only library integrated with Rcpp.
    See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.
Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.
Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit to data consisting of sampled values of functions as well as splines build over another set of knots is obtained. Podgórski, K. (2021)  <arXiv:2102.00733>.
Procedure to optimally split a dataset for training and testing. 
    'SPlit' is based on the method of support points, which is independent of modeling methods.
    Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for details.
    This work is supported by U.S. National Science Foundation grant DMREF-1921873.
An implementation of the majorization-minimization (MM) algorithm introduced by
    Xu, Chi, Yang, and Lange (2017) <arXiv:1612.05614> for solving multi-set split feasibility problems. In the multi-set split feasibility problem, we seek to find a point x
    in the intersection of multiple closed sets and whose image under a mapping also must fall in the intersection of several closed sets.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Functions to compute split generalized linear models. The approach fits 
             generalized linear models that split the covariates into groups. The 
             optimal split of the variables into groups and the regularized estimation 
             of the coefficients are performed by minimizing an objective function 
             that encourages sparsity within each group and diversity among them.
             Example applications can be found in Christidis et al. (2021)
             <arXiv:2102.08591>.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "I eat stickers all the time, dude!".
Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.
Split Knockoff is a data adaptive variable selection framework for controlling the
             (directional) false discovery rate (FDR) in structural sparsity, where variable 
             selection on linear transformation of parameters is of concern. This proposed scheme
             relaxes the linear subspace constraint to its neighborhood, often known as variable
             splitting in optimization.
             Simulation experiments can be reproduced following the Vignette. We include data
             (both .mat and .csv format) and application with our method of Alzheimer's Disease 
             study in this package.
             'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
             split the set of covariates into groups. The optimal split of the variables into groups and the 
             regularized estimation of the regression coefficients are performed by minimizing  an objective 
             function that encourages sparsity within each group and diversity among them. 
             The estimated coefficients are then pooled together to form the final fit.
Functions to generate or sample from all possible splits of features or variables
             into a number of specified groups. Also computes the best split selection estimator 
             (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) 
             <arXiv:1812.05678>.
Allows to produce and use classification trees with soft (probability) splits,
	as described in: Dvořák, J. (2019), <doi:10.1007/s00180-019-00867-1>.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into separate 
    cells. The package also includes functions to stack groups of columns and 
    to reshape wide data, even when the data are "unbalanced"---something 
    which reshape (from base R) does not handle, and which melt and dcast from 
    reshape2 do not easily handle.
Fast, lightweight toolkit for data splitting. Data sets can
    be partitioned into disjoint groups (e.g. into training, validation,
    and test) or into (repeated) k-folds for subsequent cross-validation.
    Besides basic splits, the package supports stratified, grouped as well
    as blocked splitting. Furthermore, cross-validation folds for time
    series data can be created. See e.g. Hastie et al. (2001)
    <doi:10.1007/978-0-387-84858-7> for the basic background on data
    partitioning and cross-validation.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Contains functions that fit linear mixed-effects models
        for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. 
        The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation
        is based on the R package 'lmmlasso'. 
        Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.
Automates common plotting tasks to ease data exploration.
  Makes density plots (potentially overlaid on histograms),
  scatter plots with prediction lines, or bar or line plots with error bars.
  For each type, y, or x and y variables can be plotted at levels of other variables,
  all with minimal specification.
Provides functions for fitting a sparse
        partial least squares (SPLS) regression and classification
        (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
Currently there are many functions in S-PLUS that are
  missing in R. To facilitate the conversion of S-PLUS packages to R packages,
  this package provides some missing S-PLUS functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Utility functions that help with common base-R problems relating to lists.
    Lists in base-R are very flexible. This package provides functions to quickly and easily
    characterize types of lists. That is, to identify if all elements in a list
    are null, data.frames, lists, or fully named lists. Other functionality is provided
    for the handling of lists, such as the easy splitting of lists into equally sized
    groups, and the unnesting of data.frames within fully named lists.
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
  Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
  Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
  Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Fit, summarize, and predict for a variety of spatial statistical models
    applied to point-referenced and areal (lattice) data. Parameters
    are estimated using various methods. Additional modeling features include anisotropy,
    random effects, partition factors, big data approaches, and more. Model-fit statistics are
    used to summarize, visualize, and compare models. Predictions at unobserved locations are
    readily obtainable.
Functions for estimating spatial varying coefficient models, mixed models, and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate Gaussian process modeling which is interpretable in terms of the Moran coefficient. The GP is used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) <arXiv:1703.04467>.
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* 
    proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. 
    The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, 
    detects a group of flows having similar traits in terms of directionality. 
    You provide OD data and the associated polygon to get results 
    with several parameters, some of which are defined by spdep package.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
Launches a Shiny application that allows users to carry out some of the steps that are required to curate both a linear network object based on a road structure and a point pattern that lies on such a network, becoming two previous steps to the performance of a spatial statistics analysis.
Perform spatial analysis on network.
    Implement several methods for spatial analysis on network: Network Kernel Density estimation, 
    building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation 
    for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation
    References: Okabe et al (2019) <doi:10.1080/13658810802475491>;
    Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.
Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) <doi:10.18637/jss.v103.i05>, Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924>, and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
    Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
    'eBird', Integrated Digitized
    'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information
    System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes
    functionality for retrieving species occurrence data, and combining
    those data.
Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2020) <arXiv:2001.09111>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.
It can be useful to temporarily hide some text or other HTML elements 
    in 'Shiny' applications. Building on 'Spoiler-Alert.js', it is possible to select the
    elements to hide at startup, to partially reveal them by hovering them, and to 
    completely show them when clicking on them.
Convert text (and text in R objects) to Mocking SpongeBob case 
             <https://knowyourmeme.com/memes/mocking-spongebob> and show them 
             off in fun ways. 
             CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE
             <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem 
             OFf IN Fun WayS.
Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.
Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting. The application allows for a fast random search in a compact space of hyper-parameters composed by Sequence Length and Jack-Knife Leave-N-Out.
The 'spork' syntax describes
 label formatting concisely, supporting
 mixed nesting of subscripts and superscripts
 to arbitrary depth. It intends to be easy
 to read and write in plain text, and easy
 to convert to equivalent presentations
 in 'plotmath', 'latex', and 'html'.  Greek symbols
 and a multiplication symbol are explicitly
 supported. See ?as_spork and ?as_previews.
Calculates ratings for two-player or 
  multi-player challenges. Methods included in package such as are able to 
  estimate ratings (players strengths) and their evolution in time, also able to 
  predict output of challenge. Algorithms are based on Bayesian Approximation 
  Method, and they don't involve any matrix inversions nor likelihood estimation. 
  Parameters are updated sequentially, and computation doesn't require any 
  additional RAM to make estimation feasible. Additionally, base of the package 
  is written in C++ what makes sport computation even faster. Methods used in the 
  package refers to Mark E. Glickman (1999) 
  <http://www.glicko.net/research/glicko.pdf>; 
  Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; 
  Ruby C. Weng, Chih-Jen Lin (2011) 
  <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; 
  W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
A time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. 'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.
Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).
Create scaled 'ggplot' representations of playing surfaces.
    Playing surfaces are drawn pursuant to rule-book specifications.
    This package should be used as a baseline plot for displaying any type of
    tracking data.
A set of tools for model-based optimization and tuning of
    algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment
    approaches. The main interface is spot, which uses sequentially updated
    surrogate models for the purpose of efficient optimization. The main goal is
    to ease the burden of objective function evaluations, when a single evaluation
    requires a significant amount of resources.
A graphical user interface for the Sequential Parameter Optimization Toolbox (package 'SPOT').
    It includes a quick, graphical setup for spot, interactive 3D plots, export possibilities and more.
Providing convenience functions to connect R with the 'Spotify' application programming interface ('API'). At first it aims to help setting up 
  the OAuth2.0 Authentication flow. The default output of the get_*() functions is tidy, but optionally the functions could return the raw response from the 
  'API' as well. The search_*() and get_*() functions can be combined. See the vignette for more information and examples and the official Spotify for 
  Developers website <https://developer.spotify.com/documentation/web-api/> for information about the Web 'API'.
An R wrapper for pulling data from the 'Spotify' Web API 
  <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a
  'Spotify' user's playlist.
Implements additional models, simulation tools, and interfaces as extensions to 'SPOT'.
    It provides tools for hyperparameter tuning via 'keras/tensorflow', interfacing 'mlr', for performing Markov chain simulations, 
    and for sensitivity analysis based on sequential bifurcation methods as described in Bettonvil and Kleijnen (1996). 
    Furthermore, additional plotting functions for output from 'SPOT' runs are implemented.
    Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) <doi:10.1109/CEC.2005.1554761>.
    Bartz-Beielstein T, Zaefferer M, Rehbach F (2021) <arXiv:1712.04076>.
    Bartz-Beielstein T, Rehbach F, Sen A, Zaefferer M <arXiv:2105.14625>.
    Bettonvil, B, Kleijnen JPC (1996) <doi:10.1016/S0377-2217(96)00156-7>.
An algorithm to cluster satellite hot spot data spatially and temporally.
In spatial data, information of two neighboring units are generally very similar. 
  For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study the evolution. 
  Three new methods called Orfs (Optimal Rotation with Fixed sample Size), Orsp (Optimal Rotation with Spread sample), and Spot (Spatial and Optimally Temporal Sampling) are implemented in this package. Orfs allows to select temporal samples with fixed size. Orsp selects spatio-temporal samples with random size that are well spread out in space at each point in time. And Spot generates spread sample with fixed sample size at each wave. These methods provide an optimal time rotation of the selected units using the systematic sampling.
Summary ellipses superimposed on a scatter plot contain all bi-variate summary
            statistics for regression analysis. Furthermore, the outer ellipse flags potential
            outliers. Multiple groups can be compared in terms of centers and spreads as illustrated
            in the examples.
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.
Methods for flexible estimation of conditional density and quantile function, as well as model agnostic tools for analyzing quantile covariate effect and variable importance. The estimation method implements the semi-parametric quantile regression model described in Xu and Reich (2021) <doi:10.1111/biom.13576>, and the model agnostic tools extend accumulative local effects (ALE) to quantile regression setting.
Sparse arrays interpreted as multivariate polynomials.
The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
Provides a function for the estimation of parameters 
		in a binary regression with the skew-probit link function. 
		Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
		as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.
Processing and analysis of field collected or simulated sprinkler 
    system catch data (depths) to characterize irrigation uniformity and efficiency using
    standard and other measures. Standard measures include the Christiansen coefficient
    of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295,
    "Irrigation by Sprinkling"); and distribution uniformity (DU), potential
    efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ)
    that are implementations of measures of the same notation in Keller, J. and Merriam, 
    J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management"
    <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>. spreval::DU.lh is similar to spreval::DU
    but is the distribution uniformity of the low half instead of low quarter as in DU.
    spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead
    of lateral move or solid-set sprinkler systems. The function spreval::eff is
    analogous to the method used to compute application efficiency for furrow irrigation
    presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface
    Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated
    depth compared against soil-moisture deficit (SMD), when the argument "target"
    is set equal to SMD.  The other functions contained in the package provide 
    graphical representation of sprinkler system uniformity, and other standard
    univariate parametric and non-parametric statistical measures as applied to
    sprinkler system catch depths. A sample data set of field test data spreval::catchcan
    (catch depths) is provided and is used in examples and vignettes. Agricultural systems
    emphasized, but this package can be used for landscape irrigation evaluation, and a
    landscape (turf) vignette is included as an example application.
Calculate species richness functions for rarefaction and
    extrapolation.
Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.
Computes the extended spring indices (SI-x) and false spring 
    exposure indices (FSEI). The SI-x indices are standard indices used for 
    analysis in spring phenology studies. In addition, the FSEI is also from 
    research on the climatology of false springs and adjusted to include an 
    early and late false spring exposure index. The indices  include the 
    first leaf index, first bloom index, and false spring exposure indices, 
    along with all calculations for all functions needed to calculate 
    each index. The main function returns all indices, but each function can 
    also be run separately. 
    Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> 
    Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> 
    Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266>
    Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> 
    Schwarz et al. (2013) <doi: 10.1002/joc.3625>.
An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. 
    Also includes all his song details such as lyrics and albums. Data extracted from: 
    <http://brucebase.wikidot.com/>.
An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at <arXiv:1907.08414>.
The seq_ttest() function is the implementation of Abraham
    Wald’s (1947) <doi:10.2134/agronj1947.00021962003900070011x> Sequential Probability Ratio Test (SPRT) for the test of
    a normal mean (difference) with unknown variance in R (R Core Team, 2018).
    It performs sequential t tests developed by Rushton (1950) <doi:10.2307/2332385>, Rushton (1952) <doi:10.2307/2334026> and
    Hajnal (1961) <doi:10.2307/2333131>, based on the SPRT. Specifically, seq_ttest() performs
    one-sample, two-sample, and paired t tests for testing one- and
    two-sided hypotheses.  The test is to be applied to the data during
    the sampling process, ideally after each observation. At any stage, it
    will return a decision to either continue sampling or terminate and
    accept one of the specified hypotheses. For more information on the
    SPRT t test, see Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>.
Allows for identification of cell sub-populations within tissue samples using
  Bayesian multivariate mixture models with spatial random effects to account for a wide range of 
  spatial gene expression patterns, as described in Allen et. al, 2021 <doi:10.1101/2021.06.23.449615>.
  Bayesian inference is conducted using efficient Gibbs sampling implemented using 'Rcpp'.
Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), and generate appropriate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).
The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes,
    global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided.
	Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. 
	Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) <doi:10.1002/2017WR021902>) can be calculated.
	The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model.
	All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) <doi:10.1029/2018WR024584>. 
	The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) <doi:10.2136/vzj2006.0007>) is calculated based on 
	volumetric air space and a selection of different tortuosity models: 
	(Grable, A.R., Siemer, E.G. (1968) <doi:10.2136/sssaj1968.03615995003200020011x>, Lai, S.H., Tiedje J.M., Erickson, E. (1976) <doi:10.2136/sssaj1976.03615995004000010006x>,
	Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) <doi:10.1097/00010694-199709000-00004>, Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) <doi:10.2136/sssaj2004.7500>, 
	Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) <doi:10.1097/01.ss.0000196768.44165.1f>, Millington, R.J., Quirk, J.P. (1961) <doi:10.1039/TF9615701200>,
	Penman, H.L. (1940) <doi:10.1017/S0021859600048164>, and Xu, X, Nieber, J.L. Gupta, S.C. (1992) <doi:10.2136/sssaj1992.03615995005600060014x>).
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.
A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X 
    regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references 
    Minguez, R., Lopez, F.A., and Mur, J. (2022) <doi:10.18637/jss.v104.i11>
    Mur, J., Lopez, F.A., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.
A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more.
The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.
A collection of algorithms related to Sierpinski 
  pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016) <doi:10.1080/00949655.2015.1038267>. Bakar et al., (2015) <doi:10.1111/rssc.12103>.
Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2022) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.
A set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.
Implements semiparametric transformation model two-phase estimation using calibration weights. The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine <DOI:10.1002/sim.6439>.
Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.
Uncertainty propagation analysis in spatial environmental  modelling following methodology
         described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> 
         and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions
         for examining the uncertainty propagation starting from input data and model parameters,
         via the environmental model onto model outputs. The functions include uncertainty model specification,
         stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques.
         Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled.
         Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for.
         The MC realizations may be used as input to the environmental models called from R, or externally.
Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
A set of tools for the peak filtering of mass spectrometry
  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
  region-of-interest (ROI), representing the spatial region where the informative
  signal is expected to be localized, a series of filters determine which peak
  signals are characterized by an implausible spatial distribution. The filters
  reduce the dataset dimensionality and increase its information vs noise ratio,
  improving the quality of the unsupervised analysis results, reducing data
  dimensionality and simplifying the chemical interpretation.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, 'SQLite' doesn't have a date type and 'sqliteutils' has some functions to deal with that.
Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting 'SQL' statements.
A rendering tool for parameterized SQL that also translates into
  different SQL dialects.  These dialects include 'Microsoft Sql Server', 'Oracle', 
  'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Snowflake', 
  'Azure Synapse Analytics Dedicated', 'Apache Spark', and 'SQLite'.
Provides utilities for generating SQL queries (particularly CREATE
    TABLE statements) from R model objects. The most important use case is
    generating SQL to score a generalized linear model or related model
    represented as an R object, in which case the package handles parsing
    formula operators and including the model's response function.
Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. This works by annotating the 'SQL' code with a name comment, which also will be the name of the list element.
Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].
Provides simple and powerful interfaces that facilitate interaction
    with 'ODBC' data sources. Each data source gets its own unique and dedicated
    interface, wrapped around 'RODBC'. Communication settings are remembered
    between queries, and are managed silently in the background. The interfaces
    support multi-statement 'SQL' scripts, which can be parameterised via
    metaprogramming structures and embedded 'R' expressions.
A subgroup identification method for precision medicine based on 
    quantitative objectives. This method can handle continuous, binary and 
    survival endpoint for both prognostic and predictive case. For the 
    predictive case, the method aims at identifying a subgroup for which 
    treatment is better than control by at least a pre-specified or 
    auto-selected constant. For the prognostic case, the method aims at 
    identifying a subgroup that is at least better than a 
    pre-specified/auto-selected constant. The derived signature is a linear 
    combination of predictors, and the selected subgroup are subjects with 
    the signature > 0. The false discover rate when no true subgroup exists
    is strictly controlled at a user-specified level.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. 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("SQUAREM"). Refer to the J Stat Software article: <doi:10.18637/jss.v092.i07>.
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
An implementation of statistical tools for the analysis 
    of unit quaternion time series. It relies on pre-existing 
    quaternion data structure provided by the 'Eigen' C++ library.
Data processing and visualizations for rodent vocalizations exported from
    'DeepSqueak'. These functions are compatible with the 'SqueakR' Shiny Dashboard,
    which can be used to visualize experimental results and analyses.
Fit linear and logistic regression models penalised with group-adaptive elastic net penalties.
  The group penalties correspond to groups of covariates defined by a co-data group set.
  The method accommodates inclusion of unpenalised covariates and overlapping groups.
  See Van Nee et al. (2021) <arXiv:2101.03875>.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, 
    N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) 
    <doi: 10.1111/2041-210X.12659>].
Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Connecting to databases requires boilerplate code to specify
    connection parameters and to set up sessions properly with the DBMS.
    This package provides a simple tool to fill two purposes: abstracting
    connection details, including secret credentials, out of your source
    code and managing configuration for frequently-used database connections
    in a persistent and flexible way, while minimizing requirements on the
    runtime environment.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Sparse redundancy analysis
	for high dimensional (biomedical) data. Directional multivariate analysis 
	to express the maximum variance in the predicted data set by a linear 
	combination of variables of the predictive data set. Implemented in a 
	partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.
Provides you with easy, programmatic access to SRDP data.
It is a framework to fit semiparametric regression estimators for the total parameter of a finite population
    when the interest variable is asymmetric distributed. The main references for this package are: Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) 
    and Cardozo C.A and Alonso-Malaver C.E. (2021). "Semi-parametric model assisted estimation in finite populations." In preparation.
Provides functionality for structural equation modeling for
    the social relations model (Kenny & La Voie, 1984;
    <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979,
    <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood
    estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>;
    Nestler, 2018, <doi:10.3102/1076998617741106>) and
    least squares estimation is supported (Bond & Malloy, 2018,
    <doi:10.1016/B978-0-12-811967-9.00014-X>).
The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.
Performs the change-point detection in regression coefficients of linear model 
    by partitioning the regression coefficients into two classes of smoothness. The change-point and
    the regression coefficients are jointly estimated.
Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint  (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional  delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
Read 'SubRip'
    <https://sourceforge.net/projects/subrip/> subtitle files as data
    frames for easy text analysis or manipulation.  Easily shift numeric
    timings and export subtitles back into valid 'SubRip' timestamp format
    to sync subtitles and audio.
An independent, reproducible, and flexible Spatially Resolved Transcriptomics (SRT) simulation framework that can be used to facilitate the development of SRT analytical methods for a wide variety of SRT-specific analyses. It utilizes spatial localization information to simulate SRT expression count data in a reproducible and scalable fashion. Two major simulation schemes are implemented in 'SRTsim': reference-based and reference-free.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Functions used by other packages from Statistics Norway are gathered. General data manipulation functions, and functions for hierarchical computations are included (Langsrud, 2020) <doi:10.13140/RG.2.2.27313.61283>. The hierarchy specification functions are useful within statistical disclosure control.
Provides a collection of self-labeled techniques for semi-supervised 
    classification. In semi-supervised classification, both labeled and unlabeled
    data are used to train a classifier. This learning paradigm has obtained promising
    results, specifically in the presence of a reduced set of labeled examples. 
    This package implements a collection of self-labeled techniques to construct a
    classification model. This family of techniques enlarges the original labeled set 
	using the most confident predictions to classify unlabeled data. The techniques 
	implemented can be applied to classification problems in several domains by the 
	specification of a supervised base classifier. At low ratios of labeled data, it 
	can be shown to perform better than classical supervised classifiers.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
Reference data sets of species sensitivities to compare the 
    results of fitting species sensitivity distributions 
    using software such as 'ssdtools' and 'Burrlioz'. 
    It consists of 17 primary data sets from
    four different Australian and Canadian organizations as well as five
    datasets from anonymous sources. It also includes a data set of the
    results of fitting various distributions using different software.
Functions to visually and statistically analyze single system data.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Species sensitivity distributions are cumulative probability
    distributions which are fitted to toxicity concentrations for
    different species as described by Posthuma et al.(2001)
    <isbn:9781566705783>.  The ssdtools package uses Maximum Likelihood to
    fit distributions such as the gamma, log-logistic, log-normal and
    Weibull to censored and/or weighted data.  Multiple distributions can
    be averaged using Akaike Information Criteria.  Confidence intervals
    on hazard concentrations and proportions are produced by parametric
    bootstrapping.
Provides functions to evaluate user-defined power functions for a parameter range, and draws a sensitivity plot. It also provides a resampling procedure for semi-parametric sample size estimation and methods for adding information to a Sweave report.
Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data.
Connect to a remote server over SSH to transfer files via SCP, 
    setup a secure tunnel, or run a command or script on the host while 
    streaming stdout and stderr directly to the client.
Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, and calculates the frequencies of those motifs based on HLA allele frequency data. 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) uses 'Generic Mapping Tools (GMT)' software and the 'GMT' R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. 'SSHAARP' analyzes the allele frequency data described by Solberg et al. (2008) <doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total.
Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.
Convenient tools for exchanging files securely from within R. By
    encrypting the content safe passage of files (shipment) can be provided by
    common but insecure carriers such as ftp and email. Based on asymmetric
    cryptography no management of shared secrets is needed to make a secure
    shipment as long as authentic public keys are available. Public keys used
    for secure shipments may also be obtained from external providers as part of
    the overall process. Transportation of files will require that relevant
    services such as ftp and email servers are available.
Parse Standard Schedules Information file (types 2 and 3) into a Data Frame.
    Can also expand schedules into flights.
Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random.  This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. 
    For R version 3.5 or greater, please use  
    'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.
Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.
Providing a collection of techniques for semi-supervised 
    classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
    data are used to train a classifier. The package includes a collection of 
    semi-supervised learning techniques: self-training, co-training, democratic, 
    decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface 
    that is easy to use.
Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
In order to facilitate the adjustment of the sample selection models existing in the literature, we created the 'ssmodels' package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) <doi:10.2307/1912352>), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models, introduced in the literature by Marchenko and Genton (2012) <doi:10.1080/01621459.2012.656011> and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) <doi:10.1111/sjos.12171>. We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) <doi:10.5705/ss.202021.0068>, that allows the inclusion of covariables to the dispersion and correlation parameters and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) <doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables.
Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.
Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.
Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2021)."Bayesian spatio-temporal models for stream networks" <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.
Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.
Simulation-based sampling protocol (SSP) is an R package design to estimate sampling effort in studies of
    ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015) <doi:10.1111/ele.12385> and simulation
    of ecological data. The theoretical background is described in Guerra-Castro et al. (2020) <doi:10.1101/2020.03.19.996991>.
Estimate the size of a networked population based on
    respondent-driven sampling data. The package is part of the "RDS Analyst"
    suite of packages for the analysis of respondent-driven sampling data.
    See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923> and
    Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>.
An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
A set of functions for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSrat computes
    probability distributions of the total scores for `Sympathy' (S),
    `Antipathy' (A), social `Preference' (P) and social `Impact' (I), and
    applies a set of criteria for sociometric status categorization.
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. 
 Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Calculates the power and sample size based on the difference in Restricted Mean Survival Time.
Implement the algorithm provided in scan for estimating the 
  transmission route on railway network using passenger volume. It is a 
  generalization of the scan statistic approach for railway network to identify
  the hot railway route for transmitting infectious diseases.
This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.
Tools to import survey files in the '.sss' (triple-s) format. 
    The package provides the function 'read.sss()' that reads the '.asc' 
    (or '.csv') and '.sss' files of a triple-s survey data file. 
    See also <https://www.triple-s.org/>.
Imports Variant Calling Format file into R. It can detect
             whether a sample contains contaminant from the same species.
             In the first stage of the approach, a change-point detection
             method is used to identify copy number variations for filtering.
             Next, features are extracted from the data for a support vector
             machine model. For log-likelihood calculation, the deviation
             parameter is estimated by maximum likelihood method. Using
             a radial basis function kernel support vector machine, the
             contamination of a sample can be detected.
Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
Functions for performing stochastic search variable selection (SSVS) 
    for binary and continuous outcomes and visualizing the results. 
    SSVS is a Bayesian variable selection method used to estimate the probability 
    that individual predictors should be included in a regression model. 
    Using MCMC estimation, the method samples thousands of regression models 
    in order to characterize the model uncertainty regarding both the predictor 
    set and the regression parameters. For details see Bainter, McCauley, Wager, 
    and Losin (2020) Improving practices for selecting a subset of important 
    predictors in psychology: An application to predicting pain, Advances in 
    Methods and Practices in Psychological Science 3(1), 66-80 
    <DOI:10.1177/2515245919885617>.
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and 
   a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described 
   in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>.  
   It also offers a convenient interface  to a number of other regularized 
   t-statistics commonly employed in high-dimensional case-control studies.
Efficiently estimate shape parameters of periodic time series 
    imagery with which  a statistical seasonal trend analysis (STA) is subsequently performed. 
    STA output can be exported in conventional raster formats. 
    Methods to visualize STA output are also implemented as well as the calculation 
    of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, 
    B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.
A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>)  and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist' (i.e., Nolan's 0-parameterization and 
    1-parameterization as detailed in Nolan (2020)). 
    See github for Lambert and Lindsey 1999 JRSS-C journal article, 
    which details the parameterization of the Buck (1995) stable.   
    See the Details section of the `?dstable` help file for context and 
    references.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough   to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected  and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and  Knudson (2018) <arXiv:1812:09384>.
Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.
An exploratory and heuristic approach for specification search in
    Structural Equation Modeling. The basic idea is to subsample the original data
    and then search for optimal models on each subset. Optimality is defined through
    two objectives: model fit and parsimony. As these objectives are conflicting,
    we apply a multi-objective optimization methods, specifically NSGA-II, to obtain
    optimal models for the whole range of model complexities. From these optimal
    models, we consider only the relevant model specifications (structures), i.e.,
    those that are both stable (occur frequently) and parsimonious and use those to
    infer a causal model.
An implementation of many measures for the
    assessment of the stability of feature selection. Both simple measures
    and measures which take into account the similarities between features
    are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.
Efficient regression for heavy-tailed and skewed data following a stable distribution. Generalized regression where the skewness and tail parameter of residuals are dependent on regressors is also available. Includes fast calculation of stable densities. Calculation of densities is based on efficient numerical methods from Ament and O'Neil (2017) <doi:10.1007/s11222-017-9725-y>. Parts of the code have been ported to C from Ament's 'Matlab' code available at <https://gitlab.com/s_ament/qastable>.
Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting. Both, standard stability
    selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) 
    and complementary pairs stability selection with improved error bounds 
    (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are
    implemented. The package can be combined with arbitrary user specified
    variable selection approaches.
Provides methods for inference using stacked multiple 
    imputations augmented with weights. The vignette provides example R code for 
    implementation in general multiple imputation settings. For additional details 
    about the estimation algorithm, we refer the reader to Beesley, Lauren J and 
    Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple 
    imputation incorporating the substantive model” <doi:10.1111/biom.13372>, 
    and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random 
    missingness through imputation stacking” <arXiv:2101.07954>.
Helper functions collected from StackOverflow.com, a
    question and answer site for professional and enthusiast programmers.
Model stacking is an ensemble technique
    that involves training a model to combine the outputs of many 
    diverse statistical models, and has been shown to improve 
    predictive performance in a variety of settings. 'stacks' 
    implements a grammar for 'tidymodels'-aligned model stacking.
Graphical outputs and treatment for a database of fish pass
    monitoring. It is a part of the 'STACOMI' open source project developed in
    France by the French Office for Biodiversity institute to centralize
    data obtained by fish pass monitoring. This version is available in French and
    English. See <http://stacomir.r-forge.r-project.org/> for more information on
    'STACOMI'.
S4 class wrappers for the 'ODBC' and Pool DBI connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.
Creates and fits staged event tree probability models, 
             which are probabilistic graphical models capable of representing 
             asymmetric conditional independence statements 
             for categorical variables. 
             Includes functions to create, plot and fit staged 
             event trees from data, as well as many efficient structure 
             learning algorithms.
             References:
             Collazo R. A., Görgen C. and Smith J. Q. 
             (2018, ISBN:9781498729604).
             Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) 
             <arXiv:1705.09457>.
             Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
             Barclay L. M., Hutton J. L. and Smith J. Q. (2013) 
             <doi:10.1016/j.ijar.2013.05.006>.
             Smith J. Q. and Anderson P. E. (2008) 
             <doi:10.1016/j.artint.2007.05.004>.
Provides facilities to implement and run population models of
    stage-structured species...
Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools 
    proposed by Roth and Sant'Anna (2021) <arXiv:2102.01291>.
Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.
Perform spatial temporal analysis of moving polygons; a
    longstanding analysis problem in Geographic Information Systems. Facilitates
    directional analysis, shape analysis, and some other simple functionality for
    examining spatial-temporal patterns of moving polygons.
Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Tools which allow regression variables to be placed on similar
    scales, offering computational benefits as well as easing interpretation of
    regression output.
The <http://standartox.uni-landau.de> database offers cleaned,
    harmonized and aggregated ecotoxicological test data, which can
    be used for assessing effects and risks of chemical concentrations
    found in the environment.
The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Implementation of popular mortality models using the 'rstan' 
    package, which provides the R interface to the 'Stan' C++ library for 
    Bayesian estimation. The package supports well-known models proposed in the 
    actuarial and demographic literature including the Lee-Carter (1992) 
    <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) 
    <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user 
    inputs deaths and exposures and the package outputs the MCMC simulations for
    each parameter, the log likelihoods and predictions. Moreover, the package 
    includes tools for model selection and Bayesian model averaging by leave 
    future-out validation.
An implementation of Simultaneous Truth and 
    Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>.  This
    method is used when there are multiple raters for an object, typically an
    image, and this method fuses these ratings into one rating.  It uses an
    expectation-maximization method to estimate this rating and the individual
    specificity/sensitivity for each rater.
Provides function to manipulate PDF files: 
    fill out PDF forms;
    merge multiple PDF files into one; 
    remove selected pages from a file;
    rename multiple files in a directory;
    rotate entire pdf document; 
    rotate selected pages of a pdf file;
    Select pages from a file;
    splits single input PDF document into individual pages;
    splits single input PDF document into parts from given points.
    'staplr' requires Java 8 installed on your system.
This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).
Provides modular functions and applications for quickly generating 
    plots and tables. Each modular function opens a graphical user interface 
    providing the user with options to create and customise plots and tables.
Reading, manipulating, writing and plotting
    spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL'
    bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
Data in multidimensional systems is obtained from 
    operational systems and is transformed to adapt it to the new 
    structure. Frequently, the operations to be performed aim to 
    transform a flat table into a star schema. Transformations can 
    be carried out using professional ETL (extract, transform and 
    load) tools or tools intended for data transformation for end 
    users. With the tools mentioned, this transformation can be 
    carried out, but it requires a lot of work. The main objective 
    this package is to define transformations that allow obtaining 
    star schemas from flat tables easily. In addition, it includes 
    basic data cleaning, dimension enrichment, incremental data 
    refresh and query operations, adapted to this context.
Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.
The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.
Get started with new projects by dropping a skeleton of a new
    project into a new or existing directory, initialise git repositories,
    and create reproducible environments with the 'renv' package. The
    package allows for dynamically named files, folders, file content, as
    well as the functionality to drop individual template files into
    existing projects.
Provides a generic, publisher-independent Rmarkdown template for writing 
  scientific papers and reports in Rmarkdown. The template allows for all the basic features of a
  scientific article, including a title page with author affiliations and footnotes possibly shown
  in two common formats, multi-language abstracts and keywords, page headers and footers, and 
  the ability to place figures and tables at the end of the output PDF. Smart cross-referencing of
  figures, tables, equations and sections is provided using the bookdown package. See package
  README.md for basic package usage.
Tool to automatically fetch, transform and arrange subsets of
    multi- dimensional data sets (collections of files) stored in local and/or
    remote file systems or servers, using multicore capabilities where possible.
    The tool provides an interface to perceive a collection of data sets as a single
    large multidimensional data array, and enables the user to request for automatic
    retrieval, processing and arrangement of subsets of the large array. Wrapper
    functions to add support for custom file formats can be plugged in/out, making
    the tool suitable for any research field where large multidimensional data sets
    are involved.
Contains functions for estimating the STARTS model of
    Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>,
    <DOI:10.1037/10409-008>. Penalized maximum likelihood
    estimation and Markov Chain Monte Carlo estimation are
    also provided, see Luedtke, Robitzsch and Wagner (2018) 
    <DOI:10.1037/met0000155>.
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files.  This makes it possible to keep private / secret environment variables separate from other environment variables.  It also makes it easier to share specific startup settings by simply copying a file to a directory.
Provides utilities to create or suppress start-up messages.
Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).
Performance analysis workflow that combines the power of the R
    language (and the tidyverse realm) and many auxiliary tools to
    provide a consistent, flexible, extensible, fast, and versatile
    framework for the performance analysis of task-based applications
    that run on top of the StarPU runtime (with its MPI (Message
    Passing Interface) layer for multi-node support).  Its goal is to
    provide a fruitful prototypical environment to conduct performance
    analysis hypothesis-checking for task-based applications that run
    on heterogeneous (multi-GPU, multi-core) multi-node HPC
    (High-performance computing) platforms.
Provides data about the 'Star Wars' movie franchise
    in a set of relational tables or as a complete 'DuckDB' database. All
    data was collected from the open source 'Star Wars' API
    <https://swapi.dev/>.
An interactive document on  the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
Highest Density Regions are the smallest set in the support of a
    probability distribution with the specified coverage probability. 'HDRs'
    may contain disjoint intervals, but can be calculated efficiently using
    iterative methods. One can similarly construct optimal (i.e., shortest)
    confidence intervals for some basic inferential problems, including for
    population means, variances, or proportion parameters.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.
An easy connection with R to Statistics Canada's Web Data Service. Open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) are accessible as a data frame, directly in the user's R environment.
    Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.
Extract statistics from articles and recompute p values.
The goal of statcodelists is to promote the reuse and exchange of statistical 
    information and related metadata with making the internationally standardized SDMX code 
    lists available for the R user. SDMX has been published as an ISO International Standard 
    (ISO 17369). The metadata definitions, including the codelists are updated regularly 
    according to the standard. The authoritative version of the code lists made available in this
    package is <https://sdmx.org/?page_id=3215/>.
An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
The 'cartogram' heatmaps generated by the included methods 
    are an alternative to choropleth maps for the United States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade" 
    (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>).
    "State bins" preserve as much of the geographic placement of the states as 
    possible but have the look and feel of a traditional heatmap. Functions are 
    provided that allow for use of a binned, discrete scale, a continuous scale 
    or manually specified colors depending on what is needed for the underlying data.
Create panel data consisting of independent states from 1816 to
    the present. The package includes the Gleditsch & Ward (G&W) and Correlates
    of War (COW) lists of independent states, as well as helper functions for 
    working with state panel data and standardizing other data sources to 
    create country-year/month/etc. data.
A tool that makes estimating models in state space form 
    a breeze. See "Time Series Analysis by State Space Methods" by 
    Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details 
    about the algorithms implemented.
Fast single trait Genome Wide Association Studies (GWAS) following 
    the method described in Kang et al. (2010), <doi:10.1038/ng.548>.        
    One of a series of statistical genetic packages for streamlining the 
    analysis of typical plant breeding experiments developed by Biometris.
Analysis of multi environment data of plant breeding experiments
    following the analyses described in Malosetti, Ribaut, 
    and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>.
    One of a series of statistical genetic packages for streamlining the analysis of 
    typical plant breeding experiments developed by Biometris.
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r>).
Phenotypic analysis of data coming from high throughput 
    phenotyping (HTP) platforms, including different types of outlier detection,
    spatial analysis, and parameter estimation. The package is being developed
    within the EPPN2020 project (<https://eppn2020.plant-phenotyping.eu/>).
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://www.vsni.co.uk/software/asreml>).
For biparental, three and four-way crosses Identity by Descent 
    (IBD) probabilities can be calculated using Hidden Markov Models and 
    inheritance vectors following Lander and Green
    (<https://www.jstor.org/stable/29713>) and Huang
    (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic 
    packages for streamlining the analysis of typical plant breeding experiments
    developed by Biometris.
For Multi Parent Populations (MPP) Identity By Descend (IBD) 
    probabilities are computed using Hidden Markov Models. These probabilities 
    are then used in a mixed model approach for QTL Mapping as described in 
    Li et al. (<doi:10.1007/s00122-021-03919-7>).
Phenotypic analysis of field trials using mixed models with and 
    without spatial components. One of a series of statistical genetic packages 
    for streamlining the analysis of typical plant breeding experiments developed
    by Biometris.    
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r>).
Contains statistical methods to analyze graphs, such as graph 
      parameter estimation, model selection based on the GIC (Graph Information 
      Criterion), statistical tests to discriminate two or more populations of 
      graphs (ANOGVA - Analysis of Graph Variability), correlation between 
      graphs, and clustering of graphs. 
      References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, 
      Futija et al. (2017) <doi:10.3389/fnins.2017.00066>, 
      Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, 
      Tang et al. (2017) <doi:10.3150/15-BEJ789>, 
      Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, 
      Ghoshdastidar et al. (2017) <arXiv:1705.06168>,  
      Ghoshdastidar et al. (2017) <arXiv:1707.00833>, 
      Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>, 
      Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, 
      Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
Initializes local variables statically within a function. 
  Their value will then persist across subsequent function invocations.
Acquire hourly meteorological data from stations located all over
    the world. There is a wealth of data available, with historic weather data
    accessible from nearly 30,000 stations. The available data is automatically
    downloaded from a data repository and processed into a 'tibble' for the
    exact range of years requested. A relative humidity approximation is
    provided using the 'August-Roche-Magnus' formula, which was adapted from
    Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.
Templates, guides, and scripts for
    writing documents in 'LaTeX' and 'R markdown' to produce
    guides, slides, and reports. Special care is taken to
    illustrate use of templates and customization opportunities.
    Challenges and opportunities of 'HTML' output from 'R markdown'
    receive special attention. Includes several vignettes
    to assist new users of literate programming.
A collection of miscellaneous statistical functions for 
    probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for 
    the Bernoulli distribution, and 'distr2name()', 'name2distr()' for 
    distribution names; 
    probability density estimation: 'densityfun()'; 
    most frequent value estimation: 'mfv()', 'mfv1()'; 
    other statistical measures of location: 'cv()' (coefficient of variation),
    'midhinge()', 'midrange()', 'trimean()'; 
    construction of histograms: 'histo()', 'find_breaks()'; 
    calculation of the Hellinger distance: 'hellinger()'; 
    use of classical kernels: 'kernelfun()', 'kernel_properties()'; 
    univariate piecewise-constant regression: 'picor()'.
A collection of statistical and geometrical tools
	including the aligned rank transform (ART; Higgins et al.
	1990 <doi:10.4148/2475-7772.1443>; Peterson 2002
	<doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011
	<doi:10.1145/1978942.1978963>), 2-D histograms and
	histograms with overlapping bins, a function for making all
	possible formulae within a set of constraints, amongst others.
Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Also includes advanced generalized linear model functions including Tweedie and Digamma distributional families and a secure convergence algorithm.
Statnet is a collection of packages for statistical network analysis that are 
  designed to work together because they share common data representations and 'API' 
  design.  They provide an integrated set of tools for the representation, 
  visualization, analysis, and simulation of many different forms of network data.  
  This package is designed to make it easy to install and load the 
  key 'statnet' packages in a single step.  Learn more about 'statnet' 
  at <http://www.statnet.org>.  Tutorials for many packages can be found 
  at <https://github.com/statnet/Workshops/wiki>.  For an introduction to functions in this package, 
  type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software <https://github.com/statnet>.
Get programmatic access to data from the Czech public
    budgeting and accounting database, Státní pokladna
    <https://monitor.statnipokladna.cz/>.
Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>,  Amendola et al. (2015) <doi:10.1002/for.2322> and  Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
A small collection of data on graduate statistics programs from the United States.
Implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. The confidence intervals and sample size functions are applicable to single parameters as well as differences, ratios, and linear contrasts of parameters.  The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see:  Statistical Methods for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science. Other functions allow searching
    the quotes database by key term tags, or authors or creating a word cloud.
A set of methods to implement Generalized Method of Moments and Maximal
    Likelihood methods for Random Utility Models. These methods are meant to
    provide inference on rank comparison data. These methods accept full,
    partial, and pairwise rankings, and provides methods to break down full or
    partial rankings into their pairwise components. Please see Generalized
    Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
    some of our methods.
Tools to help download, process and analyse the UK road collision data collected using the
  'STATS19' form. The data are provided as 'CSV' files with detailed road safety data about the
  circumstances of car crashes and other incidents on the roads resulting in 
  casualties in Great Britain from 1979, the types
  (including make and model) of vehicles involved and the consequential casualties.  The
  statistics relate only to personal casualties on public roads that are reported
  to the police, and subsequently recorded, using the 'STATS19' accident reporting form. See
  the Department for Transport website 
  <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more
  information on these data.
Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design:
    - Independent samples.
    - One-way and two-way Anova.
    - Paired samples (T-Test & Regression).
    - Repeated measures (Anova & Multiple Regression).
    - Clinical Assay.
Pull data from the 'STAT Search Analytics' API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.
Utilities for producing dataframes with rich details for the
    most common types of statistical approaches and tests: parametric,
    nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
    analyses, contingency table analyses, and meta-analyses. The
    functions are pipe-friendly and provide a consistent syntax to work
    with tidy data. These dataframes additionally contain expressions with
    statistical details, and can be used in graphing packages. This
    package also forms the statistical processing backend for
    'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Shiny apps for automated data analysis, annotated outputs and human-readable interpretation in natural language. Designed especially for learners and applied researchers. Currently available methods: EDA, EDA with Python, Correlation Analysis, Principal Components Analysis, Confirmatory Factor Analysis.
Data and functions to support Bayesian and frequentist inference and decision making 
            for the Coursera Specialization "Statistics with R".
            See <https://github.com/StatsWithR/statsr> for more information.
Download data from 'StatsWales' into R. Removes the need for the user to
    write their own loops when parsing data from the 'StatsWales' API. Provides functions
    for datasets (<http://open.statswales.gov.wales/en-gb/dataset>) and metadata
    (<http://open.statswales.gov.wales/en-gb/discover/metadata>) endpoints.
Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Spatio-temporal change of support (STCOS) methods are designed for statistical inference
	on geographic and time domains which differ from those on which the data were observed. In
	particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced
	by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools which
	facilitate use of STCOS models.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Functions for computing a standardized moderation effect
             in moderated regression and forming its confidence interval
             by nonparametric bootstrapping as proposed in
             Cheung, Cheung, Lau, Hui, and Vong (2022)
             <doi:10.1037/hea0001188>. Also includes simple-to-use
             functions for computing conditional effects (unstandardized
             or standardized) and plotting moderation effects.
Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.
Allows the creation and manipulation of C++ std::vector's in R.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
The steepness package computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.
A set of functions for finding and analysing Steiner trees. It has applications in
    biological pathway network analysis. Sadeghi (2013) <doi:10.1186/1471-2105-14-144>.
Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.
Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this  to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).
Manages and display stellar tracks and isochrones from 
	     Pisa low-mass database. Includes tools for isochrones
	     construction and tracks interpolation.
Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species.
    (i) Age class determination.
    (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. 
    (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. 
    (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH).
    <https://github.com/forestscientist/StemAnalysis>.
Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.
It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.
Provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.
An user-friendly framework to preprocess raw item
             scores of questionnaires into factors or scores and standardize
             them. Standardization can be made either by their normalization
             in representative sample, or by import of premade scoring table.
Starting from a Regression Model, it provides a stepwise 
              procedure to select the linear predictor.
Collection of model estimation, and model plotting functions 
             related to the STEPCAM family of community assembly models. 
             STEPCAM is a STEPwise Community Assembly Model that infers 
             the relative contribution of Dispersal Assembly, Habitat Filtering 
             and Limiting Similarity from a dataset consisting of the 
             combination of trait and abundance data. See also <http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> for more information.
Provides a step-down procedure for controlling the False
  Discovery Proportion (FDP) in a competition-based setup, implementing 
  Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy 
  competition (TDC) in computational mass spectrometry and the knockoff 
  construction in linear regression.
An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.
This is an interface for the 'python' package
  'stepmix'. This is a package multi-step estimation of latent class
  models with measurement and structural components. The package can
  also be used to fit mixture models with various observed random
  variables. Largely based on Bakk & Kuha (2018)
  <doi:10.1007/s11336-017-9592-7>.
A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two or more treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group.
Tools for power and sample size 
    calculation as well as design diagnostics for 
    longitudinal mixed model settings, with a focus on stepped wedge designs.
    All calculations are oracle estimates i.e. assume random effect variances 
    to be known (or guessed) in advance.  
    The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>,
    extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.
Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.
L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure.
Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Three most common types of stepwise regression including linear regression, logistic regression and cox proportional hazard regression can be performed to select best model with methods of forward selection, backward elimination, bidirectional selection and best subset selection. A widely used selection criteria are available for variable selection.
An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.
Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.
Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
This is a collection of various kinds of data with broad uses for teaching. 
    My students, and academics like me who teach the same topics I teach, should find 
    this useful if their teaching workflow is also built around the R programming 
    language. The applications are multiple but mostly cluster on topics of statistical
    methodology, international relations, and political economy.
Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.
These are miscellaneous functions that I find useful for my research and teaching.
    The contents include themes for plots, functions for simulating
    quantities of interest from regression models, functions for simulating various 
    forms of fake data for instructional/research purposes, and many more. All told, the functions
    provided here are broadly useful for data organization, data presentation, data recoding, 
    and data simulation.
These are my collection of 'R Markdown' templates, mostly for compilation to PDF.
    These are useful for all things academic and professional, if you are using 'R Markdown'
    for things like your CV or your articles and manuscripts.
A general spatiotemporal satellite image imputation method based on sparse functional data analytic techniques. The imputation method applies and extends the Functional Principal Analysis by Conditional Estimation (PACE). The underlying idea for the proposed procedure is to impute a missing pixel by borrowing information from temporally and spatially contiguous pixels based on the best linear unbiased prediction.
A collection of statistical hypothesis tests of functional time series. While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few.
Genomic Selection (GS) is a latest development in animal and plant breeding where whole genome markers information is used to predict genetic merit of an individuals in a practical breeding programme. GS is one of the promising tool for improving genetic gain in animal and plants in today’s scenario. This package is basically developed for genomic predictions by estimating marker effects. These marker effects further used for calculation of genotypic merit of individual i.e. genome estimated breeding values (GEBVs). Genomic selection may be based on single trait or multi traits information. This package performs genomic selection only for single traits hence named as STGS i.e. single trait genomic selection. STGS is a comprehensive package which gives single step solution for genomic selection based on most commonly used statistical methods.
A set of functions for computing the Standardized Temperature Index (STI).
In base R, object attributes are lost when objects are modified by
    common data operations such as subset, filter, slice, append, extract
    etc. This packages allows objects to be marked as 'sticky' and have
    attributes persisted during these operations or when inserted
    into or extracted from list-like or table-like objects.
Provides data frames that hold certain columns and attributes 
    persistently for data processing in 'dplyr'.
Functions for simplified emulation of time series computer model output in model parameter space using Gaussian processes. Stilt can be used more generally for Kriging of spatio-temporal fields. There are functions to predict at new parameter settings, to test the emulator using cross-validation (which includes information on 95% confidence interval empirical coverage), and to produce contour plots over 2D slices in model parameter space.
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641>
             and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree
             and a multiple regression model.
A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first- and second-order summary statistics.
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
The Structural Topic Model (STM) allows researchers 
  to estimate topic models with document-level covariates. 
  The package also includes tools for model selection, visualization,
  and estimation of topic-covariate regressions. Methods developed in
  Roberts et. al. (2014) <doi:10.1111/ajps.12103> and 
  Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
  is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.
Generates an interactive visualization of topic correlations/
    hierarchy in a Structural Topic Model (STM) of Roberts, Stewart, and Tingley.
    The package performs a hierarchical clustering of topics which are then exported
    to a JSON object and visualized using D3.
Analyses spatio-temporal data, decomposing data in n-dimensional arrays and using the median polish technique.
Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
Provides an application that acts as a GUI for the 'stm' text analysis package.
This app enables interactive validation, interpretation and visualization of structural topic models from the 'stm' package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.
Implementation of the family of generalised age-period-cohort
    stochastic mortality models. This family of models encompasses many models
    proposed in the actuarial and demographic literature including the 
    Lee-Carter (1992) <doi:10.2307/2290201> and
    the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. 
    It includes functions for fitting mortality models, analysing their 
    goodness-of-fit and performing mortality projections and simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.
Strength training prescription using percent-based approach requires
    numerous computations and assumptions. 'STMr' package allow users to estimate 
    individual reps-max relationships, implement various progression tables, and
    create numerous set and rep schemes. The 'STMr' package is originally created as
    a tool to help writing Jovanović M. (2020) Strength Training Manual
    <ISBN:979-8604459898>.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
New Version of the R package originally accompanying the paper 
  "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" 
  by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes 
  (PNAS 2014, 111(5), E626-635 <doi:10.1073/pnas.1311647111>). In this paper, we measure expression profiles 
  from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of 
  lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and 
  the parameters of these lognormal distributions from the cumulated expression measurements.
  The main difference of this new package version to the previous one is that it is now possible to use 
  different n's, i.e. a dataset where each tissue sample originates from a different number of cells.  
  We used this on pheno-seq data, see: Tirier, S.M., Park, J., Preusser, F. et al. Pheno-seq - linking visual 
  features and gene expression in 3D cell culture systems. Sci Rep 9, 12367 (2019) 
  <doi:10.1038/s41598-019-48771-4>).
Implementations of stochastic, limited-memory quasi-Newton optimizers,
	similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
	for smooth stochastic optimization. Implements the following methods:
	oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
	SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
	adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
	Provides functions for easily creating R objects
	with partial_fit/predict methods from some given objective/gradient/predict functions.
	Includes an example stochastic logistic regression using these optimizers.
	Provides header files and registered C routines for using it directly from C/C++.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is 
    integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model
    Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).
Methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation.
    Jerald E. Pinto (“Equity Asset Valuation (4th Edition)”, 2020, ISBN: 9781119628194).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).
An implementation of the UCI open communication protocol that
    ships with 'Stockfish' <https://stockfishchess.org/>, a very popular,
    open source, powerful chess engine written in C++.
Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (2019) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.
Given a list of substance compositions, a list of substances 
  involved in a process, and a list of constraints in addition to mass 
  conservation of elementary constituents, the package contains functions 
  to build the substance composition matrix, to analyze the uniqueness of 
  process stoichiometry, and to calculate stoichiometric coefficients if 
  process stoichiometry is unique. 
  (See Reichert, P. and Schuwirth, N., A generic framework for deriving 
  process stoichiometry in enviromental models, Environmental Modelling 
  and Software 25, 1241-1251, 2010 for more details.)
Provides functionality for working with tensors, alternating
           tensors, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  Functionality for Grassman algebra
	   is provided.  The canonical reference would be:
	   M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds".
Trajectory data formed by human or animal movement is often marked 
    by periods of movement interspersed with periods of standing still. It is
    often of interest to researchers to separate geolocation trajectories of
    latitude/longitude points by clustering consecutive locations to produce a
    model of this behavior. This package implements the Stay Point detection
    algorithm originally described in Ye (2009) <doi:10.1109/MDM.2009.11> that
    uses time and distance thresholds to characterize spatial regions as
    'stops'. This package also implements the concept of merging described in
    Montoliu (2013) <doi:10.1007/s11042-011-0982-z> as stay point region
    estimation, which allows for clustering of temporally adjacent stops for
    which distance between the midpoints is less than the provided threshold.
    GPS-like data from various sources can be used, but the temporal thresholds
    must be considered with respect to the sampling interval, and the spatial 
    thresholds must be considered with respect to the measurement error.
Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).
Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Implements stopping rule methods described in Goldman (1987) <doi:10.1016/0197-2456(87)90153-X>, Geller et al. (2003, ISBN:9781135524388), Ivanova, Qaqish, and Schell (2005) <doi:10.1111/j.1541-0420.2005.00311.x>, and Kulldorff et al. (2011) <doi:10.1080/07474946.2011.539924>.
Provides multiple sources of stopwords, for use in text analysis and natural language processing.
Storm is a distributed real-time computation system. Similar to how
 Hadoop provides a set of general primitives for doing batch processing, Storm
 provides a set of general primitives for doing real-time computation.

    Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
 implementation of Bolts and Spouts in languages other than Java.  This R
 extension provides implementations of utility functions to allow an application
 developer to focus on application-specific functionality rather than Storm/R
 communications plumbing.
Creates and manages simple key-value stores.  These can
    use a variety of approaches for storing the data.  This package
    implements the base methods and support for file system, in-memory
    and DBI-based database stores.
Functions for retrieving general and specific data from the Norwegian Parliament,
  through the Norwegian Parliament API at <https://data.stortinget.no>.
Download, explore, and analyze Literary Theme Ontology themes
    and thematically annotated story data. To learn more about the project
    visit <https://github.com/theme-ontology/theming> and
    <https://themeontology.org>.
An interface to explore trends in Twitter data using the 
    'Storywrangler' Application Programming Interface (API), which can be found
    here: <https://github.com/janeadams/storywrangler>.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
Tools for transport planning with an emphasis on spatial
    transport data and non-motorized modes. Create geographic "desire
    lines" from origin-destination (OD) data (building on the 'od'
    package); calculate routes on the transport network locally and via
    interfaces to routing services such as <https://cyclestreets.net/>;
    calculate route segment attributes such as bearing.  The package
    implements the 'travel flow aggregration' method described in Morgan
    and Lovelace (2020) <doi:10.1177/2399808320942779>.  Further
    information on the package's aim and scope can be found in the
    vignettes and in a paper in the R Journal (Lovelace and Ellison 2018)
    <doi:10.32614/RJ-2018-053>.
Utilities to estimate parameters of the models with survival functions 
             induced by stochastic covariates. Miscellaneous functions for data preparation 
             and simulation are also provided. For more information, see: 
             (i)"Stochastic model for analysis of longitudinal data on aging and mortality" 
             by Yashin A. et al. (2007), 
             Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>;
             (ii) "Health decline, aging and mortality: how are they related?" 
             by Yashin A. et al. (2007), 
             Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.
Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.
Generates artificial spatiotemporal (ST) point patterns 
  through the integration of microsimulation 
  (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) 
  and agent-based models 
  (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>). 
  Allows a user to define the behaviours of a set of 'walkers' (agents, 
  objects, persons, etc.) whose interactions with the spatial (landscape) 
  (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>)
  and the temporal domains produce new point events. The resulting ST 
  patterns from the point cloud can be measured and utilized for spatial 
  and/or temporal model testings and evaluations. Application: With 
  increasingly limited availability of fine-grained spatially and 
  temporally stamped point data, the package provides an alternative 
  source of data for a wide range of research in social and life sciences.
Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.
Offers a suite of functions for converting to and from
    (atomic) vectors, matrices, data.frames, and (3D+) arrays as well
    as lists of these objects. It is an alternative to the base R
    as.<str>.<method>() functions (e.g., as.data.frame.array()) that
    provides more useful and/or flexible restructuring of R objects.
    To do so, it only works with common structuring of R objects (e.g.,
    data.frames with only atomic vector columns).
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.
Provides a framework for performing discrete (share-level) simulations of
  investment strategies. Simulated portfolios optimize exposure to an input signal subject
  to constraints such as position size and factor exposure. For background see L. Chincarini
  and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".
Functions for the stratigraphic analysis of phylogenetic trees.
An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>.
  The package provides two functions, srank, which returns stratum-specific
  information, including population share and average percentile rank; and strat,
  which returns the stratification index and its approximate standard error.
  When a grouping factor is specified, strat also provides a detailed decomposition
  of the overall stratification into between-group and within-group components.
Functions in this package provide solution to classical problem in
  survey methodology - an optimum sample allocation in stratified sampling
  schemes. In this context, the optimal allocation is in the classical
  Tschuprov-Neyman's sense and it satisfies additional lower or upper bounds
  restrictions imposed on sample sizes in strata. There are few different
  algorithms available to use, and one them is based on popular sample
  allocation method that applies Neyman allocation to recursively reduced set of
  strata.
  This package also provides the function that computes a solution to the minimum
  sample size allocation problem, which is a minor modification of the classical
  optimium sample allocation. This problem lies in the determination of a vector
  of strata sample sizes that minimizes total sample size, under assumed fixed
  level of the pi-estimator's variance. As in the case of the classical optimal
  allocation, the problem of minimum sample size allocation can be complemented
  by imposing upper bounds constraints on sample sizes in strata.
A pilot matching design to automatically 
    stratify and match large datasets.  The manual_stratify() function allows
    users to manually stratify a dataset based on categorical variables of 
    interest, while the auto_stratify() function does automatically by
    allocating a held-aside (pilot) data set, fitting a prognostic score  
    (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based
    on prognostic score quantiles.  The strata_match() function then does optimal
    matching of the data set in parallel within strata.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called Biased
    Random Key Genetic Algorithms.
Identifies individuals in a social network who should be the intervention
    subjects for a network intervention in which you have a group of targets, a
    group of avoiders, and a group that is neither.
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches.
    The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies.
    Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).
A dynamic model of 
    the big-picture, whole ecosystem effects of hydrodynamics, 
	temperature, nutrients, and fishing on continental shelf marine 
	food webs. The package is described in: Heath, M.R., 
	Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) 
	<doi:10.1111/2041-210X.13510> StrathE2E2: An R package 
	for modelling the dynamics of marine food webs and 
	fisheries. 8pp.
Univariate stratification of survey populations with a generalization of the 
  Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account 
  a discrepancy between the stratification variable and the survey variable. The determination 
  of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all 
  stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure 
  that some specific units are in the sample. The well known cumulative root frequency rule of 
  Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.
A toolkit for stratified medicine, subgroup identification, and precision medicine.
    Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
    models (counterfactual patient-level quantities, such as the conditional average treatment effect), 
    (3) subgroup identification models (find subsets of patients with similar treatment effects), 
    and (4) treatment effect estimation and inference (for the overall population and discovered 
    subgroups). These tools can be customized and are directly used in PRISM 
    (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
    <arXiv:1912.03337>. This package is in beta and will be continually updated.
Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
    Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
    As it's built with C5.0 trees, it works only for classification (not for regression).
Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) <doi:10.1007/s42081-021-00134-y>. The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé <arXiv:2105.08379>.
The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.
Includes bases for litholog generation: graphical functions
    based on R base graphics, interval management functions and svg importation 
    functions among others. Also include stereographic projection functions, 
    and other functions made to deal with large datasets while keeping options
    to get into the details of the data.
    When using for publication please cite 
    Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and 
    Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178.
    The palaeomagnetism functions are based on:
    Tauxe, L., 2010. Essentials of Paleomagnetism. University of California 
    Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>;
    Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural 
    Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge
    University Press, 289 pp.;
    Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections
    with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205,
    <doi: 10.1016/j.cageo.2012.07.021>.
Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called vns.
The main function is icweib(), which fits a stratified Weibull
    proportional hazards model for left censored, right censored, interval censored,
    and non-censored survival data. We parameterize the Weibull regression model
    so that it allows a stratum-specific baseline hazard function, but where the
    effects of other covariates are assumed to be constant across strata. 
    Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and
    Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.
API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).
This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful 
    unsupervised algorithm for detecting anomalies in high-dimensional data, with a 
    strong theoretical foundation. However, it suffers from some limitations that 
    significantly hinder its performance level, under certain circumstances. This package 
    implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) 
    <arXiv:1908.04000>  for detecting anomalies in high-dimensional data
    that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
    with a large distance gap. An approach based on extreme value theory is used 
    for the anomalous threshold calculation.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.
Numerically solve and plot solutions of a parametric ordinary
        differential equations model of growth, death, and respiration of
        macroinvertebrate and algae taxa dependent on pre-defined environmental
        factors. The model (version 1.0) is introduced in Schuwirth, N. and
        Reichert, P., (2013) <DOI:10.1890/12-0591.1>. This package includes
        model extensions and the core functions introduced and used in
        Schuwirth, N. et al. (2016) <DOI:10.1111/1365-2435.12605>,
        Kattwinkel, M. et al. (2016) <DOI:10.1021/acs.est.5b04068>,
        Mondy, C. P., and Schuwirth, N. (2017) <DOI:10.1002/eap.1530>,
        and Paillex, A. et al. (2017) <DOI:10.1111/fwb.12927>.
Implementation of analytical models for estimating streamflow 
    depletion due to groundwater pumping, and other related tools. Functions
    are broadly split into two groups: (1) analytical streamflow depletion
    models, which estimate streamflow depletion for a single stream reach
    resulting from groundwater pumping; and (2) depletion apportionment 
    equations, which distribute estimated streamflow depletion among multiple
    stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707>
    for more information on depletion apportionment equations and Zipper et
    al. (2019) <doi:10.1029/2018WR024403> for more information on analytical
    depletion functions, which combine analytical models and depletion apportionment
    equations.
I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).
Functions to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.
The stress addition approach is an alternative to the traditional
    concentration addition or effect addition models. It allows the modelling
    of tri-phasic concentration-response relationships either as single toxicant
    experiments, in combination with an environmental stressor or as mixtures of
    two toxicants. See Liess et al. (2019) <doi:10.1038/s41598-019-51645-4>
    and Liess et al. (2020) <doi:10.1186/s12302-020-00394-7>.
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
There are some things that I wish were easier with the
    'stringr' or 'stringi' packages. The foremost of these is the
    extraction of numbers from strings. 'stringr' and 'stringi' make you
    figure out the regular expression for yourself; 'strex' takes care of
    this for you. There are many other handy functionalities in 'strex'.
    Contributions to this package are encouraged: it is intended as a
    miscellany of string manipulation functions that cannot be found in
    'stringi' or 'stringr'.
The strided iterator adapts multidimensional buffers to work with 
  the C++ standard library and range-based for-loops. Given a pointer or iterator
  into a multidimensional data buffer, one can generate an iterator range using
  make_strided to construct strided versions of the standard library's begin and
  end. For constructing range-based for-loops, a strided_range class is provided.
  These help authors to avoid integer-based indexing, which in some cases can impede
  algorithm performance and introduce indexing errors. This library exists
  primarily to expose the header file to other R projects.
Takes a list of character strings and forms an adjacency matrix for
    the times the specified characters appear together in the strings provided. For
    use in social network analysis and data wrangling. Simple package, comprised of
    three functions.
Extract glyph information from font data, and translate the
    outline curves to flattened paths or tessellated polygons. The converted
    data is returned as a 'data.frame' in easy-to-plot format.
Base R already ships with string handling capabilities 'out-
    of-the-box' but lacks streamlined function names and workflow. The
    'stringi' ('stringr') package on the other hand has well named functions,
    extensive Unicode support and allows for a streamlined workflow. On the other
    hand it adds dependencies and regular expression interpretation between base R
    functions and 'stringi' functions might differ. This packages aims at providing
    a solution to the use case of unwanted dependencies on the one hand but the need
    for streamlined text processing on the other. The packages' functions are solely
    based on wrapping base R functions into 'stringr'/'stringi' like function names.
    Along the way it adds one or two extra functions and last but not least provides
    all functions as generics, therefore allowing for adding methods for other text
    structures besides plain character vectors.
Implements an approximate string matching version of R's native
    'match' function. Also offers fuzzy text search based on various string
     distance measures. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences. This package is built for speed and
    runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
    Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
A collection of character string/text/natural language
    processing tools for pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation, sorting, padding,
    wrapping, Unicode normalisation, date-time formatting and parsing,
    and many more. They are fast, consistent, convenient, and -
    thanks to 'ICU' (International Components for Unicode) -
    portable across all locales and platforms.
    Documentation about 'stringi' is provided
    via its website at <https://stringi.gagolewski.com/> and
    the paper by Gagolewski (2022, <doi:10.18637/jss.v103.i02>).
A consistent, simple and easy to use set of
    wrappers around the fantastic 'stringi' package. All function and
    argument names (and positions) are consistent, all functions deal with
    "NA"'s and zero length vectors in the same way, and the output from
    one function is easy to feed into the input of another.
English is the native language for only 5% of the World population.
    Also, only 17% of us can understand this text. Moreover, the Latin alphabet
    is the main one for merely 36% of the total.
    The early computer era, now a very long time ago, was dominated by the US.
    Due to the proliferation of the internet, smartphones, social media, and
    other technologies and communication platforms, this is no longer the case.
    This package replaces base R string functions (such as grep(),
    tolower(), sprintf(), and strptime()) with ones that fully
    support the Unicode standards related to natural language and
    date-time processing. It also fixes some long-standing inconsistencies,
    and introduces some new, useful features.
    Thanks to 'ICU' (International Components for Unicode) and 'stringi',
    they are fast, reliable, and portable across different platforms.
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
Extracts plain text from RTF (Rich Text Format) file.
Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX based systems.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
A fast implementation with additional experimental features for
             testing, monitoring and dating structural changes in (linear)
             regression models. 'strucchangeRcpp' features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
             fit, plot and test fluctuation processes (e.g. cumulative/moving
             sum, recursive/moving estimates) and F statistics, respectively.
             These methods are described in Zeileis et al. (2002)
             <doi:10.18637/jss.v007.i02>.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals,
             and their magnitude as well as the model fit can be evaluated
             using a variety of statistical measures.
Spatial structural diversity refers to the spatial arrangement of landscape elements and can reveal itself as landscape features, such as patches and line features. The 'R' package 'StrucDiv' provides methods to quantify spatial structural diversity in continuous remote sensing data, or in other data in gridded field format. The methods are based on second-order texture metrics, considering the spatial arrangement of pixel pairs.
Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species.  As a general methodology, it is applicable to any type of (genomic) data with prior structure information.
Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement
    units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).
Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
An open source platform for validation and process control.
    Tools to analyze data from internal validation of forensic short tandem
    repeat (STR) kits are provided. The tools are developed to provide
    the necessary data to conform with guidelines for internal validation
    issued by the European Network of Forensic Science Institutes (ENFSI)
    DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
    (SWGDAM). A front-end graphical user interface is provided.
    More information about each function can be found in the
    respective help documentation.
Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling.
  Schultze (2017) <doi:10.17169/refubium-622>.
Download, navigate and analyse the Student-Life dataset. 
    The Student-Life dataset contains passive and automatic sensing data 
    from the phones of a class of 48 Dartmouth college students. 
    It was collected over a 10 week term. Additionally, the dataset contains ecological 
    momentary assessment results along with pre-study and post-study mental  
    health surveys. The intended use is to assess 
    mental health, academic performance and behavioral trends. 
    The raw dataset and additional information is 
    available at <https://studentlife.cs.dartmouth.edu/>.
Implements multi-study learning algorithms such as 
		merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, 
		the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. 
		Embedded within the 'caret' framework, this package allows for a wide range of 
		single-study learners (e.g., neural networks, lasso, random forests). 
		The package offers over 20 default similarity measures and allows for specification of custom 
		similarity measures for covariate-profile similarity weighting and an accept/reject step. 
		This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019)
		<doi:10.1101/856385>.
Implementations of the Single Transferable Vote counting 
    system. By default, it uses the Cambridge method for surplus allocation
    and Droop method for quota calculation.  Fractional surplus allocation
    and the Hare quota are available as options.
Pretty-prints R code without changing the user's formatting
    intent.
Estimates distinctiveness in speakers' (authors') style. Fits models that can be used for predicting speakers of new texts. Methods developed in Huang et al (2020) <doi:10.1017/pan.2019.49>.
Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html>. You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.
Calculate empirical subcopula and dependence measures from a given bivariate sample, and Bernstein copula approximations.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
A test for the existence of a subgroup with enhanced treatment 
    effect. And, a sample size calculation procedure for the subgroup 
    detection test.
A formula 'sub' is a subformula of 'formula' if all the terms
    on the right hand side of 'sub' are terms of 'formula' and their left hand 
    sides are identical. This package aids in the creation of subformulas.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
Provides functions for obtaining a variety of 
    graphical displays that may be useful in the subgroup analysis setting. 
    An example with a prostate cancer dataset is provided.
    The graphical techniques considered include level plots, mosaic plots,
    contour plots, bar charts, Venn diagrams, tree plots, forest plots, 
    Galbraith plots, L'Abbé plots, the subpopulation treatment effect pattern 
    plot, alluvial plots, circle plots and UpSet plots.
Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates.  The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz et al. (2017) <doi:10.7717/Peerj.3364>.
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>.
Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases.
This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y.  The SWA first uses a base procedure,  here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables,  according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'.  Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swa-reg/>.
Functions for computing test subscores using different
    methods in both classical test theory (CTT) and item response theory (IRT). This
    package enables three types of subscoring methods within the framework of CTT
    and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) 
    <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) 
    <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) 
    <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. 
    It also includes functions to compute Proportional Reduction 
    of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to 
    examine whether test subscores are of added value. In addition, the package includes 
    a function to assess the local independence assumption of IRT with
    Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 
    <doi:10.1111/j.1745-3984.1993.tb00423.x>).
Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation.
 The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup.
 The diagram may show thousands of them. The position of the dot in the diagram is determined by the
 sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The
 sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore,
 the diagram shows the line of no effect and the overall study results. For small subgroups, which
 are found on the left side of the plot, larger random deviations from the mean study effect are expected,
 while for larger subgroups only small deviations from the study mean can be expected to be chance findings.
 So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a
 kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.
 This approach was presented in Muysers (2020) <doi:10.1007/s43441-019-00082-6> and referenced in Ballarini (2020) <doi:10.1002/pst.2012>.
 New to version 3 is the Automatic Screening of one- or MUlti-factorial Subgroups (ASMUS) for documentation of the structured review of subgroup findings.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.
The Subsemble algorithm is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) <doi:10.1080/02664763.2013.864263>.
An interface to 'OpenSubspace', an open source framework for
  evaluation and exploration of subspace clustering algorithms in WEKA 
  (see <http://dme.rwth-aachen.de/de/opensubspace> for more
  information).  Also performs visualization.
Naive and adjusted treatment effect estimation for subgroups. Model 
  averaging (Bornkamp et.al, 2016 <doi:10.1002/pst.1796>) and bagging 
  (Rosenkranz, 2016 <doi:10.1002/bimj.201500147>) are proposed to address the
  problem of selection bias in treatment effect estimates for subgroups. 
  The package can be used for all commonly encountered type of outcomes in
  clinical trials (continuous, binary, survival, count). Additional functions
  are provided to build the subgroup variables to be used and to plot the 
  results using forest plots. For details, see Ballarini et.al. (2021) <doi:10.18637/jss.v099.i14>.
Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method.  Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.
A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. 
    The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially 
    expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease 
    reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.
Quality control charts for survival outcomes.
    Allows users to construct the Continuous Time Generalized
    Rapid Response CUSUM (CGR-CUSUM) <doi:10.48550/ARXIV.2205.07618>, 
    the Biswas & Kalbfleisch (2008)  <doi:10.1002/sim.3216> CUSUM, 
    the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data. 
    These procedures can be used to monitor survival processes for a change 
    in the failure rate.
Interface to 'Sudachi' <https://github.com/WorksApplications/Sudachi>,
    a Japanese morphological analyzer. This is a port of what is available in Python.
Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Generates, plays, and solves Sudoku puzzles.  The GUI
        playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
    This package is an alternative to the earlier sudoku-solver package,
    'sudoku'.  The present package uses a slightly different algorithm, has a
    simpler coding and presents a few more sugar tools, such as plot and print
    methods.  Solved sudoku games are of some interest in Experimental Design
    as examples of Latin Square designs with additional balance constraints.
Generates region-specific Suess and Laws corrections for 
    stable carbon isotope data from marine organisms collected between 1850 and 2021. Version
    0.1.4 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the
    Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the 
    subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental 
    data for regions currently not built into the package to generate corrections for those regions.
Create a hexagon tile map display from spatial polygons. Each 
    polygon is represented by a hexagon tile, placed as close to it's original
    centroid as possible, with a focus on maintaining spatial relationship to
    a focal point. Developed to aid visualisation and analysis of spatial 
    distributions across Australia, which can be challenging due to the 
    concentration of the population on the coast and wide open interior.
Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.
An adaptation of classical region/gene-based association analysis
    techniques to the use of summary statistics (P values and effect sizes)
    and correlations between genetic variants as input. It is a tool to perform
    the most popular and efficient gene-based tests using the results of genome-wide
    association (meta-)analyses without having the original genotypes and
    phenotypes at hand.
    See for details:
    Svishcheva et al (2019) Gene-based association tests using GWAS summary
    statistics. Bioinformatics.
    Belonogova et al (2022) SumSTAAR: A flexible framework for gene-based
    association studies using GWAS summary statistics. PLOS Comp Biol.
Functions to speed up the exploratory analysis of simple
    datasets using 'dplyr'. Functions are provided to do the 
    common tasks of calculating confidence intervals.
Shrinkage estimator for polygenic risk prediction models based on summary statistics of genome-wide association studies.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common descriptive
  (univariate) statistics in concise tables available in a
  variety of formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.
Module to compute cluster specific information for regression models
        with clustered errors, including leverage and influence statistics. 
        Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages)
        are supported. 'summclust' implements similar features as the 
        user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; 
        <arXiv:2205.03288v1>).
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645> and Li et al. (2020) <arXiv:2007.05117>.
Application of theoretical results which ensure that the summation
  of an infinite discrete series is within an arbitrary margin of error of its
  true value. The C code under the hood is shared through header files to allow
  users to sum their own low level functions as well. Based on the paper by
  Braden (1992) <doi: 10.2307/2324995>.
It allows to quickly perform permutation-based closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) <arXiv:2102.11759>.
Make interactive 'd3.js' sequence sunburst diagrams in R with the
    convenience and infrastructure of an 'htmlwidget'.
Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.),
        moon position and lunar phase for the given location and time. Most calculations are based on the 
        formulas given in Astronomy Answers articles about position of the sun and the planets : 
        <https://www.aa.quae.nl/en/reken/zonpositie.html>.
Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).
Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computing.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.
Tools for integrating spatially-misaligned GIS datasets. Part of the Sub-National Geospatial Data Archive System.
Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the
    diode of photovoltaic cells without the effect of series resistance.
    Here, Suns-Voc curves can be constructed with outdoor time-series I-V
    curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab.
    Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves.
    This material is based upon work supported by the U.S. Department of
    Energy's Office of Energy Efficiency and Renewable Energy (EERE) under
    Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. 
    Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of 
    Energy Efficiency and Renewable Energy administered by the Oak Ridge 
    Institute for Science and Education (ORISE) for the DOE. 
    ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE
    contract number DE-SC0014664. 
    [1] Wang, M. et al, 2018. 
    <doi:10.1109/PVSC.2018.8547772>. 
    [2] Walters et al, 2018
    <doi:10.1109/PVSC.2018.8548187>. 
    [3] Guo, S. et al, 2016. 
    <doi:10.1117/12.2236939>.
Implements the self-updating process clustering algorithms proposed
    in Shiu and Chen (2016) <doi:10.1080/00949655.2015.1049605>.
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc, implementing
   algorithms 'PELORA' and 'WILMA'.
Clusters features under the assumption that each cluster has a
 random effect and there is an outcome variable that is related to the random 
 effects by a linear regression. In this way the cluster analysis is 
 ``supervised'' by the outcome variable. An alternate specification is that 
 features in each cluster have the same compound symmetric normal distribution, 
 and the conditional distribution of the outcome given the features
 has the same coefficient for each feature in a cluster.
Computes standard error and confidence interval of various descriptive statistics under 
    various designs and sampling schemes. The main function, superbPlot(), can either return a plot 
    or a dataframe with the statistic and its precision interval so that other plotting package
    can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> 
    or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005)
    <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012)
    <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>,
    Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding 
    <doi:10.20982/tqmp.15.1.p001> for specific references.
Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.
Creates superpixels based on input spatial data. 
  This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters).
  It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.
A supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) <doi:10.1002/sam.11508>. This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646.
The 'superdiag' package provides a comprehensive test suite for testing
  Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence
  diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger
  distance) and plotting functions for trace plots and density histograms. The functions
  of the package can be used to present all diagnostic statistics and graphs at once
  for conveniently checking MCMC nonconvergence.
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations.  This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300.  Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo.  The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
Implements the super learner prediction method and contains a
    library of prediction algorithms to be used in the super learner.
Adds a Super Learner ensemble model method (using the 'SuperLearner' package)  
    to the 'mice' package. Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) <doi:10.1093/aje/kwab271>.
The idea is to provide a standard interface 
             to users who use both R and Python for building machine learning models. 
             This package provides a scikit-learn's fit, predict interface to 
             train machine learning models in R.
Produces ANOVA tables in the format used by Judd, McClelland,
    and Ryan (2017, ISBN: 978-1138819832) in their introductory textbook,
    Data Analysis. This includes proportional reduction in error and
    formatting to improve ease the transition between the book and R.
Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://www.ijsr.net/archive/v4i4/SUB152869.pdf>.
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.
Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap.
The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).
Downloads data supplementary materials from manuscripts,
    using papers' DOIs as references. Facilitates open, reproducible
    research workflows: scientists re-analyzing published datasets can
    work with them as easily as if they were stored on their own
    computer, and others can track their analysis workflow
    painlessly. The main function suppdata() returns a (temporary)
    location on the user's computer where the file is stored, making
    it simple to use suppdata() with standard functions like
    read.csv().
Ten distributions supplementing those built into R.
  Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
  squared, Spearman's rho, maximum F ratio, the Pearson product
  moment correlation coefficient, Johnson distributions, normal
  scores and generalized hypergeometric distributions.
Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. For details on best-worst scaling, see Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'.  The package also contains a tutorial on basic data frame management, including how to handle missing data.
Implementation of the direct Monte Carlo approach of 
             Zellner and Ando (2010) <doi:10.1016/j.jeconom.2010.04.005>
             to sample from posterior of 
             Seemingly Unrelated Regression (SUR) models. In 
             addition, a Gibbs sampler is implemented that allows 
             the user to analyze SUR models using the power prior.
An implementation of the surrogate approach to residuals and 
  diagnostics for ordinal and general regression models; for details, see Liu 
  and Zhang (2017) <doi:10.1080/01621459.2017.1292915>. These residuals can be 
  used to construct standard residual plots for model diagnostics (e.g., 
  residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). 
  The package also provides an 'autoplot' function for producing standard 
  diagnostic plots using 'ggplot2' graphics. The package currently supports 
  cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. 
  Support for binary regression models using the standard 'glm' function is also 
  available.
A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.
Estimation of gross flows under non-response and complex sampling designs, using Gutiérrez, Nascimento Silva and Trujillo (2014) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2014002/article/14113-eng.pdf> complex sampling extension of the non-response model developed by Stasny (1987) <https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/some-markov-chain-models-for-nonresponse-in-estimating-gross-labor-force-flows.pdf>. 
    It estimates the gross flows process under non-response by modelling the observable cross-tabulation counts as a two-stage Markov Chain process, combining (1) the unobservable Markov Chain describing the transition of states; and (2) the non-response process, given by the initial response probabilities and the response/non-response transition probabilities.
This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.
Create sampling designs using the surface reconstruction algorithm.
  Original method by: Olsson, D. 2002. A method to optimize soil sampling from 
  ancillary data. Poster presenterad at: NJF seminar no. 336, 
  Implementation of Precision Farming in Practical Agriculture, 10-12 
  June 2002, Skara, Sweden.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.
Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" <doi:10.1111/biom.13629>.
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
Provides a variety of functions to estimate
        time-dependent true/false positive rates and AUC curves from a
        set of censored survival data.
Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.
Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.
Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) <doi:10.1002/sim.4154>. Inference for the difference in C between two competing prediction models is also implemented.
Estimates correlation coefficients with associated
   confidence limits for bivariate, partially censored survival times. Uses
   the iterative multiple imputation approach proposed
   by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate 
   distribution, either on the original time scale or as copula.
Plots survival models from the 'survival' package. Additionally, it
    plots curves of multistate models from the 'mstate' package. Typically, a
    plot is drawn by the sequence survplot(), confIntArea(), survCurve() and
    nrAtRisk(). The separation of the plot in this 4 functions allows for great
    flexibility to make a custom plot for publication.
Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables
  in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two
  survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival
  curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.
Fits time series models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. Calculates Theil's index to measure inequality among multiple groups, and can be extended to measure inequality across multiple groups nested within geographies. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).
Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.
Survival analysis models are commonly used in medicine and other areas. Many of them
    are too complex to be interpreted by human. Exploration and explanation is needed, but
    standard methods do not give a broad enough picture. 'survex' provides easy-to-apply
    methods for explaining survival models, both complex black-boxes and simpler statistical models.
    They include methods specific to survival analysis such as SurvSHAP(t) described in Krzyzinski et al., (2022)
    <arXiv:2208.11080>, SurvLIME introduced in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as
    extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.
It computes Relative survival, AER and SMR based on French death rates.
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement.
Functions to generate K-fold cross validation (CV) folds
    and CV test error estimates that take into account
    how a survey dataset's sampling design was constructed
    (SRS, clustering, stratification, and/or unequal sampling weights).
    You can input linear and logistic regression models, along with data and a 
    type of survey design in order to get an output that can help you determine
    which model best fits the data using K-fold cross validation.
    Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
    by Wieczorek, Guerin, and McMahon (2022)
    <doi:10.1002/sta4.454>
    explains why differing how we take folds based on survey design is useful.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
At present, the only functionality is the calculation of optimal one-sided winsorizing cutoffs. The main function is optimal.onesided.cutoff.bygroup. It calculates the optimal tuning parameter for one-sided winsorisation, and so calculates winsorised values for a variable of interest. See the help file for this function for more details and an example.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Decision support tool for prioritizing sites for ecological
    surveys based on their potential to improve plans for conserving
    biodiversity (e.g. plans for establishing protected areas). Given a set of
    sites that could potentially be acquired for conservation management,
    it can be used to generate and evaluate plans for surveying additional
    sites. Specifically, plans for ecological surveys can be
    generated using various conventional approaches (e.g. maximizing expected
    species richness, geographic coverage, diversity of sampled environmental
    algorithms. After generating such survey plans, they can be evaluated using
    conditions) and maximizing value of information. Please note that several
    functions depend on the 'Gurobi' optimization software (available from
    <https://www.gurobi.com>). Additionally, the 'JAGS' software (available from
    <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical
    generalized linear models.
Contains a suite of functions for survival analysis in health economics. 
    These can be used to run survival models under a frequentist (based on maximum likelihood) 
    or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian 
    Monte Carlo). The user can specify a set of parametric models using a common notation and 
    select the preferred mode of inference. The results can also be post-processed to produce 
    probabilistic sensitivity analysis and can be used to export the output to an Excel 
    file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.
High dimensional time to events data analysis with variable selection technique.
             Currently support LASSO, clustering and Bonferroni's correction.
Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).
Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.
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.
Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.
Functions can be used for batch survival analysis, 
	but not only for it. Most importantly, it can verify any
 	P-value calculated according to the gene expression level and 
	eliminate the influence of co-expression genes.
A high-level interface to perform survival analysis, 
    including Kaplan-Meier analysis and log-rank tests and Cox regression.
    Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting.
    Builds upon the 'survminer' package for Kaplan-Meier plots and provides
    a customizable implementation for forest plots.
    Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452>
    Cox (1972) <JSTOR:2985181>
    Peto & Peto (1972) <JSTOR:2344317>.
Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models with left, right or interval censored survival data 
      using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard
      function is provided.
Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.
Facilitates building personalized survival path models. The function survivalpath() return tree structure results, which can be used to draw easily beautiful and ready-to-publish survival path tree. See Shen L, et al (2018) <doi:10.1038/s41467-018-04633-7> .
Provides estimates for the bivariate and trivariate distribution 
             functions and bivariate and trivariate survival functions for 
             censored gap times. Two approaches, using existing methodologies, 
             are considered: (i) the Lin's estimator, which is based on the 
             extension the Kaplan-Meier estimator of the distribution function 
             for the first event time and the Inverse Probability of Censoring 
             Weights for the second time (Lin DY, Sun W, Ying Z (1999) 
             <doi:10.1093/biomet/86.1.59> and (ii) another estimator
             based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
             <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). 
             The proposed methods are the landmark estimators based on 
             subsampling approach, and the estimator based on weighted cumulative
             hazard estimator. The package also provides nonparametric estimator
             conditional to a given continuous covariate. All these methods have
             been submitted to be published.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344)
Performs support vectors analysis for data sets with survival
    outcome. Three approaches are available in the package: The regression approach
    takes censoring into account when formulating the inequality constraints of
    the support vector problem. In the ranking approach, the inequality constraints
    set the objective to maximize the concordance index for comparable pairs
    of observations. The hybrid approach combines the regression and ranking
    constraints in the same model.
Several datasets which detail the results and events of each season of Survivor. This includes 
  details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is 
  useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. 
  Includes 'ggplot2' scales and colour palettes for visualisation.
Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.
Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
Provides kernel weighting methods for estimation of proportional 
    hazards models with intermittently observed longitudinal covariates. 
    Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) 
    <doi:10.1080/01621459.2014.957289>.
An implementation of popular evaluation metrics that are commonly used in survival prediction 
  including Concordance Index, Brier Score, Integrated Brier Score, 
  Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and
  (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.
In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in  Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.
Simple and quick method of exporting the most often used
    survival analysis results to an Excel sheet.
Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package.
    In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to 
    capture uncertainty in model parameters.
    Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
Fits hazard and excess hazard models with multidimensional penalized splines allowing for 
        time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for 
        multidimensional penalized hazard and excess hazard models. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics. 
	See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.
The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <arXiv:1402.0432>.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Performs the procedure proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. The method specifies a set of truncation time points tau's for calculating restricted mean survival times (RMST), performs testing for equality, and estimates the difference in RMST between two groups at the specified tau's. Multiplicity by specifying several tau's is taken into account in this procedure.
Performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) <doi:10.1002/sim.8565> have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference.
Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2019) <arXiv:1908.01444>.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) <doi:10.18637/jss.v059.i02> and Moriña D, Navarro A. (2017) <doi:10.1080/03610918.2016.1175621>.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Targeted estimates of marginal cumulative incidence in survival
    settings with and without competing risks, including estimators that respect
    bounds (Benkeser, Carone, and Gilbert. Statistics in Medicine, 2017.
    <doi:10.1002/sim.7337>).
Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.
Implements methods for variable selection in linear
    regression based on the "Sum of Single Effects" (SuSiE) model, as
    described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
    (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
    simple summaries, called "Credible Sets", for accurately
    quantifying uncertainty in which variables should be selected.
    The methods are motivated by genetic fine-mapping applications,
    and are particularly well-suited to settings where variables are
    highly correlated and detectable effects are sparse. The fitting
    algorithm, a Bayesian analogue of stepwise selection methods
    called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
    and fast, allowing the SuSiE model be fit to large data sets
    (thousands of samples and hundreds of thousands of variables).
The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. Ahmar, A.S. (2017) <doi:10.17605/osf.io/rknsv>.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
             independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), 
             smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Quickly construct standard dialog boxes for your GUI, including 
  message boxes, input boxes, list, file or directory selection, ... In case R
  cannot display GUI dialog boxes, a simpler command line version of these
  interactive elements is also provided as fallback solution.
Reimplementation of the 'svDialogs' dialog boxes in Tcl/Tk.
Generates simulated paths from various financial stochastic volatility models
 with jumps and applies the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to 
 compute likelihood evaluations, filtering distribution estimates, and maximum likelihood parameter estimates.
 The algorithm is implemented following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995>.
Obtain parameters of Svensson's Method, including percentage agreement, 
    systematic change and individual change. Also, the contingency table can be generated. 
    Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales 
    which measures the level of change both from systematic and individual aspects. For the details, 
    please refer to Svensson E. Analysis of systematic and random differences between paired ordinal 
    categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.
A graphics device for R that produces 'Scalable Vector Graphics'.
  'svglite' is a fork of the older 'RSvgDevice' package.
This 'htmlwidget' provides pan and zoom interactivity to R
    graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is
    provided through the 'svg-pan-zoom.js' library. Various options to the widget
    can tailor the pan and zoom experience to nearly any user desire.
The purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. 
    In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. 
    In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. 
    Basically, 'svgtools' transforms the statistical values into coordinates and widths/heights of the vector graphics.
    This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.
The 'SciViews' 'svGUI' package eases the management of Graphical
  User Interfaces (GUI) in R. It is independent from any particular GUI widgets
  ('Tk', 'Gtk2', native, ...). It centralizes info about GUI elements currently
  used, and it dispatches GUI calls to the particular toolkits in use in
  function of the context (is R run at the terminal, within a 'Tk' application,
  a HTML page?).
Creates 3D animated, interactive visualizations that can be viewed in a web browser.
A simple HTTP server allows to connect GUI clients to R.
Function for the GUI API to interact with external IDE/code editors.
Provides functionality to fit and simulate from stationary vine 
  copula models for time series, see Nagler et al. (2022) 
  <doi:10.1016/j.jeconom.2021.11.015>.
R-side code to implement an R editor and IDE in Komodo IDE
  with the SciViews-K extension.
Miscellaneous functions for 'SciViews' or general use: manage a
  temporary environment attached to the search path for temporary variables you
  do not want to save() or load(), test if 'Aqua', 'Mac', 'Win', ... Show
  progress bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  as described in Groenen et al. (2008) <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating 
             the squared deviations from sample variance. Besides indicating the contribution of squared 
             deviations for the sample variability, these plots are capable of detecting characteristics of the
             distribution such as symmetry, skewness and outliers. A remarkable graphical method based on 
             Sv-plot2 can determine the decision on testing hypotheses over one or two population means. 
             In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology  
             can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.
Provides tools for creating and working with survey replicate weights,
  extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>.
  Methods are provided for applying nonresponse adjustments to
  both full-sample and replicate weights as suggested by 
  Rust and Rao (1996) <doi:10.1177/096228029600500305>.
  Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) 
  <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>.
  Diagnostic functions are included to compare weights and weighted estimates
  from different sets of replicate weights.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis, EM clustering, logratio
	analysis and log-multiplicative (association) analysis. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
A socket server allows to connect clients to R.
Functions to enumerate and reference figures, tables and equations
  in R Markdown documents that do not support these features (thus not
  'bookdown' or 'quarto'. Supporting functions for using 'Sweave' and 'Knitr'
  with 'LyX'.
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part.
High level management of widgets, windows and other graphical resources.
Diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202).  Variance inflation factors are also computed for some general linear models (logistic and poisson) as described in Liao (U. Maryland thesis, 2010).
Builds, evaluates and validates a nomogram with survey data
    and right-censored outcomes. As described in Capanu (2015)
    <doi:10.18637/jss.v064.c01>, the package contains functions to create
    the nomogram, validate it using bootstrap, as well as produce the
    calibration plots.
Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.
Quickly and flexibly calculates weights for survey data, in order to correct
    for survey non-response or other sampling issues. Uses rake weighting, a common technique 
    also know as rim weighting or iterative proportional fitting.  This technique allows for weighting 
    on multiple variables, even when the interlocked distribution of the two
    variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in 
    Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking
    to the base weighting functionality in 'survey.'
This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.
An algorithm that trains a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute 
    combinations. This package works on top of the 'caret' package and proceeds in a 
    forward-step manner. More specifically, it builds and tests learners starting 
    from very few attributes until it includes a maximal number of attributes by 
    increasing the number of attributes at each step. Hence, for each fixed number
    of attributes, the algorithm tests various (randomly selected) learners and 
    picks those with the best performance in terms of training error. Throughout,
    the algorithm uses the information coming from the best learners at the previous
    step to build and test learners in the following step. In the end, it outputs
    a set of strong low-dimensional learners.
A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.
Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.
Three ensemble learning algorithms based on support vector machines. 
    They all train support vector machines on subset of data and combine the result.
There are numerous places to create and download color
    palettes. These are usually shared in 'Adobe' swatch file formats of
    some kind. There is also often the need to use standard palettes
    developed within an organization to ensure that aesthetics are carried over
    into all projects and output. Now there is a way to read these swatch
    files in R and avoid transcribing or converting color values by hand or
    or with other programs. This package provides functions to read and
    inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'),
    'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed
    descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other
    swatch file formats can be found at
    <http://www.selapa.net/swatches/colors/fileformats.php>.
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.
Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
The Swiss Ephemeris (version 2.08) is a high precision ephemeris based upon the
    DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to
    17191 CE. This package uses the semi-analytic theory by Steve Moshier.
    For faster and more accurate calculations, the compressed Swiss Ephemeris
    data is available in the 'swephRdata' package. To access this data package,
    run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/",
    type = "source")'. The size of the 'swephRdata' package is approximately
    115 MB. The user can also use the original JPL DE431 data.
Process and summarize aerial survey 'DAS' data (AirDAS) 
    <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF>
    collected using an aerial survey program from the 
    Southwest Fisheries Science Center (SWFSC) 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    PDF files detailing the relevant AirDAS data formats are included in this package.
Process and summarize shipboard 
    'DAS' <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data
    produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz' 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    This package standardizes and streamlines basic DAS data processing,
    and includes a PDF with the DAS data format requirements.
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
An efficient sensitivity analysis for stochastic models based on 
    Monte Carlo samples. Provides weights on simulated scenarios from a 
    stochastic model, such that stressed random variables fulfil given 
    probabilistic constraints (e.g. specified values for risk measures), 
    under the new scenario weights. Scenario weights are selected by 
    constrained minimisation of the relative entropy to the baseline model. 
    The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019)
    "Reverse Sensitivity Testing: What does it take to break the model" 
    <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) 
    "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
The goal of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions.  To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files.  Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community.  'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data.  This is a developmental package, not yet mature.
Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.
Use the R console as an interactive learning
    environment. Users receive immediate feedback as they are guided through
    self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
    to be used with swirl.
Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide, ambient temperature, dew point, wind speed and wind direction at 3 sites around lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.
Provides a number of useful functions to employ MrP for small area prediction in Switzerland. Based on a hierarchical model and survey data one can derive cantonal preference measures. The package allows to automatize the prediction and post-stratification steps. It further provides adequate print, summary, map, and plot functions for objects of its class.
Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via 
    an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).
An unsorted collection of visualization widgets rendered in 'Tcl/Tk'<https://www.tcl.tk/> to generate agile dashboards for your iterative simulations. Widgets include progress bars, counters, eavesdroppers, injectors, switches, and sliders for dynamic manipulation and visualization of simulation parameters.
Provides a switch-case construct for 'R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an 'R' function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a
    generalized package installation process which supports multiple repository
    and non-repository sources and tracks package provenance.
Provides a simple plugin to the switchr
	     framework which allows users to publish package and
	     session manifests as gists.
Functions to connect the widely used Storm Water Management Model (SWMM)
  of the United States Environmental Protection Agency (US EPA) 
  <https://www.epa.gov/water-research/storm-water-management-model-swmm> to R with
  currently two main goals: (1) Run a SWMM simulation from R and (2) provide fast 
  access to simulation results, i.e. SWMM's binary '.out'-files. High performance is achieved
  with help of Rcpp. Additionally, reading SWMM's '.inp' and '.rpt' files is supported to 
  glance model structures and to get direct access to simulation summaries.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
Tools for performing routine analysis and plotting tasks with environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. This package builds
    on the functionality of the 'SWMPr' package <https://cran.r-project.org/package=SWMPr>,
    which is used to retrieve and organize the data. The combined set of tools
    address common challenges associated with continuous time series data
    for environmental decision making, and are intended for use in annual reporting activities.
    References:
    Beck, Marcus W. (2016) <ISSN 2073-4859><https://journal.r-project.org/archive/2016-1/beck.pdf>
    Rudis, Bob (2014) <https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/>.
    United States Environmental Protection Agency (2015) <https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030>.
    United States Environmental Protection Agency (2012) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.646.1973&rep=rep1&type=pdf>.
Functions to speed up work flow for hydrological analysis. 
    Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).
An English language syllable counter, plus readability score
    measure-er. For readability, we support 'Flesch' Reading Ease and
    'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975)
    <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
    Automated Readability Index ('Senter' and Smith 1967)
    <https://apps.dtic.mil/sti/citations/AD0667273>,
    Simple Measure of Gobbledygook (McLaughlin 1969)
    <https://www.semanticscholar.org/paper/SMOG-Grading-A-New-Readability-Formula.-Laughlin/5fccb74c14769762b3de010c5e8a1a7ce700d17a>,
    and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The
    package has been carefully optimized and should be very efficient, both in
    terms of run time performance and memory consumption. The main methods are
    'vectorized' by document, and scores for multiple documents are computed in
    parallel via 'OpenMP'.
Implements tidy syllabification of transcription. 
  Based on @kylebgorman's 'python' implementation <https://github.com/kylebgorman/syllabify>.
Provides the hyphenation algorithm used for 'TeX'/'LaTeX' and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the
                    function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the 'koRpus' package, but later
                    released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added
                    on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions
                    on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of
                    the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).
Adds support for the English language to the 'sylly'
        package. Due to some restrictions on CRAN, the full package
        sources are only available from the project homepage. To ask
        for help, report bugs, suggest feature improvements, or discuss
        the global development of the package, please consider
        subscribing to the koRpus-dev mailing list
        (<http://korpusml.reaktanz.de>).
Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.
Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation.
 (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>,
 Breiman, L. (1996), <doi:10.1007/BF00058655>,
 Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
 Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
 Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
 Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
 Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
 Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
 Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.
Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
A collection of functions for symbolic computation using 'Ryacas'
  package for structural equation models. This package includes
  functions to calculate model-implied covariance (and correlation)
  matrix and sampling covariance matrix of functions of variables using
  the first-order Taylor approximation. Reference: McArdle and McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>.
Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.
Generates synonyms from a given word drawing from a synonym list
    from the 'moby' project <http://moby-thesaurus.org/>.
Methods for computing spatial, temporal, and spatiotemporal
    statistics as described in Gouhier and Guichard (2014) 
    <doi:10.1111/2041-210X.12188>. These methods include 
    empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Generate the same random numbers in R and Python.
Analysis of metacommunities based on functional traits and
    phylogeny of the community components. The functions that are offered here
    implement for the R environment methods that have been available in the
    SYNCSA application written in C++ (by Valerio Pillar, available at 
    <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).
Regression inference for multiple populations by integrating 
    summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and 
    Mukherjee, B. (2021) A synthetic data integration framework to leverage 
    external summary-level information from heterogeneous populations 
    <arXiv:2106.06835>.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
Set of functions to compute different types of synoptic classification methods and for analysing their effect on environmental variables. More information about the methods used in Lemus-Canovas et al. 2019 <DOI:10.1016/j.atmosres.2019.01.018>,  Martin-Vide et al. 2008 <DOI:10.5194/asr-2-99-2008>, Jenkinson and Collison 1977.
Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.
A set of functions for generating 'SPSS' syntax files from the R environment.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
Provides access to curated American Community Survey (ACS) base tables via a wrapper
  to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with
  ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated
  annealing.  SMSM is conducted in parallel by default. Lastly, we provide functionality for
  data-extensibility of micro-datasets <doi:10.18637/jss.v104.i07>.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
A critical first step in systematic literature reviews
  and mining of academic texts is to identify relevant texts from a range
  of sources, particularly databases such as 'Web of Science' or 'Scopus'.
  These databases often export in different formats or with different metadata
  tags. 'synthesisr' expands on the tools outlined by Westgate (2019)
  <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats
  (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging
  and deduplication of the resulting dataset.
Creation of an individual claims simulator which generates various
    features of non-life insurance claims. An initial set of test parameters,
    designed to mirror the experience of an Auto Liability portfolio, were set
    up and applied by default to generate a realistic test data set of
    individual claims (see vignette). The simulated data set then allows
    practitioners to back-test the validity of various reserving models and to
    prove and/or disprove certain actuarial assumptions made in claims
    modelling. The distributional assumptions used to generate this data set can
    be easily modified by users to match their experiences. Reference: Avanzi B,
    Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
    simulator with feature control" <arXiv:2008.05693>.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
A set of functions to support experimentation in the utility of partially synthetic data sets.  All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.
Drop-in replacements for the base system2() function with fine control
    and consistent behavior across platforms. Supports clean interruption, timeout, 
    background tasks, and streaming STDIN / STDOUT / STDERR over binary or text 
    connections. Arguments on Windows automatically get encoded and quoted to work 
    on different locales.
Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>).
  This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper.
Loading system fonts and Google Fonts
    <https://fonts.google.com/> into R, in order to
    support other packages such as 'R2SWF' and 'showtext'.
Provides functions for constructing mathematical models of dynamical systems from measured input-output data.
Send 'syslog' protocol messages to a remote 'syslog' server
    specified by host name and TCP network port.
In the past decade, genome-scale metabolic reconstructions have widely been 
    used to comprehend the systems biology of metabolic pathways within an organism. 
    Different GSMs are constructed using various techniques that 
    require distinct steps, but the input data, information conversion and software tools
    are neither concisely defined nor mathematically or programmatically formulated
    in a context-specific manner.The tool that quantitatively and qualitatively specifies
    each reconstruction steps and can generate a template list of reconstruction steps dynamically
    selected from a reconstruction step reservoir, constructed based on all available published papers.
Econometric estimation of simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS)
 as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>,
 Zellner and Theil (1962) <doi:10.2307/1911287>, and
 Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.
Provides system native access to the font catalogue. As font
    handling varies between systems it is difficult to correctly locate 
    installed fonts across different operating systems. The 'systemfonts' 
    package provides bindings to the native libraries on Windows, macOS and 
    Linux for finding font files that can then be used further by e.g. graphic
    devices. The main use is intended to be from compiled code but 'systemfonts'
    also provides access from R.
The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.
Deals with standard Young tableaux (field of combinatorics). Performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice.
Extracts sentiment and sentiment-derived plot arcs
    from text using a variety of sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.
Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.
Functions for tabulating and summarising categorical variables. 
    Most functions are designed to work with dataframes, and use the 'tidyverse' 
    idiom of taking the dataframe as the first argument so they work within 
    pipelines. Equivalent functions that operate directly on vectors are also 
    provided where it makes sense. This package aims to make exploratory data 
    analysis involving categorical variables quicker, simpler and more robust.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.
For writing tables with custom formats in a Excel file ready to be distributed.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Translate double and integer valued data into
    character values formatted for tabulation in manuscripts
    or other types of academic reports.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
    v1.2 - fixed "missing "no visible global function definition for .."
A collection of time-saving wrappers for reproducible export of
    summary tables commonly used in scientific articles, to .xls/.xlsx multi-tab spreadsheets, while controlling spreadsheet layout. Powered by 'XLConnect'/'rJava' utilities.
A toolbox for comparing two data frames with the aim of quick and 
  simple functionality. Using a key-column common to both tables, see which rows 
  are common and highlight differing values by column. Also included are functions
  validating keys and uniqueness of in-group values.
Create publication quality plots and tables for Item Response Theory and Classical Test theory based item analysis, exploratory and confirmatory factor analysis.
Creates some tables of clinical study. 'Table 1' is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of 'Table 2' is to explore influence factors. And 'Table 3' created by table3() is able to make stratified analysis.
An easy-to-use tool for drawing paper-quality tables with double-information encoded in grobs shapes and colors.
Higher Criticism (HC) test between two frequency tables. Test is based on an adaptation of the Tukey-Donoho-Jin HC statistic to testing frequency tables described in Kipnis (2019) <arXiv:1911.01208>.
A tool to create and style HTML tables with CSS. These can
    be exported and used in any application that accepts HTML (e.g. 'shiny',
    'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files
    (which also work with shiny).
Provides two classes extending 'data.table' class. Simple
    'tableList' class wraps 'data.table' and any additional structures together.
    More complex 'tableMatrix' class combines 'data.table' and
    'matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more
    information and examples.
Provides a user friendly interface to 
  generation of booktab style tables using 'xtable'.
Creates 'Table 1', i.e., description of baseline patient
    characteristics, which is essential in every medical research.
    Supports both continuous and categorical variables, as well as
    p-values and standardized mean differences. Weighted data are
    supported via the 'survey' package.
'R' interface to the 'Tabler' HTML template. See more here <https://tabler.io>. 
    'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different 
     layouts available such as a one page dashboard or a multi page template,
     where the navigation menu is contained in the navigation bar. A fancy example
     is available at <https://dgranjon.shinyapps.io/shinyMons/>.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
Allows to work with 'Table Schema' (<https://specs.frictionlessdata.io/table-schema/>). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction.
Presentation-quality tables are displayed as plots on an R 
  graphics device.  Although there are other packages that format tables 
  for display, this package is unique in combining two features: (a) It is 
  aware of the logical structure of the table being presented, and makes 
  use of that for automatic layout and styling of the table.  This avoids 
  the need for most manual adjustments to achieve an attractive result. 
  (b) It displays tables using 'ggplot2' graphics.  Therefore a table can 
  be presented anywhere a graph could be, with no more effort.  External 
  software such as LaTeX or HTML or their viewers is not required.  The 
  package provides a full set of tools to control the style and appearance 
  of tables, including titles, footnotes and reference marks, horizontal 
  and vertical rules, and spacing of rows and columns.  Methods are included 
  to display matrices; data frames; tables created by R's ftable(), table(), 
  and xtabs() functions; and tables created by the 'tables' and 'xtable' 
  packages.  Methods can be added to display other table-like objects.  A 
  vignette is included that illustrates usage and options available in the 
  package.
Creates a table of descriptive statistics
 for factor and numeric columns in a data frame. Displays
 these by groups, if any. Highly customizable, with support
 for 'html' and 'pdf' provided by 'kableExtra'. Respects
 original column order, column labels, and factor level order.
 See ?tablet.data.frame and vignettes.
A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.
Implements the 'TabNet' model by Sercan O. Arik et al (2019) <arXiv:1908.07442>
    and provides a consistent interface for fitting and creating predictions. It's
    also fully compatible with the 'tidymodels' ecosystem.
Collect  your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Helps the user to build and register schema descriptions of 
    disorganised (messy) tables. Disorganised tables are tables that are 
    not in a topologically coherent form, where packages such as 'tidyr' could 
    be used for reshaping. The schema description documents the arrangement of 
    input tables and is used to reshape them into a standardised (tidy) output 
    format.
An easy way to examine archaeological count data. This
    package provides several tests and measures of diversity:
    heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.),
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and
    similarity (Brainerd-Robinson, etc.). The package make it easy to
    visualize count data and statistical thresholds: rank vs abundance
    plots, heatmaps, Ford (1962) and Bertin (1977) diagrams.
Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.
The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.
The maximum likelihood classifier (MLC) is one of the most common classifiers used for 
  remote sensing imagery. 
  This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train 
  and predict over tabular data (data.frame).
  The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.
Generates pretty console output for tables allowing for full customization
  of cell colors, font type, borders and many others attributes. It also supports 'multibyte'
  characters and nested tables.
Efficient tabulation with Stata-like output.
	For each unique value of the variable, it shows the number of 
	observations with that value, proportion of observations with that
	value, and cumulative proportion, in descending order of frequency.
	Accepts data.table, tibble, or data.frame as input. 
	Efficient with big data: if you give it a data.table, 
	tab() uses data.table syntax.
Convert semi-structured log files (such as 'Apache' access.log files)
    into a tabular format (data.frame) using a standard template system.
Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
To facilitate the analysis of positron emission tomography (PET) 
    time activity curve (TAC) data, and to encourage open science and 
    replicability, this package supports data loading and analysis of multiple 
    TAC file formats. Functions are available to analyze loaded TAC data for 
    individual participants or in batches. Major functionality includes weighted
    TAC merging by region of interest (ROI), calculating models including 
    standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, 
    Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting 
    functions and calculation of cut-off values (Aizenstein et al. 2008
    <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for
    a detailed overview of 'tacmagic' functions.
Extensions to 'lattice', providing new high-level
  functions, methods for existing functions, panel functions, and a theme.
Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.
Function TailClassifier() in this package is a Tail-Classifier 
    function. The function suggests one of the following types of tail for
    your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying 
    tail; and 3) Near-exponential decaying tail.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in  
	 Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) <doi:10.1111/rssb.12114> and 
	 Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) <doi:10.1007/s10687-017-0303-7>.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.
When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting.  The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2020) <doi:10.1111/biom.13558>.
A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to  Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
The Tanaka method enhances the representation of topography on
    a map using shaded contour lines. In this simplified implementation of
    the method, north-west white contours represent illuminated topography
    and south-east black contours represent shaded topography. See Tanaka
    (1950) <doi:10.2307/211219>.
Density, distribution function, quantile function, random
    generation and survival function for the Tangent Burr Type XII Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics 
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Provides an extensible formula system to quickly and easily create
    production quality tables. The steps of the process are formula parser,
    statistical content generation from data, to rendering. Each step of the process
    is separate and user definable thus creating a set of building blocks for
    highly extensible table generation. A user is not limited by any of the 
    choices of the package creator other than the formula grammar. For example,
    one could chose to add a different S3 rendering function and output a format
    not provided in the default package. Or possibly one would rather have Gini
    coefficients for their statistical content. Routines to achieve New England
    Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are
    provided. The package contains rendering for HTML5, Rmarkdown and an indexing
    format for use in tracing and tracking are provided.
Builds tables with customizable rows. Users can specify the type
             of data to use for each row, as well as how to handle missing
             data and the types of comparison tests to run on the table
             columns.
Implementation of functions for fitting taper curves (a semiparametric 
  linear mixed effects taper model) to diameter measurements along stems. Further 
  functions are provided to estimate the uncertainty around the predicted curves, 
  to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. 
  For cases where tree heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper 
  curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees 
  and a subset of section-wise diameter measurements of these trees. The functions 
  implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013)
  <doi:10.1007/s10342-013-0715-0>.
Wrapper for using 'tapkee' command line utility,
 it allows to run it from inside R and catch the results for further analysis and plotting.
 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/>
 for installation and other details.
Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Function-oriented Make-like declarative workflows for
  Statistics and data science are supported in the 'targets' R package.
  As an extension to 'targets', the 'tarchetypes' package provides
  convenient user-side functions to make 'targets' easier to use.
  By establishing reusable archetypes for common kinds of
  targets and pipelines, these functions help express complicated
  reproducible workflows concisely and compactly.
  The methods in this package were influenced by the 'drake' R package
  by Will Landau (2018) <doi:10.21105/joss.00550>.
Measure text's sentiment with dictionaries and simple rules covering
    negations and modifiers. User-supplied dictionaries are supported, including
    Unicode emojis and multi-word tokens, so this package can also be used to
    study constructs beyond sentiment.
Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>)
	     and estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>).
As a pipeline toolkit for Statistics and data science in R,
  the 'targets' package brings together function-oriented programming and
  'Make'-like declarative workflows.
  It analyzes the dependency relationships among the tasks of a workflow,
  skips steps that are already up to date, runs the necessary
  computation with optional parallel workers, abstracts files as
  R objects, and provides tangible evidence that the results match
  the underlying code and data. The methodology in this package
  borrows from GNU 'Make' (2015, ISBN:978-9881443519)
  and 'drake' (2018, <doi:10.21105/joss.00550>).
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions.
    Functions on this package interacts with data on 'tashudata' package, a 'drat' repository.
    'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. 
    To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>.
    top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths.
    daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental.
    create_train_dataset(), create_test_dataset() is data processing function for prediction.
    Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset.
    Users can make random-forest prediction model by using create_train_model() 
    and predict amount of bicycle rental in 2015 by using predict_bike_rental().
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
You only need to type 'why pie charts are bad' on Google to find
    thousands of articles full of (valid) reasons why other types of charts 
    should be preferred over this one.
    Therefore, because of the little use due to the reasons already mentioned,
    making pie charts (and related) in R is not straightforward, so other 
    functions are needed to simplify things.
    In this R package there are useful functions to make 'tasty' pie charts 
    immediately by exploiting the many cool templates provided.
The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.
Functions to combine data.frames in ways that require additional effort in 
  base R, and to add metadata (id, title, ...) that can be used for printing and 
  xlsx export. The 'Tatoo_report' class is provided as a 
  convenient helper to write several such tables to a workbook, one table per 
  worksheet. Tatoo is built on top of 'openxlsx', but intimate knowledge of 
  that package is not required to use tatoo.
Computes the t* statistic corresponding to the tau* population
          coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514>
          and does so in O(n^2) time following the algorithm of Heller and
          Heller (2016) <arXiv:1605.08732> building off of the work of Weihs,
          Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for
          independence testing using the asymptotic distribution of t* as described by
          Nandy, Weihs, and Drton (2016) <arXiv:1602.04387>.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Provides classes for storing and manipulating taxonomic data. 
 Most of the classes can be treated like base R vectors (e.g. can be used 
 in tables as columns and can be named). Vectorized classes can store taxon names
 and authorities, taxon IDs from databases, taxon ranks, and other types of
 information. More complex classes are provided to store taxonomic trees and
 user-defined data associated with them.
Creates a local database of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
Creates a local Lightning Memory-Mapped Database ('LMDB') 
             of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
             Supported taxonomic authorities include 
             the Integrated Taxonomic Information System ('ITIS'),
             National Center for Biotechnology Information ('NCBI'),
             Global Biodiversity Information Facility ('GBIF'), 
             Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). 
             Name and identifier resolution using 'LMDB' can
             be hundreds of times faster than either relational databases or
             internet-based queries. Precise data provenance information for
             data derived from naming providers is also included.
Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.  Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod).  Classical CA is based on the Euclidean distance.  Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance.  For some tables, Taxicab CA gives more informative results than classical CA.
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as getting database specific taxonomic identifiers, verifying
    species names, getting taxonomic hierarchies, fetching downstream and
    upstream taxonomic names, getting taxonomic synonyms, converting
    scientific to common names and vice versa, and more.
Tools for working with 'taxonomic' databases, including
    utilities for downloading databases, loading them into various
    'SQL' databases, cleaning up files, and providing a 'SQL' connection
    that can be used to do 'SQL' queries directly or used in 'dplyr'.
Handling taxonomic lists through objects of class 'taxlist'.
    This package provides functions to import species lists from 'Turboveg'
    (<https://www.synbiosys.alterra.nl/turboveg/>) and the possibility to create
    backups from resulting R-objects.
    Also quick displays are implemented as summary-methods.
The NCBI taxonomy is a popular resource for taxonomic studies but it only contains
    data on species with sequence data whereas the GBIF has a more extensive coverage of
    extinct species. Taxonbridge is useful for the creation and analysis of custom taxonomies
    based on the NCBI taxonomy and GBIF backbone taxonomy.
Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
A comprehensive resource for data on Taylor Swift songs. Data is
    included for all officially released studio albums, extended plays (EPs),
    and individual singles are included. Data comes from
    'Genius' (lyrics) and 'Spotify' (song characteristics). Additional functions
    are included for easily creating data visualizations with color palettes
    inspired by Taylor Swift's album covers.
For when your colors absolutely should not be excluded from the narrative.
Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.
Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}.
We provide a forecasting model for time series forecasting problems with predictors. The offered model, which is based on a submitted research and called tree-based moving average (TBMA),  is based on the integration of the moving average approach to tree-based ensemble approach.  The tree-based ensemble models can capture the complex correlations between the predictors and response variable but lack in modelling time series components. The integration of the moving average approach to the tree-based ensemble approach helps the TBMA model to handle both correlations and autocorrelations in time series data. This package provides a tbma() forecasting function that utilizes the ranger() function from the 'ranger' package. With the help of the ranger() function, various types of tree-based ensemble models, such as extremely randomized trees and random forests, can be used in the TBMA model.
Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
Fast calculation of the Subtree Prune and Regraft (SPR),
  Tree Bisection and Reconnection (TBR) and Replug distances between 
  unrooted trees, using the algorithms of Whidden and 
  Matsen (2017) <arxiv:1511.07529>.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.
The classical two-sample t-test works well for the normally distributed data or 
    data with large sample size. The tcfu() and tt() tests implemented in this package provide 
    better type-I-error control with more accurate power when testing the equality of two-sample 
    means for skewed populations having unequal variances. These tests are especially useful 
    when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve 
    a better approximation to the true percentiles. The tt() provides transformations of the Welch's 
    t-statistic so that the sampling distribution become more symmetric. For more technical details, 
    please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/> for more information). 'TCGAretriever' is easy to use (get all the TCGA data you need with a few lines of code), enforces reliable data download (via 'httr'), and is suitable for downloading large volumes of data.
Differential analysis of tumor tissue immune cell type
    abundance based on RNA-seq gene-level expression from The Cancer
    Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.
Implementation of Time-course Gene Set Analysis (TcGSA), a method for 
	analyzing longitudinal gene-expression data at the gene set level. Method is
	detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.
Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
Provide the core functionality to transform longitudinal data to
    complex-time (kime) data using analytic and numerical techniques, visualize the original 
    time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression)
    and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021)
    "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series,
    ISBN 978-3-11-069780-3. <https://www.degruyter.com/view/title/576646>.
    The package includes 18 core functions which can be separated into three groups.
    1) draw longitudinal data, such as fMRI time-series, and forecast or transform the time-series data.
    2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas,
    report the corresponding p-values, and visualize the p-values in the 3D brain space.
    3) Laplace transform and kimesurface reconstructions of the fMRI data.
An implementation of hypothesis testing in an extended Rasch modelling framework,
    including sample size planning procedures and power computations. Provides 4 statistical tests, 
    i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), 
    and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear 
    logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three 
    types of functions for power and sample size computations are provided. Firstly, functions to 
    compute the sample size given a user-specified (predetermined) deviation from the hypothesis 
    to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the 
    power of the tests given a user-specified (predetermined) deviation from the hypothesis to be 
    tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the 
    so-called post hoc power of the tests. This is the power of the tests given the observed 
    deviation of the data from the hypothesis to be tested and a user-specified level alpha of the
    test. Power and sample size computations are based on a Monte Carlo simulation approach. It is
    computationally very efficient. The variance of the random error in computing power and sample
    size arising from the simulation approach is analytically derived by using the delta method. 
    Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011)  <doi:10.1007/s11222-010-9194-z> and others.
The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Performs the basic concentration response curve fitting used 
    in the 'tcpl' package. It is a substitute for the original tcplFit() function 
    (and sub-functions) and allows a wider variety of concentration-response models. 
    All of the models included in the 'BMDExpress' package are now part of this package,
    and the output includes a calculation of the bmd (Benchmark Dose or concentration) 
    value.
R functions for Tinkoff Investments API <https://tinkoffcreditsystems.github.io/invest-openapi/>. Using this package, analysts and traders can interact with account and market data from within R.  Clients for both REST and Streaming protocols implemented.
The 'twelvedata' REST service offers access to current and historical
 data on stocks, standard as well as digital 'crypto' currencies, and other financial
 assets covering a wide variety of course and time spans. See <https://twelvedata.com/>
 for details, to create an account, and to request an API key for free-but-capped access
 to the data.
Tools for the statistical analysis of persistent homology and for
    density clustering. For that, this package provides an R interface for the
    efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Topological data analysis is a powerful tool for finding non-linear global structure
    in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
    and machine learning practitioners so that more analyses may benefit from the
    power of topological data analysis. The main tool of topological data analysis is
    persistent homology, which computes a shape descriptor of a dataset, called
    a persistence diagram. There are five goals of this package: (1) convert persistence diagrams
    computed using the two main R packages for topological data analysis into a data frame, 
    (2) implement fast versions of both distance and kernel calculations
    for pairs of persistence diagrams, (3) provide methods for machine learning
    and inference for persistence diagrams which scale well, (4) deliver a fast implementation
    of persistent homology via a python interface, and (5) contribute tools for the interpretation of
    persistence diagrams.
A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.
Uniform random samples from simple manifolds, sometimes with noise,
    are commonly used to test topological data analytic (TDA) tools.
    This package includes samplers powered by two techniques: analytic
    volume-preserving parameterizations, as employed by Arvo (1995)
    <doi:10.1145/218380.218500>, and rejection sampling, as employed by
    Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
Tools for computing various vector summaries of persistence diagrams 
    studied in Topological Data Analysis. For improved computational efficiency, 
    all code for the vector summaries is written in 'C++' using the 'Rcpp' package.
The companion package that provides all the datasets used in the book
 "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).
An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the 'gbm' package originally developed by Greg Ridgeway.
A collection of functions for automatically creating 'Stan'
      code for transition diagnostic classification models (TDCMs) as they are
      defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>.
      This package supports automating the creation of 'Stan' code for TDCMs,
      fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal
      across all items), and multi-threaded TDCMs.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
The t-Digest construction algorithm, by 
    Dunning et al., (2019) <arXiv:1902.04023v1>, uses a variant of 1-dimensional 
    k-means clustering to produce a very compact data structure that allows 
    accurate estimation of quantiles. This t-Digest data structure can be used 
    to estimate quantiles, compute other rank statistics or even to estimate 
    related measures like trimmed means. The advantage of the t-Digest over 
    previous digests for this purpose is that the t-Digest handles data with 
    full floating point resolution. The accuracy of quantile estimates produced 
    by t-Digests can be orders of magnitude more accurate than those produced 
    by previous digest algorithms. Methods are provided to create and update 
    t-Digests and retrieve quantiles from the accumulated distributions.
Set of functions designed to help in the
    analysis of TDP sensors. Features includes dates and time conversion, weather
    data interpolation, daily maximum of tension analysis and calculations required
    to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Compute time-dependent ROC curve from censored survival data using
    nonparametric weight adjustments.
Functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.
Statistics students often have problems understanding 
    the relation between a random variable's true scale and its z-values. 
    To allow instructors to better better visualize histograms for these 
    students, the package provides histograms with two horizontal 
    axis containing z-values and the true scale of the variable. 
    The function TeachHistDens() provides a density histogram with two axis.  
    TeachHistCounts() and TeachHistRelFreq() are variations for count and 
    relative frequency histograms, respectively. TeachConfInterv() and 
    TeachHypTest() help instructors to visualize confidence levels 
    and the results of hypothesis tests.
Contains apps and gadgets for teaching data analysis and 
    statistics concepts along with how to implement them in R.  Includes 
    tools to make app development easier and faster by nesting apps together.
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
An implementation of the TEAM algorithm to identify local differences 
    between two (e.g. case and control) independent, univariate distributions, as 
    described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm 
    is based on embedding a multiple-testing procedure on a hierarchical structure 
    to identify high-resolution differences between two distributions. The 
    hierarchical structure is designed to identify strong, short-range differences 
    at lower layers and weaker, but long-range differences at increasing layers. 
    TEAM yields consistent layer-specific and overall false discovery rate control.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Package of wrapper functions using R6 class to send requests to 
  Microsoft 'Teams' <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks.
  When you need to share information or data from R to 'Teams', rather than copying/pasting, you
  can use this package to send well-formatted output from multiple R objects.
The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.
Implements methods to fit Virtual Twins models (Foster et al. 
  (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential
  effects in the context of clinical trials while controlling the probability
  of falsely detecting a differential effect when the conditional average
  treatment effect is uniform across the study population using parameter
  selection methods proposed in Wolf et al. (2022) 
  <doi:10.1177/17407745221095855>.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.
Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Provides a pure interface for the 'Telegram Bot API'
    <http://core.telegram.org/bots/api>. In addition to the pure API
    implementation, it features a number of tools to make the development of
    'Telegram' bots with R easy and straightforward, providing an easy-to-use
    interface that takes some work off the programmer.
An R interface to the TELEMAC suite for modelling
    of free surface flow. This includes methods for model initialisation, simulation,
    and visualisation. So far only the TELEMAC-2D module for 2-dimensional hydrodynamic
    modelling is implemented.
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.
Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .
Provides tools to work with template code and text in R. It aims to
    provide a simple substitution mechanism for R-expressions inside these
    templates. Templates can be written in other languages like 'SQL', can
    simply be represented by characters in R, or can themselves be R-expressions
    or functions.
Helper functions for MASCOTNUM algorithm template, for design of numerical experiments practice:
    algorithm template parser to support MASCOTNUM specification <https://www.gdr-mascotnum.fr/template.html>, 
    'ask & tell' decoupling injection (inspired by <https://search.r-project.org/CRAN/refmans/sensitivity/html/decoupling.html>) 
    to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R,
    basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim().
TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing 
    significant changes in temporal expression patterns across conditions.  Given a gene expression data set where each sample is characterized by 
    an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway
    by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls
    and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.
Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).
Compute the coordinates to produce a tendril plot. 
    In the tendril plot, each tendril (branch) represents a type of events, 
    and the direction of the tendril is dictated by on which treatment arm the 
    event is occurring. If an event is occurring on the first of the two 
    specified treatment arms, the tendril bends in a clockwise direction. 
    If an event is occurring on the second of the treatment arms, the
    tendril bends in an anti-clockwise direction. 
    Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary 
    of the incidence, significance and temporal aspects of adverse events in 
    clinical trials" - JAMIA 2018; 25(8): 1069-1073 <doi:10.1093/jamia/ocy016>.
Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with data sets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.
Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.
Interface to 'TensorFlow' <https://www.tensorflow.org/>,
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations,
  while the graph edges represent the multidimensional data arrays
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team
  within Google's Machine Intelligence research organization for the
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.
Basic functions to handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base 'array' class to some S4 classes, this package serves as an alternative resort to handle tensor only as 'array' class.
    Some functionalities related to missingness and rearrangement, discussed in Bai and Ng (2021) <arXiv:1910.06677>, are also supported.
A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. 
    See Chen and Lam (2022) <arXiv:2208.04012> for more details.
Provides methods to manipulate and store sparse tensors. Tensors 
  are multidimensional generalizations of matrices (two dimensional) and 
  vectors (one dimensional).
Implement the alternating algorithm for supervised tensor decomposition with interactive side information.
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.
Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in 
    Li et al (2021) <arXiv:2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, 
    Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <arXiv:2006.02611>.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Provides functions for the computationally efficient simulation of 
    dynamic networks estimated with the statistical framework of temporal 
    exponential random graph models, implemented in the 'tergm' package.
Creates, manipulates, queries and repairs vectors of
    parameter terms.  Parameter terms are the labels used to reference
    values in vectors, matrices and arrays. They represent the names in
    coefficient tables and the column names in 'mcmc' and 'mcmc.list'
    objects.
Implements simulated tests for the hypothesis that terminal digits are uniformly
    distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits
    are independent from preceding digits (several tests of independence for r x c 
    contingency tables). Also, for a number of distributions, implements Monte Carlo 
    simulations for type I errors and power for the test of independence.
Plots ternary diagrams (simplex plots / Gibbs triangles) and
  Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the
  standard graphics functions.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
  Includes a 'Shiny' user interface for point-and-click ternary plotting.
A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include standard operations such as intersect and buffer. Raster methods include global, local, zonal, and focal computations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. 'terra' is a replacement for the 'raster' package ('terra' can do more, and it is faster and easier to use).
Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://www.github.com/fogleman/hmm>.
Functions for the retrieval, manipulation, and visualization
    of 'geospatial' data, with an aim towards producing '3D' landscape
    visualizations in the 'Unity' '3D' rendering engine. Functions are
    also provided for retrieving elevation data and base map tiles from
    the 'USGS' National Map <https://apps.nationalmap.gov/services/>.
Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Delaunay and Voronoï tessellations, with emphasis on the two-dimensional and the three-dimensional cases (the package provides functions to plot the tessellations for these cases). Delaunay tessellations are computed in C with the help of the 'Qhull' library <http://www.qhull.org/>.
Easy install and load key packages from the 'tesselle' suite
    in a single step. The 'tesselle' suite is a collection of packages for
    research and teaching in archaeology. These packages focus on
    quantitative analysis methods developed for archaeology. The
    'tesselle' packages are designed to work seamlessly together and to
    complement general-purpose and other specialized statistical packages.
    These packages can be used to explore and analyze common data types in
    archaeology: count data, compositional data and chronological data.
    Learn more about 'tesselle' at <https://www.tesselle.org>.
Bindings to 'Tesseract': 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
Package test2norm contains functions to generate formulas for 
    normative standards applied to cognitive tests. It takes raw test scores 
    (e.g., number of correct responses) and converts them to scaled scores and 
    demographically adjusted scores, using methods described in Heaton et al. 
    (2003) <doi:10.1016/B978-012703570-3/50010-9> & Heaton et al. (2009, 
    ISBN:9780199702800). The scaled scores are calculated as quantiles of the 
    raw test scores, scaled to have the mean of 10 and standard deviation of 3, 
    such that higher values always correspond to better performance on the test. 
    The demographically adjusted scores are calculated from the residuals of a 
    model that regresses scaled scores on demographic predictors (e.g., age). 
    The norming procedure makes use of the mfp() function from the 'mfp' package 
    to explore nonlinear associations between cognition and demographic 
    variables.
Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay. This is
    an implementation of the methods described in Fay, Sachs, and Miura (2018) 
    <doi:10.1002/sim.7528>.
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2020), <https://cowles.yale.edu/sites/default/files/files/pub/d21/d2194-r.pdf>.
Test your data! An extension of the 'testthat' unit testing
    framework with a family of functions and reporting tools for checking
    and validating data frames.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: 
    listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), 
    treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; 
    Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; 
    Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), 
    item mean imputation (IM), two-way (TW) and response function (RF) imputation,
    (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
    logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) 
    imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
    Supports the following mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony',
    'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.
Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) <doi:10.1002/ajpa.1330660105>, Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
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. Two
 recent papers on the methodology are
 Ramsay, James; Li, Juan; Wiberg, Marie (2020) <doi:10.3390/psych2040026> and
 Ramsay, James; Wiberg, Marie; Li, Juan (2019) <doi:10.3102/1076998619885636>.
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.
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.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.
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.
This is a collection of functions optimized for working with
             with various kinds of text matrices. Focusing on 
             the text matrix as the primary object - represented 
             either as a base R dense matrix or a 'Matrix' package sparse 
             matrix - allows for a consistent and intuitive interface 
             that stays close to the underlying mathematical foundation 
             of computational text analysis. In particular, the package
             includes functions for working with word embeddings, 
             text networks, and document-term matrices. Methods developed in 
             Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, 
             Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, 
             Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and
             Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
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>.
Unifies different text to speech engines, such as
    Google, Microsoft, and Amazon.  Text synthesis can be done
    in any engine with a simple switch of an argument denoting
    the service requested.  The 'aws.polly' package has been
    orphaned and can be found from the CRAN archives.
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.
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'.
A tool for extracting some generic features (e.g., number of
    words, line breaks, characters per word, URLs, lower case, upper case,
    commas, periods, exclamation points, etc.) from strings of text.
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.
'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.
'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>.
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.
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.
A collection of useful tools for programming and writing-scripts. 
    Several functions are simple wrappers around base R functions that extend 
    their functionality while also providing some convenient properties–regular
    expression functions that automatically detect look-ahead and look-behind 
    statements, a read-line function that suppresses incomplete-final-line 
    warnings and automatically opens and closes connections, a version of 
    substrings that starts from the end of strings, etc. Other functions are 
    useful for checking whether packages are installed, omitting 
    missing data, and showing in-use connections.
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, <http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf>, 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.
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>.
We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical
    thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs
    from these simulations. These simulations are presented in Owen, C., Sutton, G., Martin, G., van Steenderen, C., and Coetzee, J. Sample size determination for critical thermal
    limits studies. 2022. Physiological Entomology. Under review. The GUI version of this package is available on the R Shiny online server at: 
    <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like
    to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.
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 full path of the executing script. Works when 
    running a line or selection from a script in 'RStudio', 'Rgui', and
    'VSCode', when using 'source', 'sys.source', 'debugSource' in 'RStudio',
    and 'testthat::source_file', and when running from a shell.
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://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.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/thredds/current/tds/TDS.html>) 
  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>.
We focus on the estimation of optimal thresholds and AUCs 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 missing for those subjects censored before t. Here we provide three methods (missing exclusion, imputation of censored times and using time-dependent ROC curves) to estimate optimal thresholds and AUCs 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>.
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 'List', 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.
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'.
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>).
Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science,
    data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.
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.
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.
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.
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'.
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.
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.
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.
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>.
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'.
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.
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. For more information on the synthetic control method, see Abadie et al. (2003) <doi:10.1257/000282803321455188>.
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.
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>.
Perform a Visual Predictive Check (VPC), while accounting for 
    stratification, censoring, and prediction correction. Using piping from 
    'magrittr', the intuitive syntax gives users a flexible and powerful method 
    to generate VPCs using both traditional binning and a new binless approach 
    Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile 
    Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) 
    prediction correction.
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 'SQLite' 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 <http://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>.
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.
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 universal storage engine 'TileDB' introduces a
 powerful on-disk format for multi-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.
Tools for creating and detecting tiling schemes for geospatial datasets.
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.
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.
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.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> time series data. Calculate time series model and forecast parameters in budget time series data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring deterministic and stochastic trend of the input time series data with 'ACF', 'PACF', 'Phillips Perron' test, 'Augmented Dickey Fuller (ADF)' test, 'Kwiatkowski-Phillips-Schmidt-Shin (KPSS)' test, 'Mann Kendall' test for monotonic trend and 'Cox and Stuart' trend test, decomposing with local regression models or 'stl' decomposition, fitting the appropriate 'arima' model and provide forecasts for the input 'OpenBudgets.eu' time series fiscal data. Also, can be used generally to extract visualization parameters convert them to 'JSON' format and use them as input in a different graphical interface.
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.
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; has been extensively applied to
         time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
Access 'timsTOF' mass spectrometry data, as described <https://sso.bruker.com/auth/realms/bruker/protocol/openid-connect/auth?client_id=aem-bruker.com&redirect_uri=https%3A%2F%2Fwww.bruker.com%2Fen.login.html%3FtargetUrl%3Dhttps%3A%2F%2Fwww.bruker.com%2Fen%2Fservices%2Fsoftware-downloads%2Fmass-spectrometry.html&response_type=id_token%20token&scope=openid%20profile&state=4f9d225e92f341cca3b03a55533dbd65&nonce=f550633b1e984ecfb07979ae6d9277b4&ui_locales=en> (after registering), using the 'OpenTIMS' C++ reader and save all into 'data.tables'.
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.
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.
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.
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.
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.
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. Mainly an implementation of an approach proposed by Best and colleagues (2021) is provided <doi:10.1002/pst.2093>. Further functions facilitate the specification of the robust MAP prior via expert elicitation (using the roulette method) and computation of the posterior distribution of the treatment effect with either fixed or stochastic expert-elicited weights. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
Using the idea of "tipping point" (proposed in
   Gregory Campbell, Gene Pennello and Lilly Yue(2011)
   <DOI:10.1080/10543406.2011.550094>) to visualize  the results
   of sensitivity analysis for missing data, the package provides
   a set of functions to list out all the possible combinations
   of missing values in two treatment arms, calculate
   corresponding estimated treatment effects and p values, and draw
   a colored heat-map. It could deal with randomized
   experiments with a binary outcome or a continuous outcome. In addition,
   the package provides a visualized method to compare various imputation
   methods by adding the rectangles or convex hulls on the basic plot.
'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 (2020) <doi:10.1101/2020.11.09.373795>.
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,
  ME and RS King. 2010. A new method for detecting and interpreting
  biodiversity and ecological community thresholds.  Methods in Ecology and
  Evolution 1(1): 25:37. King, RS and ME Baker. 2010. Considerations for
  identifying and interpreting ecological community thresholds.  Journal
  of the North American Benthological Association 29(3):998-1008. Baker ME
  and RS King. 2013. Of TITAN and straw men: an appeal for greater
  understanding of community data. Freshwater Science 32(2):489-506.
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.
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.
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.
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>.
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, tweets, 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://www.aclweb.org/anthology/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.
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. 
 Sample dataset in this package is from: 
 Casadebaig P, Zheng B, Chapman S et al. (2016) <doi: 10.1371/journal.pone.0146385>. 
 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.
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.
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).
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 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.
An API wrapper for 'cbbstat' and a suite of functions to pull
  and clean detailed, extensive college basketball statistics from 
  Barttorvik<https://barttorvik.com/>.
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.
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).
Tools for analyzing count data with the Touchard model (Matsushita et al., 2018, Comm Stat Th Meth <doi:10.1080/03610926.2018.1444177>). It includes univariate estimation (ML and MM) and regression tools developed by Andrade et al. (submitted).
Contains two functions related to sports competitions. One to create league tables and one to create a match schedule.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
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. A description of the analysis can be found in Blackwell et al. (2017) <doi:10.1021/acs.est.7b01613>.
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.
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.
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
             model discussed in Gomez, Olmos, Varela and Bolfarine (2018) <doi:10.1007/s11766-018-3354-x>, the
             slash truncated positive normal distribution discussed in Gomez, Gallardo and Santoro (2021)
             <doi:10.3390/sym13112164>, the bimodal truncated positive normal distribution discussed in 
             Gomez et al. (2022) <doi:10.3390/sym14040665> and the flexible truncated positive normal model.
Regression models for temporal process responses with
        time-varying coefficient.
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.
Simple but crucial functionality related to creation of reproducible and traceable output (plots) back to code. It has a graphics saver with simple automation of plot stamping with source, destination and creation time, a selection of dimensions for use in presentations. A list of plots can be saved at once.
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.
Analysis of shape and contours in regions of interest (ROIs) in image sequences and extracting midline and other kinematic data.
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>.
Datasets from An Advanced Guide to Trade Policy Analysis
    (Year: 2016, ISBN: 978-92-870-4367-2) by Yotov, et al. and functions to 
    report regression summaries with clustered robust standard errors.
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.
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.
Assistance for handling ecological trait data and applying the 
    Ecological Trait-Data Standard terminology (Schneider et al. 2019
    <doi:10.1111/2041-210X.13288>). There are two major use cases: (1) preparation of
    own trait datasets for publication, and (2) harmonizing
    trait datasets from different sources by re-formatting them into a unified
    format. See 'traitdataform' website for full documentation.
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.
Functions for analysis of bulk data generated from experiments in Randomized block design as per Panse and Sukhatme (1954) <https://books.google.co.in/books?id=Efo9AAAAYAAJ>. Computes analysis of variance; Descriptive statistics parameter like Mean, Minimum, Maximum, CV, Standard error of mean, Standard Error of deviation, CD; Genetic parameter statistics Genotypic Coefficient of Variation, Phenotypic Coefficient of Variation, Heritability in broad sense, Genetic Advance and Genetic Advance per cent mean; Variance and Co-variance matrix of genotypic, phenotypic and environmental; Correlation of genotypic, phenotypic and environmental. Further includes directly publication ready tables.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Contains a set of methods for trajectory data preparation, such as filtering, compressing and clustering, and for trajectory pattern discovery.
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 2-dimensional 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. 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, <arxiv:1910.09219>).
  Multivariate conditional transformation models (Klein et al, 2022, <DOI:10.1111/sjos.12501>) 
  can be fitted as well.
Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe 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 simple functions for extracting the most frequent 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'.
Transcribes audio to text with the HP IDOL API. Includes functions to upload files, 
	     retrieve transcriptions, and monitor jobs.
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines. The default pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> trained on the Stanford Natural Language Inference <https://nlp.stanford.edu/projects/snli/> and Multi-Genre Natural Language Inference <https://huggingface.co/datasets/multi_nli> datasets. Using similar models, zero-shot classification transformers have demonstrated superior performance relative to other natural language processing models <arXiv:1909.00161>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>}.
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>.
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.
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.
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.
Generate Total Returns (TR) from bond yield data with fixed maturity, e.g. 
  reported treasury yields. The generated TR series are very close to alternative series 
  that can be purchased (e.g. CRSP, Bloomberg), suggesting they are a high-quality 
  alternative for those, see Swinkels (2019) <doi:10.3390/data4030091>.
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.
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>.
The aim of the 'R' package 'treebalance' is to provide functions for the computation of 
    a large variety of (im)balance indices for rooted trees. The package accompanies the manuscript 
	''Tree balance indices: a comprehensive survey'' by M. Fischer, L. Herbst, S. Kersting, 
	L. Kuehn and K. Wicke (2021) <arXiv:2109.12281>, which gives a precise definition for the terms 
	'balance index' and 'imbalance index' (Section 3) and provides an overview of the terminology in 
	this manual (Section 2). For further information on (im)balance indices, see also 
	Fischer et al. (2021) <https://treebalance.wordpress.com>.
    Considering both established and new (im)balance indices, 'treebalance' provides (among 
    others) functions for calculating the following 18 established indices: the average leaf depth,
	the B1 and B2 index, the Colijn-Plazzotta rank, the normal, corrected, quadratic and equal 
	weights Colless index, the family of Colless-like indices, the family of I-based indices, the 
	Rogers J index, the Furnas rank, the rooted quartet index, the s-shape statistic, the Sackin 
	index, the symmetry nodes index, the total cophenetic index and the variance of leaf depths. 
	Additionally, we include 5 tree shape statistics that satisfy the definition of an (im)balance 
	index but have not been thoroughly analyzed in terms of tree balance in the literature yet. 
	These are: the maximum width, the maximum difference in widths, the maximal depth, the 
	stairs1 and the stairs2 index. 
    As input, most functions of 'treebalance' require a rooted (phylogenetic) tree in 'phylo' format 
    (as introduced in 'ape' 1.9 in November 2006). 'phylo' is used to store (phylogenetic) trees 
    with no vertices of out-degree one. For further information on the format we kindly refer the 
	reader to E. Paradis (2012) <http://ape-package.ird.fr/misc/FormatTreeR_24Oct2012.pdf>.
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.
The centre of mass is a crucial data for arborists in order to
    consolidate a tree using steel or dynamic cables. Given field-recorded data
    on branchiness of a tree, the package: (i) computes and plots the centre of
    mass of the tree itself, (ii) computes branches slenderness coefficient in
    order to aid the arborist identify potentially dangerous branches, and
    (iii) computes the force acting on a ground plinth and its best position
    relating to the tree centre of mass, should the tree need to be stabilized
    by a steel cable.
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).
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.
Simulates nested phylogenetic trees (gene trees in species tree, 
    symbiont trees in host trees) using birth-death processes and transfers
    between lineages. Simulations of gene trees within species trees are performed
    using a three-tree model with species trees, locus trees, and gene trees.
    The cophylogenetic birth-death process is used to simulate sets of host and
    symbiont trees with extant associations between tips.  
    For more information about the three-tree model see:
    Mallo et al. (2015) <doi:10.1093/sysbio/syv082>, 
    Rasmussen and Kellis (2012) <doi:10.1101/gr.123901.111>.
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.
Matches phylogenetic trees and trait data, and
    allows simultaneous manipulation of the tree and data using 'dplyr'.
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>.
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.
Set of tools implemented into a shiny-based application for extracting and analyzing individual tree forest attributes from LiDAR (Light Detection and Ranging) data.
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 (2021). 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>.
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.
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.
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>.
Functions to make manipulation of object attributes easier. 
    It also contains a few functions that extend the 'dplyr' package for data 
    manipulation, and it provides new pipe operators, including the pipe '%@>%' 
    similar to the 'magrittr' '%>%', but with the additional functionality to 
    enable attributes propagation.
A flexible color scale for ternary compositions with options for
  discretization, centering and scaling.
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'.
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>.
Functions for accessing and manipulating spatial data for animal
    tracking, with straightforward coercion from and to other formats. Filter
    for speed and create time spent maps from animal track 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.
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.
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>.
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>.
A compilation of fish stock assessment methods for the
    analysis of length-frequency data in the context of data-poor
    fisheries. Includes methods and examples included in the FAO
    Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish
    stock assessment" (<http://www.fao.org/documents/card/en/c/9bb12a06-2f05-5dcb-a6ca-2d6dd3080f65/>),
    as well as other more recent methods.
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.
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.
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>.
A toolkit for read and prepare immune repertoire data. 'TrustVDJ' package focuses on the reading and processing of 'TRUST4' and '10x cellranger' software output results by using 'ReadTrust' and 'Read10x' functions, respectively, and also provides a convenience function 'build_IMGT_reference' to download the 'IMGT' database reference and split its sequences by species.
Data and functions for analyzing and simulating illusory
    truth datasets, developed as part of a longitudinal study by
    Henderson, Barr, and Simons (2020). The illusory truth effect is
    the observation that people rate repeated statements as more
    likely to be true than novel statements. We tested the trajectory
    of the illusory truth effect by collecting truth ratings for
    statements repeated across four time intervals: immediately, one
    day, one week, and one month following initial presentation. The
    package contains the anonymized data from the study along with
    stimulus materials, as well as functions for analyzing the data,
    running simulations, and calculating power. Further details about
    the project are available at <https://osf.io/nvugt/>, which
    includes Stage 1 of the Registered Report at the Journal of
    Cognition (<https://osf.io/vqnx2/>).
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).
Stationary Gaussian ARMA processes and the stationary 'GARMA' distribution are fundamental in time series analysis. Here we give utilities to compute 
    the auto-covariance/auto-correlation for a stationary Gaussian ARMA process, as well as the probability functions (density, cumulative distribution, random 
    generation) for random vectors from this distribution.  We also give functions for the spectral intensity, and the permutation-spectrum test for testing 
    a time-series vector for the presence of a signal.
Transforming one or multiple time series into networks. This package is 
    useful for complex systems modeling, time series data mining, or time series analysis using networks. 
    An introduction to the topic and the descriptions of the methods implemented 
    in this package can be found in Mitchell (2006) <doi:10.1016/j.artint.2006.10.002>, 
    Silva and Zhao (2016) <doi:10.1007/978-3-319-17290-3>, and Silva et al. (2021) <doi:10.1002/widm.1404>.
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> .
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 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).
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 to forecast time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2017) <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).
This gui shows realisations of times series, currently ARMA and GARCH processes. It might be helpful for teaching and studying.
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>.
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.
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.
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.
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.
Several forms of likelihood ratio calculations are available.
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.
Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <arXiv:1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.
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.
It provides distribution, density and quantile functions of the
    Tukey's g-and-h probability distribution, as well as functions for random
    number generation, parameter estimation and testing.
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.
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.
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, and Yajnaseni Chakraborti for their valuable help with testing the package and 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.
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>.
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>.
Facilitates development and application of two-regression
    algorithms for research-grade wearable devices. It provides an easy
    way for users to access previously-developed algorithms, and also to
    develop their own. Initial motivation came from Hibbing PR, LaMunion SR,
    Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>.
    However, other algorithms are now supported. Please see the associated
    references in the package documentation for full details of the algorithms
    that are supported.
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; Parametric Bootstrap based Generalized Test and Generalized Pivotal Quantity based Generalized Test (Weerahandi and Krishnamoorthy, 2019) <doi:10.1080/03610926.2017.1419264>. 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 phantom 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.
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>.
This suite of packages is developed to predicts resistome for unknown chemical compounds. It utilizes two algorithms viz. deterministic model and stochastic model (manuscript under preparation) for the prediction of drug class. Once the drug class is predicted, the resistome profile of Escherichia coli and Pseudomonas aeruginosa for the drug class is fetched from the database.
R bindings of the 'uchardet', encoding detector library from Mozilla
  (<https://www.freedesktop.org/wiki/Software/uchardet/>). It takes a sequence
  of bytes in an unknown character encoding and without any additional
  information, and attempts to get the encoding of the text. All return names of
  the encodings are iconv-compatible.
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.
             Pedregal, DJ (2022) <doi:10.18637/jss.v103.i09>,
	     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>.
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 (2021) <doi:10.1080/03610926.2021.1916531>,
    Savi Virolainen (2021) <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 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 11. 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 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.
Unmatched regression refers to the regression setting where
    covariates and predictors are collected separately/independently and so are not paired     together, as in the usual regression setting.   Balabdaoui, Doss, and Durot (2021) <arXiv:2007.00830>    study the unmatched regression setting where the univariate regression function is known to    be monotone.     This package implements methods for computing  the estimator developed in  Balabdaoui, Doss, and Durot (2021).  The main method is an active-set-trust-region-based method.
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>.
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>.
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 15.0.0 and related utilities.
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.
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.
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. Reference: Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
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 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.
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.
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 outlined in Frühwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>.
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.
'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.
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.
S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization. SWU.
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.
This is a replacement of the archived 'USGSstates2k' package.
    It contains a map with the state boundaries of the USA obtained from the
    USGS. Irucka worked with this data set while a Cherokee Nation Technology
    Solutions (CNTS) USGS Contractor and/or USGS employee. 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>.
The boundaries for geographical units in the United States of
    America contained in this package include state, county, congressional
    district, and zip code tabulation area. Contemporary boundaries are provided
    by the U.S. Census Bureau (public domain). Historical boundaries for the
    years from 1629 to 2000 are provided form the Newberry Library's 'Atlas of
    Historical County Boundaries' (licensed CC BY-NC-SA). Additional  data is
    provided in the 'USAboundariesData' package; this package provides an
    interface to access that data.
US Census 2010 shape files and additional demographic data
        from the SF1 100 percent files. This package contains a number
        of helper functions for the UScensus2010blk,
        UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
        packages.
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.
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 in other R packages. They support some miscellaneous operations 
  on data frames, matrices and vectors like adding a row on a ternary (3-value)
  data.frame based on positive and negative vector-indicators, rearranging a 
  list of data.frames by rownames, pruning rows or columns of a data.frame 
  that contain only one specific value given by the user,
  pruning and reordering a vector according to the common elements between its 
  names and elements of another given vector, finding the non-common elements 
  between two vectors (outer-section), 
  normalization of a vector, matrix or data.frame's numeric values in a specified range, 
  pretty printing of vector names and values in an R Markdown document.
  Also included is a function that returns the statistics needed for plotting a ROC curve.
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.
A collection of functions to make building customized ready-to-export tables for publication purposes easier and creating summaries of large datasets for review a breeze.
A set of tools for preparing and summarizing data for publication purposes. Includes functions for tabulating models, means to produce human-readable summary statistics from raw data, macros for calculating duration of time, and simplistic hypothesis testing tools.
A small set of functions to aid in the production of visuals in ggplot2. Includes minimalist themes with transparent backgrounds and tools for building survival curves with risk tables.
Data utility functions for use in probability and statistics.  Includes functions for computing higher-moments for samples and their decompositions.
  Also includes utilities to examine functional mappings between factor variables and other variables in a data set.
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).
Supports the course Introduction to Methodology (POLSCI 4419/9715) at the University of Western Ontario.
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: 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.
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>.
Check whether records in a database table are valid using 
   validation rules in R syntax specified with R package 'validate'. 
   R validation checks are automatically translated to SQL using 'dbplyr'.
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).
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'.
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.
Identify and analyze conserved waters within crystallographic 
  protein structures and molecular dynamics simulation trajectories. Statistical 
  parameters for each water cluster, informative graphs, and a PyMOL session 
  file to visually explore the conserved waters and protein are returned. 
  Hydrophilicity is the propensity of waters to congregate near specific protein 
  atoms and is related to conserved waters. An informatics derived set of 
  hydrophilicity values are provided based on a large, high-quality X-ray 
  protein structure dataset.
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.
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, collected and crafted over 7 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 suppose 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.
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.
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 new mathematical formulation to merge observed data with gridded images of environmental variables using partial differential equations in a variational setting. The original method was created, developed and published by Ulloa, Samaniego, Campozano and Ballari (2018) <doi:10.1002/2017JD027982>.
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. Also extended to allow a regression in the location, scale and shape parameters.
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 and Relative 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, Paul-Henry Cournède and Estelle Kuhn (2019) <doi:10.1016/j.csda.2019.01.014>.
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>).
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'.
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.
Provides new classes for (rotated) BB1, BB6, BB7, BB8, and 
  Tawn copulas, extends the existing Gumbel and Clayton families with 
  rotations, and allows to set up a vine copula model using the 'copula' API.
  Corresponding objects from the 'VineCopula' API can easily be converted.
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.
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.
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.
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.
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>.
Implements linear, logistic, and Cox regression on vertically
    partitioned data across several data partners.  Data is not shared
    between data partners or the analysis center and the computations can
    be considered secure.  Three different protocols are implemented. 
	2-Party: two data partners which communicate directly without an 
	intermediate analysis center; 2T-Party: two data partners communicate 
	indirectly via an analysis center, and KT-Party: two or more 
	data partners plus an analysis center are all allowed to 
	communicate directly.  2-Party and 2^T-Party use a form of secure 
	multiplication as found in Karr, et. al. (2009) "Privacy-Preserving Analysis
    of Vertically Partitioned Data Using Secure Matrix Products"
    and Slavkovic et. al. (2007) "Secure Logistic Regression of Horizontally 
    and Vertically Partitioned Distributed Databases" <doi:10.1109/ICDMW.2007.114>.  
    Full details can be found in Samizo (In preparation).
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 2D statistical analysis is performed, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
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 background is described in Neeraj Dhanraj Bokde et al. (2021) <doi:10.48550/arXiv.2111.03441>,
    Karanam L. Ramakumar et al. (2011) <https:archive.org/details/PanchangamCalculations>,
    K. S. Charak et al. (2012, ISBN:8190100807),
    Satish BD et al. (2013) <https:github.com/webresh/drik-panchanga>.
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.
Draws and displays 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.
Provides variable selection and estimation routines for models
    with main effects stratified on multiple binary factors. The 'vennLasso' package
    is an implementation of the method introduced in Huling, et al. (2017) <doi:10.1111/biom.12769>.
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).
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.
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.
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://services.land.vic.gov.au/catalogue/publicproxy/guest/dv_geoserver/wfs?request=getCapabilities>, 
  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>.
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.
Contains a function to support the following paper:
    Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>,
    A sequential multiple change-point detection procedure via VIF regression,
    Computational Statistics, 31(2): 671-691.
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://tel.archives-ouvertes.fr/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.
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+).
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.
A Bayesian Phase I cancer vaccine
    trial design is implemented in this package. The design allows simultaneous evaluation
    of safety and immunogenicity outcomes in the context of vaccine studies.
    See Wang (2019) <DOI:10.1002/sim.8021> for the details of the Phase I cancer vaccine trial
    design.
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.
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.
Functions for visualizing the probabilities of early
        termination, fail and success of Simon's two-stage design.
        Functions for evaluating and visualizing 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, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.
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. 
             When both distributions are provided the animation shows a transition from 
             prior to posterior. Finally, the animation splits the distribution using the provided 
             'breaks' into bars that show the probability for each region. 
             If no 'breaks' are providers it will use zero by default.
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.
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>.
General purpose tools for voice analysis, speaker recognition and 
    mood inference. Gathers 'R' and 'Python' tools to solve problems concerning voice 
    and audio in general.
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.
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) <arXiv:2102.05801>).
'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.
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.
Estimation of varying regression segments and a change point in
    2-segment regression models with heteroscedastic variances, and with or
    without a smoothness constraint at the change point.
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). See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2>.
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>.
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.
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.
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 <http://validator.w3.org/> for more information.
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.
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>).
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.
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'.
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.
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>.
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>.
Methods for watersheds aggregation and spatial drainage network analysis.
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.
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/>.
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.
Real probability distributions can be represented as the square of
    an orthogonal sum in the Hermite basis. This representation is formally
    similar to the representation of quantum mechanical states as wave
    functions, whose squared modulus is a probability density. This is
    described in more detail in "Wave function representation of probability
    distributions," by Madeleine B. Thompson <arXiv:1712.07764>. This package
    provides a reference implementation of the technique.
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.
Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.
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. These functions provide methods for measuring the spatial
    structure of model errors and evaluating where predictions can be made 
    safely, and 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>),
    as well as an implementation of the area of applicability methodology from
    'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>).
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.
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>).
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.
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) network rewiring,
    (5) preferential attachment network generation.
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>).
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.
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, ChemIDplus, ChemSpider, ETOX,
    Flavornet, NIST Chemistry WebBook, OPSIN, PAN Pesticide Database, 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 <https://httpbin.org> web service.
The web version WebGestalt <http://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.
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.
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>.
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.
Secondary analysis of case-control studies using a weighted estimating equation (WEE) approach: logistic regression for binary secondary outcomes, linear regression and quantile regression for continuous secondary outcomes.
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>.
Performs a goodness-of-fit test of Weibull distribution (weibullness test) and provides the maximum likelihood estimates of the three-parameter Weibull distribution. Note that the threshold parameter is estimated based on the correlation from the Weibull plot. For more details, see Park (2018) <doi:10.1155/2018/6056975>. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
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).
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.
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>.
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().
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>.
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, empirical
             balancing calibration weights, 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>.
Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions
    included for converting between 'GeoJSON' to 'WKT', creating both
    'GeoJSON' features, and non-features, creating 'WKT' from R objects
    (e.g., lists, data.frames, vectors), and linting 'WKT'.
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.
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").
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.
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>.
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.
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.
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/>.
Provides an interface to the Wikipedia web application programming
    interface (API), using internet connexion.Three functions provide details for
    a specific Wikipedia page : all links that are present, all pages that link
    to, all the contributions (revisions for main pages, and discussions for talk
    pages). Two functions provide details for a specific user : all contributions,
    and general information (as name, gender, rights or groups). It provides
    additional information compared to others packages, as WikipediR. It does not
    need login. The multiplex network that can be constructed from the results
    of the functions of WikipediaR can be modeled as Stochastic Block Model as in
    Barbillon P., Donnet, S., Lazega E., and Bar-Hen A. : Stochastic Block Models
    for Multiplex networks: an application to networks of researchers, ArXiv
    1501.06444, http://arxiv.org/abs/1501.06444.
A convenience wrapper for the Wikipedia page access statistics API 
  binding the 'pageviews' package and using an additional self composed data 
  source thus covering a time span from very late 2007 up to the present for 
  daily page views.
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.
'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.
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.
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 random but perhaps useful functions.
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.
Helps for interfacing with the operating system
    particularly for Windows.
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).
A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setting), causal inference (in the presence of interventions) and learning policy decisions (in Decision Network setting). Functionalities include end-to-end implementations for data-preprocessing, structure-learning, exact inference, approximate inference, extending the learned structure to Decision Networks and policy optimization using statistically rigorous methods such as bootstraps, resampling, ensemble-averaging and cross-validation. In addition to Bayesian Decision Networks, it also features correlation networks, community-detection, graph visualizations, graph exports and web-deployment of the learned models as Shiny dashboards.
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 only
  useful in R 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'.
Implementation of an adaptive weighted k-nearest neighbours (wk-NN) imputation algorithm for clinical register data developed to explicitly handle missing values of continuous/ordinal/categorical and static/dynamic features conjointly. For each subject with missing data to be imputed, the method creates a feature vector constituted by the information collected over his/her first 'window_size' time units of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features.
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.
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>.
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/>.
Functions that help less experienced R users to make graph analysis for free associated words, or more generally for repeated nominal data for which a undirected graph analysis is meaningful. By corresponding to each word its centrality, it is possible to apply standard quantitative analysis methods in order to associate word selection with other   variables.
    The functions are implemented with the aid of the 'tibble', 'tidygraph', 'ggraph' and 'ggplot2' packages. Supported centrality functions are centrality_alpha(), centrality_authority(), centrality_betweenness(), centrality_closeness(), centrality_pagerank(), centrality_eigen(). A data set is included.
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>.
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 functions for generating bootstrap prediction
    intervals from a 'tidymodels' workflow. 'tidymodels'
    <https://www.tidymodels.org/> is a collection of packages for modeling
    and machine learning using 'tidyverse' <https://www.tidyverse.org/>
    principles. This package is not affiliated with or maintained by
    'RStudio' or the 'tidymodels' maintainers.
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 <http://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, see <https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database>).
Retrieves taxonomic information from  <http://www.marinespecies.org> using WoRMS' RESTful Webservice. Utility functions aim at taxonomic consistency.
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
    (<http://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>.
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.
Implementing weighted predictiveness curve to visualize the marker-by-treatment relationship and measure the performance of biomarkers for guiding treatment decision.
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 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.
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.
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.
Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. 
    Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. 
    Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. 
    The functions provided here help to inspect graphically the data to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement by low random values. 
    Dedicated filtering and statistical testing using the framework of package 'limma' <doi:10.18129/B9.bioc.limma> can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. 
    Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options separating 
    the data into sub-groups during normalization and testing. 
    As example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) is provided quantified by MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), ProteomeDiscoverer,
    OpenMS (<doi:10.1038/nmeth.3959>) and Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>). Meta-data provided in sdrf format can be integrated to the analysis.
    Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches.
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.
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.
Methods to calculate and interpret climate change signals and time series from climate multi-model ensembles. Climate model output in binary 'NetCDF' format is read in and aggregated over a specified region to a data.frame for statistical analysis. Global Circulation Models, as the 'CMIP5' simulations, can be read in the same way as Regional Climate Models, as e.g. the 'CORDEX' or 'ENSEMBLES' simulations. The package has been developed at the 'Wegener Center for Climate and Global Change' at the University of Graz, Austria.
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 an array of white noise hypothesis tests for functional data 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. 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>, and Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, 
  respectively.
Calculate the (weighted) win loss statistics including the win ratio, win difference and win product 
 and their variances, with which the p-values are also calculated. The variance estimation is based on 
 Luo et al. (2015) <doi:10.1111/biom.12225> and Luo et al. (2017) <doi:10.1002/sim.7284>. This package also calculates general win loss statistics with 
 user-specified win loss function with variance estimation based on 
 Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>.
 This version corrected an error when outputting confidence interval for win difference.
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.
Simulation of purebred and crossbred genomic data as well as pedigree and phenotypes are possible by this package. 'xbreed' can be used for the simulation of populations with flexible genome structures and trait genetic architectures. It can also be used to evaluate breeding schemes and generate genetic data to test statistical tools.
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.
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.