Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models.  Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.
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.
Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.
Provides diagnostic tests for assessing the informativeness of survey weights in regression models. Implements difference-in-coefficients tests (Hausman 1978 <doi:10.2307/1913827>; Pfeffermann 1993 <doi:10.2307/1403631>), weight-association tests (DuMouchel and Duncan 1983 <doi:10.2307/2288185>; Pfeffermann and Sverchkov 1999 <https://www.jstor.org/stable/25051118>; Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>; Wu and Fuller 2005 <https://www.jstor.org/stable/27590461>), estimating equations tests (Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 <doi:10.1111/insr.12509>) and extensions.
Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.578>.
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/>.
Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.
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.
Obtaining accurate and stable estimates of regression coefficients
    can be challenging when the suggested statistical model has issues related to
    multicollinearity, convergence, or overfitting. One solution is to use
    principal component analysis (PCA) results in the regression, as discussed in
    Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package
    streamlines comparisons between a raw regression model with the full set of
    raw independent variables and a principal component regression model where
    principal components are estimated on a subset of the independent variables,
    then swapped into the regression model in place of those variables. The
    swaprinc() function compares one raw regression model to one principal
    component regression model, while the compswap() function compares one raw
    regression model to many principal component regression models. Package
    functions include parameters to center, scale, and undo centering and scaling,
    as described by Harvey and Hansen (2022)
    <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>.
    Additionally, the package supports using Gifi methods to extract principal
    components from categorical variables, as outlined by Rossiter (2021)
    <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.
Function library for processing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.
Provides a pipeline for the comparative analysis of 
  collective movement data (e.g. fish schools, bird flocks, baboon troops) by 
  processing 2-dimensional positional data (x,y,t) from GPS trackers or computer
  vision tracking systems, discretizing events of collective motion, calculating
  a set of established metrics that characterize each event, and placing the
  events in a multi-dimensional swarm space constructed from these metrics.
  The swarm space concept, the metrics and data sets included are described in:
  Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, 
  Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King
  Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.
The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management. Furthermore, it contains several functions for analysis and visualization of (spatial) infection data.
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 or cohort sampling scheme (Hussey MA and Hughes JP (2007) 
	Contemporary Clinical Trials 28:182-191).
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 (the default specification assumes no-time-effect) 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/study-design-power-calculation/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'.
Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project <https://github.com/AbhikSarkar1999/SweepDiscovery> in 'GitHub'. This package has been developed by using the concept of  Pavlidis and Alachiotis (2017) <doi:10.1186/s40709-017-0064-0>.
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 2.10.03) 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.r-universe.dev",
    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 DAS data files. 
    These files are typically, but do not have to be 
    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'.
    This package standardizes and streamlines basic DAS data processing,
    and includes a PDF with the DAS data format requirements expected by the package.
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).
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.
Create carousels using the 'JavaScript' library 'Swiper' and
    the package 'htmlwidgets'. The carousels can be displayed in the
    'RStudio' viewer pane, in 'Shiny' applications and in 'R markdown'
    documents. The package also provides a 'RStudio' addin allowing to 
    choose image files and to display them in the viewer pane.
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 machine-readable access to parliamentary data of the 
    Swiss Federal Assembly via the 'OData' interface (<https://ws.parlament.ch/odata.svc/>) 
    and the 'OpenParlData' REST API (<https://api.openparldata.ch>), which also 
    offers harmonized data for selected cantonal and municipal parliaments.
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.
Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <https://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices.
            For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) <DOI:10.3389/fams.2023.1274530>.
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),
    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>.
Collection (syllogi in greek) of real and fictitious data sets for teaching purposes.
	     The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation.
	     The fictions datasets are the creation of the author, that he has found useful for teaching statistics.
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.
Powerful user interface for adding symbols, smileys, arrows, building mathematical equations using 'LaTeX' or 'r2symbols'. Built for use in development of 'Markdown' and 'Shiny' Outputs.
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>,
 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>,
 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.
Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.
Easily analyze and visualize the performance of symptom
    checkers. This package can be used to gain comprehensive insights into
    the performance of single symptom checkers or the performance of
    multiple symptom checkers. It can be used to easily compare these
    symptom checkers across several metrics to gain an understanding of
    their strengths and weaknesses. The metrics are developed in 
    Kopka et al. (2023) <doi:10.1177/20552076231194929>.
A collection of functions for symbolic computation using the
			 'caracas' package for structural equation models and other
			 statistical analyses. Among its features is the ability
			 to calculate the model-implied covariance (and correlation)
			 matrix and the sampling covariance matrix of variable functions
			 using the delta method.
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/>.
Compare directories flexibly (by date, content, or both) and synchronize files efficiently, with asymmetric and symmetric modes, helper tools, and visualization support for file management.
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>.
A framework for evaluating drug combination effects in preclinical in vivo studies.
    'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, 
    perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, 
    and assess both post-hoc and a priori statistical power.
    The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>).
    The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>),
    and Gałecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).
This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.
Decode raw SYNOP (surface synoptic observations) messages into 
    tidy data frames. Supports batch processing of messages from the same 
    station, extracting data from Sections 0, 1, and 3, including temperature, 
    dew point, pressure, wind, clouds, and precipitation. The decoding logic 
    follows the specifications defined in the World Meteorological 
    Organization (2019) "Manual on Codes, Volume I.1 (WMO-No. 306)" 
    <https://library.wmo.int/idurl/4/35713>.
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.
Draw syntenic relationships between genome assemblies.
    There are 3 functions which take a tab delimited file containing alignment data for 
    syntenic blocks between genomes to produce either a linear alignment plot, 
    an evolution highway style plot, or a painted ideogram representing syntenic relationships.
    There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format
    to the required data structure.
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.
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.
Synthesize numeric, categorical, mixed and time series data. Data 
    circumstances including mixed (or zero-inflated) distributions and missing
    data patterns are reproduced in the synthetic data. A single parameter allows
    balancing between high-quality synthetic data that represents correlations of
    the original data and lower quality but more privacy safe synthetic data
    without correlations. Tuning can be done per variable or for the whole
    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" <doi:10.48550/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>. Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data).
Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) <doi:10.2139/ssrn.3751162>, building on 
  the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) <doi:10.1016/j.jfineco.2015.10.001>, to estimate the cumulative 
  treatment effect of an event on treated firms’ stock returns.
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>.
Measures memory and CPU usage of R code by regularly taking 
    snapshots of calls to the system command 'ps'. The package provides an entry 
    point (albeit coarse) to profile usage of system resources by R code run 
    in parallel.
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.
There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of  feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.
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>.
Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction,  where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.
Deals with Young tableaux (field of combinatorics). For standard
    Young tabeaux, performs enumeration, counting, random generation, the
    Robinson-Schensted correspondence, and conversion to and from paths on
    the Young lattice. Also performs enumeration and counting of 
    semistandard Young tableaux, enumeration of skew semistandard Young 
    tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of 
    Kostka numbers.
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.
A system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients  based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and 'T2DFitTailor' details the exercise plan and predicts the intervention's effectiveness.
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.
Converting structured data from tables into XML format using
    predefined templates ensures consistency and flexibility, making it
    ideal for data exchange, reporting, and automated workflows.
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.
Produces weighted cross-tabulation tables for one or more outcome 
    variables across one or more breakdown variables, and exports them directly 
    to 'Excel'. For each outcome-by-breakdown combination, the package creates a 
    weighted percentage table and a corresponding unweighted count table, with 
    transparent handling of missing values and light, readable formatting. 
    Designed to support social survey analysis workflows that require large sets of 
    consistent, publication-ready tables.
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 toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead.
Offers a TableContainer() function to create tables enriched with row, column, and table annotations. This package is similar to 'SummarizedExperiment' in Bioconductor <doi:10.18129/B9.bioc.SummarizedExperiment>, but designed to work independently of Bioconductor, it ensures annotations are automatically updated when the table is subset. Additionally, it includes format_tbl() methods for enhanced table formatting and display.
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.
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.
Features include the ability to extract tabled content from NISO-JATS-coded XML, any native HTML or HML file, DOCX, and PDF documents, and then collapse it into a text format that is readable by humans by mimicking the actions of a screen reader. As tables within PDF documents are extracted with the 'tabulapdf' package, and the table captions and footnotes cannot be extracted, the results on tables within PDF documents have to be considered less precise. The function table2matrix() returns a list of the tables within a document as character matrices. table2text() collapses the matrix content into a list of character strings by imitating the behavior of a screen reader. The textual representation of characters and numbers can be unified with unifyMatrix() before parsing. The function table2stats() extracts the tabled statistical test results from the collapsed text with the function standardStats() from the 'JATSdecoder' package and, if activated, checks the reported and coded p-values for consistency. Due to the great variability and potential complexity of table structures, parsing accuracy may vary.
Provides functions to build interactive dashboards combining the 'Tabler UI Kit' with 'Shiny', making it
    easy to create professional-looking web applications. 'Tabler' is fully responsive and compatible with all modern
    browsers. Offers customizable layouts and components built with 'HTML5' and 'CSS3'. The underlying 'Tabler'
    (<https://github.com/tabler/tabler>) and 'Tabler Icons' (<https://github.com/tabler/tabler-icons>)
    were pre-built from source to eliminate the need for 'Node.js' and 'NPM' on package installation.
'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.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
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.
Create "good enough" tables with a single formula. 'tablespan' tables 
  can be exported to 'Excel', 'HTML', 'LaTeX', and 'RTF' by leveraging
  the packages 'openxlsx' and 'gt'. See <https://jhorzek.github.io/tablespan/> for
  an introduction.
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.
Collection of functions that allow to export data frames to excel workbook.
Implements the 'TabNet' model by Sercan O. Arik et al. (2019)
    <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label
    Classification Networks' by Giunchiglia et al.  <doi:10.48550/arXiv.2010.10151> 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/>.
Provides a consistent API for classification and regression models 
    based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions 
    on small data with a tabular foundation model," Nature, 637(8045) 
    <doi:10.1038/s41586-024-08328-6>. The calculations are served via 'Python' 
    to train and predict the model.
Provides a music notation syntax and a collection of music
    programming functions for generating, manipulating, organizing, and analyzing
    musical information in R. Music syntax can be entered directly in character
    strings, for example to quickly transcribe short pieces of music. The
    package contains functions for directly performing various mathematical,
    logical and organizational operations and musical transformations on special
    object classes that facilitate working with music data and notation. The
    same music data can be organized in tidy data frames for a familiar and
    powerful approach to the analysis of large amounts of structured music data.
    Functions are available for mapping seamlessly between these formats and
    their representations of musical information. The package also provides an
    API to 'LilyPond' (<https://lilypond.org/>) for transcribing musical
    representations in R into tablature ("tabs") and sheet music. 'LilyPond' is
    open source music engraving software for generating high quality sheet music
    based on markup syntax. The package generates 'LilyPond' files from R code
    and can pass them to the 'LilyPond' command line interface to be rendered
    into sheet music PDF files or inserted into R markdown documents. The
    package offers nominal MIDI file output support in conjunction with
    rendering sheet music. The package can read MIDI files and attempts to
    structure the MIDI data to integrate as best as possible with the data
    structures and functionality found throughout the package.
A standardized workflow to reconstruct spatial configurations of altitude-bounded biogeographic systems over time. For example, 'tabs' can model how island archipelagos expand or contract with changing sea levels or how alpine biomes shift in response to tree line movements. It provides functionality to account for various geophysical processes such as crustal deformation and other tectonic changes, allowing for a more accurate representation of biogeographic system dynamics. For more information see De Groeve et al. (2025) <doi:10.21425/fob.18.151677>.
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.
A lightweight toolkit that provides functions for printing tables from
    input data in the R console or terminal with customizable formatting. Supported
    outputs include American Psychological Association (APA)-style tables
    (American Psychological Association, 2020, ISBN:9781433832178),
    correlation matrices, contingency tables, and two-column summary tables.
Simplify reporting many tables by creating tibbles of tables. With
  'tabtibble', a tibble of tables is created with captions and automatic
  printing using 'knit_print()'.
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.). It allows to easily visualize
    count data and statistical thresholds: rank vs abundance plots,
    heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.
Bindings for the 'Tabula' <https://tabula.technology/> 'Java'
    library, which can extract tables from PDF files. This tool can reduce time
    and effort in data extraction processes in fields like investigative
    journalism. It allows for automatic and manual table extraction, the latter
    facilitated through a 'Shiny' interface, enabling manual areas selection\
    with a computer mouse for data retrieval.
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.
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>.
Make it easy to deal with multiple cross-tables in data exploration, by
  creating them, manipulating them, and adding color helpers to highlight
  important informations (differences from totals, comparisons between lines or
  columns, contributions to variance, confidence intervals, odds ratios, etc.). 
  All functions are pipe-friendly and render data frames which can be easily 
  manipulated. In the same time, time-taking operations are done with 'data.table'
  to go faster with big dataframes. Tables can be exported with formats and colors 
  to 'Excel', plot and html.
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.
The “TAD” package compiled an analytical framework based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).
Suite of tools to support the practice of tada science. It includes an
    engaging package roulette that is designed to facilitate learning about 
    new packages.
General framework to organize data, methods, and results used in
  reproducible scientific analyses. A TAF analysis consists of four scripts
  (data.R, model.R, output.R, report.R) that are run sequentially. Each script
  starts by reading files from a previous step and ends with writing out files
  for the next step. Convenience functions are provided to version control the
  required data and software, run analyses, clean residues from previous runs,
  manage files, manipulate tables, and produce figures. With a focus on
  stability and reproducible analyses, the TAF package comes with no
  dependencies. TAF forms a base layer for the 'icesTAF' package and other
  scientific applications.
Provides functions for attaching tags to R objects,
    searching for objects based on tags, and removing tags from objects.
    It also includes a function for removing all tags from an object, as well
    as a function for deleting all objects with a specific tag from the R
    environment. The package is useful for organizing and managing large
    collections of objects in R.
High-resolution movement-sensor tags typically include accelerometers 
    to measure body posture and sudden movements or changes in speed, 
    magnetometers to measure direction of travel, and pressure sensors
    to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; 
    calibration (from raw data to calibrated data in scientific units); 
    visualization (for example, multi-panel time-series plots); 
    data processing (such as event detection, calculation of derived metrics like jerk and 
    dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).
Functions and example files to calculate the tRNA adaptation index,
  a measure of the level of co-adaptation between the set of tRNA genes and the 
  codon usage bias of protein-coding genes in a given genome. The methodology is
  described in dos Reis, Wernisch and Savva (2003) <doi:10.1093/nar/gkg897>, and 
  dos Reis, Savva and Wernisch (2004) <doi:10.1093/nar/gkh834>.
The function TailClassifier() 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. The function also provides an estimate of the parameter for the classified-distribution as a reference.
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>.
The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025)<doi:10.4230/LIPIcs.ECRTS.2025.20>.
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.
Postprocessors refine predictions outputted from machine
    learning models to improve predictive performance or better satisfy
    distributional limitations. This package introduces 'tailor' objects,
    which compose iterative adjustments to model predictions. A number of
    pre-written adjustments are provided with the package, such as
    calibration. See Lichtenstein, Fischhoff, and Phillips (1977) 
    <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose 
    new adjustments are also included. Tailors are tightly integrated with the 
    'tidymodels' framework.
Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and 
            Klar (2024) <doi:10.1080/00031305.2024.2413081>. 
            One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. 
            Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>.
            The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.
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 (2022) <doi:10.1080/00031305.2022.2063944>.
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.
Visualisation, analysis and quality control of conversational data.
             Rapid and visual insights into the nature, timing and quality of
             time-aligned annotations in conversational corpora.
             For more details, see 
             Dingemanse et al., (2022) <doi:10.18653/v1/2022.acl-long.385>.
An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. 
             It is a versatile, general-purpose tool for analyzing textual data. 
             'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.
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>.
Agglomerative hierarchical clustering with a bespoke distance 
  measure based on medication similarities in the Anatomical Therapeutic 
  Chemical Classification System, medication timing and 
  medication amount or dosage. Tools for summarizing, illustrating and 
  manipulating the cluster objects are also available.
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>.
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.
Feature selection algorithm that extracts features in highly
    correlated spaces. The extracted features are meant to be fed into
    simple explainable models such as linear or logistic regressions. The
    package is useful in the field of explainable modelling as a
    way to understand variable behavior.
Provides an extensible formula system to quickly and easily create
    production quality tables. The processing steps are a formula parser,
    statistical content generation from data as defined by formula, followed by
    rendering into a table. Each step of the processing is separate and user
    definable thus creating a set of composable building blocks for
    highly customizable 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 in a resulting table. 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>.
Providing new german-wide 'TapeR' Models and functions for their 
  evaluation. Included are the most common tree species in Germany (Norway 
  spruce, Scots pine, European larch, Douglas fir, Silver fir as well as 
  European beech, Common/Sessile oak and Red oak). Many other species are mapped
  to them so that 36 tree species / groups can be processed. Single trees are 
  defined by species code, one or multiple diameters in arbitrary measuring 
  height and tree height. The functions then provide information on diameters 
  along the stem, bark thickness, height of diameters, volume of the total or 
  parts of the trunk and total and component above-ground biomass. It is also 
  possible to calculate assortments from the taper curves. Uncertainty 
  information is provided for diameter, volume and component biomass estimation.
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.
Swift and seamless Single Sign-On (SSO) integration. Designed
    for effortless compatibility with popular Single Sign-On providers
    like Google and Microsoft, it streamlines authentication, enhancing
    both user experience and application security. Elevate your 'shiny'
    applications for a simplified, unified, and secure authentication process.
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 pipelines 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 pipelines concisely and compactly.
  The methods in this package were influenced by the 'targets' 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 (AIPW) estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
         variable importance and conditional average treatment effects (CATE)
         (van der Laan (2006) <doi:10.2202/1557-4679.1008>),
	     estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
         linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
Pipeline tools coordinate the pieces of computationally
  demanding analysis projects.
  The 'targets' package is a 'Make'-like pipeline tool for statistics and
  data science in R. The package skips costly runtime for tasks
  that are already up to date,
  orchestrates the necessary computation with implicit parallel computing,
  and abstracts files as R objects. If all the current output matches
  the current upstream code and data, then the whole pipeline is up
  to date, and the results are more trustworthy than otherwise.
  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().
Implements a task queue system for asynchronous parallel computing 
    using 'PostgreSQL' <https://www.postgresql.org/> as a backend. Designed for 
    embarrassingly parallel problems where tasks do not communicate with each other.
    Dynamically distributes tasks to workers, handles uneven load balancing, and 
    allows new workers to join at any time. Particularly useful for running large 
    numbers of independent tasks on high-performance computing (HPC) clusters with 
    'SLURM' <https://slurm.schedmd.com/> job schedulers.
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.
A collection of recipe datasets scraped from <https://www.allrecipes.com/>, containing two complementary datasets: 'allrecipes' with 14,426 general recipes, and 'cuisines' with 2,218 recipes categorized by country of origin. Both datasets include comprehensive recipe information such as ingredients, nutritional facts (calories, fat, carbs, protein), cooking times (preparation and cooking), ratings, and review metadata. All data has been cleaned and standardized, ready for analysis.
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.
Functions to support economic modelling in R based on the methods of the 
    Dutch guideline for economic evaluations in healthcare <https://www.zorginstituutnederland.nl/documenten/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg>, 
    CBS data <https://www.cbs.nl/>, and OECD data <https://www.oecd.org/en.html>.
A clinically meaningful measures of treatment effects for right-censored data 
  are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures.
  Two plots of tau processes, with the option to account for the cure fraction or not, are available.
  The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time.
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) <DOI:10.48550/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) <DOI:10.1214/16-EJS1166>.
'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.
A novel feature-wise normalization method based on a zero-inflated negative binomial model. This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) <doi:10.1101/2023.10.31.563648>.
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 application programming interfaces
    (API) 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. Some of the services
    supported include 'NCBI E-utilities' (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>),
    'Encyclopedia of Life' (<https://eol.org/docs/what-is-eol/data-services>),
    'Global Biodiversity Information Facility' (<https://techdocs.gbif.org/en/openapi/>),
    and many more. Links to the API documentation for other supported services are
    available in the documentation for their respective functions in this package.
Download taxonomic databases, convert them into 'SQLite' format, 
    and query them locally for fast, reliable, and reproducible access to 
    taxonomic data.
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.
A collection of functions used to format taxonomic names in Markdown
    documents. Those functions work with data structured according to
    Alvarez and Luebert (2018) <doi:10.3897/bdj.6.e23635>.
Computes phylogenetic distances between any two taxa using 
    hierarchical lineage data retrieved from The Taxonomicon 
    <http://taxonomicon.taxonomy.nl>, a comprehensive curated classification 
    of all life based on Systema Naturae 2000 (Brands, 1989 
    <http://taxonomicon.taxonomy.nl>). Given any two taxon names, retrieves 
    their full lineages, identifies the most recent common ancestor (MRCA), 
    and computes a dissimilarity index based on the depth of the most recent 
    common ancestor. Supports individual distance queries, pairwise distance 
    matrices, clade filtering, and lineage utilities.
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.
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 'SoundStat' (song characteristics). Additional functions
    are included for easily creating data visualizations with color
    palettes inspired by Taylor Swift's album covers.
The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples).  The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors).  To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" <doi:10.1037/h0057079>.
For when your colors absolutely should not be excluded from the narrative.
A collection of interactive 'shiny' applications for performing comprehensive analyses in the field of tree breeding and genetics. The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079).
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.
Functions are provided for prior specification in divergence time
    estimation using fossils as well as other kinds of data. It
    provides tools for interacting with the input and output of Bayesian
    platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes',
    or 'MCMCTree'.
    It Implements a simple measure similarity between probability
    density functions for comparing prior and
    posterior Bayesian densities, as well as code for calculating the
    combination of distributions using conflation of Hill (2008). Functions for estimating the
    origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and
    stratigraphic intervals (Marshall 2010) are also available.
    Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>,
    Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>,
    Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.
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>.
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) <doi:10.48550/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 <https://www.cbioportal.org/> for more information).
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>.
Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks.  R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'.  This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' <https://github.com/GeoscienceAustralia/tcrm> and then coded up in 'Cuda' 'cpp' by 'TCwindgen' <https://github.com/CyprienBosserelle/TCwindgen>.
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 Functional magnetic resonance imaging(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 modeling 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. 
    Additionally, functions to compute the power of the tests as a function of an effect measure 
    interpreted as explained variance are provided.
    Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.
A series of additional Tcl commands and Tk widgets 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>.
The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, 'invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts.
The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts.
Convert T Cell Receptor (TCR) gene names between the 10X Genomics, Adaptive Biotechnologies, and ImMunoGeneTics (IMGT) nomenclatures.
Implements methods for selecting the number of factors in Poisson
  factor models, with a primary focus on Thinning Cross-Validation (TCV). The
  TCV method is based on the 'data thinning' technique, which probabilistically
  partitions each count observation into training and test sets while preserving
  the underlying factor structure. The Poisson factor model is then fit on the
  training set, and model selection is performed by comparing predictive
  performance on the test set. This toolkit is designed for researchers working
  with high-dimensional count data in fields such as genomics, text mining, and
  social sciences. The data thinning methodology is detailed in Dharamshi et al.
  (2025) <doi:10.1080/01621459.2024.2353948> and Wang et al. (2025)
  <doi:10.1080/01621459.2025.2546577>.
Framework provides functions to parse 'Training Center XML (TCX)' files
    and extract key activity metrics such as total distance, total time, calories burned,
    maximum altitude, and power values (watts). This package is useful for analyzing workout
    and training data from devices that export 'TCX' format.
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 Topological Data Analysis. The package focuses on statistical analysis of persistent homology and 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 methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8>  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. The main tool of topological data analysis is persistent homology, which computes
    a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides 
    useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference,
    and these functions can also interface with other data science packages to form flexible and integrated
    topological data analysis pipelines.
Topological data analytic methods in machine learning rely on
    vectorizations of the persistence diagrams that encode persistent
    homology, as surveyed by Ali &al (2000)
    <doi:10.48550/arXiv.2212.09703>.  Persistent homology can be computed
    using 'TDA' and 'ripserr' and vectorized using 'TDAvec'.  The
    Tidymodels package collection modularizes machine learning in R for
    straightforward extensibility; see Kuhn & Silge (2022,
    ISBN:978-1-4920-9644-3).  These 'recipe' steps and 'dials' tuners make
    efficient algorithms for computing and vectorizing persistence
    diagrams available for Tidymodels workflows.
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>.
Provides a set of tools for managing time-series data,
    with a particular emphasis on defining various frequency types such
    as daily and weekly. It also includes functionality for converting
    data between different frequencies.
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>.
Provides 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' and 'RcppArmadillo' packages.
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.
Estimate the transition diagnostic classification model (TDCM) 
    described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a 
    longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in 
    Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM 
    subsumes many other diagnostic classification models (DCMs), many other DCMs 
    can be estimated longitudinally via the TDCM. The 'TDCM' package includes 
    functions to estimate the single-group and multigroup TDCM, summarize 
    results of interest including item parameters, growth proportions, 
    transition probabilities, transitional reliability, attribute correlations, 
    model fit, and growth plots.
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 total deviation index (TDI) is an unscaled statistical measure used to evaluate the deviation between paired quantitative measurements when assessing the extent of agreement between different raters. It describes a boundary such that a large specified proportion of the differences in paired measurements are within the boundary (Lin, 2000) <https://pubmed.ncbi.nlm.nih.gov/10641028/>.
    This R package implements some methodologies existing in the literature for TDI estimation and inference in the case of two raters.
The main purpose of this package is to propose a rigorous framework to fairly compare trip distribution laws and models as described in Lenormand et al. (2016) <doi:10.1016/j.jtrangeo.2015.12.008>.
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>).
This contains functions that can be used to estimate the time-dependent 
              precision-recall curve (PRC) and the corresponding area under the PRC 
              for right-censored survival data. It also compute time-dependent 
              ROC curve and its corresponding area under the ROC curve (AUC). See
              Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.
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.)
The tdROC package facilitates the estimation of time-dependent ROC 
    (Receiver Operating Characteristic) curves and the Area Under the time-dependent 
    ROC Curve (AUC) in the context of survival data, accommodating scenarios with 
    right censored data and the option to account for competing risks. In addition 
    to the ROC/AUC estimation, the package also estimates time-dependent Brier score and 
    survival difference. Confidence intervals of various estimated quantities can be 
    obtained from bootstrap. The package also offers plotting functions for visualizing 
    time-dependent ROC curves.
Functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.
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.
STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.
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.
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.
A 'shiny' based interactive exploration framework for
    analyzing clinical trials data. 'teal' currently provides a dynamic
    filtering facility and different data viewers. 'teal' 'shiny'
    applications are built using standard 'shiny' modules.
Introduction of 'qenv' S4 class, that facilitates code
    execution and reproducibility in 'teal' applications.
Provides a 'teal_data' class as a unified data model for
    'teal' applications focusing on reproducibility and relational data.
Utilizing the 'logger' framework to record events within a
    package, specific to 'teal' family of packages.  Supports logging
    namespaces, hierarchical logging, various log destinations,
    vectorization, and more.
Provides user-friendly tools for creating and customizing
    clinical trial reports. By leveraging the 'teal' framework, this
    package provides 'teal' modules to easily create an interactive panel
    that allows for seamless adjustments to data presentation, thereby
    streamlining the creation of detailed and accurate reports.
Prebuilt 'shiny' modules containing tools for viewing data,
    visualizing data, understanding missing and outlier values within your
    data and performing simple data analysis.  This extends 'teal'
    framework that supports reproducible research and analysis.
Prebuilt 'shiny' modules containing tools for the generation
    of 'rmarkdown' reports, supporting reproducible research and analysis.
Data filtering module for 'teal' applications.  Allows for
    interactive filtering of data stored in 'data.frame' and
    'MultiAssayExperiment' objects. Also displays filtered and unfiltered
    observation counts.
A standardized user interface for column selection, that
    facilitates dataset merging in 'teal' framework.
Collection of 'shiny' widgets to support 'teal' applications.
    Enables the manipulation of application layout and plot or table
    settings.
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>.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Models the direction of the maximum horizontal stress using
    relative plate motion parameters. Statistical algorithms to evaluate
    the modeling results compared with the observed data. Provides plots
    to visualize the results. Methods described in Stephan et al. (2023)
    <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998)
    <doi:10.1016/S0079-1946(98)00091-3>.
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.
Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.
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>.
A simple wrapper around the Telegram Bot API (<https://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.
Analyze telemetry datasets generalized to allow
    any technology. The filtering steps check for false positives caused by 
    reflected transmissions from surfaces and false pings from other noise 
    generating equipment. The filters are based on JSATS filtering algorithms
    found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats>
    but have been generalized to allow the user to define many of the filtering 
    variables. Additionally, this package contains scripts used to help identify 
    an optimal maximum blanking period as defined in Capello et al (2015) 
    <doi:10.1371/journal.pone.0134002>. The functions were written according to
    their manuscript description, but have not been reviewed by the authors for 
    accuracy. It is included here as is, without warranty.
Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>.
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.
Streamline the processing of 'Telraam' data, sourced from open data
    mobility sensors. These tools range from data retrieval (without the need for
    API knowledge) to data visualization, including data preprocessing.
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.
Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.
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> .
Implements the template ICA (independent components analysis) model
    proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the 
    spatial template ICA model proposed in proposed in Mejia et al. (2022)
    <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level 
    brain as deviations from known population-level networks, which are 
    estimated using standard ICA algorithms. Both models employ an 
    expectation-maximization algorithm for estimation of the latent brain 
    networks and unknown model parameters. Includes direct support for 'CIFTI',
    'GIFTI', and 'NIFTI' neuroimaging file formats.
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 / RT-UQ <https://uq.math.cnrs.fr/> algorithm template, for design of numerical experiments practice:
    algorithm template parser to support MASCOTNUM specification <https://github.com/MASCOTNUM/algorithms>, 
    '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().
Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, <doi:10.3791/53584>), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, <doi:10.1037/1064-1297.16.3.264>), scoring questionnaires according to the methods of Kirby et al. (1999, <doi:10.1037//0096-3445.128.1.78>) and Wileyto et al (2004, <doi:10.3758/BF03195548>), Bayesian model selection using a range of discount functions (Franck et al., 2015, <doi:10.1002/jeab.128>), drift diffusion models of discounting (Peters & D'Esposito, 2020, <doi:10.1371/journal.pcbi.1007615>), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, <doi:10.1901/jeab.2001.76-235>) and ED50 (Yoon & Higgins, 2008, <doi:10.1016/j.drugalcdep.2007.12.011>).
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.
Implements the Temporal Forest algorithm for feature selection in 
    high-dimensional longitudinal data. The method combines time-aware network 
    construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability 
    selection using tree-based models. This package provides tools for 
    reproducible longitudinal analysis, closely following the methodology 
    described in Shao, Moore, and Ramirez (2025) <https://github.com/SisiShao/TemporalForest>.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.
A collection of methods to estimate parameters of different tempered stable 
  distributions (TSD). Currently, there are seven different tempered stable 
  distributions to choose from: Tempered stable subordinator distribution, 
  classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid 
  decreasing TSD, and Kim-Rachev TSD.
  The package also provides functions to compute density and probability 
  functions and tools to run Monte Carlo simulations.
  This package has already been used for the estimation of tempered stable 
  distributions (Massing (2023) <arXiv:2303.07060>).
  The following references form the theoretical background for various functions
  in this package. References for each function are explicitly listed in its 
  documentation:
  Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4>
  Bianchi et al. (2011) <doi:10.1137/S0040585X97984632>
  Carrasco (2017) <doi:10.1017/S0266466616000025>
  Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x>
  Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656>
  Hansen (1982) <doi:10.2307/1912775>
  Hofert (2011) <doi:10.1145/2043635.2043638>
  Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014>
  Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004>
  Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5>
  Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015>
  Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012>
  Rachev et al. (2011) <doi:10.1002/9781118268070>.
TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.
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).
Implements methods and functions to calibrate 
  time-specific niche models (multi-temporal calibration), letting users 
  execute a strict calibration and selection process of niche models based 
  on ellipsoids, as well as functions to project the potential distribution in 
  the present and in global change  scenarios.The 'tenm' package has functions
  to recover information that may be lost or overlooked while applying a data 
  curation protocol. This curation involves preserving occurrences that may 
  appear spatially redundant (occurring in the same pixel) but originate from 
  different time periods. A novel aspect of this package is that it might 
  reconstruct the fundamental niche more accurately than mono-calibrated 
  approaches. The theoretical background of the package can be found in 
  Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.
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>.
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.
Provides methods for low-rank tensor regression with tensor-valued predictors
    and scalar covariates. Model estimation is performed using stochastic optimization
    with random-walk updates for low-rank factor matrices. Computationally intensive
    components for coefficient estimation and prediction are implemented in C++ via
    'Rcpp'. The package also includes tools for cross-validation and prediction error
    assessment.
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 data classes, this package serves as an alternative resort to handle tensor only as 'array' class.
    Some functionalities related to missingness 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 (2023) <arXiv:2208.04012> for more details.
Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 <doi:10.1080/10618600.2021.1978471>.
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) <doi:10.48550/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) <doi:10.48550/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 general principle relies on calculating the cumulative signal of nascent
    RNA sequencing over the gene body of any given gene or transcription unit.
    'tepr' can identify transcription attenuation sites by comparing profile to
    a null model which assumes uniform read density over the entirety of the
    transcription unit. It can also identify increased or diminished
    transcription attenuation by comparing two conditions. Besides rigorous
    statistical testing and high sensitivity, a major feature of 'tepr' is its
    ability to provide the elongation pattern of each individual gene, including
    the position of the main attenuation point when such a phenomenon occurs.
    Using 'tepr', users can visualize and refine genome-wide aggregated analyses
    of elongation patterns to robustly identify effects specific to subsets of
    genes. These metrics are suitable for internal comparisons (between genes in
    each condition) and for studying elongation of the same gene in different
    conditions or comparing it to a perfect theoretical uniform elongation.
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.
Provides a set of functions
    that allow users for styling their R code according to
    the 'tidyverse' style guide. The package uses a native
    Rust implementation to ensure the highest performance.
    Learn more about 'tergo' at <https://rtergo.pagacz.io>.
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.
Defines a graphics device and functions for graphical output in
    terminal emulators that support graphical output. Currently terminals that
    support the Terminal Graphics Protocol
    (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel 
    (<https://en.wikipedia.org/wiki/Sixel>) are supported.
Table, Listings, and Graphs (TLG) library for common outputs
    used in clinical trials.
Generalized estimating equations (GEE) are a popular choice
    for analyzing longitudinal binary outcomes. This package provides an
    interface for fitting GEE, currently for logistic regression, within
    the 'tern' <https://cran.r-project.org/package=tern> framework (Zhu,
    Sabanés Bové et al., 2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> (Becker, Waddell et al.,
    2023).  It builds on 'geepack' <doi:10.18637/jss.v015.i02> (Højsgaard,
    Halekoh and Yan, 2006) for the actual GEE model fitting.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see for example Cnaan, Laird and Slasor
    (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>.
    This package provides an interface for fitting MMRM within the 'tern'
    <https://cran.r-project.org/package=tern> framework by Zhu et al.
    (2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> by Becker et al. (2023).
    It builds on 'mmrm' <https://cran.r-project.org/package=mmrm> by
    Sabanés Bové et al. (2023) for the actual MMRM computations.
Plots ternary diagrams (simplex plots / Gibbs triangles) and
  Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the
  standard graphics functions.
  Allows custom annotation, interpolating, contouring and scaling of plotting
  region.
  Includes a 'Shiny' user interface for point-and-click ternary plotting.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
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 geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. 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/> to get started.
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://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>.
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.
A reliable and validated tool that calculates unit test coverage for R packages with standard testing frameworks and non-standard testing frameworks.
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 mfp2() function from the 'mfp2' 
    package to explore nonlinear associations between cognition and demographic 
    variables.
This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition,  it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface.
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>.
Provides a method for comparing the results of two binary diagnostic tests using paired data. 
    Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons.
    Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) <doi:10.1186/s12874-020-00988-y>.
    This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests.
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 (2022), <https://www.cambridge.org/core/journals/econometric-theory/article/abs/robust-tests-for-white-noise-and-crosscorrelation/4D77C12C52433F4C6735E584C779403A>, <https://elischolar.library.yale.edu/cowles-discussion-paper-series/57/>.
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: 'Rsymphony',
    'highs', '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 statistics. The package 
    also contains an R Markdown template with the required formatting for 
    assignments in my former courses.
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}.
Allows users to test characteristics of common R objects.
Add tests in-line in examples. Provides standalone functions for
    facilitating easier test writing in Rd files. However, a more familiar
    interface is provided using 'roxygen2' tags. Tools are also provided for
    facilitating package configuration and use with 'testthat'.
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.
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.
An R interface to load testing data in the 'OMOP' Common Data Model ('CDM'). An input file, csv or xlsx, can be converted to a 'CDMConnector' object. This object can be used to execute and test studies that use the 'CDM' <https://www.ohdsi.org/data-standardization/>.
Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diago, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.
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.
Provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.
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'.
Automates documentation of test_that() calls within R test files. 
    The package scans test sources, extracts human-readable test titles (even when 
    composed with functions like paste() or glue::glue(), ... etc.), and generates 
    reproducible roxygen2-style listings that can be inserted both globally and 
    per-section. It ensures idempotent updates and supports customizable numbering 
    templates with hierarchical indices. Designed for developers, QA teams, and package
    maintainers seeking consistent, self-documenting test inventories.
Runs tests using the 'testthat' package but allows for multiple
    attempts for a single test. This is useful for noisy or flaky tests
    that generally pass but can fail due to occasional random errors,
    such as numeric instability or using random data.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.
Uses the Distorted Wave Born Approximation (DWBA) to compute the
  acoustic backward scattering, the geometry of the object is formed by a
  volumetric mesh, composed of tetrahedrons. This computation is done efficiently
  through an analytical 3D integration that allows for a solution which is
  expressed in terms of elementary functions for each tetrahedron.
  It is important to note that this method is only valid for objects whose
  acoustic properties, such as density and sound speed, do not vary significantly
  compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). 
  TetraScatt model: Born approximation for the estimation of acoustic dispersion of 
  fluid-like objects of arbitrary geometries. arXiv preprint <arXiv:2312.16721>).
A game inspired by 'Tetris'. Opens a plot window device and starts a game of 'tetRys' in it. Steer the tetrominos with the arrow keys, press Pause to pause and Esc to end the game.
When using the R package  'exams'  to write mathematics questions in 'Sweave' files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the  topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra.  The output of the developed functions can be used in 'Sweave' files.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
Articles in the 'R Journal' were first authored in 'LaTeX', which performs 
    admirably for 'PDF' files but is less than ideal for modern online interfaces.
    The 'texor' package does all the transitional chores and conversions necessary
    to move to the online versions.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
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>.)
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.
Allows users to analyze text and classify emotions
    such as happiness, sadness, anger, fear, and neutrality.
    It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) 
    feature extraction, and Random Forest classification to predict emotions
    and map them to corresponding emojis for enhanced sentiment visualization.
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 see Meier, Mata & Wulff (2025) <doi:10.32614/RJ-2024-005> and Wulff, Meier & Mata (2024) <doi:10.1007/s11625-024-01516-3>.
This is a companion package for the 'text2sdg' package. It contains the trained ensemble models needed by the 'detect_sdg' function from the 'text2sdg' package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the 'LaTeX' tabular environment using '.tex' code. It can compile '.tex' code to 'PDF' automatically.
It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. It also plots the n-grams and corresponding counts as a bar chart.
A lightweight and focused text annotation tool built with 'shiny'. Provides an 
    interactive graphical user interface for coding text documents,
    managing code hierarchies, creating memos, and analyzing coding patterns.
    Features include code co-occurrence analysis, visualization of coding patterns,
    comparison of multiple coding sets, and export capabilities. Supports
    collaborative qualitative research through standardized annotation formats
    and analysis tools.
Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text
    datasets on the disk and load them when needed. Includes various
    sentiment lexicons and labeled text data sets for classification and
    analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
Implement text and sentiment analysis with 'texter'. 
             Generate sentiment scores on text data and also visualize sentiments.
             'texter' allows you to quickly generate insights on your data.
             It includes support for lexicons such as 'NRC' and 'Bing'.
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. Do note that to avoid the risk of CRAN archival that you should put packages as much as possible in Suggests.
    Package 'ggalt' is available in the CRAN archive: <https://cran.r-project.org/package=ggalt>.
A toolkit for web scraping, modular NLP pipelines, and text
    preparation for large language models. Organized around four core
    actions: fetching, reading, processing, and searching. Covers the full
    pipeline from raw web data acquisition to structural text processing and
    BM25 indexing. Supports multiple retrieval strategies including regex,
    dictionary matching, and ranked keyword search. Pipe-friendly with no
    heavy dependencies; all outputs are plain data frames or data.tables.
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/>.
A wrapper for the 'TexTra' API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, 
    a web service for translating texts between different languages. 
    'TexTra' API account is required to use the service.
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.
Provides S3 vector types for functional data represented on
    grids, in spline bases, or via functional principal components.
    Supports arithmetic and summary methods, plotting, derivation,
    integration, smoothing, registration, and data import/export for these
    functional vectors. Includes data-wrangling tools for re-evaluation,
    subsetting, sub-assignment, zooming into sub-domains, and extracting
    functional features such as minima, maxima, and their locations.
    Enables joint analysis of functional and scalar variables by integrating
    functional vectors into standard data frames.
R implementation of 'TFactS' to predict which are the 
    transcription factors (TFs), regulated in a biological condition based on
    lists of differentially expressed genes (DEGs) obtained from transcriptome
    experiments. This package is based on the 'TFactS' concept by 
    Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands
    it. It allows users to perform 'TFactS'-like enrichment approach. The
    package can import and use the original catalogue file from the 'TFactS'
    as well as users' defined catalogues of interest that are not supported 
    by 'TFactS' (e.g., Arabidopsis).
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).
Build customized transfer function and ARIMA models with multiple operators 
    and parameter restrictions. Provides tools for model identification, estimation 
    using exact or conditional maximum likelihood, diagnostic checking, automatic outlier 
    detection, calendar effects, forecasting, and seasonal adjustment. The new version 
    also supports unobserved component ARIMA model specification and estimation 
    for structural time series analysis.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for
    building complex input pipelines from simple, re-usable pieces.
    See <https://www.tensorflow.org/guide> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/guide/estimator>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. 
  Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training 
  progress of machine learning models.
'TensorFlow' Hub is a library for the publication, discovery, and
    consumption of reusable parts of machine learning models. A module is a 
    self-contained piece of a 'TensorFlow' graph, along with its weights and 
    assets, that can be reused across different tasks in a process known as
    transfer learning. Transfer learning train a model with a smaller dataset,
    improve generalization, and speed up training.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
Provides functions to retrieve headers, titles, and footnotes
    from structured metadata sources (e.g., Excel or CSV files) for
    annotating tables, listings, and figures in clinical study reports
    (CSRs) or other formal deliverables. It supports separation of
    metadata from analysis code in clinical reporting workflows.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15. <doi:10.1186/1748-7188-2-15>.
Provides a framework for the creation and use of Neural ordinary
    differential equations with the 'tensorflow' and 'keras' packages. 
    The idea of Neural ordinary differential equations comes from 
    Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and 
    presents a novel way of learning and solving differential systems.
The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, "Nonparametric bootstrap inference for the eigenvalues of geophysical tensors", accepted by the Journal of American Statistical Association). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.
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.)
Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.
Creates a framework to store and apply display metadata to
    Analysis Results Datasets (ARDs). The use of 'tfrmt' allows users to
    define table format and styling without the data, and later apply the
    format to the data.
Provides an interactive interface to the 'tfrmt' package. Users 
    can import, modify, and export tables and templates with little to no code.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
Extension of 'funHDDC' Schmutz et al. (2018) 
    <doi:10.1007/s00180-020-00958-4> for cases including
    outliers by fitting t-distributions for robust groups. 'TFunHDDC' can cluster
    univariate or multivariate data produced by the 'fda' package for data using
    a b-splines or Fourier basis.
Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Efficient implementation of K-Means++ algorithm. For more
    information see (1) "kmeans++ the advantages of the k-means++
    algorithm" by David Arthur and Sergei Vassilvitskii (2007),
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
    algorithms, Society for Industrial and Applied Mathematics,
    Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of
    Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval
    Rabani, Leonard J. Schulman and Chaitanya Swamy
    <doi:10.1145/2395116.2395117>.
Generalization of the classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.
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>).
Contains data sets used in other packages Torsten Hothorn
  maintains.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood using posterior samples and
    unnormalized log posterior values via reciprocal importance sampling.
    Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). 
    Bayesian Analysis. <doi:10.1214/24-BA1422>.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood for uni- and multivariate mixture 
    models using posterior samples and unnormalized log posterior values via 
    reciprocal importance sampling.
    Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025)
    <doi:10.48550/arXiv.2504.21812>.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
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 suite of functions for analysing, interpreting, and visualising
    time-series features calculated from different feature sets from the 'theft' package. 
    Implements statistical learning methodologies described in Henderson, T.,  
    Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.
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 <doi:10.48550/arXiv.1106.1813>,
    BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008
    <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the
    number of majority cases using NearMiss 2003
    <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
    link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Utilizing the 'OpenAI' API as the back end (<https://platform.openai.com/docs/api-reference>), 'TheOpenAIR' offers 'R' wrapper functions for the 'ChatGPT' endpoint and several high-level functions that enable the integration of 'ChatGPT' capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.
An integrated suite of tools for creating, maintaining, and reusing
    FAIR (Findable, Accessible, Interoperable, Reusable) theories.
    Designed to support transparent and collaborative theory development,
    the package enables users to formalize theories,
    track changes with version control,
    assess pre-empirical coherence,
    and derive testable hypotheses.
    Aligning with open science principles and workflows,
    'theorytools' facilitates the systematic improvement of theoretical frameworks
    and enhances their discoverability and usability.
Provides simple and fast date manipulation functions operating on
    character strings in the format 'DD.MM.YYYY'. Functions include extraction
    of day, month, and year components, computation of differences between two
    dates, and addition or subtraction of months.
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. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R
    package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. 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.
Provides tools for decomposing differences in rate metrics between
    two groups into contributions from individual subgroups and visualizing them
    as a "Theseus Plot". Inspired by the story of the Ship of Theseus, the method
    replaces subgroup data from one group with that of another step by step,
    recalculating the overall metric at each stage to quantify subgroup contributions.
    A Theseus Plot combines the stepwise progression of a waterfall plot with
    the comparative bars of a bar chart, offering an intuitive way to understand
    subgroup-level effects.
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.
Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.
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.
Provides comprehensive tools for conducting research
    using the Thinking Grid framework, a psychological measurement
    approach for understanding the stream of thought. Includes functions
    for generating Qualtrics surveys with the thinking grid, processing
    survey responses, calculating quadrant depths, and creating various
    visualization types including heatmaps, animations, and statistical
    plots. See Irving, Z. C., Murray, S., Kuvar, V., Urena, M., and
    Mills, C. (2025) "Consciousness, Just in Time: Fluctuations in the
    Stream of Consciousness during Tasks and Rest" (manuscript under
    review, draft available from zci7c@virginia.edu).
Some tools for cleaning up messy 'Excel' files to be suitable
    for R. People who have been working with 'Excel' for years built more
    or less complicated sheets with names, characters, formats that are
    not homogeneous. To be able to use them in R nowadays, we built a set
    of functions that will avoid the majority of importation problems and
    keep all the data at best.
Determine the path of the executing script. Compatible
        with several popular GUIs: 'Rgui', 'RStudio', 'Positron',
        'VSCode', 'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible
        with several functions and packages: 'source()',
        'sys.source()', 'debugSource()' in 'RStudio',
        'compiler::loadcmp()', 'utils::Sweave()', 'box::use()',
        'knitr::knit()', 'plumber::plumb()', 'shiny::runApp()',
        'package:targets', and 'testthat::source_file()'.
Provides utility functions for plotting. Includes functions for color manipulation, plot customization, panel size control, data optimization for plots, and layout adjustments.
Provides utility functions for data analysis and computing. Includes functions for logging, parallel processing, and other computational tasks to streamline workflows.
Functions for admin needs of employees of
      Thomas Jefferson University and Thomas Jefferson
      University Hospital, Philadelphia, PA.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://www.symas.com/mdb>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Creates some 'WebGL' shaders. They can be used as the background of a 'Shiny' app. They also can be visualized in the 'RStudio' viewer pane or included in 'Rmd' documents, but this is pretty useless, besides contemplating them.
A tool that allows users to estimate tree height in the long-term forest experiments in Sweden. It utilizes the multilevel nonlinear mixed-effect height models developed for the forest experiments and consists of four functions for the main species, other conifer species, and other broadleaves. Each function within the system returns a data frame that includes the input data and the estimated heights for any missing values. Ogana et al. (2023) <doi:10.1016/j.foreco.2023.120843>\n Arias-Rodil et al. (2015) <doi:10.1371/JOURNAL.PONE.0143521>.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/tds/>) 
  catalogs, and access dataset metadata and resources.
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. Please check the publication by Wang, Magnotti, Zhang,
    and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>) for electrode 
    localization. This viewer has been fully adopted by 'RAVE' 
    <https://openwetware.org/wiki/RAVE>, an interactive toolbox to 
    analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>). Please check 
    'citation("threeBrain")' for details.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Access open data from <https://www.threesixtygiving.org>, a 
    database of charitable grant giving in the UK operated by '360Giving'.
    The package provides functions to search and retrieve data on charitable 
    grant giving, and process that data into tidy formats. It relies on the 
    '360Giving' data standard, described at 
    <https://standard.threesixtygiving.org/>.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.
We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.
Allow to compute and visualise convective parameters commonly
  used in the operational prediction of severe convective storms. Core algorithm
  is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly
  efficient engine allows to derive thermodynamic and kinematic parameters from
  large numerical datasets such as reanalyses or operational Numerical Weather 
  Prediction models in a reasonable amount of time. Package has been developed
  since 2017 by research meteorologists specializing in severe thunderstorms.
  The most relevant methods used in the package based on the following publications
  Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>,
  McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>,
  Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>,
  Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>,
  Showalter (1953) <doi:10.1175/1520-0477-34.6.250>,
  Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>,
  Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>,
  Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>,
  Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>,
  Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>,
  Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.
Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References:
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.;
  Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Calculate time intelligence metrics for financial planning and
    analysis. 'ti' provides functions for period-over-period comparisons
    (year-over-year, month-over-month), period-to-date calculations (YTD, MTD,
    QTD), and customer segmentation (ABC analysis, cohorts). Supports standard
    and retail calendars (4-4-5, 4-5-4, 5-4-4) with both in-memory and database
    backends via 'dbplyr'.
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.
Plots and analyzes time-intensity curve data, such as data from 
  (contrast-enhanced) ultrasound. Values such as peak intensity, time to 
  peak, area under the curve, wash in rate and wash out rate are calculated.
This is a small package to provide consistent tick marks for plotting 'ggplot2' figures. 
    It provides breaks and labels for 'ggplot2' without requiring 'ggplot2' to be installed.
Description: Provides affine-invariant, distribution-free tests of multivariate independence, applied either directly to observed data or to estimated independent components. In the latter case, the procedures can be used to assess the validity of independent component models. The tests are based on L2-type distances between characteristic functions, with inference carried out using permutation or bootstrap resampling schemes. The methods are described in Hallin et al. (2024) <doi:10.48550/arXiv.2404.07632>.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
Code execution timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'StackList', which is a
    stack implemented as a
    list, both of which support operations 'push', 'pop', 'first_element',
    'last_element' and 'clear'.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps using the harmonic representation of inequalities.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Provides 'Apache Spark' style window aggregation for R dataframes and remote 'dbplyr' tables via 'mutate' in 'dplyr' flavour.
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Provides pipeline audit trails and data diagnostics for
    'tidyverse' workflows. The audit trail system captures lightweight
    metadata snapshots at each step of a pipeline, building a structured
    record without storing the data itself. Operation-aware taps enrich
    snapshots with join match rates and filter drop statistics.
    Trails can be serialized to 'JSON' or 'RDS' and exported as
    self-contained 'HTML' visualizations. Also includes diagnostic
    functions for interactive data analysis including frequency tables,
    string quality auditing, and data comparison.
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.
Census and administrative data in South Korea are a basic source of
    quantitative and mixed-methods research for social and urban scientists.
    This package provides a 'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) 
    based standardized workflow based on direct open API access to the major census and
    administrative data sources and pre-generated files in South Korea.
Changepoint detection algorithms for R are widespread but have
    different interfaces and reporting conventions. 
    This makes the comparative analysis of results difficult. 
    We solve this problem by providing a tidy, unified interface for several 
    different changepoint detection algorithms. 
    We also provide consistent numerical and graphical reporting leveraging 
    the 'broom' and 'ggplot2' packages.
There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
A common interface to specifying clustering models, in the
    same style as 'parsnip'. Creates unified interface across different
    functions and computational engines.
Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.
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.
A comprehensive and user-friendly interface for accessing, manipulating, 
    and analyzing country-level data from around the world. It allows users to retrieve detailed 
    information on countries, including names, regions, continents, populations, currencies, calling codes, 
    and more, all in a tidy data format. The package is designed to work seamlessly within 
    the 'tidyverse' ecosystem, making it easy to filter, arrange, and visualize country-level data in R.
Core parts of the C API of R are wrapped in a C++ namespace via a set
 of inline functions giving a tidier representation of the underlying data structures
 and functionality using a header-only implementation without additional dependencies.
The Common Workflow Language <https://www.commonwl.org/> is an
    open standard for describing data analysis workflows. This package takes
    the raw Common Workflow Language workflows encoded in JSON or 'YAML'
    and turns the workflow elements into tidy data frames or lists.
    A graph representation for the workflow can be constructed and visualized
    with the parsed workflow inputs, outputs, and steps. Users can embed the
    visualizations in their 'Shiny' applications, and export them
    as HTML files or static images.
Provides model specifications, tuning parameters for models in 
    'dann' package. Models based on Hastie (1996) 
    <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
Visualize your 'Tidyverse' data analysis pipelines via the 
    'Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.
Delta Method implementation to estimate standard errors with known 
    asymptotic properties within the 'tidyverse' workflow. The Delta Method is a
    statistical tool that approximates an estimator’s behaviour using a Taylor 
    Expansion. For a comprehensive explanation, please refer to Chapter 3 of van
    der Vaart (1998, ISBN: 9780511802256).
Cleans spectrophotometry data obtained from the Denovix instrument. The package also provides an option to normalize the data in order to compare the quality of the samples obtained.
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'.
Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for 'dplyr'.
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.
A tidy-style interface for applying differential privacy to data frames.
    Provides pipe-friendly functions to add calibrated noise, compute private statistics,
    and track privacy budgets using the epsilon-delta differential privacy framework.
    Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and
    the Gaussian mechanism for achieving differential privacy as described in Dwork and
    Roth (2014) <doi:10.1561/0400000042>.
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.
Streamline the process of accessing fundamental financial data from the United States Securities and Exchange Commission's ('SEC') Electronic Data Gathering, Analysis, and Retrieval system ('EDGAR') 'API' <https://www.sec.gov/edgar/sec-api-documentation>, transforming it into a tidy, analysis-ready format.
Takes objects of class edsurvey.data.frame and converts them to a data.frame within the calling environment of 'dplyr' and 'ggplot2' functions. Additionally, for plotting with 'ggplot2', users can map aesthetics to subject scales and all plausible values will be used. This package supports student level data; to work with school or teacher level data, see '?EdSurvey::getData'.
Unicodes are not friendly to work with, and not all Unicodes are
    Emoji per se, making obtaining Emoji statistics a difficult task. This
    tool can help your experience of working with Emoji as smooth as possible,
    as it has the 'tidyverse' style.
The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.
Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym)
    climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
Helper functions for empirical research in financial
    economics, addressing a variety of topics covered in Scheuch, Voigt,
    and Weiss (2023) <doi:10.1201/b23237>.  The package is designed to
    provide shortcuts for issues extensively discussed in the book,
    facilitating easier application of its concepts. For more information
    and resources related to the book, visit
    <https://www.tidy-finance.org/r/index.html>.
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'.
Provides functions that compute predictions from Generalised
    Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble.
    These can be plotted with a generic plot()-method that uses 'ggplot2' or
    plotted as any other data frame. The main function is predict_gam().
A toolset that allows you to easily import and tidy data sheets retrieved
    from Gapminder data web tools. It will therefore contribute to reduce the time
    used in data cleaning of Gapminder indicator data sheets as they are very messy.
Interactively gate points on a scatter plot. Interactively drawn gates are 
  recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is 
  based on the package gatepoints by Wajid Jawaid.
Variant determination and genotyping from high throughput sequences
    from multilocus amplicon libraries, typically sequenced in Illumina MiSeq or similar.
    It provides a set of core functions for the central steps: demultiplex by locus,
    truncate reads, variant calling, and genotype calling. Additionally, it provides a set of functions
    for diagnosis and estimation of best running parameters and multiple extensions for
    genotype/variants manipulation and reformatting. Output variants and genotypes are 
    output in 'tidy' format, thus facilitating reformatting, manipulation and potential
    connection to other R packages.
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.
The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.
Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources and then applies tidy data principles.
A reproducible, tidyverse-style framework for intensive longitudinal
  data analysis in R, with built-in methodological safeguards, provenance
  tracking, and reporting tools. Encodes time structure, enforces
  within-between decomposition, provides spacing-aware lags, and integrates
  diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(),
  and related functions for a unified pipeline from raw EMA/diary data to
  interpretable models.
Turn complex 'JSON' data into tidy data frames.
Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package.
Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII
    Standard) well log files into tidy, analysis-ready tabular formats. The
    package separates LAS header information and log data into structured
    components, builds a searchable index across collections of LAS files,
    and enables reproducible subsetting of wells based on metadata or curve
    availability. Output tables can be written to CSV or Parquet formats to
    support large-scale statistical, machine learning, and earth science
    workflows. The tidy data structure follows Wickham (2014) <doi:10.18637/jss.v059.i10>.
    The LAS file structure follows the Canadian Well Logging Society LAS standard
    <https://www.cwls.org/wp-content/uploads/2017/02/Las2_Update_Jan2017.pdf>.
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.
Provides a unified tidyverse-compatible interface to R's machine
    learning packages. Wraps established implementations from 'glmnet',
    'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster',
    'dbscan', and others - providing consistent function signatures, tidy tibble
    output, unified 'ggplot2'-based visualization, and optional formatted 'gt'
    tables via the tl_table() family of functions. The underlying algorithms
    are unchanged; 'tidylearn' simply makes them easier to use together. Access
    raw model objects via the $fit slot for package-specific functionality.
    Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>,
    LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
    support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>,
    and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.
A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'Openai', 'Gemini','Mistral' and local models via 'Ollama' into R workflows. The package supports text and media-based interactions, interactive message history, batch request APIs, and a tidy, pipeline-oriented interface for streamlined integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.
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.
Easily carry out latent profile analysis ("LPA"), determine the
    correct number of classes based on best practices, and tabulate and plot the
    results. Provides functionality to estimate commonly-specified models with
    free means, variances, and covariances for each profile. Follows a tidy
    approach, in that output is in the form of a data frame that can
    subsequently be computed on. Models can be estimated using the free open
    source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program
    'MPlus', via the 'MplusAutomation' package.
Framework to run Monte Carlo simulations over a parameter grid. 
             Allows to parallelize the simulations. 
             Generates plots and 'LaTeX' tables
             summarizing the results from the simulation.
The tidy modeling "verse" is a collection of packages for
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.
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 drop-in replacements for common R functions (mean(), sum(), sd(), min(), etc.) that default to 'na.rm = TRUE' and issue warnings when missing values are removed. It handles some special cases. The table() default is set to 'useNA = ifany'.
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().
An implementation of tidy speaker vowel normalization.
    This includes generic functions for defining new normalization methods for
    points, formant tracks, and Discrete Cosine Transform coefficients, as well
    as convenience functions implementing established normalization methods.
    References for the implemented methods are:
    Johnson, Keith (2020) <doi:10.5334/labphon.196>
    Lobanov, Boris (1971) <doi:10.1121/1.1912396>
    Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf>
    Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381>
    Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.
Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.
The goal of 'tidyplate' is to help researchers convert
    different types of microplates into tibbles which can be used in data
    analysis. It accepts xlsx and csv files formatted in a specific way as
    input. It supports all types of standard microplate formats such as
    6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and, 1536-well
    plates.
The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.
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.
We provide a tidy grammar of population genetics, facilitating 
    the manipulation and analysis of data on biallelic single nucleotide
    polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets
    by storing genotypes on disk, and performing operations on them in 
    chunks, without ever loading all data in memory. The full
    functionalities of the package are described in Carter et al. (2025)
    <doi:10.1111/2041-210x.70204>.
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(), rpart(), earth(),
    xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and
    ctree() models.
Easily construct prompts and associated logic for interacting with 
  large language models (LLMs). 
  'tidyprompt' introduces the concept of prompt wraps, which are building
  blocks that you can use to quickly turn a simple prompt into a complex
  one. Prompt wraps do not just modify the prompt text, but also add
  extraction and validation functions that will be applied to the response
  of the LLM. This ensures that the user gets the desired output.
  'tidyprompt' can add various features to prompts and their evaluation
  by LLMs, such as structured output, automatic feedback, retries, reasoning
  modes, autonomous R function calling, and R code generation and evaluation.
  It is designed to be compatible with any LLM provider that offers
  chat completion.
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.
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).
Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the 'epitools' package.
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.
Allows users to quickly load multiple patients' electrocardiographic 
    (ECG) data at once and conduct relevant time analysis of heart rate variability 
    (HRV) without manual edits from a physician or data cleaning specialist. 
    The package provides the unique ability to iteratively filter, plot, 
    and store time analysis results in a data frame while writing plots to a 
    predefined folder. This streamlines the workflow for HRV analysis across 
    multiple datasets. Methods are based on Rodríguez-Liñares et al. (2011) 
    <doi:10.1016/j.cmpb.2010.05.012>. Examples of applications using this 
    package include Kwon et al. (2022) <doi:10.1007/s10286-022-00865-2> and 
    Lawrence et al. (2023) <doi:10.1016/j.autneu.2022.103056>.
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.
Provides functions to scale, log-transform and fit linear models within a 'tidyverse'-style R code framework.
    Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in 'broom' Robinson (2017) <doi:10.21105/joss.00341>.
Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.
Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  models and metrics specific to SDMs,
  as well as a number of specialised functions to process occurrences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2024) <doi:10.1111/2041-210X.14406>.
A backend for the selecting functions of the 'tidyverse'.  It
    makes it easy to implement select-like functions in your own packages
    in a way that is consistent with other 'tidyverse' interfaces for
    selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
    Includes functionality to estimate latent class analyses, and to plot
    'dagitty' and 'igraph' objects.
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.
Enables the analysis of spectroscopy data such as infrared ('IR'), Raman, and nuclear magnetic resonance ('NMR') using the tidy data framework from the 'tidyverse'. The 'tidyspec' package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" <doi:10.1021/ac60214a047>, Sternberg (1983) "Biomedical Image Processing" <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1654163>, Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" <doi:10.1016/0168-583X(95)00908-6>, Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" <doi:10.1177/0003702820987847>, Wickham et al. (2019) "Welcome to the tidyverse" <doi:10.21105/joss.01686>, and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" <https://CRAN.R-project.org/package=recipes>.
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.
Provides a tidy set of functions for summarising data, 
    including descriptive statistics, frequency tables with normality testing, 
    and group-wise significance testing. Designed for fast, readable, 
    and easy exploration of both numeric and categorical data.
Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. For method details see Motulsky H J (2016) <https://www.graphpad.com/guides/prism/10/statistics/index.htm> and d'Agostino R B (1971) <doi:10.1093/biomet/58.2.341>.
A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.
A system built on 'tidymodels' for generating synthetic tabular
    data. We provide tools for ordering a sequential synthesis, feature and 
    target engineering, sampling, hyperparameter tuning, enforcing constraints, 
    and adding extra noise during a synthesis.
A tidy interface to 'data.table',
  giving users the speed of 'data.table' while using tidyverse-like syntax.
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 version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, 'titanic', reproduces the Kaggle competition files with minimal preprocessing, 'tidytitanic' combines the train and test datasets into the single dataset, 'passengers', for exploration and summary across all passengers. It also extracts personal identifiers—such as first names, last names, and titles from the raw 'name' field, enabling demographic analysis. The 'passengers' data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets 'tidy_titanic' and 'flat_titanic' based on the 'Titanic' data set from 'datasets' for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases.
Generate tables, listings, and graphs (TLG) using 'tidyverse'.
  Tables can be created functionally, using a standard TLG process, or by
  specifying table and column metadata to create generic analysis summaries.
  The 'envsetup' package can also be leveraged to create environments for table
  creation.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
Calculates the community four 'moments' (mean, variance, skewness,
    and kurtosis) of a given trait based on the moments described in Wieczynski 
    et al. (2019) <doi:10.1073/pnas.1813723116>. These functional metrics are 
    extremely useful in characterizing the distribution of traits in a plant 
    community. It also provides tidyverse-friendly wrappers to seamlessly calculate
    advanced functional diversity indices (e.g., FDis, Rao's Q) using 'fundiversity' 
    (Grenie et al. 2023 <doi:10.1111/ecog.06585>) and functional rarity indices 
    using 'funrar' (Grenie et al. 2017 <doi:10.1111/ddi.12629>). Evaluating these 
    community-weighted moments and diversity metrics allows researchers to evaluate
    shifts in optimal phenotypes and understand ecological filtering with exactness.
'TidyTuesday' is a project by the 'Data Science Learning
    Community' in which they 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.
Utilities to retrieve and tidy U.S. macroeconomic data series
    from public government data providers. Functions streamline access to series
    from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED),
    the Bureau of Labor Statistics flat files, and the Bureau of Economic
    Analysis National Income and Product Accounts tables, then return
    consistent, tidy data frames ready for modeling and graphics. The package includes
    helpers for date alignment, log-linear projections, and common macro diagnostics,
    along with convenience plot builders for quick publication-quality charts.
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.
Provides multiple water chemistry-based models and published empirical models in one standard format.
    As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability.
    To learn more, read the references provided below for the models implemented.
    Functions can be chained together to model a complete treatment process and are designed to work in a 'tidyverse' workflow.
    Models are primarily based on these sources:
    Benjamin, M. M. (2002, ISBN:147862308X),
    Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473),
    USEPA. (2001) <https://www.epa.gov/sites/default/files/2017-03/documents/wtp_model_v._2.0_manual_508.pdf>.
Functions are collected to analyse weather data for agriculture 
    purposes including to read weather records in multiple formats, 
    calculate extreme climate index. Demonstration data are included the
    SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).
Query 'Wikidata' API <https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.
Imports non-tabular from Excel files into R.  Exposes cell content,
    position and formatting in a tidy structure for further manipulation.
    Tokenizes Excel formulas.  Supports '.xlsx' and '.xlsm' via the embedded
    'RapidXML' C++ library <https://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
Functions to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors, including native image representation.
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
The R implementation of TIGER. 
    TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters.
    TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared.
    Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.
Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of 
    any novel alleles. This information is then used to correct existing V 
    allele calls from among the sample sequences.
    Citations:
    Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>,
    Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.
The functions needed to perform tight clustering Algorithm.
Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>).  The main function is tighten().  The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.
Write modelling results into a database for
    'tigreBrowser', a web-based tool for browsing figures and summary
    data of independent model fits, such as Gaussian process models
    fitted for each gene or other genomic element. The browser is
    available at <https://github.com/PROBIC/tigreBrowser>.
Download TIGER/Line shapefiles from the United States Census Bureau
    (<https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>)
    and load into R as 'sf' objects.
Tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index (WQI/IQA), the Trophic State Index (TSI/IET) after Carlson (1977) <doi:10.4319/lo.1977.22.2.0361> and Lamparelli (2004) <https://www.teses.usp.br/teses/disponiveis/41/41134/tde-20032006-075813/publico/TeseLamparelli2004.pdf>, and the National Sanitation Foundation Water Quality Index (NSF WQI) <doi:10.1007/s11157-023-09650-7>. The package also checks compliance with Brazilian standard CONAMA Resolution 357/2005 <https://conama.mma.gov.br/?id=450&option=com_sisconama&task=arquivo.download> and generates reproducible reports for routine monitoring workflows.
    The example dataset (`wq_demo`) is now a real subset from monitoring data (BURANHEM river, 2020-2024, 4 points, 20 rows, 14 columns including extra `rio`, `lat`, `lon`). All core examples and vignettes use this realistic sample, improving reproducibility and documentation value for users.
Collect  marketing data from TikTok Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
The modern database 'TileDB' introduces a powerful on-disk
  format for storing and accessing any complex data based on 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. This package provides the R support.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
Implements an algorithm for generating maps, known as tile maps,
    in which each region is represented by a single tile of the same shape and
    size. The algorithm was first proposed in "Generating Tile Maps" by Graham
    McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow
    users to generate, plot, and compare square or hexagon tile maps.
Creates geographic map tiles from geospatial map files or 
    non-geographic map tiles from simple image files. This package provides a 
    tile generator function for creating map tile sets for use with packages 
    such as 'leaflet'. In addition to generating map tiles based on a common 
    raster layer source, it also handles the non-geographic edge case, producing 
    map tiles from arbitrary images. These map tiles, which have a 
    non-geographic, simple coordinate reference system (CRS), can also be used 
    with 'leaflet' when applying the simple CRS option. Map tiles can be created 
    from an input file with any of the following extensions: tif, grd and nc for 
    spatial maps and png, jpg and bmp for basic images. This package requires 
    'Python' and the 'gdal' library for 'Python'. 'Windows' users are 
    recommended to install 'OSGeo4W' (<https://trac.osgeo.org/osgeo4w/>) as an 
    easy way to obtain the required 'gdal' support for 'Python'.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.
Provides data frames for forest or tree data structures. You can 
    create forest data structures from data frames and process them based on 
    their hierarchies.
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.
Displays processing time in a clear and structured way. One function supports iterative workflows by predicting and showing the total time required, while another reports the time taken for individual steps within a process.
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>.
Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) <doi:10.1002/bimj.200310051>.
Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits.
  The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes.
  The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes.
  References:
  Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x>
  Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814>
  Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311>
  Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.
Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.
Computation of t-year survival probabilities and t-year
    risks with right censored survival data. The Kaplan-Meier estimator
    is used to provide estimates for data without competing risks and
    the Aalen-Johansen estimator is used when there are competing risks.
    Confidence intervals and p-values are obtained using either usual
    Wald-type inference or empirical likelihood inference, as described
    in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315>
    and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for
    both one-sample and two-sample inference are provided. Unlike Wald-type
    inference, empirical likelihood inference always leads to consistent
    conclusions, in terms of statistical significance, when comparing
    two risks (or survival probabilities) via either a ratio or a difference.
Manage time-series data frames across time zones, resolutions, and date ranges, 
    while filling gaps using weekday/hour patterns or simple fill helpers or plotting them interactively.
    It is designed to work seamlessly with the tidyverse and dygraphs environments.
Produce an HTML page containing horizontal strips that symbolize events in a person's lsife. Since this is entirely a visualization, the image <https://barryzee.github.io/henry-timeline/henry.html> will show the basic use to show a timeline of events. The image <https://barryzee.github.io/vermeer/cssOverlay.html> shows how to correlate two timelines of events. A brief description is available at <https://barryzee.github.io/timeLineGraphics_manuscript/golden_age.html>.
Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.
Simple definition of time intervals for the current, previous, and next week, month, quarter and year.
A set of fast tidy functions for wrangling, completing and
    summarising date and date-time data. It combines 'tidyverse' syntax
    with the efficiency of 'data.table' and speed of 'collapse'.
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.
'S4' classes and various tools for financial time series:
  Basic functions such as scaling and sorting, subsetting,
  mathematical operations and statistical functions.
Provides a diverse collection of time series datasets
    spanning various fields such as economics, finance, energy, healthcare, and more.
    Designed to support time series analysis in R by offering datasets from
    multiple disciplines, making it a valuable resource for researchers and analysts.
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 duration are handled. Both can be regularly or unevenly spaced (overlapping duration 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 pipeline of tools for analysing circadian 
    time-series data using functional data analysis (FDA). The package 
    supports smoothing of rhythmic time series, functional principle 
    component analysis (FPCA), and extraction of group-level traits from 
    functional representations. Analyses can incorporate multiple curve 
    derivatives and optional temporal segmentation, enabling comparative 
    analysis of circadian dynamics across experimental groups and time 
    windows.
Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.
Computes how the correlation between 2 time-series changes over time.
    To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>.
    It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy).
    An automatic selection procedure for the bandwidth parameter is implemented.
    Alternative kernels can be used (Epanechnikov, box and normal).
    Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps or R markdown documents.
    'timevis' includes an extensive API to manipulate a timeline after creation,
    and supports getting data out of the visualization into R. Based on the
    'vis.js' Timeline JavaScript library.
Unleash the power of time-series data visualization with ease using our package. Designed with simplicity 
 in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series 
 charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays 
 Averages per country presented in tables with accompanying sparklines, providing a quick and attractive 
 overview of the data. The last tab presents A customizable world map colored based on user-defined 
 variables for any chosen number of countries, offering an advanced visual approach to understanding 
 geographical data distributions. This package operates with just a few simple arguments, enabling users 
 to conduct sophisticated analyses without the need for complex programming skills. Transform your 
 time-series data analysis experience with our user-friendly tool.
Estimates time varying regression effects under Cox type models in
    survival data using classification and regression tree. The codes in this package were 
    originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression
    Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315.
    Development of this package was supported by NIH grants AG053983 and AG057707,
    and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442.
    The content is solely the responsibility of the authors and does not necessarily
    represent the official views of the NIH.
    The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).
A problem solving environment (PSE) for fitting separable
    nonlinear models to measurements arising in physics and chemistry
    experiments, as described by Mullen & van Stokkum (2007)
    <doi:10.18637/jss.v018.i03> for its use in fitting time resolved
    spectroscopy data, and as described by Laptenok et al. (2007)
    <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence
    Lifetime Imaging Microscopy (FLIM) data, in the study of Förster
    Resonance Energy Transfer (FRET).  `TIMP` also serves as the
    computation backend for the `GloTarAn` software, a graphical user
    interface for the package, as described in Snellenburg et al. (2012)
    <doi:10.18637/jss.v049.i03>.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
Provides an easy-to-use tind class representing time indices
    of different types (years, quarters, months, ISO 8601 weeks, dates,
    time of day, date-time, and arbitrary integer/numeric indices).
    Includes an extensive collection of functions for calendrical computations
    (including business applications), index conversions, index parsing,
    and other operations. Auxiliary classes representing time differences and
    time intervals (with set operations and index matching functionality) are
    also provided. All routines have been optimised for speed in order
    to facilitate computations on large datasets. More details regarding
    calendars in general and calendrical algorithms can be found in
    "Calendar FAQ" by Claus Tøndering <https://www.tondering.dk/claus/calendar.html>.
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.
Palettes generated from Tintin covers. There is one palette per
  cover, with a total of 24 palettes of 5 colours each. Includes functions to
  interpolate colors in order to create more colors based on the provided
  palettes.The data is based on Cyr, et al. (2004) <doi:10.1503/cmaj.1041405>
  and Wikipedia <https://en.wikipedia.org/wiki/The_Adventures_of_Tintin>.
The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.
Adds some functions to help in your coding etiquette.
    'tinycodet' primarily focuses on 4 aspects.
    1) Safer decimal (in)equality testing,
    standard-evaluated alternatives to with() and aes(),
    and other functions for safer coding.
    2) A new package import system,
    that attempts to combine the benefits of using a package without attaching it,
    with the benefits of attaching a package.
    3) Extending the string manipulation capabilities of the 'stringi' R package.
    4) Reducing repetitive code.
    Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Optimize and compress images using 'Rust' libraries to reduce
    file sizes while maintaining image quality. Supports PNG palette
    reduction and dithering via the 'exoquant' crate before lossless PNG
    optimization via the 'oxipng' crate. The package provides functions to
    optimize individual image files or entire directories, with configurable
    compression levels.
Assign, extract, or remove variable labels from R vectors.
  Lightweight and dependency-free.
Provides utilities to create and use lenses to simplify data
    manipulation. Lenses are composable getter/setter pairs that provide a
    functional approach to manipulating deeply nested data structures, e.g.,
    elements within list columns in data frames. The implementation is based on
    the earlier 'lenses' R package <https://github.com/cfhammill/lenses>,
    which was inspired by the Haskell 'lens' package by Kmett (2012)
    <https://github.com/ekmett/lens>, one of the most widely referenced
    implementations of lenses. For additional background and history on the
    theory of lenses, see the 'lens' package wiki:
    <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
Lightweight extension of the base R graphics system, with support
      for automatic legends, facets, themes, and various other enhancements.
Provides functions to get personal 'Google Scholar'
    profile data from web API and show it in table or figure format.
A lightweight, 'WebSocket'-enabled proxy server for hosting multiple
    'shiny' applications with automatic health monitoring, session management,
    and resource cleanup. Provides a simple entry point to run the server using
    a JSON configuration file.
Snapshots for unit tests using the 'tinytest' framework for R. Includes expectations to test base R and 'ggplot2' plots as well as console output from print().
An R wrapper for the 'Spotify' Web API 
  <https://developer.spotify.com/web-api/>.
Create highly customized tables with this simple and dependency-free package. Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package.
Provides a lightweight (zero-dependency) and easy to use 
    unit testing framework. Main features: install tests with 
    the package. Test results are treated as data that can be stored and 
    manipulated. Test files are R scripts interspersed with test commands, that
    can be programmed over. Fully automated build-install-test sequence for 
    packages. Skip tests when not run locally (e.g. on CRAN). Flexible and 
    configurable output printing. Compare computed output with output stored 
    with the package. Run tests in parallel. Extensible by other packages.
    Report side effects.
Unit testing is a solid component of automated CI/CD pipelines. 
	'tinytest' - a lightweight, zero-dependency  alternative to 'testthat' was developed.
	To be able to integrate 'tinytests' results into common CI/CD systems the test results from 
	tinytest need to be caputred and converted to JUnit XML format. 
	'tinytest2JUnit' enables this conversion while staying also lightweight 
	and only have 'tinytest' as its dependency.
Helper functions to install and maintain the 'LaTeX' distribution
  named 'TinyTeX' (<https://yihui.org/tinytex/>), a lightweight, cross-platform,
  portable, and easy-to-maintain version of 'TeX Live'. This package also
  contains helper functions to compile 'LaTeX' documents, and install missing
  'LaTeX' packages automatically.
Themes for 'ggplot2' are a convenient way to style plots. The 'hrbrthemes' package
 contains a particularly nice one, but brings along a significant tail of dependencies. So this
 (currently experimental) package brings along just the 'theme_ipsum_rc' theme using the 'Roboto'
 'Condensed' font.  Should the font not be installed on your system, see the help in the package
 'hrbrthemes' on how to install 'Roboto Condensed'. Note that 'hrbrthemes' is now archived at
 CRAN.
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.
Fits a wide variety of multivariate spatio-temporal models
    with simultaneous and lagged interactions among variables (including
    vector autoregressive spatio-temporal ('VAST') dynamics)
    for areal, continuous, or network spatial domains.  
    It includes time-variable, space-variable, and space-time-variable 
    interactions using dynamic structural equation models ('DSEM') 
    as expressive interface, and the 'mgcv' package to specify splines 
    via the formula interface.  See Thorson et al. (2025)
    <doi:10.1111/geb.70035> for more details.
Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.
Accurately estimates phase shifts by accounting for period
  changes and for the point in the circadian cycle at which the stimulus occurs.
  See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.
Assists performing tip-dating of phylogenetic trees with BEAST 
    BEAST is a popular software for phylogenetic analysis.
    The package assists the implementation of various phylogenetic tip-
	dating tests using BEAST. It contains two main functions.
	The first one allows preparing date randomization analyses, 
	which assess the temporal signal of a data set. 
	The second function allows performing leave-one-out analyses,
	which test for the consistency between independent calibration sequences
	and allow pinpointing those leading to potential bias.
	The included tutorial provides detailed step-by-step instructions.
	An expanded description of the package can be found in article:
	Rieux, A. and Khatchikian, C.E. (2017), 
	TIPDATINGBEAST: an R package to assist the implementation of phylogenetic
	tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613.
	<onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.
Provides access to the complete Pali Canon, or
    Tipitaka, the canonical scripture for Theravadin Buddhists
    worldwide. Based on the Chattha Sangayana Tipitaka version
    4 (Vipassana Research Institute, 1990). Includes word
    frequency data and tools for Pali string sorting. For a
    lemmatized critical edition with sutta-level granularity,
    see the companion package 'tipitaka.critical'.
A lemmatized critical edition of the complete Pali Canon
    (Tipitaka), the canonical scripture of Theravadin Buddhism. Based
    on a five-witness collation of the Pali Text Society (PTS) edition
    (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute
    (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT),
    and the Thai Royal Edition. All text is lemmatized using the
    'Digital Pali Dictionary', grouping inflected forms by dictionary
    headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma)
    with 5,777 individual text units. The companion package 'tipitaka'
    provides the original VRI edition data and Pali text tools.
    For background on the collation method, see Zigmond (2026)
    <https://github.com/dangerzig/tipitaka.critical>.
Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.
The strength of evidence provided by epidemiological and
    observational studies is inherently limited by the potential for
    unmeasured confounding.  We focus on three key quantities: the
    observed bound of the confidence interval closest to the null, the
    relationship between an unmeasured confounder and the outcome, for
    example a plausible residual effect size for an unmeasured continuous
    or binary confounder, and the relationship between an unmeasured
    confounder and the exposure, for example a realistic mean difference
    or prevalence difference for this hypothetical confounder between
    exposure groups. Building on the methods put forth by Cornfield et al.
    (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983),
    Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli &
    Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele
    (2016), we can use these quantities to assess how an unmeasured
    confounder may tip our result to insignificance.
Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.
It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).
Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
Compute and visualize the 'Tissot Indicatrix' for map projections. 
    The indicatrix characterizes projection distortion by computing scale 
    factors, angular deformation, areal distortion, and convergence at 
    arbitrary points. Based on the calculations shared by Bill Huber on 
    <https://gis.stackexchange.com/a/5075/482>. Uses 'GDAL' for coordinate transformation. 
    Developed using the method published in Snyder, JP (1987)  <doi:10.3133/pp1395>.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include Baker and King 
  (2010) <doi:10.1111/j.2041-210X.2009.00007.x>, King and Baker (2010) 
  <doi:10.1899/09-144.1>, and Baker and King (2013) <doi:10.1899/12-142.1>.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.
Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones.
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) <doi:10.1002/sim.9580> .
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 AA, Davidian M, Holloway ST (2023) <doi:10.1111/biom.13603>.
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 'TRexSelector').
    The package is based on the papers Machkour, Muma, and Palomar (2022) <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>.
Provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.
Support functions and datasets to facilitate the analysis of linguistic
    data. The current focus is on the calculation of corpus-linguistic dispersion
    measures as described in Gries (2021) <doi:10.1007/978-3-030-46216-1_5> and 
    Soenning (2025) <doi:10.3366/cor.2025.0326>. The most commonly used parts-based 
    indices are implemented, including different formulas and modifications that are 
    found in the literature, with the additional option to obtain frequency-adjusted 
    scores. Dispersion scores can be computed based on individual count variables or 
    a term-document matrix.
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 wrappers function in the 'tableone' 
    package to make the results knit-able.
Documentation for commonly-used objects included in the base distribution of 
    R. Note that 'tldrDocs' does not export any functions itself, its purpose is
    to write .Rd files during its installation for 'tldr()' to find.
This comprehensive toolkit for T-distributed regression is designated as "TLIC" (The LIC for T Distribution Regression Analysis) analysis. It is predicated on the assumption that the error term adheres to a T-distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.
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. The methods are described in Barrera-Gomez
    and Basagana (2015) <doi:10.1097/EDE.0000000000000247>.
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.
Fast, reproducible detection and quantitative analysis of tertiary lymphoid structures (TLS) in multiplexed tissue imaging. Implements Independent Component Analysis Trace (ICAT) index, local Ripley's K scanning, automated K Nearest Neighbor (KNN)-based TLS detection, and T-cell clusters identification as described in Amiryousefi et al. (2025) <doi:10.1101/2025.09.21.677465>.
A framework for text mining applications within R.
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.
A robust computational framework for analyzing complex multimodal data. Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" <doi:10.18608/jla.2025.8423>.
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.
Provides new layer functions to 'tmap' for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible.
Provides new layer functions to 'tmap' for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of 'tmap' and allow flexible control over size, layout, and appearance.
The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes.
Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions.
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.
Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.
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 comprehensive R package for calculating melting temperatures of nucleic acid sequences. 
    Implements three calculation methods:
    1. Wallace rule (Thein & Wallace, 1986)
    2. Empirical formulas based on GC content (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; 
       Untergasser, 2012; von Ahsen, 2001)
    3. Nearest neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; 
       SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; 
       Sugimoto, 1995; Allawi, 1997; Santalucia, 2005)
    Includes corrections for:
    - Salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008)
    - Chemical compounds (dimethyl sulfoxide, formamide)
    Supports both direct sequence input and FASTA file input.
A Text mining toolkit for Chinese, which includes facilities for 
    Chinese string processing, Chinese NLP supporting, encoding detecting and 
    converting. Moreover, it provides some functions to support 'tm' package 
    in Chinese.
Provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <doi:10.2140/astat.2023.14.37>, Barnhill et al. (2023) <doi:10.48550/arXiv.2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <doi:10.1007/s11538-024-01327-8>, Yoshida et al. (2022) <doi:10.1109/TCBB.2024.3420815>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis. See Zyla et al (2019)
  <doi:10.1093/bioinformatics/btz447>.
Define general templates with tags that can be replaced by content depending on arguments and objects to modify the final output of the document.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Sensitivity analysis using the trimmed means estimator.
Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). 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 simulating various types 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) <doi:10.48550/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>.
Provides tools for performing Transition Network Analysis (TNA) to 
    study relational dynamics, including functions for building and plotting TNA 
    models, calculating centrality measures, and identifying dominant events and 
    patterns. TNA statistical techniques (e.g., bootstrapping and permutation 
    tests) ensure the reliability of observed insights and confirm that 
    identified dynamics are meaningful. See (Saqr et al., 2025) 
    <doi:10.1145/3706468.3706513> for more details on TNA.
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.
Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>.
      Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official 'CKAN' API.
Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.
Performing the hypothesis tests for the two
    sample problem based on order statistics and power comparisons. 
    Calculate the test statistic, density, distribution function, 
    quantile function, random number generation and others.
Provides access to the Taxonomic Name Resolution Service <https://github.com/ojalaquellueva/tnrsapi> through R.  The user supplies plant taxonomic names and the package returns resolved taxonomic names along with information on decisions.  Optionally, the package can also be used to parse taxonomic names.
Create interactive tables, calendars, charts and markdown WYSIWYG editor with 'TOAST UI' <https://ui.toast.com/> libraries to
  integrate in 'shiny' applications or 'rmarkdown' 'HTML' documents.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).
This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Use the <https://toggl.com> time tracker api through R.
Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by  in Algeri and van Dyk (2018) <arXiv:1803.03858>.
Interfaces with the 'Hugging Face' tokenizers library to provide implementations
  of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm 
  <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both 
  training new vocabularies and tokenizing texts.
Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.
Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.
Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.
Email Finder R Client Library.
        Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet.
        Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name.
        Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.
Simple toolkit for working with TOML text. Based on tomledit which
    allows for modifying TOML while preserving order, comments,and whitespace.
A toolkit for working with 'TOML' files in R while preserving
    formatting, comments, and structure. 'tomledit' enables serialization of R
    objects such as lists, data.frames, numeric, logical, and date vectors.
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>. Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package.
Tools to calculate stability indices with parametric,
 non-parametric and probabilistic approaches. The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. 
 Function "table_stability" offers the summary table of all stability indices in this package. 
 This R package toolStability is part of the main publication: 
 Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. 
 Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG>. 
 Sample dataset in this package is derived from another publication:  
 Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. 
 For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. 
 Indices used in this package are from: 
 Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. 
 Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. 
 Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. 
 Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. 
 Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. 
 Lin CS, Binns MR (1988). 
 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.
Token-Oriented Object Notation (TOON) is a compact, human-readable 
    serialization format designed for passing structured data to Large Language
    Models with significantly reduced token usage. It's intended for LLM input
    as a lossless, drop-in representation of JSON data.
A minimal-dependency, performance-first R package for reading,
    writing, validating, streaming, and converting TOON (Token-Oriented Object
    Notation) data. Optimized for very large tabular files with robust
    diagnostics. Supports lossless JSON conversion and tabular CSV/Parquet/Feather
    conversion.
Language specific cardinal to ordinal number conversion.
Two- and three-dimensional morphometric maps of enamel and 
	dentine thickness and multivariate analysis. 
	Volume calculation of dental materials. 
	Principal component analysis of thickness maps with 
	associated morphometric map variations.
Several datasets which describe the chef contestants in Top Chef, 
    the challenges that they compete in, and the results of those challenges. 
    This data is useful for practicing data wrangling, graphing, and analyzing 
    how each season of Top Chef played out.
The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>).  The authors published an implementation of their method as an R script (two different versions; also available in this package).  This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation.  It also provides a small number of bug fixes to the original code.
Data collected on movement behavior is often in the form of time-
    stamped latitude/longitude coordinates sampled from the underlying movement
    behavior. These data can be compressed into a set of segments via the Top-
    Down Time Ratio Segmentation method described in Meratnia and de By (2004)
    <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, 
    can both reduce the size of the data as well as provide corrective smoothing
    mechanisms to help reduce the impact of measurement error.  This is an
    improvement on the well-known Douglas-Peucker algorithm for segmentation
    that operates not on the basis of perpendicular distances. Top-Down Time
    Ratio segmentation allows for disparate sampling time intervals by
    calculating the distance between locations and segments with respect to
    time. Provided a trajectory with timestamps, tdtr() returns a set of straight-
    line segments that can represent the full trajectory. McCool, Lugtig,
    and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as
    implemented here in more detail.
Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for 
    Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
    For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), 
    pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.
Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios.
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 <doi:10.48550/arXiv.1907.04907>.
Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit <https://r-topics.org/> and <https://www.r-text.org/>.
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>.
Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) <doi:10.1111/jedm.70001>. 
    The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to 
    account for local item dependence in testlets using latent topics from student 
    textual responses.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package 'TopKLists' <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.
A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from <https://www.gurobi.com/>).
A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) <doi:10.1111/j.1365-294X.2009.04338.x>), weighted topographic paths (Zhan et al. (1993) <doi:10.1007/3-540-57207-4_29>), and topographic least cost paths (Wang and Summers (2010) <doi:10.1111/j.1365-294X.2009.04465.x>). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques). The package is
             based on the method described in Massa M.S., Chiogna M., Romualdi
             C. (2010) <doi:10.1186/1752-0509-4-121>.
Description: Implementation of topological data analysis methods based on 
    graph-theoretic approaches for discovering topological structures in data.
    The core algorithm constructs topological spaces from graphs following
    Nada et al. (2018) <doi:10.1002/mma.4726> "New types of topological 
    structures via graphs".
Deciphering hierarchy of agents exhibiting observable dominance events is a crucial problem in several disciplines, in particular in behavioural analysis of social animals, but also in social sciences and game theory. This package implements an inference approach based on graph theory, namely to extract the optimal acyclic subset of a weighted graph of dominance; this allows for hierarchy estimation through topological sorting. The package also contains infrastructure to investigate partially defined hierarchies and hierarchy dynamics.
Flexible and ergonomic topological sorting implementation for
    R. Supports a variety of input data encoding (lists of edges or
    adjacency matrices, graphs edge direction), stable sort variants as
    well as cycle detection with detailed diagnosis.
A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.
Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.
The goal of tor (to-R) is to help you to import multiple
    files from a single directory at once, and to do so as quickly,
    flexibly, and simply as possible.
Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <doi:10.48550/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.
Torch code for computing multi-class Area Under The Minimum,
 <https://www.jmlr.org/papers/v24/21-0751.html>, Generalization.
 Useful for optimizing Area under the curve.
Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
Provides access to datasets, models and preprocessing
    facilities for deep learning with images. Integrates seamlessly
    with the 'torch' package and it's 'API' borrows heavily from
    'PyTorch' vision package.
Implements additional operators for computer vision models, including
    operators necessary for image segmentation and object detection deep learning
    models.
Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.
Draws tornado plots for model sensitivity to univariate changes.  Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package.  Also draws variable importance plots.
A framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.
Performs Thresholded Ordered Sparse Canonical Correlation Analysis (CCA). For more details see Senar, N. (2024) <doi:10.1093/bioadv/vbae021> and Senar, N. et al. (2025) <doi:10.48550/arXiv.2503.15140>.
Operators and functions provided by base R sometimes lack some
    features found in other programming languages, such as the ability to
    concatenate strings using + or to repeat strings using *. This package aims
    at providing such functionality without breaking existing code, i.e., only
    statements, that would throw errors in pure base R are patched.
Base R sometimes requires verbose statements for simple,
    often recurring tasks, such as printing text without trailing
    space, ending with newline. This package aims at providing
    shorthands for such tasks.
A general framework of two directional simultaneous inference
    is provided for high-dimensional as well as the fixed dimensional models with manifest
    variable or latent variable structure, such as high-dimensional mean models, high-
    dimensional sparse regression models, and high-dimensional latent factors models.
    It is making the simultaneous inference on a set of parameters from two directions,
    one is testing whether the estimated zero parameters indeed are zero and the other is
    testing whether there exists zero in the parameter set of non-zero. More details can be 
    referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.
Ports the 'Stata' ado package 'tost' which provides a suite of commands to perform two one-sided tests for equivalence following the approach by Schuirman (1987) <doi:10.1007/BF01068419>. Commands are provided for t tests on means, z tests on proportions, McNemar's test (1947) <doi:10.1007/BF02295996> on proportions and related tests, tests on the regression coefficients from OLS linear regression (not yet implementing all of the current regression options from the 'Stata' 'tostregress' command, e.g., survey regression options, estimation options, etc.), Wilcoxon's (1945) <doi:10.2307/3001968> signed rank tests, Wilcoxon-Mann-Whitney (1947) <doi:10.1214/aoms/1177730491> rank sum tests, supporting inference about equivalence for a number of paired and unpaired, parametric and nonparametric study designs and data types. Each command tests a null hypothesis that samples were drawn from populations different by at least plus or minus some researcher-defined level of tolerance, which can be defined in terms of units of the data or rank units (Delta), or in units of the test statistic's distribution (epsilon) except for tost.rrp() and tost.rrpi().  Enough evidence rejects this null hypothesis in favor of equivalence within the tolerance.  Equivalence intervals for all tests may be defined symmetrically or asymmetrically.
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>.
R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://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).
Several datasets which describe the challenges and results of
    competitions in Tournament of Champions.  This data is useful for
    practicing data wrangling, graphing, and analyzing how each season of
    Tournament of Champions played out.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
Find the optimal decision rules (AKA progression criteria)
    and sample size for clinical trials with three (stop/pause/go)
    outcomes. Both binary and continuous endpoints can be accommodated,
    as can cases where an adjustment is planned following a pause outcome. 
    For more details see Wilson et al. (2024) <doi:10.1186/s12874-024-02351-x>.
The best way to implement middle ware for 'shiny' Applications. 'tower'
    is designed to make implementing behavior on top of 'shiny' easy with a layering
    model for incoming HTTP requests and server sessions. 'tower' is a very minimal
    package with little overhead, it is mainly meant for other package developers
    to implement new behavior.
Non-imputational method for handling missing values in 
   a prediction context, meaning that not only are there missing
   values in the training dataset, but also some values may be missing
   in future cases to be predicted. Based on the notion of regression
   averaging (Matloff (2017, ISBN: 9781498710916)).
Computation of stopping boundaries for a single-arm trial using a 
    Bayesian criterion. 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>.
Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.
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 (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.github.io/>) 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>.
A 'shiny' app that generates plots and summary tables from repeat-dose
        toxicology study results to facilitate holistic evaluation of the drug safety of 
        active pharmaceutical ingredients (API) prior to initiation of clinical trials.
Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the 
  Tissue-Adjusted Pathway Analysis of cancer (TPAC) method.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc.
Constraint-based causal discovery using the PC algorithm while
    accounting for a partial node ordering, for example a partial temporal ordering
    when the data were collected in different waves of a cohort study.
    Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>  
    provide a guide how to use tpc to analyse cohort data.
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.
Provides functions to construct two-phase design layouts, compute treatment- and block-incidence matrices, derive C-matrices for residual, direct, and interaction effects, and calculate the efficiency factor for two-phase experimental designs with factorial treatment structure.
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.
Contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.
Provides a simple approach for constructing dynamic materials
 modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It
 can easily generate various processing-maps based on this model as well. The
 calculation result in this package contains full materials constants, information
 about power dissipation efficiency factor, and rheological properties, can
 be exported completely also, through which further analysis and customized
 plots will be applicable as well.
Estimation of transition probabilities for the
	illness-death model and or the three-state progressive model.
Provide data generation and estimation tools for the truncated positive normal (tpn)
             model discussed in Gomez, Olmos, Varela and Bolfarine (2018) 
             <doi:10.1007/s11766-018-3354-x>, the slash tpn distribution discussed in Gomez, 
             Gallardo and Santoro (2021) <doi:10.3390/sym13112164>, the bimodal tpn distribution 
             discussed in Gomez et al. (2022) <doi:10.3390/sym14040665>, the flexible tpn model
	     <doi:10.3390/math11214431> and the unit tpn distribution <doi:10.1016/j.chemolab.2025.105322>.
Regression models for temporal process responses with
        time-varying coefficient.
Density, distribution function, the quantile function, 
    random generation function, and maximum likelihood estimation.
The two-parameter Xgamma and Poisson Xgamma distributions are analyzed, covering standard distribution and regression functions, maximum likelihood estimation, quantile functions, probability density and mass functions, cumulative distribution functions, and random number generation. References include: "Sen, S., Chandra, N. and Maiti, S. S. (2018). On properties and applications of a two-parameter XGamma distribution. Journal of Statistical Theory and Applications, 17(4): 674--685. <doi:10.2991/jsta.2018.17.4.9>." "Wani, M. A., Ahmad, P. B., Para, B. A. and Elah, N. (2023). A new regression model for count data with applications to health care data. International Journal of Data Science and Analytics. <doi:10.1007/s41060-023-00453-1>."
Enables the acquisition of Korean financial market data, 
            designed to integrate seamlessly with the 'tidyquant' package.
Automates translating the instructions of 'iatgen' generated qsf
             (Qualtrics survey files) to other languages using either officially
             supported or user-supplied translations (for tutorial see Santos
             et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).
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 (The Ecological Flora
    of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, 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.
A pipeline for short tandem repeat instability analysis from fragment analysis data. Inputs of fsa files or peak tables, and a user supplied metadata data-frame. The package identifies ladders, calls peaks, identifies the modal peaks, calls repeats, then calculates repeat instability metrics (e.g. expansion index from Lee et al. (2010) <doi:10.1186/1752-0509-4-29>).
Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in  C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.
Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.
Functions to access the database of 217 data-frames with aggregate 
    study-level characteristics (that may act as effect modifiers) extracted 
    from published systematic reviews with network meta-analysis. The database shall 
    only be used for developing and appraising the methodology to assess 
    the transitivity assumption quantitatively.
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.
Tracks parameter value, gradient, and Hessian at each iteration 
    of numerical optimizers. Useful for analyzing optimization progress, 
    diagnosing issues, and studying convergence behavior.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.
Perform and Runtime statistical comparisons between models.
    This package aims at choosing the best model for a particular dataset, 
    regarding its discriminant power and runtime.
Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An
    Advanced Guide to Trade Policy Analysis" and functions to report regression
    summaries with clustered robust standard errors.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods.
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 a state/space algorithm. The normalized sample entropy method
            has been implemented which produces accurate entropy estimation even on smaller datasets.
            On a separate stream, trades from the five major assets classes and also
            functionality to use pricing curves, rating tables, Credit Support Annex 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. Back testing has also been included for the 'EuroMillions',
      			the 'EuroJackpot', the UK Lotto, the Set For Life and the UK 'ThunderBall' lotteries.
      			Furthermore, some basic functionality about climate risk has been included.
Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.
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>.
Split a dataframe, tibble, or data.table into training and test sets. Return either a list, an index, or directly assign training and test sets into memory.
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 'geographiclib' package.
Calculates trait moments from trait and community data using the 
  methods developed in Maitner et al (2021) <doi:10.22541/au.162196147.76797968/v1>.
Identifies clusters of individual longitudinal trajectories. In the spirit of Leffondre et al. (2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of the Spectral Clustering algorithm.
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.
Estimation of group-based trajectory models, including finite mixture models for longitudinal data, 
    supporting censored normal, zero-inflated Poisson, logit, and beta distributions, 
    using expectation-maximization and quasi-Newton methods, with tools for model selection, 
    diagnostics, and visualization of latent trajectory groups,
    <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.
Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
A toolbox to assist with statistical analysis of animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. Some support is provided for 3-dimensional trajectories. 
    McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Getting 'TikTok' data (<https://www.tiktok.com/>) through the official 
  and unofficial APIs—in other words, you can track 'TikTok'.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). 
  Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
  transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
  <DOI:10.1093/biostatistics/kxac048>) and a tutorial explains applications in survival analysis
  (Siegfried et al., 2025, <DOI:10.48550/arXiv.2402.06428>). Multivariate conditional transformation models 
  (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
  <DOI:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of 
  a doubly robust score test, described in  Kook et al. (2024, <DOI:10.1080/01621459.2024.2395588>).
Extends invariant causal prediction (Peters et al., 2016, 
    <doi:10.1111/rssb.12167>) to generalized linear and transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291>).
    The methodology is described in Kook et al. (2023,
    <doi:10.1080/01621459.2024.2395588>).
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.
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 the 'mgcv' notation. Additive mixed-effects transformation
    models are described in Tamasi (2025) <doi:10.18637/jss.v114.i11>.
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) <doi:10.48550/arXiv.1703.03373>, respectively.
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'. Application to shift-scale
    transformation models are described in Siegfried et al. (2024,
    <doi:10.1080/00031305.2023.2203177>).
Performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP  <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.
Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000)  <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980)  <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.
Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.
Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).
Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References:  Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.
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>.
Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.
Provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.
Analyse data from longitudinal studies to characterise changes in values of
   semi-quantitative outcome variables within individual subjects, using high performance C++
   code to enable rapid processing of large datasets. A flexible methodology is available for
   codifying these state transitions.
Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.
Provides a robust and user-friendly solution for transliterating Ukrainian strings into Latin symbols.
An object model for source text and translations. Find and extract
    translatable strings. Provide translations and seamlessly retrieve them at
    runtime.
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>.
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.
Doubly-robust, non-parametric estimators for the transported average treatment effect from 
  Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the 
  intent-to-treatment average treatment effect from Rudolph and van der Laan (2017)
  <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are 
  estimated using the Super Learner algorithm.
A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.
Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette (optional), install 'fwelnet' and 'ecpc' from <https://github.com/kjytay/fwelnet> and <https://github.com/Mirrelijn/ecpc>, respectively.
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.
Hospitals, hospital systems, and even trauma systems that
    provide care to injured patients may not be aware of robust metrics
    that can help gauge the efficacy of their programs in saving the lives
    of injured patients.  'traumar' provides robust functions driven by
    the academic literature to automate the calculation of relevant
    metrics to individuals desiring to measure the performance of their
    trauma center or even a trauma system.  'traumar' also provides some
    helper functions for the data analysis journey. Users can refer to the
    following publications for descriptions of the methods used in
    'traumar'.  TRISS methodology, including probability of survival, and
    the W, M, and Z Scores - Flora (1978)
    <doi:10.1097/00005373-197810000-00003>, Boyd et al. (1987,
    PMID:3106646), Llullaku et al. (2009) <doi:10.1186/1749-7922-4-2>,
    Singh et al. (2011) <doi:10.4103/0974-2700.86626>, Baker et al. (1974,
    PMID:4814394), and Champion et al. (1989)
    <doi:10.1097/00005373-198905000-00017>. For the Relative Mortality
    Metric, see Napoli et al. (2017) <doi:10.1080/24725579.2017.1325948>,
    Schroeder et al. (2019) <doi:10.1080/10903127.2018.1489021>, and
    Kassar et al. (2016) <doi:10.1177/00031348221093563>. For more
    information about methods to calculate over- and under-triage in
    trauma hospital populations and samples, please see the following
    publications - Peng & Xiang (2016) <doi:10.1016/j.ajem.2016.08.061>,
    Beam et al. (2022) <doi:10.23937/2474-3674/1510136>, Roden-Foreman et
    al. (2017) <doi:10.1097/JTN.0000000000000283>.
'Travel Time' API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using ‘as the crow flies’ distance.
  Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.
Contains R functions for simulating and estimating integer-valued trawl processes as 
    described in the article Veraart (2019),"Modeling, simulation and inference for
    multivariate time series of counts using trawl processes", Journal of Multivariate Analysis,
    169, pages 110-129,
    <doi:10.1016/j.jmva.2018.08.012> and for 
    simulating random vectors from the bivariate negative binomial and the bi- and trivariate 
    logarithmic series distributions.
A new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>.
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.
Truncation of univariate probability distributions. The probability distribution can come from other packages so long as the function names follow the standard d, p, q, r naming format. Also other univariate probability distributions are included.
Download daily interest rates from the US Treasury XML feed.
    Leveraging
    <https://home.treasury.gov/treasury-daily-interest-rate-xml-feed>,
    this package serves as a wrapper, facilitating the retrieval of daily
    treasury rates across various categories, including par yield curves,
    treasury bills, long-term rates, and real yield curves.
Computes treatment patterns within a given cohort using the Observational
    Medical Outcomes Partnership (OMOP) common data model (CDM). As described
    in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations.  Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.
A modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).
There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.
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) <doi:10.48550/arXiv.1906.10845>.
Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk.
Interface to the API for 'TreeBASE' <http://treebase.org>
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found 
    in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.
Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology,
    see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.
An implementation that combines trait data and a phylogenetic tree (or trees) into a 
    single object of class 'treedata.table'. The resulting object can be easily 
    manipulated to simultaneously change the trait- and tree-level sampling.
    Currently implemented functions allow users to use a 'data.table' syntax when
    performing operations on the trait dataset within the 'treedata.table' object.
    For more details see Roman-Palacios et al. (2021) <doi:10.7717/peerj.12450>.
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. For more details see Volz and Frost (2017) <doi:10.1093/ve/vex025>.
The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) <doi:10.1093/treephys/14.7-8-9.1069>. Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) <doi:10.1016/j.ufug.2006.01.007>. US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213.
Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).
Perform test to detect differences in structure between families of
             trees. The method is based on cophenetic distances and aggregated
             Student's tests.
Testing for trajectory presence and heterogeneity on
 multivariate data. Two statistical methods (Tenha & Song 2022) 
 <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
 test quantifies the statistical evidence for trajectory presence. The
 subset specificity measure summarizes pattern heterogeneity using the
 minimum subtree cover. There is no user tunable parameters for either
 method. Examples are included to illustrate how to use the methods on 
 single-cell data for studying gene and pathway expression dynamics and
 pathway expression specificity.
Implements measures of tree similarity, including 
  information-based generalized Robinson-Foulds distances
  (Phylogenetic Information Distance, Clustering Information Distance,
  Matching Split Information Distance; Smith 2020)
  <doi:10.1093/bioinformatics/btaa614>; 
  Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
  <doi:10.1007/978-3-642-40453-5_13>, 
  including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
  the Matching Split Distance (Bogdanowicz & Giaro 2012)
  <doi:10.1109/TCBB.2011.48>;
  the Hierarchical Mutual Information (Perotti et al. 2015)
  <doi:10.1103/PhysRevE.92.062825>;
  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 identifying islands of trees (Silva and Wilkinson 2021)
  <doi:10.1093/sysbio/syab015>,
  for calculating the median of sets of trees,
  and for computing the information content of trees and splits.
Perform two types of analysis: 1) checking the
    goodness-of-fit of tree models to your single-cell gene expression
    data; and 2) deciding which tree best fits your data.
Creates interpretable decision tree visualizations with the
    data represented as a heatmap at the tree's leaf nodes.  'treeheatr'
    utilizes the customizable 'ggparty' package for drawing decision
    trees.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
Implementation of unconditional Bernoulli Scan Statistic developed
             by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> 
             for hierarchical tree structures. Tree-based Scan Statistics are an
             exploratory method to identify event clusters across the space of a 
             hierarchical tree.
Implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" <doi:10.48550/arXiv.2507.17229>.
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.
Record all tree-ring Shapefile of tree disk with GIS soft 'Qgis' and interpolating model from high resolution tree disk image.
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>.
Provides bindings to 'Tree-sitter', an incremental parsing
    system for programming tools. 'Tree-sitter' builds concrete syntax
    trees for source files of any language, and can efficiently update
    those syntax trees as the source file is edited. It also includes a
    robust error recovery system that provides useful parse results even
    in the presence of syntax errors.
Provides bindings to a 'C' grammar for Tree-sitter, to be used
    alongside the 'treesitter' package. Tree-sitter builds concrete syntax trees
    for source files and can efficiently update them or generate code like producing 
    R C API wrappers from C functions, structs and global definitions from header files.
Provides bindings to an 'R' grammar for 'Tree-sitter', to be
    used alongside the 'treesitter' package. 'Tree-sitter' builds concrete
    syntax trees for source files of any language, and can efficiently
    update those syntax trees as the source file is edited.
Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. It enables users to cut trees in any orientation, such as rootwardly (from root to tips) and tipwardly (from tips to its root), or allows users to define a specific time interval of interest. It can also be used to create multiple tree pieces of equal temporal width. Moreover, it allows the assessment of novel temporal rates for various phylogenetic indexes, which can be quickly displayed graphically.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a 'shiny' interface which can be started from R using
    treespaceServer(). 
    For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Combine a list of taxa with a phylogeny to generate a starting tree for use in
    total evidence dating analyses.
Collection of phylogenetic tree statistics,
             collected throughout the literature. All functions have been 
             written to maximize computation speed. The package includes 
             umbrella functions to calculate all statistics, all balance 
             associated statistics, or all branching time related statistics. 
             Furthermore, the 'treestats' package supports summary statistic 
             calculations on Ltables, provides speed-improved coding of 
             branching times, Ltable conversion and includes algorithms to
             create intermediately balanced trees. Full description can be
             found in Janzen (2024) <doi:10.1016/j.ympev.2024.108168>.
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.
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, Lemant et al. 2022)
  <doi:10.1016/j.mbs.2012.10.005>, <doi:10.1093/sysbio/syac027>;
  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.
Measuring tree architecture from terrestrial lidar data, 
  including tree-level properties, crown characteristics, and structural 
  attributes derived from quantitative structure models (QSMs).
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.
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.
Fitting models for, and simulation of, trend locally stationary 
    wavelet (TLSW) time series models, which take account of time-varying 
    trend and dependence structure in a univariate time series. The TLSW model, 
    and its estimation, is described in McGonigle, Killick and Nunes (2022a) 
    <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will 
    likely want to start with the TLSW function.
Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.
Extract trends from monthly and quarterly economic time series.
    Provides two main functions: augment_trends() for pipe-friendly 'tibble' workflows
    and extract_trends() for direct time series analysis. Includes key econometric
    filters and modern parameter experimentation tools.
Provides a set of exploratory data analysis (EDA) tools for 
    visualizing trends, diagnosing data types for beginner-friendly workflows, 
    and automatically routing to suitable statistical tests or trend exploration 
    models. Includes unified plotting functions for trend lines, grouped boxplots, 
    and comparative scatterplots; automated statistical testing (e.g., t-test, 
    Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size 
    calculation; and model-based trend analysis using generalized additive 
    models (GAM) for count data, generalized linear models (GLM) for continuous 
    data, and zero-inflated models (ZIP/ZINB) for count data with potential 
    zero-inflation. 
    Also supports time-window continuity checks, cross-year 
    handling in compare_monthly_cases(), and ARIMA-ready preparation with 
    stationarity diagnostics, ensuring consistent parameter styles for 
    reproducible research and user-friendly workflows.Methods are 
    based on R Core Team (2024) <https://www.R-project.org/>, 
    Wood, S.N.(2017, ISBN:978-1498728331),
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, 
    Simon Jackman (2024) <https://github.com/atahk/pscl/>,    
    Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.
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 (2022) <arXiv:2110.06048>.
Tri-hierarchical incomplete block design is defined as an arrangement of v  treatments each replicated r  times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.
Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) <doi:10.7717/peerj.17036>.
Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.
Implements target trial emulation methods to apply randomized
    clinical trial design and analysis in an observational setting. Using
    marginal structural models, it can estimate intention-to-treat and
    per-protocol effects in emulated trials using electronic health
    records. A description and application of the method can be found in
    Danaei et al (2013) <doi:10.1177/0962280211403603>.
A collection of clinical trial designs and methods, implemented in 
    'rstan' and R, including: the Continual Reassessment Method by O'Quigley et 
    al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) 
    <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of
    Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the 
    Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and
    more. We provide functions to aid model-fitting and analysis. 
    The 'rstan' implementations may also serve as a cookbook to anyone looking 
    to extend or embellish these models. We hope that this package encourages 
    the use of Bayesian methods in clinical trials. There is a preponderance of 
    early phase trial designs because this is where Bayesian methods are used 
    most. If there is a method you would like implemented, please get in touch.
Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.
Functions and Examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.  Also includes maximum likelihood estimation of parameters.
Measuring angles between points in a landscape is much easier
    than measuring distances. When the location of three points is known the
    position of the observer can be determined based solely on the angles between
    these points as seen by the observer. This task (known as triangulation)
    however requires onerous calculations - these calculations are automated by this
    package.
Compositional data consisting of three-parts can be color
  mapped with a ternary color scale. Such a scale is provided by
  the tricolore packages with options for discrete and continuous
  colors, mean-centering and scaling. See 
  Jonas Schöley (2021) "The centered ternary balance scheme. A technique
  to visualize surfaces of unbalanced three-part compositions"
  <doi:10.4054/DemRes.2021.44.19>,
  Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data
  on the Lexis surface" <doi:10.4054/DemRes.2017.36.21>, and
  Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures
  at a glance" <doi:10.1016/S0140-6736(18)31194-2>.
Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( 
    Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual
    parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria 
    (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared
    (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. 
    See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.
Processing and analysis of pathomics, omics and other medical datasets.
    'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, 
    plotting and many other methods for exploratory analysis of high-dimensional datasets.
A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.
Triad Log-Linear modelling of Imprinting
    Environmental interactions, and Maternal effects (TriLLIEM).
    This is an implementation of the log-linear model described in a series of
    papers, see for example Ainsworth et al. (2010) <doi:10.1002/gepi.20547>.
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.
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.
  See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis
  and Gatsonis (2023), ISBN 9781482233704.
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
Access and manipulate spatial tracking data, with straightforward 
 coercion from and to other formats. Filter for speed and create time spent 
 maps from tracking data. There are coercion methods to convert between 'trip'
 and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 
 'spatstat'. Trip objects can be created from raw or grouped data frames, and 
 from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, 
 Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
  Please note that most of the functions are now also covered in package
  interp, which is a re-implementation from scratch under a free license
  based on a different triangulation algorithm.
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.
Implements triple-difference (DDD) estimators for both average
              treatment effects and event-study parameters. Methods include regression
              adjustment, inverse-probability weighting, and doubly-robust estimators,
              all of which rely on a conditional DDD parallel-trends assumption and
              allow covariate adjustment across multiple pre- and post-treatment
              periods. The methodology is detailed in Ortiz-Villavicencio and
              Sant'Anna (2025) <doi:10.48550/arXiv.2505.09942>.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
Attain excellent covariate balance by matching two treated units
    and one control unit or vice versa within strata. Using such triples, as
    opposed to also allowing pairs of treated and control units, 
    allows easier interpretation of the two possible 
    weights of observations and better insensitivity to unmeasured bias in the test
    statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio
    allows for the match to be feasible in more situations.
    The 'rrelaxiv' package, which provides an alternative solver for the underlying 
    network flow problems, carries an academic license and is not available on CRAN, but
    may be downloaded from 'GitHub' at <https://github.com/josherrickson/rrelaxiv/>.
    The 'Gurobi' commercial optimization software is required to use the two functions
    [infsentrip()] and [triplesIP()]. These functions are not essential
    to the main purpose of this package. A free academic license can be obtained at 
    <https://www.gurobi.com/features/academic-named-user-license/>. 
    The 'gurobi' R package can then be installed following 
    the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Uses read counts for biallelic single nucleotide polymorphisms (SNPs)
    to compare the likelihoods for the observed read counts given that a sample is 
    either diploid or triploid. It allows parameters to be specified to account for 
    sequencing error rates and allelic bias. For details of the algorithm, please see
    Delomas (2019) <doi:10.1111/1755-0998.13073>.
Overall predictive performance is measured by a mean score
    (or loss), which decomposes into miscalibration, discrimination, and
    uncertainty components. The main focus is visualization of these distinct
    and complementary aspects in joint displays.
    See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.
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.
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" (<https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>),
    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.
Provides methods for generating .dat files for use with the 'AMPL' 
    software using spatial data, particularly rasters. It includes support for 
    various spatial data formats and different problem types. By automating the 
    process of generating 'AMPL' datasets, this package can help streamline 
    optimization workflows and make it easier to solve complex optimization 
    problems. The methods implemented in this package are described in detail
    in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).
Bayesian trophic position models using 'stan' by leveraging 'brms' for 
             stable isotope data. Trophic position models are derived by using 
             equations from 
             Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>,
             Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, 
             and 
             Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.
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 rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials.
Provides a convenient R interface to the 'National Health Service 
    NHS Technology Reference Update Distribution (TRUD) API', allowing users 
    to list available releases for their subscribed items, retrieve metadata, 
    and download release files. For more information on the API, see 
    <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.
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.
    Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>.
    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.".
This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range.  Unlike conventional metrics such as TWAP (Time-Weighted Average Price), which focuses solely on time, or VWAP (Volume-Weighted Average Price), which emphasizes volume, 'TrueWAP' captures fluctuating market behavior by reflecting true price movement within high/low performance boundaries.
Implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the 
   same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.
Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. The output is a plot with the effect size of risk variants in the Y axis, and the allele frequency spectrum in the X axis. Corte et al (2023) <doi:10.1101/2023.04.21.23288923>.
Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets 'simu', 'simu_c1', and 'simu_c2', which are used to illustrate the usage of the functions in this package.
    Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.
A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Handles truncated members from the exponential family of
	probability distributions. Contains functions such as rtruncnorm() and
	dtruncpois(), which are truncated versions of rnorm() and dpois() from the
	stats package that also offer richer output containing, for example, the
	distribution parameters. It also provides functions to retrieve the original
	distribution parameters from a truncated sample by maximum-likelihood
	estimation.
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Finds the posterior modes for the mean and standard deviation for a
  truncated normal distribution with one or two known truncation points.
  The method used extends Bayesian methods for parameter estimation for a singly
  truncated normal distribution under the Jeffreys prior (see Zhou X,
  Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated
  data with applications to operational risk measurement".
  <doi:10.1080/14697688.2012.752103>). This package additionally allows for a
  doubly truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Estimators for semi-parametric linear regression models with truncated response variables (fixed truncation point). The estimators implemented are the Symmetrically Trimmed Least Squares (STLS) estimator introduced by Powell (1986) <doi:10.2307/1914308>, the Quadratic Mode (QME) estimator introduced by Lee (1993) <doi:10.1016/0304-4076(93)90056-B>, and the Left Truncated (LT) estimator introduced by Karlsson (2006) <doi:10.1007/s00184-005-0023-x>.
Computes the product moments of the truncated multivariate normal 
    distribution, particularly for cases involving patterned 
    variance-covariance matrices. It also has the capability to calculate these 
    moments with arbitrary positive-definite matrices, although performance may 
    degrade for high-dimensional variables.
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>.
Makes data wrangling with ID-related aspects more comfortable.
    Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility.
    Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.
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>.
Provides tools for evaluating the trustworthiness of machine
    learning models in production and research settings. Computes a
    Stability Index that quantifies the consistency of model predictions
    across multiple runs or resamples, and a Robustness Score that measures
    model resilience under small input perturbations. Designed for data
    scientists, ML engineers, and researchers who need to monitor and ensure
    model reliability, reproducibility, and deployment readiness.
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).
Differentiate client errors (4xx)
  from server errors (5xx) for the 'plumber' and 
  'RestRserve' HTTP API frameworks. The package
  also includes a built-in logging mechanism
  to standard output (STDOUT) or standard error
  (STDERR) depending on the log level.
Provides functions to calculate the Surface Temperature (Ts) from geospatial raster data. 
    These functions use albedo, Normalized Difference Vegetation Index (NDVI), and air temperature (Ta) 
    to estimate Ts, facilitating hydrological, ecological, and remote sensing analyses.
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.
A suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the 'tsoutliers' package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series components—such as trend, seasonal, ARMA, irregular, and anomalies—in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, 'Softplus-Logit' and Sigmoid.
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"
    <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) 
    "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" <doi:10.18637/jss.v114.i07>.
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. (2022) <doi:10.1515/demo-2022-0105>.
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.
Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series,  white noise is added in the original time series to form a new time series, etc.  'tsdataleaks' package can be used to detect data leakages in a collection of  time series.
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>.
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>.
First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.
Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.
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>.
The library allows to perform a multivariate time series
    classification based on the use of Discrete Wavelet Transform for
    feature extraction, a step wise discriminant to select the most
    relevant features and finally, the use of a linear or quadratic
    discriminant for classification. Note that all these steps can be done
    separately which allows to implement new steps.  Velasco, I., Sipols,
    A., de Blas, C. S., Pastor, L., & Bayona, S. (2023)
    <doi:10.1186/S12938-023-01079-X>.  Percival, D. B., & Walden, A. T.
    (2000,ISBN:0521640687).  Maharaj, E. A., & Alonso, A. M. (2014)
    <doi:10.1016/j.csda.2013.09.006>.
Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 <doi:10.1017/psrm.2026.10087>). We also provide the actual formulae for these effects.
Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.
Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application.
    A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ).
    In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ).
    After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule.
    'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.
Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        <http://www.mpipks-dresden.mpg.de/~tisean/>.
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Multiscale multifractal analysis (MMA) (Gierałtowski et al.,
    2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method,
    designed to describe scaling properties of fluctuations within the signal
    analyzed. The main result of this procedure is the so called Hurst surface
    h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation
    scaling exponent) on the multifractal parameter q and the scale of observation s
    (data window width).
Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) OBES <doi:10.1111/obes.12647>;  unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.
A set of tools designed to perform descriptive data analysis on assets, manage asset portfolios and capital allocation, and download, organize, and maintain data from the "Tehran Stock Exchange" and "NOBITEX" platforms.
Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
A general regression neural network (GRNN) is a variant of a
    Radial Basis Function Network characterized by a fast single-pass learning.
    'tsfgrnn' allows you to forecast time series using a GRNN model Francisco 
    Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco
    Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting
    horizon is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. You can consult and plot how the
    prediction was done. It is also possible to assess the forecasting accuracy
    of the model using rolling origin evaluation.
Allows forecasting time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.
Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.
Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.
A collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.
Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.
The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories.
    It is designed to model the progression of an epidemic over time while accounting for the various uncertainties
    inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach,
    using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in
    epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves.
    The forecasts generated by the package are of value to public health officials and researchers who need to
    understand and predict the course of an epidemic to inform decision-making. Beyond its application in public
    health, the package is also a useful resource for researchers and practitioners in fields where the trajectories
    of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for
    data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy.
    The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in
    Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021)
    <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024)
    <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.
Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained.
To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).
Two-stage procedure compares hazard rate functions,
    which may or may not cross each other.
Provides a 'tbl_ts' class (the 'tsibble') for
    temporal data in an data- and model-oriented format. The 'tsibble'
    provides tools to easily manipulate and analyse temporal data, such as
    filling in time gaps and aggregating over calendar periods.
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
A shared tsibble data easily communicates between
    htmlwidgets on both client and server sides, powered by 'crosstalk'. A
    shiny module is provided to visually explore periodic/aperiodic
    temporal patterns.
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).
An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <doi:10.1371/journal.pone.0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <doi:10.1111/1467-9876.00187>.
Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.
Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) <doi:10.1080/01621459.2024.2326621>. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) <doi:10.1214/11-AOAS514>).
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically designed to handle time series with an exogenous variable, allowing users to denote whether data was available for a particular period or not. The package encompasses various functionalities, including hyperparameter tuning, custom loss function support, model evaluation, and one-step-ahead forecasting. With an emphasis on ease of use and flexibility, it empowers users to explore, evaluate, and deploy LSTM models for accurate time series predictions and forecasting in diverse applications. More details can be found in Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.
A novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. It also simultaneously performs variable selection for each autoregressive model and hence the order selection.
Generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages. Additional methods for time series prediction ensembles and probabilistic plotting of predictions is included. A more detailed description is available at <https://www.nopredict.com/packages/tsmethods> which shows the currently implemented methods in the 'tsmodels' framework.
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.
Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.
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 prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. 
  TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. 
  Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. 
  It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. 
  More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.
Provides threshold sweep methods for Qualitative Comparative 
    Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), 
    Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), 
    and Dual Threshold Sweep (DTS) for systematic exploration of threshold 
    calibration effects on crisp-set QCA results. These methods extend 
    traditional robustness approaches by treating threshold variation as an 
    exploratory tool for discovering causal structures. Also provides Fiss 
    (2011) <doi:10.5465/amj.2011.60263120> core/peripheral condition 
    classification via compute_fiss_core() and generate_fiss_chart(), enabling 
    four-symbol configuration charts that distinguish core conditions (present 
    in both parsimonious and intermediate solutions) from peripheral conditions 
    (intermediate only). Built on top of the 'QCA' package by Dusa (2019) 
    <doi:10.1007/978-3-319-75668-4>, with function arguments following 'QCA' 
    conventions. Based on set-theoretic methods by Ragin (2008) 
    <doi:10.7208/chicago/9780226702797.001.0001> and established robustness 
    protocols by Rubinson et al. (2019) <doi:10.1177/00491241211036158>.
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. Main
    algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.
It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). 
          This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness.
          ---
          Guerrero, V.M (2007)  <DOI:10.1016/j.spl.2007.03.006>.
          Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (2020, ISBN: 978-0-367-18733-0).
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.
Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.
Plot official statistics' time series conveniently: automatic
    legends, highlight windows, stacked bar chars with positive and
    negative contributions, sum-as-line option, two y-axes with automatic
    horizontal grids that fit both axes and other popular chart types.
    'tstools' comes with a plethora of defaults to let you plot without
    setting an abundance of parameters first, but gives you the
    flexibility to tweak the defaults. In addition to charts, 'tstools'
    provides a super fast, 'data.table' backed time series I/O that allows
    the user to export / import long format, wide format and transposed
    wide format data to various file types.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Fitting tree-structured varying coefficient models (Berger et al. (2019), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.
Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). 'tsvio' is an R wrapper to 'C' code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.
An 'RStudio' add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using 'plotly' package.
Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott.  It is helpful for data analysis and for time series instruction.
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
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 via the 'diffobj' package for 'diff'-style
 textual comparison of R objects, as well as via 'tinysnapshot' package for visual
 differences in plots.
Analysis of treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) <doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Likelihood ratio and maximum likelihood statistics are provided 
     that can be used as alternatives to p-values Colquhoun (2017) 
     <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. 
     together with degrees of freedom. For the function 'tTOlr', the argument
     'twoSided' has the default 'twoSided = TRUE'.
A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.
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. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called 'Rare-Screening' which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called 'Firth-ttScreening' which uses 'ttScreening' method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates.
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) <doi:10.48550/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
Simple utilities to generate a Dockerfile from a directory or
    project, build the corresponding Docker image, push the image to
    DockerHub, and publicly share the project via Binder.
Cooperative game theory models decision-making situations in
    which a group of agents, called players, may achieve certain benefits
    by cooperating to reach an optimal outcome. It has great potential in
    different fields, since it offers a scenario to analyze and solve
    problems in which cooperation is essential to achieve a common goal.
    The 'TUGLab' (Transferable Utility Games Laboratory) R package
    contains a set of scripts that could serve as a helpful complement to
    the books and other materials used in courses on cooperative game
    theory, and also as a practical tool for researchers working in this
    field. The 'TUGLab' project was born in 2006 trying to highlight the
    geometrical aspects of the theory of cooperative games for 3 and 4
    players. 'TUGlabWeb' is an online platform on which the basic
    functions of 'TUGLab' are implemented, and it is being used all over
    the world as a resource in degree, master's and doctoral programs.
    This package is an extension of the first versions and enables users
    to work with games in general (computational restrictions aside). The
    user can check properties of games, compute well-known games and
    calculate several set-valued and single-valued solutions such as the
    core, the Shapley value, the nucleolus or the core-center. The package
    also illustrates how the Shapley value flexibly adapts to various
    cooperative game settings, including weighted players and coalitions,
    a priori unions, and restricted communication structures. In keeping
    with the original philosophy of the first versions, special emphasis
    is placed on the graphical representation of the solution concepts for
    3 and 4 players.
Provides tools to perform multiple comparison analyses, based on the well-known Tukey's "Honestly Significant Difference" (HSD) test. In models involving interactions, 'TukeyC' stands out from other R packages by implementing intuitive and easy-to-use functions. In addition to accommodating traditional R methods such as lm() and aov(), it has also been extended to objects of the lmer() class, that is, mixed models with fixed effects. For more details see Tukey (1949) <doi:10.2307/3001913>.
Functions for density, cumulative density, quantile and
       simulation of Tukey g-and-h (1977) distributions. The
       quantile-based transformation (Hoaglin 1985
       <doi:10.1002/9781118150702.ch11>) and its reverse
       transformation, as well as the letter-value based estimates
       (Hoaglin 1985), are also provided.
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").
Includes the results of general, local, and presidential elections held in Turkey between 1995 and 2024, broken down by provinces and overall national results. It facilitates easy processing of this data and the creation of visual representations based on these election results.
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    <http://en.wikipedia.org/wiki/Turtle_graphics>.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
Helps the R users to get data from 'Tushare Pro'<https://tushare.pro>.
    'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area.
    We support financial data such as stock price, financial report statements and digital coins data.
Helper functions for creating, editing, and testing tutorials
    created with the 'learnr' package. Provides a simple method for allowing
    students to download their answers to tutorial questions. For examples
    of its use, see the 'r4ds.tutorials' package.
The main objective of cooperative Transferable-Utility games (TU-games) 
  is to allocate a good among the agents involved. The package implements major 
  solution concepts including the Shapley value, Banzhaf value, and egalitarian
  rules, alongside their extensions for structured games: 
  the Owen value and Banzhaf-Owen value for games with a priori unions, and the
  Myerson value for communication games on networks. To address the inherent exponential 
  computational complexity of exact evaluation, the package offers both exact 
  algorithms and linear approximation methods based on sampling, enabling the 
  analysis of large-scale games. Additionally, it supports core set-based 
  solutions, allowing computation of the vertices and the centroid of the core.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model can also be run in a semi-distributed fashion and with dual representation of soil layer.
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Create a time-varying dataset using features, exposure, and
    look back specifications.
Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) <doi:10.1093/jrsssa/qnaf035>.
Total variation denoising can be used to approximate a given
    sequence of noisy observations by a piecewise constant sequence, with 
    adaptively-chosen break points. An efficient linear-time algorithm for 
    total variation denoising is provided here, based on Johnson (2013)
    <doi:10.1080/10618600.2012.681238>.
Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time.  These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>.  We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. 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 Terasvirta (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.
Estimates the time-varying (tv) parameters of the GARCH(1,1) model, enabling the modeling of non-stationary volatilities by allowing the model parameters to change gradually over time. The estimation and prediction processes are facilitated through the application of the Kalman filter and state-space equations. This package supports the estimation of tv parameters for various deterministic functions, which can be identified through exploratory analysis of different time periods or segments of return data. The methodology is grounded in the framework presented by Ferreira et al. (2017) <doi:10.1080/00949655.2017.1334778>.
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).
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 the estimation of a time-dependent covariance matrix of returns with the intended use for portfolio optimization. The package offers methods for determining the optimal number of factors to be used in the covariance estimation, a hypothesis test of time-varying covariance, and user-friendly functions for portfolio optimization and rolling window evaluation. The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017) <doi:10.1016/j.jeconom.2016.12.004>. The approach to time-varying portfolio optimization follows Fan et al. (2024) <doi:10.1016/j.jeconom.2022.08.007>. The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019) <doi:10.1016/j.jeconom.2019.04.025>.
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.
Utilities for restricted mean survival time (RMST) and
  time-varying restricted mean survival time quantities computed from survival
  curves provided on a time grid. The package is model-agnostic and accepts
  only a time vector and survival matrices, returning RMST-based quantities and
  bootstrap summaries. For restricted mean survival time methodology, see Royston 
  and Parmar (2013) <doi:10.1186/1471-2288-13-152>.
Longitudinal data offers insights into population changes over
 time but often requires a flexible structure, especially with varying
 follow-up intervals. Panel data is one way to store such records, though
 it adds complexity to analysis. The 'tvtools' package for R simplifies
 exploring and analyzing panel data.
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.
High-performance parsing of 'Tableau' workbook files into
    tidy data frames and dependency graphs for other visualization tools
    like R 'Shiny' or 'Power BI' replication.
An open-access tool/framework that constitutes the core functions
  to analyze terrestrial water cycle data across various spatio-temporal scales.
Implements Time-Weighted Dynamic Time Warping (TWDTW), 
    a measure for quantifying time series similarity. The TWDTW algorithm, 
    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
    Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional 
    time series of various resolutions. It is particularly suitable for comparing 
    time series with seasonality for environmental and ecological data analysis, 
    covering domains such as remote sensing imagery, climate data, hydrology, 
    and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, 
    efficient 'Fortran' routines for TWDTW calculations, flexible time weighting 
    definitions, as well as utilities for time series preprocessing and visualization.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion  (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that can be used as basis for animation. Furthermore 
    it adds a number of vectorized interpolaters for common R data 
    types such as numeric, date and colour.
An interface to 'twitter-text', a 'JavaScript' library which
   is responsible for determining the length/validity of a tweet and 
   identifying/linking any URLs or special tags (e.g. mentions or
   hashtags) which may be present.
Generates a game of 2048 that can be played in the
    console.  Supports grids of arbitrary sizes, undoing the last move,
    and resuming a game that was exited during the current session.
An R wrapper around the API of TheyWorkForYou, a parliamentary 
    monitoring site that scrapes and repackages Hansard (the UK's parliamentary 
    record) and augments it with information from the Register of Members' 
    Interests, election results, and voting records to provide a unified 
    source of information about UK legislators and their activities. See 
    <http://www.theyworkforyou.com> for details.
Provides tools for building decision and cost-effectiveness analysis models. It enables users to write these models concisely, simulate outcomes—including probabilistic analyses—efficiently using optimized vectorized processes and parallel computing, and produce results. The package employs a Grammar of Modeling approach, inspired by the Grammar of Graphics, to streamline model construction. For an interactive graphical user interface, see 'DecisionTwig' at <https://www.dashlab.ca/projects/decision_twig/>. Comprehensive tutorials and vignettes are available at <https://hjalal.github.io/twig/>.
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.
A global-local approximation framework for large-scale Gaussian process modeling. 
                Please see Vakayil and Joseph (2024) <doi:10.1080/00401706.2023.2296451> for details.
                This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873.
An efficient algorithm for data twinning. This work is supported by U.S. National Science 
	Foundation grants DMREF-1921873 and CMMI-1921646.
Collect  your data on digital marketing campaigns from Twitter Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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.
Comprehensive functions to calculate sample size and power for 
    clinical trials with two co-primary endpoints. The package supports five 
    endpoint combinations: two continuous endpoints (Sozu et al. 2011 
    <doi:10.1080/10543406.2011.551329>), two binary endpoints using asymptotic 
    methods (Sozu et al. 2010 <doi:10.1002/sim.3972>) and exact methods (Homma 
    and Yoshida 2025 <doi:10.1177/09622802251368697>), mixed continuous and 
    binary endpoints (Sozu et al. 2012 <doi:10.1002/bimj.201100221>), and mixed 
    count and continuous endpoints (Homma and Yoshida 2024 
    <doi:10.1002/pst.2337>). All methods appropriately account for correlation 
    between endpoints and provide both sample size and power calculation 
    capabilities.
Provides a calculator for the two-dimensional clinical Disease Activity
    index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure
    that complements the conventional clinical DAPSA score. TwoDcDAPSA captures
    residual variation in patient-reported outcomes (pain and patient global assessment)
    and joint counts (swollen and tender) after adjusting for standardized cDAPSA using
    natural spline coefficients derived from published models. Residuals are
    standardized and combined with fixed principal component loadings to yield two
    continuous component scores: the PROs-Joint Contrast (PJC) and the
    Swollen–Tender joints Contrast (STC), along with quartile-based groupings
    (including optional combined quartile groupings). The package applies pre-specified
    coefficients, residual standardization, and loadings to new datasets but does not
    estimate spline models or principal components itself.
Implements two-mode clustering (biclustering) using 
    genetic algorithms. The method was first introduced in 
    Hageman et al. (2008) <doi:10.1007/s11306-008-0105-7>. The 
    package provides tools for fitting, visualization, and 
    validation of two-mode cluster structures in data matrices.
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and
  confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Density, distribution function, quantile function, 
    and random generation function, maximum likelihood estimation (MLE), 
    penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM),
    and median rank estimation (MEDRANK) for the two-parameter exponential distribution.
    MLE and PMLE are based on Mengjie Zheng (2013)<https://scse.d.umn.edu/sites/scse.d.umn.edu/files/mengjie-thesis_masters-1.pdf>.
    QM is based on Entisar Elgmati and Nadia Gregni (2016)<doi:10.5539/ijsp.v5n5p12>.
    MEDRANK is based on Matthew Reid (2022)<doi:10.5281/ZENODO.3938000>.
Tools for constructing and analyzing two-phase experimental designs under correlated error structures. Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) <doi:10.2307/3001770>).
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>.
An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales. If covariates are available, estimate a proportional hazards model with such a two-dimensional baseline hazard. 
  Functions are provided to prepare the raw data for estimation, to fit the model and to plot the two-dimensional smooth hazard. 
  Extension to a competing risks model are implemented. For details about the method please refer to Carollo et al. (2025) <doi:10.1002/sim.10297>.
Implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.
Carries out analyses of two-way tables with one observation per cell, together with graphical displays
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.
Estimates the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions.
    Clément de Chaisemartin, Xavier D'Haultfœuille (2020) <DOI: 10.1257/aer.20181169>.
Performs two-way tests in independent groups designs. These are two-way ANOVA, two-way ANOVA under heteroscedasticity: parametric bootstrap based generalized test and generalized pivotal quantity based generalized test, two-way ANOVA for medians, trimmed means, M-estimators. 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  (Dag et al., 2024, <doi:10.1016/j.softx.2024.101862>).
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.
A model for the growth of self-limiting populations using three, four, or five parameter 
    functions, which have wide applications in a variety of fields.
    The dependent variable in a dynamical modeling could be the population size at time x, 
    where x is the independent variable. In the analysis of quantitative polymerase chain reaction 
    (qPCR), the dependent variable would be the fluorescence intensity and the independent variable 
    the cycle number. This package then would calculate the TWW cycle threshold.
Provides several confidence interval and testing procedures, based on either 
    semiparametric (using event-specific win ratios) or nonparametric measures, 
    including the ratio of integrated cumulative hazard (RICH) and the ratio of 
    integrated transformed cumulative hazard (RITCH), for treatment effect inference 
    with terminal and non-terminal events under competing risks. The semiparametric 
    results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the 
    nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). 
    For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 
    <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), 
    Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 
    <doi:10.1093/biostatistics/kxv032>) are included. The package also supports 
    univariate survival analysis with a single event. In this package, effect size 
    estimates and confidence intervals are obtained for each event type, and several 
    testing procedures are implemented for the global null hypothesis of no treatment 
    effect on either terminal or non-terminal events. Furthermore, a test of proportional 
    hazards assumptions, under which the event-specific win ratios converge to hazard 
    ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment 
    effect across all events, confidence intervals for linear combinations of the 
    event-specific win ratios, RICH, or RITCH are available using pre-determined or 
    data-driven weights. Asymptotic properties of these inference procedures are 
    discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 
    <doi:10.1002/sim.70205>).
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.
Simulates typing of R script files for presentations and demonstrations.
    Provides character-by-character animation with optional live code execution.
    Supports R scripts (.R), R Markdown (.Rmd), and Quarto (.qmd) documents.
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.
Compile 'Typst' files using the 'typst-cli' (<https://typst.app>)
    command line tool. Automatically falls back to rendering via embedded 'Typst'
    from 'Quarto' (<https://quarto.org>) if 'Typst' is not installed. Includes
    utilities to check for 'typst-cli' availability and run 'Typst' commands.
Create 'Typst' table markup from data frames. Features a pipe-friendly interface for column, row, and cell styling with support for grouped headers, grouped rows, and data-driven formatting.
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!
An educational toolkit for learning statistical concepts through 
    interactive exploration. Provides functions for basic statistics (mean, 
    variance, etc.) and probability distributions with step-by-step explanations 
    and interactive learning modes. Each function can be used for simple 
    calculations, detailed learning with explanations, or interactive practice 
    with feedback.
A comprehensive educational package combining clustering algorithms with 
    detailed step-by-step explanations. Provides implementations of both traditional 
    (hierarchical, k-means) and modern (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), 
    Gaussian Mixture Models (GMM), genetic k-means) clustering methods 
    as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>. 
    Includes educational datasets highlighting different clustering challenges, based on 
    'scikit-learn' examples (Pedregosa et al., 2011) 
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>. Features detailed 
    algorithm explanations, visualizations, and weighted distance calculations for 
    enhanced learning.
Despite there being a section in RFC 7231
    <https://datatracker.ietf.org/doc/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 ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller. Additionally it contains some useful tools for digitizing and data manipulation.
Analyzes the impact of external conditions on air quality using counterfactual approaches, featuring methods for data preparation, modeling, and visualization.
The framework proposed in Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>, together with an interactive Shiny dashboard. 'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (<doi:10.2307/2531628>). 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'.
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>.
Basic statistical analyses. The package has been developed
    to be used in statistics courses at Bocconi University (Milan, Italy).
    Currently, the package includes some exploratory and inferential analyses
    usually presented in introductory statistics courses.
Calculates the Urban Centrality Index (UCI) as in Pereira et al., 
             (2013) <doi:10.1111/gean.12002>. The UCI measures the 
             extent to which the spatial organization of a city or region varies 
             from extreme polycentric to extreme monocentric in a continuous 
             scale from 0 to 1. Values closer to 0 indicate more polycentric patterns
             and values closer to 1 indicate a more monocentric urban form.
Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.
Find and import datasets from the
    University of California Irvine Machine Learning (UCI ML) Repository into R.
    Supports working with data from UCI ML repository inside of R scripts, notebooks, 
    and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at
    <https://archive.ics.uci.edu/>.
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 
             time series models of many kinds.
             Harvey AC (1989) <doi:10.1017/CBO9781107049994>.
             Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>.
             Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>.
             Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>.
             Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>.
             Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
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.
Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
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.
Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution
    based on Ayuyuen, S., & Bodhisuwan, W. (2024)<doi:10.18187/pjsor.v20i1.4307>.
An R API providing easy access to a relational database with macroeconomic, 
             financial and development related time series data for Uganda. 
             Overall more than 5000 series at varying frequency (daily, monthly, 
             quarterly, annual in fiscal or calendar years) can be accessed through 
             the API. The data is provided by the Bank of Uganda, 
             the Ugandan Ministry of Finance, Planning and Economic Development,
             the IMF and the World Bank. The database is being updated once a month.
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR),
    Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, 
    quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. 
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, 
    Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>,
    Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.
Unit-Gompertz density, cumulative distribution, quantile functions and random deviate
   	generation of the unit-Gompertz distribution. In addition, there are a function for fitting the 
   	Generalized Additive Models for Location, Scale and Shape.
Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).
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>.
Retrieve data from the UNESCO Institute for Statistics (UIS) API
    <https://api.uis.unesco.org/api/public/documentation/>. UIS provides public 
    access to more than 4,000 indicators focusing on education, science and
    technology, culture, and communication.
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.
Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.
A suite of utilities for working with the UK Biobank 
    <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) 
    metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. 
    Includes functions for extracting biomarkers from decoded UK Biobank field 
    data, removing unwanted technical variation from biomarker concentrations,
    computing an extended set of lipid, fatty acid, and cholesterol fractions, 
    and for re-deriving composite biomarkers and ratios after adjusting data 
    for unwanted biological variation. For further details on methods see
    Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.
Provides easy access to a curated selection of pre-processed data 
  sets relevant to the COVID-19 outbreak in the UK for teaching and 
  demonstration purposes.
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 14. 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). "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>). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/help/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.
Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the 'ukcovid19' package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see <https://ukhsa-dashboard.data.gov.uk/access-our-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.
Extracts coordinates of an event location from text based on dictionaries of landmarks, roads, and areas. Only returns the location of an event of interest and ignores other location references; for example, if determining the location of a road traffic crash from the text "crash near [location 1] heading towards [location 2]", only the coordinates of "location 1" would be returned. Moreover, accounts for differences in spelling between how a user references a location and how a location is captured in location dictionaries. For more information on the algorithm, see Milusheva et al. (2021) <doi:10.1371/journal.pone.0244317>.
Universally unique identifiers ('UUIDs') can be sub-optimal for many
  uses-cases because they are not 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. Providing an alternative, 'ULIDs'
  (<https://github.com/ulid/spec>) have 128-bit compatibility with 'UUID', 1.21e+24
  unique 'ULIDs' per millisecond, support standard (text) sorting, canonically encoded
  as a 26 character string, as opposed to the 36 character 'UUID', use 'base32'
  encoding for better efficiency and readability (5 bits per character), are case
  insensitive, have no special characters (i.e. are 'URL' safe) and have a monotonic
  sort order (correctly detects and handles the same millisecond).
A tool to define the rare biosphere. 'ulrb' solves the problem of the 
    definition of rarity by replacing arbitrary thresholds with an unsupervised machine 
    learning algorithm (partitioning around medoids, or k-medoids). This algorithm 
    works for any type of microbiome data, provided there is an abundance table.
    This method also works for non-microbiome data.
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.
Plots traced ultrasound tongue imaging data according to a polar coordinate system. 
    There is currently support for plotting means and standard deviations of each category's trace;
    Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well.  The origin of the 
    polar coordinates may be defined manually or automatically determined based on different 
    algorithms.
    Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' 
    (<https://github.com/SwatPhonLab/UltraTrace>). 'UltraTrace' is capable of importing data from 
    Articulate Instruments AAA.
    'read_textgrid.R' is required for opening TextGrids to determine category and alignment
    information of ultrasound traces.
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'. Based on the recognized data structures, the method can be used to generate new data.
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.
Provides tools for assigning molecular formulas from exact masses 
    obtained by ultrahigh-resolution mass spectrometry. The methodology follows 
    the workflow described in Leefmann et al. (2019) <doi:10.1002/rcm.8315>. 
    The package supports the inspection, filtering and visualization of 
    molecular formula data and includes utilities for calculating common 
    molecular parameters (e.g., double bond equivalents, DBE). A graphical 
    user interface is available via the 'shiny'-based 'ume' application.
Tools for analyzing sequencing data containing unique
    molecular identifiers generated by 'UMIErrorCorrect'
    (<https://github.com/stahlberggroup/umierrorcorrect>).
Calculates one-sample unbiased central moment estimates and 
    two-sample pooled estimates up to 6th order, including estimates of 
    powers and products of central moments. Provides the machinery for 
    obtaining unbiased central moment estimators beyond 6th order by generating
    expressions for expectations of raw sample moments and their powers and 
    products. 
    Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>)
    for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
The Ultimate Microrray Prediction, Reality and Inference
  Engine (UMPIRE) is a package to facilitate the simulation of realistic
  microarray data sets with links to associated outcomes. See Zhang and
  Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the
  ability to simulate realistic mixed-typed clinical data.
Quickly create, run, and report structural equation models, and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
    Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R.
    Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
If a procedure consists of several stages and there are several models that can be selected for each stage,
	uncertainty of the procedure can be decomposed by stages or models.
	This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method.
	Yongdai Kim et al. (2019) <doi:10.1016/j.hydroa.2019.100024> is a related paper which is accessible via the URL below.
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Implements Minimum Torsion for portfolio diversification as
    described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.
Model data with a suspected clustering structure (either in 
    co-variate space, regression space or both) using a Bayesian product model 
    with a logistic regression likelihood. Observations are represented 
    graphically and clusters are formed through various edge removals or 
    additions. Cluster quality is assessed through the log Bayesian evidence of 
    the overall model, which is estimated using either a Sequential Monte Carlo 
    sampler or a suitable transformation of the Bayesian Information Criterion 
    as a fast approximation of the former. The internal Iterated Batch 
    Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is 
    made available as a free standing function.
A framework for estimating difference-in-differences with
 unpoolable data, based on Karim, Webb, Austin, and Strumpf (2025)
 <doi:10.48550/arXiv.2403.15910>. Supports common or staggered adoption, multiple
 groups, and the inclusion of covariates. Also computes p-values for the
 aggregate average treatment effect on the treated via the
 randomization inference procedure described in MacKinnon and Webb (2020)
 <doi:10.1016/j.jeconom.2020.04.024>.
Detects values imported from spreadsheets that were auto-converted 
    to Excel date serials and reconstructs the originally intended day.month
    decimals (for example, '30.3' that Excel displayed as '30/03/2025').
    The functions work in a vectorized manner, preserve non-serial values, and 
    support both the 1900 and 1904 date systems.
A variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation.
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>.
Create United Nations High Commissioner for Refugees (UNHCR)
    branded documents, presentations, and reports using R Markdown templates.
    This package provides customized formats that align with UNHCR's official
    brand guidelines for creating professional PDF reports, Word documents,
    PowerPoint presentations, and HTML outputs.
A 'ggplot2' theme and color palettes following
    the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.
Verb-like functions to work with messy data, often derived from 
             spreadsheets or parsed PDF tables. Includes functions for unwrapping 
             values broken up across rows, relocating embedded grouping values, 
             and to annotate meaningful formatting in spreadsheet files.
Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).
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.
An R client to fetch SDMX (Statistical Data and Metadata eXchange)
    CSV series from the UNICEF Data Warehouse <https://data.unicef.org/>.
    Part of a trilingual suite also available for 'Python' and 'Stata'.
    Features include automatic pagination, caching with memoisation, country
    name lookups, metadata versioning (vintages), and comprehensive indicator
    support for SDG (Sustainable Development Goals) monitoring.
UNIfied Cross-Omics deconvolution (Unico) deconvolves
    standard 2-dimensional bulk matrices of samples by features into a
    3-dimensional tensors representing samples by features by cell types.
    Unico stands out as the first principled model-based deconvolution
    method that is theoretically justified for any heterogeneous genomic
    data. For more details see Chen and Rahmani et al. (2024)
    <doi:10.1101/2024.01.27.577588>.
Data from Unicode 16.0.0 and related utilities.
Most universities use specific color combinations to express their unique brand identity. The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.
Testing whether two discrete variables have a functional
  relationship under null distributions where the two variables are
  statistically independent with fixed marginal counts.
  The fast enumeration algorithm was based on (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>.
Uniform sampling of Directed Acyclic Graphs (DAG) using exact
 enumeration by relating each DAG to a sequence of outpoints (nodes with no
 incoming edges) and then to a composition of integers as suggested by
 Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.
Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).
In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules.
Provides a unified R6-based interface for various machine learning models with automatic interface detection, consistent cross-validation, model interpretations via numerical derivatives, and visualization. Supports both regression and classification tasks with any model function that follows R's standard modeling conventions (formula or matrix interface).
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.
In diagnostic contexts, individuals are often assessed using multiple
 tests that measure the same latent variable (e.g., intelligence). These test scores
 are typically not exactly identical. Simple averaging neglects the correlation
 between tests and the reduced variance of their combination. The 'unifyR' package
 provides functions to compute statistically accurate unified scores, reliabilities
 and validities of multiple tests. The underlying algorithms build on and extend the
 method proposed by Evans (1996, <DOI:10.3758/BF03204767>) and have been validated
 through simulations.
A unified R graphics backend. Render R graphics fast and easy to many common file formats.
    Provides a thread safe 'C' interface for asynchronous rendering of R graphics.
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.
Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class 'glmnet', so that all of the methods for 'glmnet' are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.
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 <doi:10.1016/j.jprot.2019.103613>.
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.
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.
Tools to detect and correct measurement-unit errors in multivariate numeric data using model-based clustering. Gaussian mixture models with user-defined translation vectors identify clusters of records that differ in scale or unit. Core functionality includes cluster assignment via the EM algorithm, error correction based on posterior probabilities and pairwise scatterplot visualizations. For more details see Di Zio, Guarnera and Luzi (2005) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2005001/article/8087-eng.pdf>.
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.
Implementation of the unity forest (UFO) framework (Hornung & Hapfelmeier, 2026,
  <doi:10.48550/arXiv.2601.07003>).
  UFOs are a random forest variant designed to better take covariates with purely
  interaction-based effects into account, including interactions for which none of the involved
  covariates exhibits a marginal effect.
  While this framework tends to improve discrimination and predictive accuracy compared
  to standard random forests, it also facilitates the identification and interpretation of
  (marginal or interactive) effects: In addition to the UFO algorithm for tree construction,
  the package includes the unity variable importance measure (unity VIM), which quantifies
  covariate effects under the conditions in which they are strongest - either marginally 
  or within subgroups defined by interactions - as well as covariate-representative tree
  roots (CRTRs) that provide interpretable visualizations of these conditions.
  Categorical and continuous outcomes are supported.
  This package is a fork of the R package 'ranger' (main author: Marvin N. Wright), which
  implements random forests using an efficient C++ backend.
Assess essential unidimensionality using external validity
    information  using the procedure proposed by Ferrando & Lorenzo-Seva
    (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing
    differential  and incremental validity, both based on a second-order 
    modelling schema for the general factor.
User-friendly maximum likelihood estimation (Fisher (1921) 
    <doi:10.1098/rsta.1922.0009>) of univariate densities.
Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them.
    The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).
    The details of the indices in this package can be found in:
    J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>,
    T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>,
    C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>,
    D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>,
    J. C. Dunn (1973) <doi:10.1080/01969727308546046>,
    F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>,
    M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>,
    S. H. Kwon (1998) <doi:10.1049/EL:19981523>,
    S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>,
    G. W. Miligan (1980) <doi:10.1007/BF02293907>,
    M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>,
    M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>,
    S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>,
    A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>,
    Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>,
    N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>,
    N. Wiroonsri, O. Preedasawakul (2023) <doi:10.48550/arXiv.2308.14785>,
    C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495>, 
    X. Xie, G. Beni (1991) <doi:10.1109/34.85677> and
    Rousseeuw (1987) and Kaufman and Rousseeuw(2009) <doi:10.1016/0377-0427(87)90125-7> and 
    <doi:10.1002/9780470316801>
    C. Alok. (2010).
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.
Provides well-known techniques for detecting univariate
    outliers. Methods for handling skewed distributions are included.
    The Hidiroglou-Berthelot (1986) method for detecting outliers in
    ratios of historical data is also implemented. When available,
    survey weights can be incorporated in the detection process.
Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.
Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.
Separate a data frame in two based on key columns. The function
 unjoin() provides an inside-out version of a nested data frame. This is used
 to identify duplication and normalize it (in the database sense) by linking
 two tables with the redundancy removed. This is a basic requirement for
 detecting topology within spatial structures that has motivated the need for
 this package as a building block for workflows within more applied projects.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Tools for fitting and assessing Bayesian multilevel regression 
  models that account for unmeasured confounders.
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.
Methods for managing under- and over-enrollment in Simon's Two-Stage Design are offered by providing adaptive threshold adjustments and sample size recalibration. It also includes post-inference analysis tools to support clinical trial design and evaluation. The package is designed to enhance flexibility and accuracy in trial design, ensuring better outcomes in oncology and other clinical studies. Yunhe Liu, Haitao Pan (2024). Submitted.
Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.
Wraps the 'unrtf' utility <https://www.gnu.org/software/unrtf/> to 
    extract text from RTF files. Supports document conversion to HTML, LaTeX or
    plain text. Output in HTML is recommended because 'unrtf' has limited 
    support for converting between character encodings.
Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.
Reconstructs all possible raw data that could have led to reported
    summary statistics. Provides a wrapper for the 'Rust' implementation of the
    'CLOSURE' algorithm.
Tools for clustering individualized survival curves using the
    Partitioning Around Medoids (PAM) algorithm, with monotonic enforcement,
    optional smoothing, weighted distances (L1/L2), automatic K selection via
    silhouette width, prediction for new curves, basic stability checks, and
    plotting helpers. The clustering strategy follows Kaufman and Rousseeuw
    (1990, ISBN:0471878766).
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.
This program realizes a universal estimation approach that accommodates
    multi-category variables and effect scales, making up for the deficiencies
    of the existing approaches when dealing with non-binary exposures and
    complex models. The estimation via bootstrapping can simultaneously provide results of
    causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects' difference.
    The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates,
    panel data, etc. The high flexibility and compatibility
    make it possible to apply for any type of model, greatly meeting the needs of
    current empirical researches.
We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.
Displays percentage changes by height and absolute changes by area for up to three nested or non-nested levels. The plots visualise changes in indices and markets, showing how the changes for sectors or for individual components contribute to the overall change. Data can be classified by up to three levels of grouping variables in a layered, hierarchical plot. Each level can be ordered in several ways including by baseline, by percentage change, and by absolute change. The vignettes give examples.
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 a package is loaded, the source repository is checked for
    new versions and a message is shown in the console indicating whether the
    package is out of date.
When updating major or minor R versions all packages should
    be re-installed. The utilities in this package assist in getting a
    user up-and-running again by installing all previously installed R
    packages. The package uses 'renv' to install; immediately replenishing
    your 'renv' package cache.
Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allele bias, overdispersion, and sequencing
       error. The main functions are flexdog() and multidog(), 
       which allow the specification
       of many different genotype distributions. Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See
       Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
       Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details 
       on the implemented methods.
Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".
Up-and-Down (UD) is the most popular design approach for dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 <doi:10.1097/ALN.0000000000004282>. Recent methodological overview: Oron and Flournoy, 2024 <doi:10.51387/24-NEJSDS74>.
Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on different models. These contributions were derived from variation partitioning (VP) and hierarchical partitioning (HP), applying the algorithm of "Lai et al. (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>".
'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>.
The Upsilon test assesses association among
 categorical variables against the null hypothesis of
 independence (Luo 2021 MS thesis; ProQuest Publication
 No. 28649813). While promoting dominant function patterns,
 it demotes non-dominant function patterns. It is robust
 to low expected count---continuity correction
 like Yates's seems unnecessary. Using a common null
 population following a uniform distribution, contingency
 tables are comparable by statistical significance---not
 the case for most association tests defining a varying
 null population by tensor product of observed marginals.
 Although Pearson's chi-squared test, Fisher's exact test,
 and Woolf's G-test (related to mutual information) are
 useful in some contexts, the Upsilon test appeals to
 ranking association patterns not necessarily following
 same marginal distributions, such as in count data
 from DNA and RNA sequencing---a rapidly expanding frontier
 in modern science.
Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.
Variance approximations for the 
    Horvitz-Thompson total estimator in Unequal Probability Sampling
    using only first-order inclusion probabilities. 
    See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.
'Elasticsearch' is an open-source, distributed, document-based datastore
    (<https://www.elastic.co/products/elasticsearch>).
    It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that
    'API' was not designed for common data science workflows like pulling large batches of
    records and normalizing those documents into a data frame that can be used as a training
    dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch'
    that is explicitly designed to make these data science workflows easy and fun.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. Goehring (2024) <https://bengoehring.github.io/improving-content-analysis-tools-for-working-with-undergraduate-research-assistants.pdf> argues that supervisors---especially supervisors of small teams---should utilize computational tools to monitor reliability in real time. As such, this package provides easy-to-use functions for calculating inter-rater reliability statistics and measuring the reliability of one coder compared to the rest of the team.
Calculate unified measures
  that quantify the effect of a covariate on a binary dependent variable
  (e.g., for meta-analyses).
  This can be particularly important
  if the estimation results are obtained
  with different models/estimators
  (e.g., linear probability model, logit, probit, ...)
  and/or with different transformations of the explanatory variable of interest
  (e.g., linear, quadratic, interval-coded, ...).
  The calculated unified measures are:
  (a) semi-elasticities of linear, quadratic, or interval-coded covariates and
  (b) effects of linear, quadratic, interval-coded, or categorical covariates
  when a linear or quadratic covariate changes between distinct intervals,
  the reference category of a categorical variable
  or the reference interval of an interval-coded variable needs to be changed,
  or some categories of a categorical covariate
  or some intervals of an interval-coded covariate need to be grouped together.
  Approximate standard errors of the unified measures are also calculated.
  All methods that are implemented in this package 
  are described in the 'vignette'  
  "Extracting and Unifying Semi-Elasticities and Effect Sizes
  from Studies with Binary Dependent Variables"
  that is included in this package.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Provide the URL checking tools available in R 4.1+ as a
    package for earlier versions of R. Also uses concurrent requests so
    can be much faster than the serial versions.
Offers tools for parsing and analyzing URL datasets,
    extracting key components and identifying common patterns. It aids in 
    examining website architecture and identifying SEO issues, helping users
    optimize web presence and content strategy.
A fast and simple 'URL' parser package for 'R'. This package provides
    functions to parse 'URLs' into their components, such as scheme, user, password, host, port,
    path, query, and fragment.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth 2.0) and 'is.gd' (no API key).
A toolkit for all URL-handling needs, including encoding and decoding,
    parsing, parameter extraction and modification. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
Conduct unit root tests based on 'EViews' (<https://eviews.com>) routines and report them in tables. 'EViews' (Econometric Views) is a commercial software for econometrics.
S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
A simple interface to the Geographic Header information
  from the "2010 US Census Summary File 2".  The entire Summary File 2
  is described at
  <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>,
  but note that this package only provides access to parts of the
  geographic header ('geoheader') of the file.  In particular, only
  the first 101 columns of the geoheader are included and, more
  importantly, only rows with summary levels (SUMLEVs) 010 through 050
  (nation down through county level) are included.  In addition to
  access to (part of) the geoheader, the package also provides a
  decode function that takes a column name and value and, for certain
  columns, returns "the meaning" of that column (i.e., a "SUMLEV"
  value of 40 means "State"); without a value, the decode function
  attempts to describe the column itself.
Updated versions of the 1970's "US State Facts and Figures"
    objects from the 'datasets' package included with R. The new data is
    compiled from a number of sources, primarily from United States Census
    Bureau or the relevant federal agency.
Contains a WGS84 datum map of the USA, which includes all
    Commonwealth and State boundaries & also includes Puerto Rico and the U.S.
    Virgin Islands. This map is a reprojection of the NAD83 datum map from the
    USGS National Map. This package contains a subset of the data included in
    the 'USA.state.boundaries.data' package, which is available in a 'drat'
    repository. To install that data package, please follow the instructions at
    <https://gitlab.com/iembry/usa.state.boundaries.data>.
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.
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.
Fetch United States Congressional Records from their API <https://api.govinfo.gov/docs/> such as congressional 
    speeches, speaker names, and metadata about congressional sessions, and detailed granule records. 
    Optional parameters allow users to specify congressional sessions, 
    and the maximum number of speeches to retrieve. Data is parsed, cleaned, and 
    returned in a structured dataframe for analysis.
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.
Provides a diverse collection of U.S. datasets 
    encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more.
    It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.
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).)
Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.
Provides functions for uniform sampling of the environmental space, designed to assist species distribution modellers in gathering ecologically relevant pseudo-absence data. The method ensures balanced representation of environmental conditions and helps reduce sampling bias in model calibration. Based on the framework described by Da Re et al. (2023) <doi:10.1111/2041-210X.14209>.
Functions to re-arrange, extract, and work with distances.
Consistent with 'knitr' syntax highlighting, 'usedthese' adds
    a summary table of package & function usage to a Quarto document and
    enables aggregation of usage across a website.
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
A set of general functions that I have used in various
    projects and other R packages. Miscellaneous operations on data
    frames, matrices and vectors, ROC and PR statistics.
Code snippets to fit models using the tidymodels framework
    can be easily created for a given data set.
Automate package and project setup tasks that are otherwise
    performed manually. This includes setting up unit testing, test
    coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp',
    'RStudio' projects, and more.
Compiled and cleaned the county-level estimates of fertilizer, 
    nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA).  The commercial fertilizer data were originally 
    generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated
    based on county-level population data of livestock, poultry, and other animals.
    See the user manual for detailed data sources and cleaning methods.  
    'usfertilizer' utilized the tidyverse to clean the original data and provide 
    user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.
Provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.
Provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.
Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.
Calculates federal and state income taxes in the United States. It acts as a wrapper 
    to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts 
    the calculations, while 'usincometaxes' prepares the data for TAXSIM 35, sends the data to 
    TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it 
    into a data frame. All without the user worrying about this process.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law (Gunther 2007)
    <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Obtain United States map data frames of varying region types (e.g. county,
    state). The map data frames include Alaska and Hawaii conveniently placed to the
    bottom left, as they appear in most maps of the US. Convenience functions for plotting
    choropleths, visualizing spatial data, 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 as new map data 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.
Tools package to extract and analyze data from U SPORTS, the governing body of university sport in Canada.
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.
Provides decorators, transformators, and utility functions
    to extend the 'teal' framework for interactive data analysis
    applications. Implements methods for data visualization enhancement,
    statistical data transformations, and workflow integration tools.
    Designed to support clinical and pharmaceutical research workflows
    within the 'teal' ecosystem through modular and reusable components.
An implementation of Lind and Mehlum's (2010) <doi:10.1111/j.1468-0084.2009.00569.x> Utest to test for the presence of a U shaped or inverted U shaped relationship between 
              variables in (generalized) linear models. It also implements a test of upward/downward sloping relationships at the lower and upper 
              boundary of the data range.
Process and print 'UTF-8' encoded international
    text (Unicode). Input, validate, normalize, encode, format, and
    display.
Format text (bold, italic, ...) and numbers using UTF-8. 
    Offers functions to search for emojis and include them in your text.
Functions for building customized ready-to-export tables for publication.
Tools for formatting and summarizing data for outcomes research.
A set of functions to aid in the production of visuals in ggplot2.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
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.
An engine for univariate time series forecasting using
    different regression models in an autoregressive way. The engine
    provides an uniform interface for applying the different models. 
    Furthermore, it is extensible so that users can easily apply their
    own regression models to univariate time series forecasting and 
    benefit from all the features of the engine, such as preprocessings
    or estimation of forecast accuracy.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
Estimate ambient vitamin D-effective or erythemal dose using ultraviolet radiation (UV) data from the 'TEMIS' database, based on date and geographical location.
A tool for checking how much information is disclosed when
    reporting summary statistics.
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
An implementation of the Uniform Manifold Approximation and
    Projection dimensionality reduction by McInnes et al. (2018)
    <doi:10.48550/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) <doi:10.48550/arXiv.1602.00370>
    is also provided. This is a complete re-implementation in R (and C++,
    via the 'Rcpp' package): no Python installation is required. See the
    uwot website (<https://github.com/jlmelville/uwot>) for more
    documentation and examples.
Provides convenience functions for user experience research
    with an emphasis on quantitative user experience testing and reporting.
    The functions are designed to translate statistical approaches to 
    applied user experience research.
An R interface to V8 <https://v8.dev>: Google's open source JavaScript
    and WebAssembly engine. This package can be compiled either with V8 or NodeJS 
    when built as a shared library.
Calibrates population-level cause-specific mortality fractions (CSMFs) that are derived using computer-coded verbal autopsy (CCVA) algorithms. Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;<https://champshealth.org/>) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;<doi:10.1214/24-AOAS2006>) and are analyzed in Pramanik et al. (2026;<doi:10.1136/bmjgh-2025-021747>). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;<doi:10.1214/24-AOAS2006>) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute.
Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) <doi:10.1016/j.vaccine.2015.02.042> and Machado et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.45.1900268> for influenza vaccination impact.
Various semiparametric and nonparametric statistical tools for
    immune correlates analysis of vaccine clinical trial data. This includes
    calculation of summary statistics and estimation of risk, vaccine efficacy,
    controlled effects (controlled risk and controlled vaccine efficacy), and
    mediation effects (natural direct effect, natural indirect effect,
    proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022)
    <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023)
    <doi:10.48550/arXiv.2208.06465>.
Provides tools for estimating vaccine effectiveness and
    related metrics. The 'vaccineff_data' class manages key features for
    preparing, visualizing, and organizing cohort data, as well as
    estimating vaccine effectiveness. The results and model performance
    are assessed using the 'vaccineff' class.
A method to visualize pharmacometric analyses which are impacted by
    covariate effects. Variability-aligned covariate harmonized-effects
    and time-transformation equivalent ('vachette') facilitates intuitive
    overlays of data and model predictions, allowing for comprehensive
    comparison without dilution effects. 'vachette' improves upon previous
    methods Lommerse et al. (2021) <doi:10.1002/psp4.12679>, enabling its
    application to all pharmacometric models and enhancing Visual Predictive
    Checks (VPC) by integrating data into cohesive plots that can highlight
    model misspecification.
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>.
A set of basic tools to transform functions into functions with
    input validation checks, in a manner suitable for both programmatic and
    interactive use.
Provides a robust and reproducible pipeline for extracting,
    cleaning, and analyzing athlete performance data generated by 'VALD'
    'ForceDecks' systems. The package supports batch-oriented data processing
    for large datasets, standardized data transformation workflows, and
    visualization utilities for sports science research and performance
    monitoring. It is designed to facilitate reproducible analysis across
    multiple sports with comprehensive documentation and error handling.
Provides helper functions and wrappers to simplify authentication, 
    data retrieval, and result processing from the 'VALD' 'APIs'. 
    Designed to streamline integration for analysts and researchers working 
    with 'VALD's external 'APIs'.
    For further documentation on integrating with 'VALD' 'APIs', see:
    <https://support.vald.com/hc/en-au/articles/23415335574553-How-to-integrate-with-VALD-APIs>.
    For a step-by-step guide to using this package, see:
    <https://support.vald.com/hc/en-au/articles/48730811824281-A-guide-to-using-the-valdr-R-package>.
Provides a 'shiny' dashboard and plotting utilities to explore and
    report 'VALD ForceDecks' testing data. Includes interactive modules for
    metric exploration, radar charts, longitudinal comparisons, quadrant plots,
    and athlete reports.
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>.
Uses large language models to create poems about R packages.
   Currently contains the roses() function to make "roses are red, ..." style 
   poems and the prompt() function to only assemble the prompt without submitting 
   it to the model.
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 between R and the 'Valhalla' API. 'Valhalla' is a
    routing service based on 'OpenStreetMap' data.
    See <https://valhalla.github.io/valhalla/> for more information.
    This package enables the computation of routes, trips, isochrones and
    travel distances matrices (travel time and kilometer distance).
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>.
Functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?
Declare data validation rules and data quality indicators;
        confront data with them and analyze or visualize the results.
        The package supports rules that are per-field, in-record,
        cross-record or cross-dataset. Rules can be automatically
        analyzed for rule type and connectivity. Supports checks implied
        by an SDMX DSD file as well. See also Van der Loo
        and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6
        and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.
Generate suggestions for validation rules 
  from a reference data set, which can be used as a starting point for domain specific 
   rules to be checked with package 'validate'.
Rule sets with validation rules may contain redundancies or contradictions. 
  Functions for finding redundancies and problematic rules are provided, 
  given a set a rules formulated with 'validate'.
Given a partition resulting from any clustering algorithm, the implemented tests allow valid post-clustering inference by testing if a given variable significantly separates two of the estimated clusters. 
             Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). 
             "Post-clustering difference testing: valid inference and practical considerations", <arXiv:2210.13172>.
Deploy, execute, and analyze the results of models hosted on the
  'ValidMind' Platform <https://validmind.ai>. This package interfaces with the
  'Python' Library API in order to allow advanced diagnostics and insight
  into trained models all from an 'R' environment.
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 
    <https://bioinformatics.mx/index.php/bioinfo-tools/>.
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>.
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.
Provides tools for analyzing the relationship between direct
    prices (based on labor values) and prices of production using Bayesian
    generalized linear models, panel data methods, partial least squares
    regression, canonical correlation analysis, and panel vector
    autoregression. Includes functions for model comparison, out-of-sample
    validation, and structural break detection. Here, methods use raw accounting data with explicit temporal structure, following Gomez Julian (2023) <doi:10.17605/OSF.IO/7J8KF>
    and standard econometric techniques for panel data analysis.
Provides statistical methods for analytical method comparison and 
    validation studies. Implements Bland-Altman analysis for assessing agreement 
    between measurement methods (Bland & Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>), 
    Passing-Bablok regression for non-parametric method comparison 
    (Passing & Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>), and Deming 
    regression accounting for measurement error in both variables 
    (Linnet (1993) <doi:10.1093/clinchem/39.3.424>). Also includes tools for 
    setting quality goals based on biological variation (Fraser & Petersen (1993) 
    <doi:10.1093/clinchem/39.7.1447>) and calculating Six Sigma metrics, 
    precision experiments with variance component analysis, precision
    profiles for functional sensitivity estimation (Kroll & Emancipator (1993) 
    <https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical 
    laboratory method validation. Provides publication-ready plots and 
    comprehensive statistical summaries.
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>.
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. It 
    comprises functions to adjust discrimination statistics for the 
    representativeness effect through case-weighting, along with functions for 
    visualizing the outcomes. Originally outlined in: 
    Jiménez-Valverde (2022) The uniform AUC: dealing with the 
    representativeness effect in presence-absence models. Methods Ecol. Evol, 
    13, 1224-1236.
Provides 'ggplot2'-compatible colour palettes inspired by Vincent van Gogh's paintings. Each palette contains five colours, manually selected by hexadecimal values. Includes tools for assessing colour vision deficiency (CVD) accessibility.
Simplifies functions to conduct univariate, 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.
The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them.  These are induced by strong peaks of the marginal spectra of each series at the corresponding frequencies. The spectral characteristics also determine how strongly these short-, medium-, or long-term fluctuations of the two series are correlated between the two series. Information on this is provided by the Coherency spectrum at the corresponding frequencies. Finally, certain fluctuations of the two series may be lagged to each other. Information on this is provided by the Phase spectrum at the corresponding frequencies. The idea in this package is to define a VAR (Vector autoregression) model with desired spectral characteristics by specifying a number of polynomials, required to define the VAR. See Ioannidis(2007) <doi:10.1016/j.jspi.2005.12.013>. These are specified via their roots, instead of via their coefficients. This is an idea borrowed from the Time Series Library of R. Dahlhaus, where it is used for defining ARMA models for univariate time series. This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage.
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.
Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024)
    <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional 
    Vector Auto-regressive Models". This package provides tools for detecting change points 
    in the transition matrices of VAR models, effectively identifying shifts in temporal 
    and cross-correlations within high-dimensional time series data.
Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.
Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.
Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).
Abstract descriptions of (yet) unobserved variables.
Implements variable screening techniques for ultra-high
    dimensional regression settings.  Techniques for independent (iid) data,
    varying-coefficient models, and longitudinal data are implemented. The package
     currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
     and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
     simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
    Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
    Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>,
    Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086>
    Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and
    Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). 
             Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP).
             For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
Provides functions for the variance gamma
        distribution. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizations for the variance gamma distribution.
This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is 
  a framework based on 'scala' and 'spark' to analyze genome datasets, 
  see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples 
  each one containing 80 million features in either unsupervised clustering approaches 
  and supervised applications, like classification and regression. The genome datasets
  are usually writing in VCF, a specific text file format used 
  in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great 
  tool for genome research, because it is able to read VCF files, run analyses and return 
  the output in a 'spark' data frame.
Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>, Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test.
Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for civilian women, women military service 
    members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the 
    existing American College of Cardiology/American Heart Association CVD risk assessment
    tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic 
    minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. 
    (2021) <doi:10.1161/JAHA.120.019217>.
Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information.
The goal of the package is to equip the 'jmcm' package (current version 0.2.1) 
    with estimations of the covariance of estimated parameters. Two methods are provided. 
    The first method is to use the inverse of estimated Fisher's information matrix, see 
    M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and 
    J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) 
    <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) 
    <doi:10.1214/aos/1176351062> for reference.
A continuous version of the receiver operating characteristics (ROC) curve to assess both classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.
A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.
In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you.
    We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates,
    Outer Control Variates and Importance Sampling algorithms are available in the framework. User can
    write its own simulation function and use the Variance Reduction techniques in this package to
    obtain more efficient simulations. An implementation of Asian Option simulation is already 
    available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Utilizes multiple variable selection methods to estimate Average Treatment Effect.
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
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>.
Performs 20 omnibus tests for testing the composite hypothesis of variance homogeneity.
An implementation of the Likelihood ratio Test (LRT) for testing that,
    in a (non)linear mixed effects model, the variances of a subset of the random
    effects are equal to zero. There is no restriction on the subset of variances
    that can be tested: for example, it is possible to test that all the variances
    are equal to zero. Note that the implemented test is asymptotic.
    This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'.
    Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.
Implements wild bootstrap tests for autocorrelation in Vector
    Autoregressive (VAR) models based on Ahlgren and Catani (2016)
    <doi:10.1007/s00362-016-0744-0>, a combined Lagrange Multiplier (LM)
    test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR
    models from Catani and Ahlgren (2016) <doi:10.1016/j.ecosta.2016.10.006>,
    and bootstrap-based methods for determining the cointegration rank from
    Cavaliere, Rahbek, and Taylor (2012) <doi:10.3982/ECTA9099> and
    Cavaliere, Rahbek, and Taylor (2014) <doi:10.1080/07474938.2013.825175>.
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)
    <doi:10.48550/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.
Provides probability density, cumulative distribution,
    quantile, and random number generation functions for the Vasicek
    distribution. In addition, two functions are available for fitting
    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 raster grid logic, operations that describe a discretized
 rectangular domain and do not require access to materialized data. Grids are
 arrays with dimension and extent, and many operations are functions of dimension
 only: number of columns, number of rows, or they are a combination of the
 dimension and the extent the range in x and the range in y in that order. Here
 we provide direct access to this logic without need for connection to any
 materialized data or formats. Grid logic includes functions that relate the cell
 index to row and column, or row and column to cell index, row, column or cell
 index to position. These methods are described in Loudon, TV, Wheeler, JF,
 Andrew, KP (1980)  <doi:10.1016/0098-3004(80)90015-1>, and implementations were
 in part derived from Hijmans R (2024) <doi:10.32614/CRAN.package.terra>.
An R client for the 'vatcheckapi.com' VAT number validation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://vatcheckapi.com/docs> .
Provides an interface to a 'HashiCorp' vault server over
  its http API (typically these are self-hosted; see
  <https://www.vaultproject.io>).  This allows for secure storage and
  retrieval of secrets over a network, such as tokens, passwords and
  certificates.  Authentication with vault is supported through
  several backends including user name/password and authentication via
  'GitHub'.
Provides tools to analyze vaccine coverage data and simulate potential disease outbreak scenarios. It allows users to calculate key epidemiological metrics such as the effective reproduction number (Re), outbreak probabilities, and expected infection counts based on county-level vaccination rates, disease characteristics, and vaccine effectiveness. The package includes historical kindergarten vaccination data for Florida counties and offers functions for generating summary tables, visualizations, and exporting the underlying plot data.
Estimate vaccine efficacy (VE) using immunogenicity data.
    The inclusion of immunogenicity data in regression models can increase precision in VE. 
    The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).
Position adjustments for 'ggplot2' to implement "visualize as
    you randomize" principles, which can be especially useful when
    plotting experimental data.
Provides tools for analysis blinding in confirmatory research contexts
  by masking and scrambling test-relevant aspects of data. Vector-, data frame-, and
  row-wise operations support blinding for hierarchical and repeated-measures designs.
  For more details see MacCoun and Perlmutter (2015) <doi:10.1038/526187a> and
  Dutilh, Sarafoglou, and Wagenmakers (2019) <doi:10.1007/s11229-019-02456-7>.
Computes the Gaussian variational approximation of the Bayesian 
    empirical likelihood posterior. This is an implementation of the function 
    found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
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>.
A Variational Bayesian algorithm for high-dimensional multi-source 
    heterogeneous linear models. More details have been written up in a paper 
    submitted to the journal Statistics in Medicine, and the details of variational
    Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
    It simultaneously performs parameter estimation and variable selection. The 
    algorithm supports two model settings: (1) local models, where variable selection
    is only applied to homogeneous coefficients, and (2) global models, where variable
    selection is also performed on heterogeneous coefficients. Two forms of 
    Spike-and-Slab priors are available: the Laplace distribution and the Gaussian 
    distribution as the Slab component.
Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms.  Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.
Add publication-quality custom legends with vertical brackets. Designed for displaying statistical comparisons between groups,
    commonly used in scientific publications for showing significance levels.
    Features include adaptive positioning, automatic bracket spacing for overlapping
    comparisons, font family inheritance, and support for asterisks, p-values,
    or custom labels. Compatible with 'ggplot2' graphics.
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.
Vector binary tree provides a new data structure, to
 make your data visiting and management more efficient. If the
 data has structured column names, it can read these names and
 factorize them through specific split pattern, then build the mappings
 within double list, vector binary tree, array and tensor mutually, through
 which the batched data processing is achievable easily. The methods of
 array and tensor are also applicable. Detailed methods are described in
 Chen Zhang et al. (2020) <doi:10.35566/isdsa2019c8>.
Time series decomposition for univariate time series using the
    "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
    described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
    Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
    und Finanzwissenschaften', by
    Hebbel and Steuer, Springer Berlin Heidelberg, 2022
    <doi:10.1007/978-3-662-65638-9>, or
    'Decomposition of Time Series using the Generalised Berlin 
    Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
    Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
    Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
    Series: Advanced Studies in Theoretical and Applied Econometrics.
    Springer 2014, p. 9-40.
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.
Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2024) <doi:10.1214/24-BA1470>.
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 the 'vcd2df' function, which loads a IEEE 1364-1995/2001
    VCD (.vcd) file, specified as a parameter of type string containing exactly
    a file path, and returns an R dataframe containing values over time.  
    A VCD file captures the register values at discrete timepoints from a 
    simulated trace of execution of a hardware design in Verilog or VHDL.
    The returned dataframe contains a row for each register, by name, and a
    column for each time point, specified VCD-style using octothorpe-prefixed 
    multiples of the timescale as strings. 
    The only non-trivial implementation details are that (1) VCD 'x' and 'z' 
    non-numerical values are encoded as negative value -1 (as otherwise all bit
    values are positive) and (2) registers with repeated names in distinct 
    modules are ignored, rather than duplicated, as we anticipate these 
    registers to have the same values.
    Read more in arXiv preprint: 'vcd2df' -- Leveraging Data Science Insights 
    for Hardware Security Research <doi:10.48550/arXiv.2505.06470>.
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.
Streams and parses variant call format file headers without reading full files. Provides structured metadata, validation, inference, and HTML reporting. For details on the specifications used see Danecek et al. (2021) <doi:10.1093/gigascience/giab008>.
The 'vcfpp.h' (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use 'C++' 'API' of 'htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files. Once
    VCF data is read into R a parser function extracts matrices of data. This
    information can then be used for quality control or other purposes. Additional
    functions provide visualization of genomic data. Once processing is complete
    data may be written to a VCF file (*.vcf.gz). It also may be converted into
    other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between
    VCF data and familiar R software.
Provides an 'htmlwidgets' interface to 'VChart.js'. 
      'VChart', more than just a cross-platform charting library, but also an expressive data storyteller.
      'VChart' examples and documentation are available here: <https://www.visactor.io/vchart>.
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.
Estimating the disparity between two groups based on the extended model of the Peters-Belson (PB) method. Our model is the first work on the longitudinal data, and also can set a varying variable to find the complicated association between other variables and the varying variable. Our work is an extension of the Peters-Belson method which was originally published in Peters (1941)<doi:10.1080/00220671.1941.10881036> and Belson (1956)<doi:10.2307/2985420>.
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 recording real 'HTTP'
    requests/responses on disk in 'cassettes', and then replaying
    matching responses on subsequent requests.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression. A description of a part of this package was published by Burgin and Ritschard (2017) <doi:10.18637/jss.v080.i06>.
Defines new notions of prototype and size that are used to
    provide tools for consistent and well-founded type-coercion and
    size-recycling, and are in turn connected to ideas of type- and
    size-stability useful for analysing function interfaces.
A way of visualizing collections of time series and, optionally
    their future values, forecasts for their future values and prediction
    intervals for the forecasts. A web-based GUI can be used to display the
    information in a collection of time series.
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019) <doi:10.1111/2041-210X.13253>. Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A worked sample on the body shape of cichlid fishes is included, based on the dataset from Kerschbaumer et al. (2013) <doi:10.5061/dryad.fc02f>.
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
    The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
    The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.
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>.
Comprehensive set of tools for analyzing and manipulating functional
 data with non-uniform lengths. This package addresses two common scenarios in
 functional data analysis: Variable Domain Data, where the observation domain
 differs across samples, and Partially Observed Data, where observations are
 incomplete over the domain of interest. 'VDPO' enhances the flexibility and
 applicability of functional data analysis in 'R'.
 See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>.
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'.
Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.
Provides functions for estimation (parametric, semi-parametric and non-parametric)
             of copula-based dependence coefficients between a finite collection of random vectors,
             including phi-dependence measures and Bures-Wasserstein dependence measures. 
             An algorithm for agglomerative hierarchical variable clustering is also implemented.
             Following the articles De Keyser & Gijbels (2024) <doi:10.1016/j.jmva.2024.105336>,
             De Keyser & Gijbels (2024) <doi:10.1016/j.ijar.2023.109090>, and De Keyser & Gijbels (2024)
             <doi:10.48550/arXiv.2404.07141>.
Implements the Vector Matching algorithm to match multiple
    treatment groups based on previously estimated generalized propensity
    scores. The package includes tools for visualizing initial confounder
    imbalances, estimating treatment assignment probabilities using various
    methods, defining the common support region, performing matching across
    multiple groups, and evaluating matching quality. For more details, see 
    Lopez and Gutman (2017) <doi:10.1214/17-STS612>.
The 'base'  tools  union() intersect(), etc.,  follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::sets (alias for all the items) tools functionality.
A tool for fast, efficient bitwise operations along the
    elements within a vector. Provides such functionality for AND, OR and
    XOR, as well as infix operators for all of the binary bitwise
    operations.
Facilitate the analysis of inter-limb and intra-limb coordination in human movement. 
  It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks.
  Needham, R., Naemi, R., & Chockalingam, N. (2014) <doi:10.1016/j.jbiomech.2013.12.032>.
  Needham, R., Naemi, R., & Chockalingam, N. (2015) <doi:10.1016/j.jbiomech.2015.07.023>.
  Tepavac, D., & Field-Fote, E. C. (2001) <doi:10.1123/jab.17.3.259>.
  Park, J.H., Lee, H., Cho, Js. et al. (2021) <doi:10.1038/s41598-020-80237-w>.
Machine learning utilities for fast vectorized model training.
    Methods are based on standard statistical learning references such as
    Hastie et al. (2009) <doi:10.1007/978-0-387-84858-7>.
Provides pedagogical tools for visualization and numerical computation in vector calculus. Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) <ISBN:9781429215084>, Stewart (2015) <ISBN:9781285741550>, Thomas, Weir and Hass (2018) <ISBN:9780134438986>, Larson and Edwards (2016) <ISBN:9781285255869>, Apostol (1969) <ISBN:9780471000051>, Spivak (1971) <ISBN:9780805390216>, Schey (2005) <ISBN:9780071369080>, Colley (2019) <ISBN:9780321982384>, Lizarazo Osorio (2020) <ISBN:9789585450103>, Sievert (2020) <ISBN:9780367180165>, and Borowko (2013) <ISBN:9781439870791>.
Allows registered 'VectorSurv' <https://vectorsurv.org/> users access to data through the 'VectorSurv API' <https://api.vectorsurv.org/>. Additionally provides functions for analysis and visualization.
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.
A lightweight vector database for text retrieval in R with embedded
    machine learning models and no external API (Application Programming
    Interface) keys. Supports dense and hybrid search, optional HNSW
    (Hierarchical Navigable Small World) approximate nearest-neighbor indexing,
    faceted filters with ACL (Access Control List) metadata, command-line
    tools, and a local dashboard built with 'shiny'. The HNSW method is
    described by Malkov and Yashunin (2018) <doi:10.1109/TPAMI.2018.2889473>.
Mixed type vectors are useful for combining semantically similar classes. Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The 'vecvec' data structure facilitates efficient storage and computation across multiple vectors within the same object.
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.
Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.
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>.
It provides a comprehensive toolkit for calculating a suite of common vegetation indices (VIs) derived from remote sensing imagery. VIs are essential tools used to quantify vegetation characteristics, such as biomass, leaf area index (LAI) and photosynthetic activity, which are essential parameters in various ecological, agricultural, and environmental studies. Applications of this package include biomass estimation, crop monitoring, forest management, land use and land cover change analysis and climate change studies. For method details see, Deb,D.,Deb,S.,Chakraborty,D.,Singh,J.P.,Singh,A.K.,Dutta,P.and Choudhury,A.(2020)<doi:10.1080/10106049.2020.1756461>. Utilizing this R package, users can effectively extract and analyze critical information from remote sensing imagery, enhancing their comprehension of vegetation dynamics and their importance in global ecosystems. The package includes the function vegetation_indices().
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.
Earth system dynamics, such as plant dynamics, water bodies, and fire regimes, are widely monitored using spectral indicators obtained from multispectral remote sensing products. There is a great need for spectral index catalogues and computing tools as a result of the quick rise of suggested spectral indices. Unfortunately, the majority of these resources lack a standard Application Programming Interface, are out-of-date, closed-source, or are not linked to a catalogue. We now introduce 'VegSpecIndex', a standardised list of spectral indices for studies of the earth system. A thorough inventory of spectral indices is offered by 'VegSpecIndex' and is connected to an R library. For every spectral index, 'VegSpecIndex' provides a comprehensive collection of information, such as names, formulae, and source references. The user community may add more items to the catalogue, which will keep 'VegSpecIndex' up to date and allow for further scientific uses. Additionally, the R library makes it possible to apply the catalogue to actual data, which makes it easier to employ remote sensing resources effectively across a variety of Earth system domains.
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.
Predicate helper functions for testing atomic vectors in R. All functions take
  a single argument 'x' and check whether it's of the target type of base-R
  atomic vector (i.e. no class extensions nor attributes other than 'names'),
  returning TRUE or FALSE. Some additionally check for value (e.g. absence of
  missing values, infinities, blank characters, or 'names' attribute; or having
  length 1).
Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny applications.
A close to zero dependency package to draw and display Venn 
    diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams.
Generate Venn diagrams from two or three sets, displaying the 
    overlapping items as lists in the appropriate sections. The lists can be
    split into columns or shortened for large sets and the plot is generated
    using 'ggplot2' allowing further customisations.
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, using the algorithm in Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>. 'verbalisr' is part of the
    'pedsuite' collection of packages for pedigree analysis. For a
    demonstration of 'verbalisr', see the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>.
Provides access to data collected by the Ecuadorian Truth Commission.
    Allows users to extract and analyze systematized information for human rights 
    research in Ecuador. The package contains datasets documenting human rights 
    violations from 1984-2008, including victim information, violation types, 
    perpetrators, and geographic distribution.
Facilitates use and analysis of data about the armed conflict
    in Colombia resulting from the joint project between La Jurisdicción
    Especial para la Paz (JEP), La Comisión para el Esclarecimiento de la
    Verdad, la Convivencia y la No repetición (CEV), and the Human Rights Data
    Analysis Group (HRDAG). The data are 100 replicates from a multiple
    imputation through chained equations as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. With the replicates
    the user can examine four human rights violations that occurred in the
    Colombian conflict accounting for the impact of missing fields and fully
    missing observations.
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.
Extendable 'R6' file comparison classes, including a 'shiny' app for combining the comparison functionality into a file comparison application. The package idea originates from pharma companies' drug development processes, where statisticians and statistical programmers need to review and compare different versions of the same outputs and datasets. The package implementation itself is not tied to any specific industry and can be used in any context for easy file comparisons between different file version sets.
The biomarker data set by Vermeulen et al. (2009)
    <doi:10.1016/S1470-2045(09)70154-8> is provided. The data
    source, however, is by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>. The original data set may
    be downloaded from
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This data set is for a real-time quantitative polymerase chain reaction
    (PCR) experiment that comprises the raw fluorescence data of 24,576
    amplification curves. This data set comprises 59 genes of interest and 5
    reference genes. Each gene was assessed on 366 neuroblastoma complementary
    DNA (cDNA) samples and on 18 standard dilution series samples (10-fold
    5-point dilution series x 3 replicates + no template controls (NTC) x 3
    replicates).
R data pipelines commonly require reading and writing data to versioned
    directories. Each directory might correspond to one step of a multi-step process,
    where that version corresponds to particular settings for that step and a chain of
    previous steps that each have their own versions. This package creates a configuration
    object that makes it easy to read and write versioned data, based on YAML
    configuration files loaded and saved to each versioned folder.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
A lightweight package for sorting version codes in various forms. No strong dependencies guaranteed.
A toolset for interactively exploring the differences between two data frames.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Provides functions to run statistical analyses on
    surface-based neuroimaging data, computing measures including cortical
    thickness and surface area of the whole-brain and of the hippocampi.
    It can make use of 'FreeSurfer', 'fMRIprep', 'XCP-D', 'HCP' and 'CAT12' 
    preprocessed datasets, 'HippUnfold' hippocampal outputs and 'SubCortexMesh'
    subcortical outputs for a given sample by restructuring the data values into 
    a single file. The single file can then be used by the package for analyses
    independently from its base dataset and without need for its access.
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.
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.
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. (2022) <doi:10.1111/biom.13509>.
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.
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', www.aacb.asn.au/AACB/Resources/Variance-Function-Program). 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>).
If f <- function(x){x^2} and g <- function(x){x+1} it is
     a constant source of annoyance that "f+g" is not defined.
     Package 'vfunc' allows you to do this, and we have (f+g)(2)
     returning 5.  The other arithmetic operators are similarly
     implemented.  A wide class of coding bugs is eliminated.
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, doubly constrained
    RR-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 variational inference. 
    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> and Goplerud (2024) <doi:10.1017/S0003055423000035> 
    provide 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().
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
The base class 'VirtualArray' is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived 'XArray' class is defined to be used for homogeneous lists that contain a single class of objects. The 'RasterArray' and 'SfArray' classes enable the use of stacked spatial data instead of lists.
'Via Foundry' API provides streamlined tools for interacting with and extracting data from structured responses, particularly for use cases involving hierarchical data from Foundry's API. It includes functions to fetch and parse process-level and file-level metadata, allowing users to efficiently query and manipulate nested data structures. Key features include the ability to list all unique process names, retrieve file metadata for specific or all processes, and dynamically load or download files based on their type. With built-in support for handling various file formats (e.g., tabular and non-tabular files) and seamless integration with API through authentication, this package is designed to enhance workflows involving large-scale data management and analysis. Robust error handling and flexible configuration ensure reliable performance across diverse data environments. Please consult the documentation for the API endpoint for your installation.
Practicals, data sets, helper functions and interactive 'Shiny' apps
    used in the introductory course on Bayesian inference at the
    Valencia International Bayesian Summer School. Installing 'vibass' installs
    all the other packages used during the course and downloads all necessary
    materials for working off line.
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>".
A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) <doi:10.48550/arXiv.2406.16227>.
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>.
Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. For the details of the methods, see the reference section of 'GitHub' README.md <https://github.com/rikenbit/Vicus>.
Video interactivity within 'shiny' applications using 'video.js'. Enables
    the status of the video to be sent from the UI to the server, and allows events
    such as playing and pausing the video to be triggered from the server.
Interface to the 'Video Game Insights' API
    <https://app.sensortower.com/vgi/> for video game market analytics and
    intelligence. Provides functions to retrieve game metadata, developer and
    publisher information, player statistics (concurrent players, daily and monthly
    active users), revenue and sales data, review analytics, wish-list tracking,
    and platform-specific rankings. The package includes data processing utilities
    to analyze player demographics, track pricing history, calculate player overlap
    between games, and monitor market trends. Supports analysis across multiple
    gaming platforms including 'Steam', 'PlayStation', 'Xbox', and 'Nintendo' with
    unified data structures for cross-platform comparison.
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', 'polyqtlR', 'GWASpoly', and 'HIDECAN' packages.
A collection of functions to make R a more effective viewscape analysis 
    tool for calculating viewscape metrics based on computing the viewable area for 
    given a point/multiple viewpoints and a digital elevation model.The method of calculating 
    viewscape metrics implemented in this package are based on the work of 
    Tabrizian et al. (2020) <doi:10.1016/j.landurbplan.2019.103704>. The algorithm of computing 
    viewshed is based on the work of 
    Franklin & Ray. (1994) <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=555780f6f5d7e537eb1edb28862c86d1519af2be>.
Perform the analysis of the World Health Organization
       (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version),
       <https://who-umc.org/>
       e.g., load data, perform data management,
       disproportionality analysis, and descriptive statistics. Intended for
       pharmacovigilance routine use or studies.
       This package is NOT supported nor reflect the opinion of the WHO, or the
       Uppsala Monitoring Centre.
       Disproportionality methods are described by Norén et 
       al (2013) <doi:10.1177/0962280211403604>.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.
This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.
Provides methods for imputation and visualization of 
    missing values. It includes graphical tools to explore the amount, structure 
    and patterns of missing and/or imputed values, supporting exploratory 
    data analysis and helping to investigate potential missingness mechanisms
    (details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>. 
    The quality of imputations can be assessed visually using a wide range of 
    univariate, bivariate and multivariate plots. 
    The package further provides several imputation methods, 
    including efficient implementations of k-nearest neighbour and hot-deck 
    imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>, 
    iterative robust model-based multiple 
    imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>; 
    Templ 2023, <doi:10.3390/math11122729>), and machine learning–based 
    approaches such as robust GAM-based multiple imputation 
    (Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as gradient boosting 
    (XGBoost) and transformer-based methods 
    (Niederhametner et al., <doi:10.1177/18747655251339401>). 
    General background and practical guidance on imputation are provided in the 
    Springer book by 
    Templ (2023) <doi:10.1007/978-3-031-30073-8>.
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>.
Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.
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>.
The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>.
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) <doi:10.48550/arXiv.1705.08310>.
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) <doi:10.48550/arXiv.1805.04755>. A 
  variance-based method for quantifying the relative strength of interaction 
  effects is also included (see the previous reference for details).
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available.
A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.
Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi: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 for reporting and forecasting viral respiratory infections,
    using case surveillance data. Report generation tools for short-term forecasts,
    and validation metrics for an arbitrary number of customizable respiratory viruses.
    Estimation of the effective reproduction number is based on the 'EpiEstim' framework 
    described in work by 'Cori' and colleagues. (2013) <doi:10.1093/aje/kwt133>.
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.
Constructs a virtual population from fertility and mortality rates for any country,
	calendar year and birth cohort in the Human Mortality Database <https://www.mortality.org> and the Human Fertility Database <https://www.humanfertility.org>.  Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population.
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.
A collection of tools for downstream analysis of
    'VirusHunterGatherer' output. Processing of hittables 
    and plotting of results, enabling better interpretation,
    is made easier with the provided functions.
Provides a comprehensive R interface to the VirusTotal API (v2 and v3), 
    a Google service that analyzes files and URLs for viruses, worms, trojans and 
    other malware. Features include file/URL scanning, domain categorization, 
    passive DNS information, IP reputation analysis, and comment/voting systems.
    Implements rate limiting, error handling, and response validation for robust 
    security analysis workflows.
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>.
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or
  generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009),
  "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>.
  These include better tabular presentation of collinearity diagnostics that highlight the important numbers,
  a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient,
  and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: 
   - components descriptive analysis
   - heat plot of the two-by-two component combinations 
   - leaving one component combination out scatter plot 
   - violin plot for specific component combinations' effects
   - density plot for components' effects  
   - waterfall plot for the interventions' effects that differ by a certain component combination 
   - network graph of components
   - rank heat plot of components for multiple outcomes.
   The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.
Functionality for creating phase portraits of functions in the
    complex number plane. Works with R base graphics, whose full 
    functionality is available. Parallel processing is used for optimum 
    performance.
Visualizing of distributions of covariance matrices.
    The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) <https://sites.stat.columbia.edu/gelman/research/unpublished/Visualization.pdf>.
Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.
Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.
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 the underlying model that 'VisitorCounts' uses can be found at 
    Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) <doi:10.1002/for.2965> .
Provides a suite of analytical functionalities to process and analyze
    visual meteor observations from the Visual Meteor Database
    of the International Meteor Organization <https://www.imo.net/>.
A comprehensive suite of static and interactive visual diagnostics for assessing the quality of multiply-imputed data obtained from packages such as 'mixgb' and 'mice'. The package supports inspection of distributional characteristics, diagnostics based on masking observed values and comparing them with re-imputed values, and convergence diagnostics.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
Provides methods to transform omop_result objects into
    formatted tables and figures, facilitating the visualisation of study
    results working with the Observational Medical Outcomes Partnership
    (OMOP) Common Data Model.
Provides tools for visibility analysis in geospatial data. It
    offers functionality to perform isovist calculations, using arbitrary
    geometries as both viewpoints and occluders.
Built on graph theory and the high-performance 'data.table' framework, 
    this package provides a comprehensive suite of tools for tidying, analyzing, 
    and visualizing animal pedigrees. By modeling pedigrees as directed acyclic 
    graphs using 'igraph', it ensures robust loop detection, efficient 
    generation assignment, and optimal sub-population splitting. Key features 
    include standardizing pedigree formats, flexible ancestry tracing, and 
    generating legible vector-based PDF graphs. A unique compaction algorithm 
    enables the visualization of massive pedigrees by grouping full-sib families. 
    Furthermore, the package implements high-performance C++ algorithms for 
    calculating and visualizing genetic relationship matrices (A, D, AA, and 
    their inverses) and inbreeding coefficients.
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.)
A library for creating time based charts, like Gantt or timelines. Possible outputs 
  include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be
  used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the 
  interactive outputs created by vistime() and hc_vistime(), you can interact with the 
  plot using mouse hover or zoom.
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs.
  In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits.
  Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.
Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.
Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.
Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martínez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martínez (2023, in press).
A collection of tools for analyzing the field of vision. It
    provides a framework for development and use of innovative methods for
    visualization, statistical analysis, and clinical interpretation of
    visual-field loss and its change over time. It is intended to be a tool for
    collaborative research. The package is described in Marin-Franch and Swanson
    (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative
    (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].
Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.
Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror.
To visualize the probabilities of early termination,
       fail and success of Simon's two-stage design.  To evaluate
       and visualize the operating characteristics of Simon's
       two-stage design.
Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', '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 under the 'XAI' paradigm.
Visualize Variance is an intuitive 'shiny' applications tailored for agricultural research data analysis, including one-way and two-way analysis of variance, correlation, and other essential statistical tools. Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) <doi:10.1198/000313002533>.
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.
Analysing vital statistics based on tools
    consistent with the tidyverse. Tools are provided for data visualization,
    life table calculations, computing net migration numbers, Lee-Carter
    modelling; functional data modelling and forecasting.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
A port of 'Inspect', a widely adopted 'Python' framework for
    large language model evaluation. Specifically aimed at 'ellmer' users
    who want to measure the effectiveness of their large language model-based
    products, the package supports prompt engineering, tool usage,
    multi-turn dialog, and model graded evaluations.
Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.
Analysis of minor alleles in Illumina sequencing data of viral
    genomes. Functions in 'vivaldi' primarily operate on vcf files.
A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.
Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.
Interactive visualization for Bayesian prior and posterior distributions. 
    This package facilitates an animated transition between prior and posterior distributions. 
    Additionally, it splits the distribution into bars based on the provided 'breaks,' displaying 
    the probability for each region. If no 'breaks' are provided, it defaults to zero.
Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.
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.
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> and Phillips et al. (2023) <doi:10.3897/BDJ.11.e96480>.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.
A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.
'RcppArmadillo' implementation for the Matlab code of the 'Variational Mode Decomposition' and 'Two-Dimensional Variational Mode Decomposition'. For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, <doi:10.1109/TSP.2013.2288675>; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, <doi:10.1007/978-3-319-14612-6_15>.
Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii)  Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.
A variety of tools to allow the quantification of videos of the 
    lymphatic vasculature taken under an operating microscope. Lymphatic vessels
    that have been injected with a variety of blue dyes can be tracked throughout
    the video to determine their width over time. Code is optimised
    for efficient processing of multiple large video files. Functions to calculate
    physiologically relevant parameters and generate graphs from these values 
    are also included.
Provides fast sampling from von Mises-Fisher distribution using the method proposed by Andrew T.A Wood (1994) <doi:10.1080/03610919408813161>.
Manage, provision and use Virtual Machines pre-configured for R.
             Develop, test and build package in a clean environment.
             'Vagrant' tool and a provider (such as 'Virtualbox') have to be installed.
Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using 'NumPyro' and 'PyTorch' backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The 'vmsae' package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.
Data version management on the file system for smaller projects.
    Manage data pipeline outputs with symbolic folder links, structured logging
    and reports, using 'R6' classes for encapsulation and 'data.table' for
    speed. Directory-specific logs used as source of truth to allow portability
    of versioned data folders.
An algorithm for nonlinear global optimization based on the 
    variable neighbourhood trust region search (VNTRS) algorithm proposed by 
    Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" 
    <doi:10.1287/ijoc.1090.0343>. The algorithm combines variable neighbourhood 
    exploration with a trust-region framework to efficiently search the solution 
    space. It can terminate a local search early if the iterates are converging 
    toward a previously visited local optimum or if further improvement within 
    the current region is unlikely. In addition to global optimization, the 
    algorithm can also be applied to identify multiple local optima.
Create adjacency matrices of vocalisation graphs from
  dataframes containing sequences of speech and silence intervals,
  transforming these matrices into Markov diagrams, and generating
  datasets for classification of these diagrams by 'flattening' them
  and adding global properties (functionals) etc.  Vocalisation
  diagrams date back to early work in psychiatry (Jaffe and Feldstein,
  1970) and social psychology (Dabbs and Ruback, 1987) but have only
  recently been employed as a data representation method for machine
  learning tasks including meeting segmentation (Luz, 2012)
  <doi:10.1145/2328967.2328970> and classification (Luz,
  2013) <doi:10.1145/2522848.2533788>.
Methods to calculate the expected value of information from a decision-analytic model.  This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS).  A range of alternative computational methods are provided under the same user interface.  See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.
Provides tools for audio data analysis, including feature extraction, pitch detection, and speaker identification. Designed for voice research and signal processing applications.
Simplifies and largely automates practical voice analytics for social science research. This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 <doi:10.1016/j.jbusres.2020.09.020>.
Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. The estimating function allows flexible prior specification, specification of fixed parameters and several options for Markov Chain Monte Carlo posterior simulation. A basic version of the algorithm is described in: Cannas M. and Piras, N. (2025) <doi:10.1007/978-3-031-96303-2_53>.
'R' implementation of the 'vol2bird' software for generating vertical profiles
    of birds and other biological signals in weather radar data. See Dokter et al.
    (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.
Trading Strategies for high Option Volatility environment are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Generates interactive plots for analysing and visualising 
    three-class high dimensional data. It is particularly suited to visualising 
    differences in continuous attributes such as gene/protein/biomarker 
    expression levels between three groups. Differential gene/biomarker 
    expression analysis between two classes is typically shown as a volcano 
    plot. However, with three groups this type of visualisation is particularly 
    difficult to interpret. This package generates 3D volcano plots and 3-way 
    polar plots for easier interpretation of three-class data.
Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the 'safetyGraphics' R package.
Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume
             of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments
             (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms
             for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for
             estimating copulas useful in computational finance. Methods implemented in 'volesti' are described
             in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.
Craft polished tables and plots in Markdown reports. 
             Simply choose whether to treat your data as counts or metrics, 
             and the package will automatically generate well-designed default tables and plots for you.
             Boiled down to the basics, with labeling features and simple interactive reports.
             All functions are 'tidyverse' compatible.
Simulates and evaluates stochastic scenarios of death and lapse events in life reinsurance contracts with profit commissions. The methodology builds on materials published by the Institute of Actuaries of Japan <https://www.actuaries.jp/examin/textbook/pdf/modeling.pdf>. A paper describing the detailed algorithms will be published by the author within a few months after the initial release of this package.
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>.
Creates visualization plots for 2D data including ellipse plots, Voronoi tesselation plots, and combined ellipse-Voronoi plots. Designed to visualize class separation in 2D data, raw of from projection techniques like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or others. For more details see Lotsch and Kringel (2026) and Lotsch and Ultsch (2024) <doi:10.1016/j.imu.2024.101573>.
Fortune's (1987, <doi:10.1007/BF01840357>) algorithm is a very efficient method to perform Voronoi tessellation and Delaunay triangulation. This package is a port of the original code published in the early 1990's by Steven Fortune.
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>.
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 Mastodon, YouTube,
    Reddit and Web 1.0 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).
Calculate solar potential for LiDAR point clouds using the 'VOSTOK'
    (Voxel Octree Solar Toolkit) algorithm. This R program provides an interface to
    the original 'VOSTOK' C++ implementation by Bechtold and Hofle (2020), enabling
    efficient ray casting and solar position algorithms to compute solar irradiance
    for each point while accounting for shadowing effects. Integrates seamlessly with
    the 'lidR' package for LiDAR data processing workflows. The original 'VOSTOK'
    toolkit is available at <doi:10.11588/data/QNA02B>.
Counting election votes and determining election results by different methods, including
    the single transferable vote or ranked choice, approval, score, plurality, condorcet and two-round 
    runoff methods (Raftery et al., 2021 <doi:10.32614/RJ-2021-086>).
'ProPublica' <https://projects.propublica.org/represent/> makes United States
    Congress member votes available and has developed their own unique cartogram to visually
    represent this data. Tools are provided to retrieve voting data, prepare voting data 
    for plotting with 'ggplot2', create vote cartograms and theme them.
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 R interface to the 'vouch' project
    (<https://github.com/mitchellh/vouch>), which bills itself as "a
    community trust management system based on explicit vouches to
    participate". 'vouch' is a 'Nushell' module, so 'voucher' provides a R
    application programming interface (API) to modify the 'VOUCHED.td'
    database without 'Nushell'. 'voucher' does not depend on 'vouch' or
    'Nushell'.
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.
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>).
R functions are not supposed to print text without giving
 the user the option to turn the printing off or on using a Boolean
 'verbose' in a construct like 'if(verbose) print(...)'. But this black/white
 approach is rather rigid, and an approach with shades of gray might be more
 appropriate in many circumstances.
Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.
The goal of 'vroom' is to read and write data (like 'csv',
    'tsv' and 'fwf') quickly. When reading it uses a quick initial
    indexing step, then reads the values lazily , so only the data you
    actually use needs to be read.  The writer formats the data in
    parallel and writes to disk asynchronously from formatting.
A  collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.
Performs variable selection/feature reduction under a clustering or 
  classification framework. In particular, it can be used in an automated fashion 
  using mixture model-based methods ('teigen' and 'mclust' are currently supported).
  Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix').
  See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023)
  <doi:10.48550/arXiv.2305.16464>.
Methods for calculating the variance scale exponent to
    identify memory patterns in time series data. Includes tests for white
    noise, short memory, and long memory. See Fu, H. et al. (2018)
    <doi:10.1016/j.physa.2018.06.092>.
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>.
Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data and penalized estimating equations for generalized linear models with multiple imputation.
  Reference:
  Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized estimating equations for generalized linear models with multiple imputation", <doi:10.1214/22-AOAS1721>.
  Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data", <doi:10.1093/jrsssc/qlad028>.
A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
Provides fast spectral estimation of latent factors in random
    dot product graphs using the vsp estimator. Under mild assumptions,
    the vsp estimator is consistent for (degree-corrected) stochastic
    blockmodels, (degree-corrected) mixed-membership stochastic
    blockmodels, and degree-corrected overlapping stochastic blockmodels.
A nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <arXiv:2303.10018>.
Implementation of 'Azure DevOps' <https://azure.microsoft.com/> API calls. 
    It enables the extraction of information about repositories, build and release definitions and individual releases. 
    It also helps create repositories and work items within a project without logging into 'Azure DevOps'. 
    There is the ability to use any API service with a shell for any non-predefined call.
Three steps variable selection procedure based on random forests.
    Initially developed to handle high dimensional data (for which number of
    variables largely exceeds number of observations), the package is very
    versatile and can treat most dimensions of data, for regression and
    supervised classification problems. First step is dedicated to eliminate
    irrelevant variables from the dataset. Second step aims to select all
    variables related to the response for interpretation purpose. Third step
    refines the selection by eliminating redundancy in the set of variables
    selected by the second step, for prediction purpose.
    Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015)
    <https://journal.r-project.org/articles/RJ-2015-018/>.
Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.
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. <doi:10.18637/jss.v114.i04>.
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>.
Offers a comprehensive set of assertion tests to help users validate the integrity of their data. 
    These tests can be used to check for specific conditions or properties within a dataset and 
    help ensure that data is accurate and reliable. 
    The package is designed to make it easy to add quality control checks to data analysis 
    workflows and to aid in identifying and correcting any errors or inconsistencies in data.
Provides functions to securely retrieve secrets from a 'Bitwarden Secrets Manager' vault using the 
              'Bitwarden CLI', enabling secret and configuration management within R packages and workflows. 
              For more information visit <https://bitwarden.com/products/secrets-manager/>.
Allow R users to interact with the 'Canvas' Learning Management System (LMS) API (see
    <https://canvas.instructure.com/doc/api/all_resources.html> for details).
    It provides a set of functions to access and manipulate course data, assignments, grades, users, 
    and other resources available through the 'Canvas' API.
Provides a set of functions for data transformations.
    Transformations are performed on character and numeric data. As the scope of the package is
    within Student Analytics, there are functions focused around the academic year.
Provides a user-friendly R 'shiny' app for performing various statistical tests on datasets. 
  It allows users to upload data in numerous formats and perform statistical analyses. 
  The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons.
  The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, 
  making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.
Provides numerous functions to fill data.
    These can be applied either to missing or skewed data.
    The functions are designed within the scope of Student Analytics.
Offers a wide range of functions for reading and writing data in various file formats, 
    including CSV, RDS, Excel and ZIP files. 
    Additionally, it provides functions for retrieving metadata associated with files, 
    such as file size and creation date, making it easy to manage and organize large data sets. 
    This package is designed to simplify data import and export tasks, 
    and provide users with a comprehensive set of tools to work with different types of data files.
Provides a set of functions for manipulating data frames in accordance with specific business rules. 
    In addition, it includes wrapper functions for commonly used functions from the popular 'tidyverse' package, 
    making it easy to integrate these functions into data analysis workflows. 
    The package is designed to streamline data preprocessing and help users quickly and efficiently 
    perform data transformations that are specific to their business needs.
Provides an R interface for interacting with the 'Tableau' Server.
    It allows users to perform various operations such as publishing workbooks, refreshing data extracts, 
    and managing users using the 'Tableau' REST API (see <https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_ref.htm> for details). Additionally, 
    it includes functions to perform manipulations on local 'Tableau' workbooks.
Provides an R interface for interacting with the 'Semestry TermTime' services.
    It allows users to retrieve scheduling data from the API. see
    <https://github.com/vusaverse/vvtermtime/blob/main/openapi_7.7.0.pdf> for details.
Provides R functions to draw lines and curves with the width
             of the curve allowed to vary along the length of the curve.
Gaze data from the Visual World Paradigm requires significant
    preprocessing prior to plotting and analyzing the data. This package 
    provides functions for preparing visual world eye-tracking data for 
    statistical analysis and plotting. It can prepare data for linear 
    analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as
    well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM).
    Additionally, it contains various plotting functions for creating grand average and
    conditional average plots. See the vignette for samples of the functionality.
    Currently, the functions in this package are designed for handling data
    collected with SR Research Eyelink eye trackers using Sample Reports created
    in SR Research Data Viewer.  While we would like to add functionality 
    for data collected with other systems in the future, the current package is 
    considered to be feature-complete; further updates will mainly entail maintenance
	and the addition of minor functionality.
A reference implementation of the Vertical Weighted Strips method
    explored by Raim, Livsey, and Irimata (2025) <doi:10.48550/arXiv.2401.09696>
    for rejection sampling.
Set of functions that improves the graphical presentations of the functions: wave.correlation and spin.correlation (waveslim package, Whitcher 2012) and the wave.multiple.correlation and wave.multiple.cross.correlation (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in a scientific paper: Polanco-Martinez and Fernandez-Macho (2014), <doi:10.1109/MCSE.2014.96>.
R interface to a W3C Markup Validation service.
  See <https://validator.w3.org/> for more information.
Provides a set of utility function to prevent the spread of
    utility scripts in W4M (Workflow4Metabolomics) tools, and centralize
    them in a single package.
    To note, some are meant to be replaced by the use of dedicated packages in the future, like
    the parse_args() function: it is here only to prepare the ground for
    more global changes in W4M scripts and tools.
    This package is used by part of the W4M Galaxy modules, some of them  
    being available on the community-maintained GitHub repository for 
    Metabolomics' Galaxy tools <https://github.com/workflow4metabolomics/tools-metabolomics>. 
    See Delporte et al (2025) <doi:10.1002/cpz1.70095> for more details.
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.
An R interface to the 'WebAIM' 'WAVE' accessibility evaluation
    API <https://wave.webaim.org/api/>. This package provides tools for analyzing web pages for
    accessibility issues, generating reports, and comparing accessibility
    across multiple websites.
Full screen and partial loading screens for 'Shiny' with spinners, progress bars, and notifications.
Generates random data sets including: data.frames, lists,
        and vectors.
Read 'Quake' assets including bitmap images and textures in 'wal' file format. This package also provides support for extracting these assets from 'WAD' and 'PAK' file archives. It can also read models in 'MDL' and 'MD2' formats.
Compare complex R objects and reveal the key differences.
    Designed particularly for use in testing packages where being able to
    quickly isolate key differences makes understanding test failures much
    easier.
Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.
Efficient Bayesian generalized linear models with time-varying coefficients 
    as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial 
    observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, the package uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
Easily collect walk scores, bike scores, and transit scores (where
    available) from the 'Walk Score' API <https://www.walkscore.com/professional/api.php>, 
    a proprietary API that assigns locations a walkability score between 0 and 100.
A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.
Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.
A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.
Provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, 
    that contains data about stores, Value of the day and products which 
    includes names, sale prices, shipping rates and taxonomies.
A toolbox of common robust statistical tests, including robust
  descriptives, robust t-tests, and robust ANOVA. It is also available as a
  module for 'jamovi' (see <https://www.jamovi.org> for more information).
  Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on
  the scripts and work of Rand Wilcox. These analyses are described in depth in
  the book 'Introduction to Robust Estimation & Hypothesis Testing'.
Implements Weighted-Average Least Squares model averaging
    for negative binomial regression models of Huynh (2024) <doi:10.48550/arXiv.2404.11324>,
    generalized linear models of De Luca, Magnus, Peracchi (2018) 
    <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of 
    Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also 
    Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares
    for the linear regression model is based on the original 'MATLAB' code by 
    Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also 
    Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and 
    De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.
The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.
'MIME' types are shorthand descriptors for
      file contents and can be determined from "magic"
      bytes in file headers, file contents or intuited
      from file extensions. Tools are provided to
      perform curated "magic" tests as well as mapping
      'MIME' types from a database of over 1,500
      extension mappings.
Provides the functions to perform a Welch's one-way Anova with fixed effects
  based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test
  for multiple comparisons and provides the effect size estimator adjusted omega squared.
  In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo
  simulation is included to bootstrap residual normality and homoscedasticity
  Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330>
  Kirk, R. E. (1996) <doi:10.1177/0013164496056005002>
  Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304>
  Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004>
  Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979>
  Levy, K. J. (1978a) <doi:10.1080/00949657808810246>
  Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.
Utilities for using a probability sample to reweight prevalence estimates calculated from the All of Us research program. Weighted estimates will still not be representative of the general U.S. population. However, they will provide an early indication for how unweighted estimates may be biased by the sampling bias in the All of Us sample.
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 packages 'tuneR' and 'seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 <doi:10.1111/2041-210X.12624>).
Toolkit to support and perform discrete event simulations with and without
    resource constraints in the context of health technology assessments (HTA).
    The package focuses on cost-effectiveness modelling and aims to be submission-ready
    to relevant HTA bodies in alignment with 'NICE TSD 15'
    <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>.
    More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.
Provides estimation and inference for while-alive regression models targeting the while-alive loss rate for composite endpoints that include recurrent events and a terminal event. The implementation supports flexible time-varying covariate effects through user-selected time bases, including B-splines, natural splines, M-splines, step functions, truncated linear
    bases, interval-local bases, and piecewise polynomials. Inference can be performed using cluster-robust variance estimators for cluster-randomized trials, with subject-level (IID) variance as a special case. The package includes prediction and plotting utilities and K-fold cross-validation for
    selecting basis and tuning parameters. Methodology is based on Fang et al. (2025) <doi:10.1093/biostatistics/kxaf047>.
Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. 
    It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations.     
    see:
    Bartholdi, J. J.,  Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1."
    The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. 
    Georgia Institute of Technology. 
    <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.
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.
Infectious disease surveillance requires early outbreak detection. This
    package provides statistical tools for analyzing time-series monitoring 
    data through three core methods: 
    a) EWMA (Exponentially Weighted Moving Average)
    b) Modified-CUSUM (Modified Cumulative Sum)
    c) Adjusted-Serfling models
    Methodologies are based on:
    - Wang et al. (2010) <doi:10.1016/j.jbi.2009.08.003>
    - Wang et al. (2015) <doi:10.1371/journal.pone.0119923>
    Designed for epidemiologists and public health researchers working with
    disease surveillance systems.
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.
Create plots and tables in a consistent style with WaSHI
    (Washington Soil Health Initiative) branding. Use 'washi' to easily
    style your 'ggplot2' plots and 'flextable' tables.
A toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.
The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.
Provides a set of functions to implement decision-making systems
    based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment),
    Chakraborty and Zavadskas (2012) <doi:10.5755/j01.eee.122.6.1810>.
    So this package offers functions that analyze and validate the
    raw data, which must be entered in a determined format;
    extract specific vectors and matrices from this raw database;
    normalize the input data; calculate rankings by intermediate methods;
    apply the lambda parameter for the main method; and a function that does
    everything at once. The package has an example database called choppers,
    with which the user can see how the input data should be organized so that
    everything works as recommended by the decision methods based on multiple
    criteria that this package solves. Basically, the data are composed of a set
    of alternatives, which will be ranked, a set of choice criteria, a matrix
    of values for each Alternative-Criterion relationship, a vector of weights
    associated with the criteria, since certain criteria are considered more
    important than others, as well as a vector that defines each criterion as
    cost or benefit, this determines the calculation formula, as there are those
    criteria that we want the highest possible value (e.g. durability)
    and others that we want the lowest possible value (e.g. price).
Functions to compute Wasserstein barycenters
    of subset posteriors using the swapping algorithm developed by Puccetti, 
    Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The 
    Wasserstein barycenter is a geometric approach for combining subset 
    posteriors. It allows for parallel and distributed computation of the 
    posterior in case of complex models and/or big datasets, thereby increasing
    computational speed tremendously.
R binding for 'libfswatch', a file system monitoring library.
    Watch files, or directories recursively, for changes in the
    background.  Log activity, or call an R function, upon every change
    event.
Calculates the water balance of starch potatoes from Normalized Distance Vegetation Index (NDVI) images, German Weather Service (DWD) reference evapotranspiration, German Weather Service RADOLAN precipitation data and irrigation information. For more details see Piernicke et al. (2025) <doi:10.3390/rs17183227>.
A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
The main purpose of waterquality is to quickly and easily convert
    satellite-based reflectance imagery into one or many well-known water quality
    algorithms designed for the detection of harmful algal blooms or the following
    pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity.
    Johansen et al. (2019) <doi:10.21079/11681/35053>.
Provides Water Year Hydrologic Classification Indices based on measured 
    unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources
    and subject to revision.
Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
    Longitudinal trajectories are shown in both Cartesian and polar coordinates.
    In many scenarios, a WATS plot more clearly shows the existence and effect size of
    of an intervention.
    This package accompanies
    "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs"
    by Rodgers, Beasley, & Schuelke (2014)
    <doi:10.1080/00273171.2014.946589>;
    see 'citation("Wats")' for details.
Tools for fitting and simulating mixtures of Watson distributions. 
             The package is described in Sablica, Hornik and Leydold (2026) <doi:10.18637/jss.v115.i04>.
             The random sampling scheme of the package offers two sampling 
             algorithms that are based of the results of Sablica, Hornik and Leydold (2022)
             <doi:10.1080/10618600.2024.2416521>.
             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) <doi:10.1090/mcom/3690> and 
             Sablica and Hornik (2024) <doi:10.1016/j.jmaa.2024.128262>.
Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. 
  Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit 
  PCM and other encodings.
Computes Bayesian wavelet shrinkage credible intervals for
	nonparametric regression.
	The method uses cumulants to derive Bayesian credible intervals for
	wavelet regression estimates.
	The first four cumulants of the posterior distribution of the
	estimates are expressed in terms of the observed data and integer
	powers of the mother wavelet functions.
	These powers are closely approximated by linear combinations of
	wavelet scaling functions at an appropriate finer scale.
	Hence, a suitable modification of the discrete wavelet transform allows
	the posterior cumulants to be found efficiently for any data set.
	Johnson transformations then yield the credible intervals themselves.
	Barber, S., Nason, G.P. and Silverman, B.W. (2002)
	<doi:10.1111/1467-9868.00332>.
Makes available code necessary to reproduce figures and
        tables in papers on the WaveD method for wavelet deconvolution
        of noisy signals as presented in The WaveD Transform in R,
        Journal of Statistical Software Volume 21, No. 3, 2007.
The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS  (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.
Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <DOI:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>.
Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021)  <doi:10.1007/s00500-021-06087-4>.
Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.
The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the  supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.
Contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.
The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.
Wavelet routines that calculate single sets of wavelet
    multiple regressions and correlations, and cross-regressions and
    cross-correlations from a multivariate time series.  Dynamic versions
    of the routines allow the wavelet local multiple (cross-)regressions
    and (cross-)correlations to evolve over time.
Functions for calculating the
    fetch (length of open water distance along given directions)
    and estimating wave energy from wind and wave monitoring data.
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.
An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s43538-025-00581-1>.
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>.
Converts pathways from 'WikiPathways' GPML format or
  'KEGG' KGML format into 'igraph' objects. Includes tools to find all
  cycles in the resulting graphs and determine which ones involve
  negative feedback (inhibition).
Convert, validate, format and elegantly print geographic coordinates and waypoints
   (paired latitude and longitude values) in decimal degrees, degrees and minutes, and degrees,
   minutes and seconds using high performance C++ code to enable rapid conversion and formatting
   of large coordinate and waypoint datasets.
Provides routing based on the 'path-tree' 'Rust' crate. The routing
    is general purpose in the sense that any type of R object can be associated
    with a path, not just a handler function.
Assessing predictive models of spatial data can be
    challenging, both because these models are typically built for
    extrapolating outside the original region represented by training data
    and due to potential spatially structured errors, with "hot spots" of
    higher than expected error clustered geographically due to spatial
    structure in the underlying data. Methods are provided for assessing
    models fit to spatial data, including approaches for measuring the
    spatial structure of model errors, assessing model predictions at
    multiple spatial scales, and evaluating where predictions can be made
    safely. Methods are particularly useful for models fit using the
    'tidymodels' framework. Methods include Moran's I ('Moran' (1950)
    <doi:10.2307/2332142>), Geary's C ('Geary' (1954)
    <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)
    <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from
    'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement
    metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>)
    and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an
    implementation of the area of applicability methodology from 'Meyer'
    and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an
    implementation of multi-scale assessment as described in 'Riemann'
    'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
The BACON algorithms are methods for multivariate outlier
    nomination (detection) and robust linear regression by Billor, Hadi,
    and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension
    to weighted problems is due to Beguin and Hulliger (2008)
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see
    also <doi:10.21105/joss.03238>.
Computation of the Wasserstein Bipolarization Index as 
    described in Lee and Sobel (Forthcoming) 
    <doi:10.48550/arXiv.2408.03331>. Provides both 
    asymptotic (Sommerfeld, 2017 
    <https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1>) 
    and bootstrap methods (Efron and Narasimhan, 2020 
    <doi:10.1080/10618600.2020.1714633>) for calculating 
    confidence intervals.
Access and analyze the World Bank's International Debt Statistics
  (IDS) <https://www.worldbank.org/en/programs/debt-statistics/ids>.
  IDS provides creditor-debtor relationships between countries, regions, and
  institutions. 'wbids' enables users to download, process and work with IDS
  series across multiple geographies, counterparts, and time periods.
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>.
Access and analyze the World Bank's World Development Indicators 
  (WDI) using the corresponding API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>. 
  WDI provides more than 24,000 country or region-level indicators for various
  contexts. 'wbwdi' enables users to download, process and work with WDI
  series across multiple countries, aggregates, and time periods.
Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <https://dataexplorer.wittgensteincentre.org/>.
A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.
K-means clustering, hierarchical clustering, and PCA with observational 
    weights and/or variable weights. It also includes the corresponding functions 
    for data nuggets which serve as representative samples of large datasets.
    Cherasia et al., (2022) <doi:10.1007/978-3-031-22687-8_20>. 
    Amaratunga et al., (2009) <doi:10.1002/9780470317129>.
Retrieval the leaf area index (LAI) and soil moisture (SM) from  microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.
Implements inferential and graphic procedures for the semiparametric proportional 
  means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 
  2016, <doi:10.1093/biostatistics/kxv050>).
Allows users to create weighted confusion matrices and accuracy
    metrics that help with the model selection process for classification
    problems, where distance from the correct category is important. The
    package includes several weighting schemes which can be parameterized, as
    well as custom configuration options. Furthermore, users can decide
    whether they wish to positively or negatively affect the accuracy score
    as a result of applying weights to the confusion matrix. Functions are
    included to calculate accuracy metrics for imbalanced data. Finally,
    'wconf' integrates well with the 'caret' package, but it can also work
    standalone when provided data in matrix form.
    References:
    Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package"
    <doi:10.18637/jss.v028.i05>
    Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization
    (and the mewto R package)" <doi:10.2139/ssrn.3805911>
    Monahov, A. (2024) "Improved Accuracy Metrics for Classification with
    Imbalanced Data and Where Distance from the Truth Matters, with the wconf R
    Package" <doi:10.2139/ssrn.4802336>
    Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced
    Data Classification Results. Pattern Recognition and Image Analysis, 295–302
    Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023)
    "A general framework for implementing distances for categorical variables"
    <doi:10.48550/arXiv.2301.02190>.
Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
A new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.
Standardizes and reconciles scientific plant names against
    a World Checklist of Vascular Plants ('WCVP')-style taxonomic
    backbone. The package parses names into taxonomic components and
    applies staged exact and fuzzy matching for binomial and trinomial
    inputs, including infraspecific rank-aware checks. It also returns
    accepted-name context and row-level matching flags to support
    reproducible, auditable preprocessing for downstream biodiversity,
    spatial, and trait analyses. A user-supplied backbone can be passed
    through 'target_df'; when the optional companion package 'wcvpdata'
    is installed, its default checklist can also be used.
Analyzes and models data subject to sampling biases.
    Provides functions to estimate the density and cumulative distribution
    functions from biased samples of continuous distributions. Includes
    the estimators proposed by Bhattacharyya et al. (1988)
    <doi:10.1080/03610928808829825> and Jones (1991) <doi:10.2307/2337020>
    for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta
    (2022) <doi:10.1007/s00184-021-00824-3> for distribution, with
    different bandwidth selectors. Also includes a real length-biased
    dataset on shrub width from Muttlak (1988)
    <https://www.proquest.com/openview/3dd74592e623cdbcfa6176e85bd3d390/1?cbl=18750&diss=y&pq-origsite=gscholar>.
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 functions to calculate the Water Deficit Index (WDI) and 
    the Evaporative Fraction (EF) using geospatial raster data such as fractional 
    vegetation cover (FVC) and surface-air temperature difference (TS-TA). 
    The package automates regression-based edge fitting and produces continuous 
    spatial maps of surface moisture and evaporative dynamics.
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.
Assortativity coefficients, centrality measures, 
    and clustering coefficients for weighted and directed networks.
    Rewiring unweighted networks with given assortativity coefficients.
    Generating general preferential attachment networks.
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/prioritizr/prepr>). For more information on this
    package, see Hanson (2022) <doi:10.21105/joss.04594>.
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, Embrace Plus, and Nowatch data, perform several
    transformations, perform signal processing and analyses, including
    batch analyses.
Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, <doi:10.54302/mausam.v31i4.3477>. Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,<https://www.environmentandecology.com/volume-40-2022>.
High-level tools to attach gridded weather data from the NASA POWER Project
    to event-based datasets. The package plans efficient spatio-temporal API calls via the 'nasapower'
    R package, caches downloaded segments locally, and joins weather variables back to the input
    table using exact or rolling joins. This package is not affiliated with or endorsed by NASA.
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 automated downloading, parsing and formatting of
    weather data for Australia through API endpoints provided by the
    Department of Primary Industries and Regional Development (DPIRD) of
    Western Australia and by the Science and Technology Division of the
    Queensland Government's Department of Environment and Science (DES).
    As well as the Bureau of Meteorology (BOM) of the Australian
    government precis and coastal forecasts,
    and downloading and importing radar and satellite imagery files.
    DPIRD weather data are accessed through public APIs provided by
    DPIRD, <https://www.dpird.wa.gov.au/online-tools/apis/>, providing
    access to weather station data from the DPIRD weather station
    network.  Australia-wide weather data are based on data from the
    Australian Bureau of Meteorology (BOM) data and accessed through
    SILO (Scientific Information for Land Owners) Jeffrey et al. (2001)
    <doi:10.1016/S1364-8152(01)00008-1>.  DPIRD data are made available
    under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU)
    license <https://creativecommons.org/licenses/by/3.0/au/deed.en>.
    SILO data are released under a Creative Commons Attribution 4.0
    International licence (CC BY 4.0)
    <https://creativecommons.org/licenses/by/4.0/>. BOM data are (c)
    Australian Government Bureau of Meteorology and released under a
    Creative Commons (CC) Attribution 3.0 licence or Public Access Licence
    (PAL) as appropriate, see
    <http://www.bom.gov.au/other/copyright.shtml> for further details.
A comprehensive suite of functions for processing,
  analyzing, and visualizing textual data from tweets is offered.  
  Users can clean tweets, analyze their sentiments, visualize data, 
  and examine the correlation between sentiments and environmental 
  data such as weather conditions. Main features include text processing,  
  sentiment analysis, data visualization, correlation analysis, and 
  synthetic data generation. Text processing involves cleaning and preparing  
  tweets by removing textual noise and irrelevant words. Sentiment analysis 
  extracts and accurately analyzes sentiments from tweet texts using advanced 
  algorithms. Data visualization creates various charts like word clouds
  and sentiment polarity graphs for visual representation of data. Correlation 
  analysis examines and calculates the correlation between tweet sentiments 
  and environmental variables such as weather conditions. Additionally, 
  random tweets can be generated for testing and evaluating the performance 
  of analyses, empowering users to effectively analyze and interpret 'Twitter' 
  data for research and commercial purposes.
Enables interaction with the National Weather Service application programming web-interface for fetching of real-time and forecast meteorological data. Users can provide latitude and longitude, Automated Surface Observing System identifier, or Automated Weather Observing System identifier to fetch recent weather observations and recent forecasts for the given location or station. Additionally, auxiliary functions exist to identify stations nearest to a point, convert wind direction from character to degrees, and fetch active warnings. Results are returned as simple feature objects whenever possible.
Provides Apache and IIS log analytics for transaction performance, client populations and workload definitions.
Chemical information from around the web. This package interacts 
    with a suite of web services for chemical information. Sources include: Alan
    Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver,
    ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet,
    NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata.
An easy-to-use interface for interacting with 'WebDAV' servers,
    including 'OwnCloud'. It simplifies the use of 'WebDAV' methods such as COPY, MOVE, DELETE and others. 
    With built-in authentication and request handling, it allows for easy
    management of files and directories over the 'WebDAV' protocol.
Organizational framework for web development in R including functions to  
    serve static and dynamic content via HTTP methods, includes the html5 package to
    create HTML pages, and offers other utility functions for common tasks related 
    to web development.
A client for the 'WebDriver' 'API'. It allows driving a
    (probably headless) web browser, and can be used to test web
    applications, including 'Shiny' apps. In theory it works with any
    'WebDriver' implementation, but it was only tested with 'PhantomJS'.
Functions for easily creating interactive web pages using
    'R Markdown' that students can use in self-guided learning.
Create a web app that makes it easier to test web clients
    without using the internet. It includes a web app framework with path
    matching, parameters and templates. Can parse various 'HTTP' request
    bodies. Can send 'JSON' data or files from the disk. Includes a web
    app that implements the 'httpbin.org' web service.
The web version WebGestalt <https://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.
Displays geospatial data on an interactive 3D globe in the web browser.
Creates interactive web maps using the 'JavaScript' 'Leaflet'
    library with base layers of 'The National Map' ('TNM'). 'TNM' services
    provide access to base geospatial information that describes the landscape
    of the United States and its territories. This package is dependent on, and
    intended to be used with, the 'leaflet' package.
Stubbing and setting expectations on 'HTTP' requests.
    Includes tools for stubbing 'HTTP' requests, including expected
    request conditions and response conditions. Match on
    'HTTP' method, query parameters, request body, headers and
    more. Can be used for unit tests or outside of a testing
    context.
Lossless webp images are 26% smaller in size compared to PNG. Lossy
    webp images are 25-34% smaller in size compared to JPEG. This package reads
    and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using
    conventions from the 'jpeg' and 'png' packages.
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Several analysis-related functions for the book entitled
    "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9)
    by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for
    the object of class 'htest'.
R is used by a vast array of people for a vast array of purposes
    - including web analytics. This package contains functions for consuming and
    munging various common forms of request log, including the Common and Combined
    Web Log formats and various Amazon access logs.
A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.
Functions that allow for accessing domains and a number of search engines.
Takes screenshots of web pages, including Shiny applications and R
    Markdown documents.
Takes screenshots of web pages, including Shiny applications
    and R Markdown documents. 'webshot2' uses headless Chrome or Chromium
    as the browser back-end.
Provides a 'WebSocket' client interface for R.
    'WebSocket' is a protocol for low-overhead real-time communication:
    <https://en.wikipedia.org/wiki/WebSocket>.
Data structures and methods to work with web tracking data. The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 <doi:10.17605/OSF.IO/M3U9P>; Stier et al., 2022 <doi:10.1017/S0003055421001222>).
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Parses http request data in application/json, multipart/form-data, 
    or application/x-www-form-urlencoded format. Includes example of hosting
    and parsing html form data in R using either 'httpuv' or 'Rhttpd'.
Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.
Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.
Provides functions for analysing Water-Energy-Food-Nutrient-Carbon
    (WEFNC) nexus interactions in agricultural production systems. Includes
    functions for computing water use efficiency (WUE), water productivity (WP),
    and water footprint (WF) including green, blue, and grey components following
    the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy
    budgeting tools for energy use efficiency (EUE), energy return on investment
    (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use
    efficiency (NUE) metrics including agronomic efficiency (AE), physiological
    efficiency (PE), recovery efficiency (RE), and partial factor productivity
    (PFP) as defined by Dobermann (2007)
    <https://digitalcommons.unl.edu/agronomyfacpub/316/> and
    Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Estimates carbon
    footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC)
    stocks, and global warming potential (GWP) using Intergovernmental Panel on
    Climate Change (IPCC) Sixth Assessment Report (AR6) default values
    (CH4 = 27, N2O = 273) as reported in Forster et al. (2021)
    <doi:10.1017/9781009157896.009>. Computes composite
    Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices,
    trade-off correlation matrices, and generates radar and heatmap
    visualizations for comparing agricultural treatments. Supports conservation
    agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid
    production environments. Methods follow Lal (2004)
    <doi:10.1016/j.envint.2004.03.005> for carbon emissions from farm
    operations, and Hoover et al. (2023) <doi:10.1016/j.scitotenv.2022.160992>
    for water use efficiency indicators.
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>.
Provides maximum likelihood estimation methods for eight modified 
    Weibull-type distributions. It returns parameter estimates, 
    log-likelihood, AIC, and BIC, and also supports model fitting, validation, 
    and comparison across different distributional forms. These methods can be 
    applied to reliability, survival, and lifetime data analysis, making the package 
    useful for researchers and practitioners in statistics, engineering, and medicine. 
    The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, 
    Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009.
Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations.
  For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, 
  Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. 
  This work was supported by the National Research Foundation of Korea (NRF) grants funded by 
  the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).
Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).
Graphical data analysis of accelerated life tests. Methods derived from  Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6).
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 (2023) <doi:10.1016/j.csda.2022.107654>. 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, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT).
The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.
A conditional independence test that can be applied both to
    univariate and multivariate random variables. The test is based on a
    weighted form of the sample covariance of the residuals after a
    nonlinear regression on the conditioning variables. Details are
    described in Scheidegger, Hoerrmann and Buehlmann (2022) "The Weighted
    Generalised Covariance Measure" <http://jmlr.org/papers/v23/21-1328.html>.
    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" <doi:10.1214/19-AOS1857>.
An implementation of the Weighted Portmanteau Tests described
      in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing"
      published by the Journal of the American Statistical Association, Volume 107, 
      Issue 498, pages 777-787, 2012.
Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2024) <doi:10.1080/01621459.2023.2221402>.  The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>.  The package can increase design sensitivity using the conditioning tactic in Rosenbaum (2025) <doi:10.1093/jrsssb/qkaf007>.  The main functions are wgtRank(), wgtRankCI(), wgtRanktt() and wgtRankC().
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>.
Provides survival analysis functions with support for time-dependent 
    and subject-specific (e.g., propensity score) weighting. Implements weighted 
    estimation for Cox models, Kaplan-Meier survival curves, and treatment 
    differences with point-wise and simultaneous confidence bands. Includes 
    restricted mean survival time (RMST) comparisons evaluated across all 
    potential truncation times with both point-wise and simultaneous confidence 
    bands. See Cole, S. R. & Hernán, M. A. (2004) 
    <doi:10.1016/j.cmpb.2003.10.004> for methodological background.
Treemaps are a visually appealing graphical representation of
    numerical data using a space-filling approach. A plane or 'map' is
    subdivided into smaller areas called cells.  The cells in the map are
    scaled according to an underlying metric which allows to grasp the
    hierarchical organization and relative importance of many objects at
    once. This package contains two different implementations of treemaps,
    Voronoi treemaps and Sunburst treemaps.  The Voronoi treemap function
    subdivides the plot area in polygonal cells according to the highest
    hierarchical level, then continues to subdivide those parental cells
    on the next lower hierarchical level, and so on. The Sunburst treemap
    is a computationally less demanding treemap that does not require
    iterative refinement, but simply generates circle sectors that are
    sized according to predefined weights.  The Voronoi tesselation is
    based on functions from Paul Murrell (2012)
    <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.
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 those that rely on parametric modeling, optimization, and machine learning. Also
             allows for assessment of weights and checking of covariate balance by interfacing directly
             with the 'cobalt' package. Methods for estimating weighted regression models that take into account 
             uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how
             to install any package 'WeightIt' uses, including those that may not be on CRAN.
Applies the item weighting method from Kilic & Dogan (2019) <doi:10.21031/epod.516057>. To improve construct validity, this method re-computes scores by utilizing the item discrimination index in conjunction with a condition established upon person ability and item difficulty.
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 as well as simple weighting graphics including weighted histograms, box plots, bar plots, and violin plots.  Also includes software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights and summarizing various types of regressions. Some portions of this package were assisted by AI-generated suggestions using OpenAI's GPT model, with human review and integration.
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.
All functions and data sets required for the examples in the book
    Hyndman (2026) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>.
    All packages needed to run the examples are also loaded.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing 
    any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some 
    improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. 
    The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and
    published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.
Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).
Palettes generated mostly from 'Wes Anderson' movies.
Implements a functional approximation of the four panel
    cointegration tests developed by Westerlund (2007)
    <doi:10.1111/j.1468-0084.2007.00477.x>. The tests are based on structural
    rather than residual dynamics and allow for heterogeneity in both the
    long-run cointegrating relationship and the short-run dynamics. The package
    includes logic for automated lag and lead selection via AIC/BIC, Bartlett 
    kernel long-run variance estimation, and a bootstrap procedure to handle 
    cross-sectional dependence. It also includes a bootstrapping distribution
    visualization function for diagnostic purposes.
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.
Computes the exact observation weights for the Kalman filter and smoother, based on the method described in Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>.
    The package supports in-depth exploration of state-space models, enabling researchers and practitioners to extract meaningful insights from time series data.
    This functionality is especially valuable in dynamic factor models, where the computed weights can be used to decompose the contributions of individual variables to the latent factors.
    See the README file for examples.
A suite of routines for Weyl algebras.  Notation follows
   Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic
   D-Modules").  Uses 'disordR' discipline
   (Hankin 2022 <doi:10.48550/arXiv.2210.03856>).  To cite
   the package in publications, use Hankin
   2022 <doi:10.48550/arXiv.2212.09230>.
Manage skills for large language model coding agents. Supports
    installing skills from 'GitHub' or local directories, tracking versions in
    a lock file, and keeping installations current. Installations can be scoped
    to a single project or shared globally across projects.
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.
Provides function, wget_set(), to change the method (default to 'wget -c') using in download.file().
  Using 'wget -c' allowing continued downloading, which is especially useful for slow internet connection and for downloading large files.
  User can run wget_unset() to restore previous setting.
Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.
An enhanced implementation of Whittaker-Henderson smoothing for the graduation 
    of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks.
    'WH' is based on the methods described in Biessy (2025) <doi:10.48550/arXiv.2306.06932>.
    Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, 
    automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.
Provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types.  The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision.  For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) <doi:10.1111/mms.12745>. For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) <doi:10.21105/joss.06473>.
Provides an 'R' interface to the 'Whapi' 'API' <https://whapi.cloud>, enabling sending and receiving 'WhatsApp' messages directly from 'R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (buttons and lists). Also includes 'webhook' parsing utilities and channel health checks.
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>.
Simulates the results of completed randomized controlled
    trials, as if they had been conducted as adaptive Multi-Arm Bandit
    (MAB) trials instead. Augmented inverse probability weighted
    estimation (AIPW), outlined by Hadad et al. (2021)
    <doi:10.1073/pnas.2014602118>, is used to robustly estimate the
    probability of success for each treatment arm under the adaptive
    design. Provides customization options to simulate perfect/imperfect
    information, stationary/non-stationary bandits, blocked treatment
    assignments, along with control augmentation, and other hybrid
    strategies for assigning treatment arms. The methods used in
    simulation were inspired by Offer-Westort et al.  (2021)
    <doi:10.1111/ajps.12597>.
Imports 'WhatsApp' chat logs and parses them into
    a usable dataframe object. The parser works on chats exported 
    from Android or iOS phones and on Linux, macOS and Windows. The parser has  multiple options for extracting smileys and emojis
    from the messages, extracting URLs and domains from the messages, extracting names and types of sent 
    media files from the messages, extracting timestamps from messages, extracting and anonymizing author 
    names from messages. Can be used to create anonymized versions of data.
Use the 'what3words' API 
    <https://developer.what3words.com/public-api> to return three words which 
    uniquely identify every 3m x 3m square on Earth. It is also possible to 
    return coordinates from any valid three words location. Supports multiple 
    languages.
Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as 'LeftOf', 'RightOf', 'TopOf', and 'Beneath' and more. Other features include clustering, dendrograms and integration with 'ggplot2' generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.
In Multidimensional Systems the When dimension allows us to
    express when the analysed facts have occurred. The purpose of this
    package is to provide support for implementing this dimension in the
    form of date and time tables for Relational On-Line Analytical Processing 
    star database systems.
A set of tools for processing and analyzing data developed in the
    context of the "Who Has Eaten the Planet" (WHEP) project, funded by the
    European Research Council (ERC). For more details on multi-regional
    input–output model "Food and Agriculture Biomass Input–Output" (FABIO) see 
    Bruckner et al. (2019) <doi:10.1021/acs.est.9b03554>.
Provides a clean syntax for vectorising the use of
    Non-Standard Evaluation (NSE), for example in 'ggplot2', 'dplyr', or
    'data.table'.
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.
Logging of scripts suitable for clinical trials using
    'Quarto' to create nice human readable logs. 'whirl' enables
    execution of scripts in batch, while simultaneously creating logs for
    the execution of each script, and providing an overview summary log of
    the entire batch execution.
Speech-to-text transcription using a native R 'torch' implementation
    of 'OpenAI' 'Whisper' model <https://github.com/openai/whisper>. Supports
    multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters)
    with integrated download from 'HuggingFace' <https://huggingface.co/> via the
    'hfhub' package. Provides automatic speech recognition with optional language
    detection and translation to English. Audio preprocessing, mel spectrogram
    computation, and transformer-based encoder-decoder inference are all
    implemented in R using the 'torch' package.
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.
A collection of functions related to novel methods for estimating R(t), 
  created by the lab of Professor Laura White. Currently implemented methods include 
  two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, 
  adapted in 'STAN' from Li (2021) <doi:10.1371/journal.pcbi.1009210>, 
  and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from 
  Zhou (2021) <doi:10.1371/journal.pcbi.1010434>.
Implements the whitening methods (ZCA, PCA, Cholesky,
 ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
 "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
 as well as the whitening approach to canonical correlation analysis allowing
 negative canonical correlations described in Jendoubi and Strimmer (2019)
 "A whitening approach to probabilistic canonical correlation analysis for omics
 data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers
 functions to simulate random orthogonal matrices, compute (correlation) loadings
 and explained variation. It also contains four example data sets (extended UCI 
 wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).
Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.
Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008>
    introduced 'WhiteStripe', an intensity-based normalization of T1 
    and T2 images, where normal 
    appearing white matter performs well, but requires segmentation.
    This method performs white matter mean and standard deviation
    estimates on data that has been rigidly-registered to the 'MNI'
    template and uses histogram-based methods.
Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 <doi:10.32614/RJ-2021-048>). In addition to parallel methods, data wrangling and additional statistical attributes are provided.
This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth.  It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) <doi:10.1111/eva.12659>, and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin.
Look up the username and full name of the current user,
    the current user's email address and 'GitHub' username,
    using various sources of system and configuration information.
The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey
    instrument to assess the distribution of disability within a country or 
    region, grounded in the International Classification of Functioning, 
    Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions 
    for calculating and presenting the results from this survey, as used by 
    the WHO. The package primarily provides functions for implementing
    Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to
    calculate a metric scale for disability.
Interactive tools for generating random samples. Users select an
    .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the
    sample type (Simple Random Sample or Stratified), the number of backups
    desired, and a "stratify_on" value (if desired). The sample size is determined
    using a normal approximation to the hypergeometric distribution based on
    Nicholson (1956) <doi:10.1214/aoms/1177728270>. An .xlsx file is created
    with the sample and key metadata for reference. It is menu-driven and lets
    users pick an output directory. See vignettes for a detailed walk-through.
Tools to download data from the online World Inequality Database directly into R. The World Inequality
    Database is an extensive source on the historical evolution of the
    distribution of income and wealth both within and between countries.
    It relies on the combined effort of an international network of over a
    hundred researchers covering more than seventy countries from all continents.
Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.
Construct a Canonical Variate Analysis Biplot via the Generalised Singular Value Decomposition, for cases when the number of samples is less than the number of variables. For more information on biplots, see Gower JC, Lubbe SG, Le Roux NJ (2011) <doi:10.1002/9780470973196> and for more information on the generalised singular value decomposition, see Edelman A, Wang Y (2020) <doi:10.1137/18M1234412>.
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.
Interface to the World Inequality Database (WID) API <https://wid.world>. Downloads distributional national accounts data with filters for country, year, percentile, age group, and population type. Includes code validation and reference tables. Independent implementation unaffiliated with the World Inequality Lab (WIL) or the Paris School of Economics.
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.
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".
Query Wikidata and get facts from current and historic Wikipedia main pages.
Scrape lake metadata tables from Wikipedia <https://www.wikipedia.org/>.
A wrapper for the MediaWiki API, aimed particularly at the
    Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
    page text, information about users or the history of pages, and elements of
    the category tree.
Queries online 'WikiPathway' graphics and allows mapping user data (e.g., expression values) on the graph. The package designs a grammar of graphic syntax that using pipe operator to add graphic layer.
'Taxonomic' information from 'Wikipedia', 'Wikicommons',
    'Wikispecies', and 'Wikidata'. Functions included for getting
    taxonomic information from each of the sources just listed, as
    well performing taxonomic search.
A set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.
Access 'Wikipedia' through the several 'MediaWiki' APIs
    (<https://www.mediawiki.org/wiki/API>), as well as through the
    'XTools' API (<https://www.mediawiki.org/wiki/XTools/API>). Ensure
    your API calls are correct, and receive results in tidy tibbles.
An implementation of the 1-Sample Wilcoxon Sign rank test for medians. 
              It includes 2 functions, W_stat(), which computes the 
              exact probabilities of the Wilcoxon Sign Rank Test Statistic, W.
              The second function, Wilcox.m.test() allows the user to conduct the
              1-Sample Wilcoxon Sign Rank hypothesis test for medians, this also 
              allows the user to conduct the hypothesis test for the normal approximation, 
              based on the techniques of Bickel and Doksum (1973, ISBN:013850363X).
Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version >= 0.3) also has new tools for automating contact analysis in large tracking datasets. The package (as of version 1.0) uses the 'move2' class of objects for working with tracking dataset.
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.
Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data.
    This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.
Obtain the native stack trace and fuse it with R's
    stack trace for easier debugging of R packages with native code.
Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.
Provides a tool to fit and compare the wind turbine power curves 
    with successful curve fitting techniques. Facilitates to examine and compare 
    the performance of a user-defined power curve fitting techniques. 
    Also, provide features to generate power curve discrete points from a graphical 
    power curves. Data on the power curves of the wind turbine from major 
    manufacturers are provided.
Analysing convergent evolution using the Wheatsheaf index, described in Arbuckle et al. (2014) <doi: 10.1111/2041-210X.12195>, and some other unrelated but perhaps useful functions.
Evaluation of prediction performance of smaller regions of
    spectra for Chemometrics. Segmentation of spectra, evolving dimensions
    regions and sliding windows as selection methods. Election of the best
    model among those computed based on error metrics. Chen et al.(2017)
    <doi:10.1007/s00216-017-0218-9>.
Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the 'Windsor.ai' API <https://www.windsor.ai/api-fields/>.
Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes Gini, Theil, Leti index, Palma ratio, 20:20 ratio, Allison and Foster index, Jenkins index, Cowell and Flechaire index, Abul Naga and Yalcin index, Apouey index, Blair and Lacy index. Bootstrap provides distribution of inequality measures enabling significance tests.
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/data/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.
Generate continuous maps of genetic diversity using moving windows with options for rarefaction, interpolation, and masking as described in Bishop et al. (2023) <doi:10.1111/2041-210X.14090>.
Using a time-varying random parameters model developed in
    Koutchade et al., (2024) <https://hal.science/hal-04318163>, this
    package allows allocating variable input costs among crops produced by
    farmers based on panel data including information on input expenditure
    aggregated at the farm level and acreage shares. It also considers in
    fairly way the weighting data and can allow integrating time-varying
    and time-constant control variables.
A multi-visit clinical trial may collect participant responses on an ordinal scale and may utilize a stratified design, such as randomization within centers, to assess treatment efficacy across multiple visits. Baseline characteristics may be strongly associated with the outcome, and adjustment for them can improve power. The win ratio (ignores ties) and the win odds (accounts for ties) can be useful when analyzing these types of data from randomized controlled trials. This package provides straightforward functions for adjustment of the win ratio and win odds for stratification and baseline covariates, facilitating the comparison of test and control treatments in multi-visit clinical trials. For additional information concerning the methodologies and applied examples within this package, please refer to the following publications:
	1. Weideman, A.M.K., Kowalewski, E.K., & Koch, G.G. (2024). “Randomization-based covariance adjustment of win ratios and win odds for 	randomized multi-visit studies with ordinal outcomes.” Journal of Statistical Research, 58(1), 33–48. <doi:10.3329/jsr.v58i1.75411>.
	2. Kowalewski, E.K., Weideman, A.M.K., & Koch, G.G. (2023). “SAS macro for randomization-based methods for covariance and stratified adjustment 	of win ratios and win odds for ordinal outcomes.” SESUG 2023 Proceedings, Paper 139-2023.
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, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis.
Performs an analysis of time-to-event clinical trial data using various "win time" methods, 
  including 'ewt', 'ewtr', 'rmt', 'ewtp', 'rewtp', 'ewtpr', 'rewtpr', 'max', 'wtr', 'rwtr', 'pwt', and 'rpwt'. These methods are used to calculate and compare 
  treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment 
  arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and 
  usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. 
  (2024)<https://pubmed.ncbi.nlm.nih.gov/38417455/>. For more information, see the package documentation or the vignette titled "Introduction to wintime."
Implementation of the weighted iterative proportional fitting (WIPF) procedure for updating/adjusting a N-dimensional array given a weight structure and some target marginals.
   Acknowledgements:
   The author wish to thank Conselleria de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031), Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.
Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Laplace or Normal shape; and a shrunk quantile predictor that fits a few quantile regressions over time and interpolates to a full predictive. The function then couples the per-series mixtures on a common transform (additive/multiplicative/log-multiplicative), simulates coherent draws, and returns both transformed- and level-scale samplers and summaries.
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).
Provides functions for computing moments and coefficients related to the Beta-Wishart and Inverse Beta-Wishart distributions. 
    It includes functions for calculating the expectation of matrix-valued functions of the Beta-Wishart distribution, 
    coefficient matrices C_k and H_k, expectation of matrix-valued functions of the inverse Beta-Wishart distribution, 
    and coefficient matrices \tilde{C}_k and \tilde{H}_k. For more details, refer Hillier and Kan (2024) <https://www-2.rotman.utoronto.ca/~kan/papers/wishmom.pdf>, 
    "On the Expectations of Equivariant Matrix-valued Functions of Wishart and Inverse Wishart Matrices".
Adds ... to a function's argument list so that it can
    tolerate non-matching arguments.
Lets you temporarily execute an expression or a local block with a different here() root in the 'here' package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.
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.
A comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', 'MongoDB', 'Qualtrics', 'SQL', 'ORACLE') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) <doi:10.20900/jpbs.20210011>.
Provides a minimal R and C++ API for parsing
  well-known binary and well-known text representation of
  geometries to and from R-native formats.
  Well-known binary is compact
  and fast to parse; well-known text is human-readable
  and is useful for writing tests. These formats are
  useful in R only if the information they contain can be
  accessed in R, for which high-performance functions
  are provided here.
Utility functions to convert between the 'Spatial' classes
  specified by the package 'sp', and the well-known binary '(WKB)'
  representation for geometry specified by the 'Open Geospatial Consortium'.
  Supports 'Spatial' objects of class 'SpatialPoints',
  'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
  'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
  types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
  'MultiPolygon'. Includes extensions to enable creation of maps with
  'TIBCO Spotfire'.
Establishes and maintains vertex pool topology for geometry 
    handled by 'wk'. Segments are the atomic unit, vertices are shared via
    integer references into a pool. Topology is made discoverable via coincident
    vertex detection while not requiring modification of the input data. 
    Topological data models follow principles described in Worboys and
    Duckham (2004, ISBN:978-0415283755). The edge-based topology geometry decomposed 
    into vertices and directed edge pairs is a simplification of the quad-edge case in
    Guibas & Stolfi (1985) <doi:10.1145/282918.282923>.
Converts weekly data to monthly data.
     Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.
It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
Use various regression models for the analysis of win loss endpoints 
             adjusting for non-binary and multivariate covariates.
Streamlines the process of transitioning between data formats commonly used in survival analysis. Functions convert longitudinal data between formats used as input for survival models as well as support overall preparation. Users are able to focus on model building rather than data wrangling.
Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.
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>.
Provides inference for the Wilcoxon-Mann-Whitney test under the null hypothesis H0: AUC = 0.5 for continuous, discrete or mixed random variables. Traditional implementations test H0: F = G, which is inappropriately broad and leads to erroneous inferences. Methods are described in M. Grendar (2025) "Wilcoxon-Mann-Whitney Test of No Group Discrimination" <doi:10.48550/arXiv.2511.20308>.
Performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.
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.
    See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.
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.
Implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.
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 (2019) <doi:10.1080/01621459.2018.1537911>.
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. (2023). 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. (2023). 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 <doi:10.48550/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/>.
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/>.
Application to estimate statistical values using properties provided by a group of individuals to describe
  concepts using 'shiny'. It estimates the underlying distribution to generate new descriptive words
  Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate 
  the probability that two persons describe the same words based on their descriptions
  Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.
Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.
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>).
  The WordNet database files need to be made available separately,
  either via package 'wordnetDicts' from <https://datacube.wu.ac.at>,
  installing system packages where available, or direct download from
  <https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>.
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.
The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. If all letters are revealed before your guesses run out, you win this game; otherwise you fail. You may run multiple games to guess different words.
Serves for rendering MS Word documents with R inline code and inserting tables and plots.
List of english scrabble words as listed in the OTCWL2014
    <https://www.scrabbleplayers.org/w/Official_Tournament_and_Club_Word_List_2014_Edition>.
    Words are collated from the 'Word Game Dictionary' <https://www.wordgamedictionary.com/word-lists/>.
Provides access to various word embedding methods (GloVe, 
    fasttext and word2vec) to extract word vectors using a unified framework to
    increase reproducibility and correctness.
An interactive laboratory for research on distributional semantic models ('DSM',
             see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
Create dense vector representation of words and documents using 'quanteda'. Implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546>, Doc2vec (Le & Mikolov, 2014) <doi:10.48550/arXiv.1405.4053> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.
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).
Create, store, read and manage structured collections of
    datasets and other objects using a 'workspace', then bundle it into a
    compressed archive.  Using open and interoperable formats makes it
    possible to exchange bundled data from 'R' to other languages such as
    'Python' or 'Julia'.  Multiple formats are supported 'Parquet',
    'JSON', 'yaml', spatial data and raster data are supported.
Download and search data from the 'World Bank' APIs,
    including the 'Indicators' API, the 'Poverty and Inequality Platform
    (PIP)' API, the 'Finances One' API, and the 'Projects' API. See
    <https://datahelpdesk.worldbank.org/knowledgebase/articles/889386-developer-information-overview>
    for further details.
World Flora Online is an online flora of all known plants, available from <https://www.worldfloraonline.org/>. Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (<http://www.theplantlist.org/>), a working list of plant names that has become static since 2013.
Allow users to obtain clean and tidy
    football (soccer) game, team and player data. Data is collected from a
    number of popular sites, including 'FBref',
    transfer and valuations data from
    'Transfermarkt'<https://www.transfermarkt.com/> and shooting location
    and other match stats data from 'Understat'<https://understat.com/>
    and 'fotmob'<https://www.fotmob.com/>. It gives users the
    ability to access data more efficiently, rather than having to export
    data tables to files before being able to complete their analysis.
Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map.
Functions to import data from more than 30,000 surface
    meteorological sites around the world managed by the National Oceanic
    and Atmospheric Administration (NOAA) Global Historical Climate
    Network (GHCN) and Integrated Surface Database (ISD).
A toolkit to detect clusters from distance matrices. 
    The distance matrices are assumed to be calculated between the cells of 
    multiple animals ('Caenorhabditis elegans') from input time-series matrices. 
    Some functions for generating distance matrices, performing clustering, 
    evaluating the clustering, and visualizing the results of clustering and 
    evaluation are available. We're also providing the download function to 
    retrieve the calculated distance matrices from 
    'figshare' <https://figshare.com>.
Client for World Register of Marine Species
    (<https://www.marinespecies.org/>). Includes functions for each
    of the API methods, including searching for names by name, date and
    common names, searching using external identifiers, fetching
    synonyms, as well as fetching taxonomic children and
    taxonomic classification.
Generate wordsearch and crossword puzzles using
    custom lists of words (and clues).  Make them easy or hard,
    and print them to solve offline with paper and pencil!
R clients to the 'Web of Science' and 'InCites' 
  <https://clarivate.com/products/data-integration/> APIs, which 
  allow you to programmatically download publication and citation data
  indexed in the 'Web of Science' and 'InCites' databases.
It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.
This method generates a tour path by interpolating between d-D frames in p-D using Givens rotations. The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See more details in Buj, Cook, Asimov and Hurley (2005) <doi:10.1016/S0169-7161(04)24014-7> and Batsaikhan, Cook and Laa (2023) <doi:10.48550/arXiv.2311.08181>.
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.
Analyzing pedigree data of wild
    populations. While primarily designed to process outputs from the
    'COLONY' (Jones & Wang (2010) <doi:10.1111/j.1755-0998.2009.02787.x>)
    pedigree reconstruction software, it can also accommodate
    data from other sources. By linking reconstructed pedigrees with
    genetic sample metadata, 'wpeR' produces spatial and temporal
    visualizations as well as tabular summaries that support
    interpretation of family structures and dynamics. The main goal of the
    package is to provide a solution for the analysis of
    complex wild pedigree data and to help the user to gain insights
    into genetic relationships within wild animal populations.
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.
Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <doi:10.48550/arXiv.2012.09999>.
Estimate and plot wavelet quantile correlations(Kumar and Padakandla,2022) between two time series. Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) <doi:10.1016/j.frl.2022.102707> for further details.
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.
The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition.
Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 <doi:10.1289/EHP10570>).
Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using
  Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error
  Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>.
  Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and
  visualization of results.
Implements various win ratio methodologies for composite endpoints of death and 
  non-fatal events, including the (stratified) proportional win-fractions (PW) regression models 
  (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly 
  recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 
  2021 <doi:10.1111/biom.13501>).
Supports systematic scrutiny, modification, and integration of
    data. The function status() counts rows that have missing values in 
    grouping columns (returned by na() ), have non-unique combinations of 
    grouping columns (returned by dup() ), and that are not locally sorted
    (returned by unsorted() ). Functions enumerate() and itemize() give 
    sorted unique combinations of columns, with or without occurrence counts,
    respectively. Function ignore() drops columns in x that are present in y,
    and informative() drops columns in x that are entirely NA; constant() returns
    values that are constant, given a key.  Data that have defined unique 
    combinations of grouping values behave more predictably during merge operations.
The main functionalities of 'wrappedtools' are:
    adding backticks to variable names; rounding to desired precision 
    with special case for p-values; 
    selecting columns based on pattern and storing their position, name, 
    and backticked name; computing and formatting of descriptive statistics 
    (e.g. mean±SD), comparing groups and creating publication-ready tables with 
    descriptive statistics and p-values; creating specialized plots for 
    correlation matrices. Functions were mainly written for my own daily work or 
    teaching, but may be of use to others as well.
Helper functions to easily add functionality to functions.  The package can 
  assign functions to have an lazy evaluation allowing you to save and update the 
  arguments before and after each function call.  You can set a temporary working 
  directory within functions and wrap console messages around other functions.
Tools for writing and debugging R code. Provides: 
    '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return),
    'build_frame()'/'draw_frame()' ('data.frame' example tools),
    'qc()' (quoting concatenate), 
    ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard
    evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.
A wrapper around Michel Scheffers's 'libassp' (<https://libassp.sourceforge.net/>).
    The 'libassp' (Advanced Speech Signal Processor) library aims at providing
    functionality for handling speech signal files in most common audio formats
    and for performing analyses common in phonetic science/speech science. This
    includes the calculation of formants, fundamental frequency, root mean
    square, auto correlation, a variety of spectral analyses, zero crossing
    rate, filtering etc. This wrapper provides R with a large subset of
    'libassp's signal processing functions and provides them to the user in a
    (hopefully) user-friendly manner.
Provides simple functions for accessing data from 'Wharton Research
    Data Services' ('WRDS'), a widely used financial database in academic
    research. Includes credential management via the system keyring,
    database tools, and functions for downloading generic tables, 'Compustat'
    fundamentals, and linking tables.
A collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.
Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) <doi:10.1002/sim.9297>.
Additional options for making graphics in the context of analyzing high-throughput data are available here.
  This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, 
  automatic checking for optimal location of legends in plots, small histograms to insert as legends, 
  histograms re-transforming axis labels to linear when plotting log2-transformed data,
  a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats, 
  principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples, 
  generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>, 
  staggered count plots and generation of mouse-over interactive html pages.
Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Imports variables from 'ReaderBench' (Dascalu et al.,
    2018)<doi:10.1007/978-3-319-66610-5_48>, 'Coh-Metrix' (McNamara et
    al., 2014)<doi:10.1017/CBO9780511894664>, and/or 'GAMET' (Crossley et
    al., 2019) <doi:10.17239/jowr-2019.11.02.01> output files; downloads
    predictive scoring models described in Mercer & Cannon
    (2022)<doi:10.31244/jero.2022.01.03> and Mercer et
    al.(2021)<doi:10.1177/0829573520987753>; and generates predicted
    writing quality and curriculum-based measurement (McMaster & Espin,
    2007)<doi:10.1177/00224669070410020301> scores.
Provides unified syntax to write data from lazy 'dplyr' 'tbl' or
    'dplyr' 'sql' query or a dataframe to a database table with modes such as
    create, append, insert, update, upsert, patch, delete, overwrite,
    overwrite_schema.
Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'
    <https://libxlsxwriter.github.io>. 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). 
  A group of 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. 
  Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples.
  Many times large experimental datasets need some additional filtering, adequate functions are provided. 
  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.
  Batch reading (or writing) of sets of files and combining data to arrays is supported, too.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Tools for constructing, simulating, and analyzing large-scale
    water resources systems. The package provides functions to represent
    system components such as reservoirs, aquifers, rivers, diversions,
    and demand sites, and to simulate system behavior under Standard
    Operating Policy. It also supports the development and evaluation
    of water allocation strategies and hydropower operations within
    integrated water resources systems.
A collection of implementations of classical and
    novel algorithms for weighted sampling without replacement.
    Implementations include the algorithm of Efraimidis and Spirakis (2006)
    <doi:10.1016/j.ipl.2005.11.003> and Wong and Easton (1980)
    <doi:10.1137/0209009>.
An adaptation for estuaries (tidal waters) of weighted regression
    on time, discharge, and season to evaluate trends in water quality time series. 
    Please see Beck and Hagy (2015) <doi:10.1007/s10666-015-9452-8> for 
    details.
Top-Down mass spectrometry aims to identify entire proteins as well as their (post-translational) modifications or ions bound (eg Chen et al (2018) <doi:10.1021/acs.analchem.7b04747>). 
    The pattern of internal fragments (Haverland et al (2017) <doi:10.1007/s13361-017-1635-x>) may reveal important information about the original structure of the proteins studied 
    (Skinner et al (2018) <doi:10.1038/nchembio.2515> and Li et al (2018) <doi:10.1038/nchem.2908>). 
    However, the number of possible internal fragments gets huge with longer proteins and subsequent identification of internal fragments remains challenging, 
    in particular since the the accuracy of measurements with current mass spectrometers represents a limiting factor.   
    This package attempts to deal with the complexity of internal fragments and allows identification of terminal and internal fragments from deconvoluted mass-spectrometry data.
Predicts individual race/ethnicity using surname, first name,
    middle name, geolocation, and other attributes, such as gender and
    age. The method utilizes Bayes' Rule (with optional measurement error
    correction) to compute the posterior probability of each racial
    category for any given individual. The package implements methods
    described in Imai and Khanna (2016) "Improving Ecological Inference by
    Predicting Individual Ethnicity from Voter Registration Records"
    Political Analysis <DOI:10.1093/pan/mpw001> and Imai, Olivella, and
    Rosenman (2022) "Addressing census data problems in race imputation
    via fully Bayesian Improved Surname Geocoding and name supplements"
    <DOI:10.1126/sciadv.adc9824>.  The package also incorporates the data
    described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity
    data for first, middle, and surnames"
    <DOI:10.1038/s41597-023-02202-2>.
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>.
This dataset was collected using a new four-arm within-study comparison design. The study aimed to examine the impact of a mathematics training intervention and a vocabulary study session on post-test scores in mathematics and vocabulary, respectively. The innovative four-arm within-study comparison design facilitates both experimental and quasi-experimental identification of average causal effects.
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.
Within-subject mediation analysis using structural equation modeling.
    Examine how changes in an outcome variable between two conditions are mediated 
    through one or more variables. Supports within-subject mediation analysis using 
    the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, 
    and extends Monte Carlo confidence interval estimation to missing data scenarios 
    using the 'semmcci' package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.
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.
Weighted versions of common descriptive statistics (variance,
    standard deviation, covariance, correlation, quantiles).
Implements the Welch-Satterthwaite approximation for differences
    of non-standardized t-distributed random variables in both univariate and 
    multivariate settings. The package provides methods for computing effective
    degrees of freedom and scale parameters, as well as distribution functions
    for the approximated difference distribution. The methodology extends the
    classical Welch-Satterthwaite framework from variance combinations to
    t-distribution differences through careful moment matching. Methods build
    on the classical Welch-Satterthwaite approach described in Welch (1947)
    <doi:10.1093/biomet/34.1-2.28> and Satterthwaite (1946) <doi:10.2307/3002019>.
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 <doi:10.1186/s12859-018-2351-7>.
Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).
Allows form managers to download entries from their respondents
    using Wufoo JSON API (<https://www.wufoo.com>). Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.
Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <doi:10.48550/arXiv.1607.05858>.
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'.  
   Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, 
   conditioned scatter plot with marginal densities,
   box and stem with matching theoretical distribution, and density with matching theoretical distribution.
Book is "Linear Mixed Models: A Practical Guide Using
        Statistical Software" published in 2006 by Chapman Hall / CRC
        Press.
Provides a collection of white noise hypothesis tests for functional time series and related visualizations. 
  These include tests based on the norms of autocovariance operators that are built under both strong and weak 
  white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
  dimensional reduction are included, which are built under strong white noise assumptions. 
  Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
  These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) 
  <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, 
  and Kim et al. (2023) <doi: 10.1214/23-SS143>
  respectively.
A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc.
    Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation.
    Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.
Meta testing is the ability to test a function without having to 
    provide its parameter values.
    Those values will be generated, based on semantic naming of parameters, as 
    introduced by package 'wyz.code.offensiveProgramming'.
    Value generation logic can be completed with your own data types 
    and generation schemes. This to meet your most specific requirements and to 
    answer to a wide variety of usages, from general use case to very specific
    ones.
    While using meta testing, it becomes easier to generate stress test 
    campaigns, non-regression test campaigns and robustness test campaigns, as 
    generated tests can be saved and reused from session to session. 
    Main benefits of using 'wyz.code.metaTesting' is ability to discover valid 
    and invalid function parameter combinations, ability to infer valid 
    parameter values, and to provide smart summaries that allows you to focus
    on dysfunctional cases.
Allows to turn standard R code into offensive programming code. 
    Provides code instrumentation to ease this change and tools to assist and 
    accelerate code production and tuning while using offensive programming code 
    technics. 
    Should improve code robustness and quality. Function calls can be easily 
    verified on-demand or in batch mode to assess parameter types and length 
    conformities. 
    Should improve coders productivity as offensive programming reduces the code
    size due to reduced number of controls all along the call chain. 
    Should speed up processing as many checks will be reduced to one single check.
Allows to generate on-demand or by batch, any R documentation file,
    whatever is kind, data, function, class or package. It populates
    documentation sections, either automatically or by considering
    your input. Input code could be standard R code or offensive programming code. 
    Documentation content completeness depends on the type of code you use. With
    offensive programming code, expect generated documentation to be fully 
    completed, from a format and content point of view. With some standard R 
    code, you will have to activate post processing to fill-in any section that 
    requires complements. Produced manual page validity is automatically tested 
    against R documentation compliance rules. Documentation language 
    proficiency, wording style, and phrasal adjustments remains your job.
Allows to generate automatically 'testthat' code files from offensive 
    programming test cases. Generated test files are complete and ready to run.
    Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of
    errors in test case production, be able to test immediately generated files 
    without any need to view or modify them, and enter a zero time latency between 
    code implementation and industrial testing. As with 'testthat', you may
    complete provided test cases according to your needs to push testing further, 
    but this need is nearly void when using 'wyz.code.offensiveProgramming'.
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.
We consider the problem where we observe k vectors (possibly of different lengths), each representing an independent multinomial random vector. For a given function that takes in the concatenated vector of multinomial probabilities and outputs a real number, this is a Monte Carlo estimation procedure of an exact p-value and confidence interval. The resulting inference is valid even in small samples, when the parameter is on the boundary, and when the function is not differentiable at the parameter value, all situations where asymptotic methods and the bootstrap would fail. For more details see Sachs, Fay, and Gabriel (2025) <doi:10.48550/arXiv.2406.19141>.
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.
Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones.  The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, <doi:10.3354/meps13814>).
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>).
Hamiltonian Monte Carlo for both continuous and discontinuous posterior 
    distributions with a customizable trajectory length termination criterion. 
    See Nishimura et al. (2020) <doi:10.1093/biomet/asz083> for the original 
    Discontinuous Hamiltonian Monte Carlo; Hoffman et al. (2014) 
    <doi:10.48550/arXiv.1111.4246> and Betancourt (2016) <doi:10.48550/arXiv.1601.00225> 
    for the definition of possible Hamiltonian Monte Carlo termination criteria.
High-level functions to render 'LaTeX' fragments in plots, 
             including as labels and data symbols in 'ggplot2' plots, plus
             low-level functions to 
             author 'LaTeX' fragments (to produce 'LaTeX' documents), 
             typeset 'LaTeX' documents (to produce 'DVI' files), 
             read 'DVI' files (to produce "DVI" objects), 
             and render "DVI" objects.
Miscellaneous functions used for x-engineering (feature engineering) or 
  for supporting in other packages maintained by 'Shichen Xie'.
Implementation of a scalable, highly configurable, and 
        e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms.
        Multiple representations (binary, real-coded, permutation, and 
        derivation-tree), a rich collection of genetic operators, 
        as well as an extended processing pipeline are provided 
        for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)),
        differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005)
        <doi:10.1007/3-540-31306-0>), simulated annealing (Aarts, E., and Korst, J.
        (1989, ISBN:0-471-92146-7)), grammar-based genetic programming 
        (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution 
        (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>), 
        and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in  
        Arabinia, H. (2006, ISBN:978-193-241596-3).
        All algorithms reuse basic adaptive mechanisms for performance optimization.
        For 'xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
        Sequential or parallel execution (on multi-core machines, 
        local clusters, and high-performance computing environments) 
        is available for all algorithms. See 
        <https://github.com/ageyerschulz/xega/tree/main/examples/executionModel>.
Translates a BNF (Backus-Naur Form) specification of a 
             context-free language into an R grammar object
             which consists of the start symbol, the symbol table, 
             the production table, and a short production table.
             The short production table is non-recursive. 
             The grammar object contains the file name from 
             which it was generated (without a path).
             In addition, it provides functions to determine the type 
             of a symbol (isTerminal() and isNonterminal()) and functions
             to access the production table (rules() and derives()).
             For the BNF specification, see Backus, John et al. (1962)
             "Revised Report on the Algorithmic Language ALGOL 60".
             (ALGOL60 standards page <http://www.algol60.org/2standards.htm>,
              html-edition <https://www.masswerk.at/algol60/report.htm>)
             A preprocessor for macros which expand to standard BNF 
             is included. 
             The grammar compiler is an extension of the APL2 implementation in 
             Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X).
Derivation tree operations are needed for implementing 
            grammar-based genetic programming and grammatical evolution:  
            Generating a random derivation trees of a context-free grammar 
            of bounded depth, decoding a derivation tree, 
            choosing a random node in a derivation tree, 
            extracting a tree whose root is a specified node, and 
            inserting a subtree into a derivation tree at a specified node.
            These operations are necessary for the initialization and 
            for decoders of a random population of programs, 
            as well as for implementing crossover and mutation operators.
            Depth-bounds are guaranteed by switching to a grammar 
            without recursive production rules. 
            For executing the examples, the package 'BNF' is needed.
            The basic tree operations for generating, extracting, and 
            inserting derivation trees as well as the conditions 
            for guaranteeing complete derivation trees have been 
            presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).
            The use of random integer vectors for the generation 
            of derivation trees has been introduced in 
            Ryan, C., Collins, J. J., and  O'Neill, M. (1998)
            <doi:10.1007/BFb0055930> for grammatical evolution.
Representation-dependent gene-level operations  
        for genetic and evolutionary algorithms with real-coded genes
        used in the R-package 'xega' <https://CRAN.R-project.org/package=xega>
        are collected in this package. The common feature of the gene
        operations is that all of them are useful for derivation-free
        optimization algorithms. At the moment the package  
        implements initialization, mutation, crossover, and replication 
        operations for differential evolution as described in
        Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005)
        <doi:10.1007/3-540-31306-0>.  In addition, several (more recent) 
        methods for determining the scale factor are provided.
        For 'xega''s architecture,
        see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
Representation-dependent gene level operations of a 
        genetic algorithm with binary coded genes
        for the R-package 'xega' <https://CRAN.R-project.org/package=xega>:
        Initialization of random binary genes, several gene maps for 
        binary genes, several mutation operators, several crossover
        operators with 1 and 2 kids, replication 
        pipelines for 1 and 2 kids, and, last but not least, function 
        factories for configuration. 
        See Goldberg, D. E. (1989, ISBN:0-201-15767-5).
        For crossover operators, see 
        Syswerda, G. (1989, ISBN:1-55860-066-3),
        Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9).
        For mutation operators, see 
        Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).
        For 'xega''s architecture,
        see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
Grammatical evolution  (see O'Neil, M. and 
        Ryan, C.  (2003,ISBN:1-4020-7444-1)) uses decoders to 
        convert linear (binary or integer genes) into programs.  
        In addition, automatic determination of codon precision 
        with a limited rule choice bias is provided.
        For a recent survey of grammatical evolution, 
        see Ryan, C., O'Neill, M., and Collins, J. J. (2018)
        <doi:10.1007/978-3-319-78717-6>.
An implementation of
        the representation-dependent gene level operations of grammar-based
        genetic programming with genes which are derivation trees
        of a context-free grammar: Initialization of a gene with a 
        complete random derivation tree, decoding of a derivation tree.
        Crossover is implemented by exchanging subtrees. Depth-bounds
        for the minimal and the maximal depth of the roots of the subtrees
        exchanged by crossover can be set. 
        Mutation is implemented by replacing a subtree by a random subtree. 
        The depth of the random subtree and the insertion node are 
        configurable. For details, 
        see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).
An implementation of 
        representation-dependent gene level operations for
        genetic algorithms with genes representing permutations: 
        Initialization of genes, mutation, and crossover.
        The crossover operation provided is position-based crossover 
        (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). 
        For mutation, several variants are included: Order-based mutation
        (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), 
        randomized Lin-Kernighan heuristics
        (Croes, G. A. (1958) <doi:10.1287/opre.6.6.791> and
         Lin, S. and Kernighan. B. W. (1973) 
        <doi:10.1287/opre.21.2.498>), 
        and randomized greedy operators.
        A random mix operator for mutation selects a mutation variant 
        randomly.
This collection of gene representation-independent functions 
   implements the population layer of extended evolutionary and genetic 
   algorithms and its support
   for the R-package 'xega' <https://CRAN.R-project.org/package=xega>.
   The population layer consists of functions 
   for initializing, logging, observing, evaluating a population of genes, 
   as well as of computing the next population. For parallel evaluation of a 
   population of genes 4 execution models - named Sequential, MultiCore, 
   FutureApply, and Cluster - are provided. They are implemented by 
   configuring the lapply() function. The execution model FutureApply can be 
   externally configured as recommended by Bengtsson (2021) 
   <doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling 
   schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) 
   <doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J. 
   (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized 
   approximate search procedure elements. Adaptive crossover and mutation 
   rates depending on population statistics generalize the approach of 
   Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). 
   For 'xega''s architecture, 
   see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.
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://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms
  in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, 
  and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Extreme Gradient Boosting, which is an efficient implementation
    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
    This package is its R interface. The package includes efficient linear
    model solver and tree learning algorithms. The package can automatically
    do parallel computation on a single machine which could be more than 10
    times faster than existing gradient boosting packages. It supports
    various objective functions, including regression, classification and ranking.
    The package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
Compute surrogate explanation groves for predictive machine learning models and analyze complexity vs. explanatory power of an explanation according to Szepannek, G. and von Holt, B. (2023) <doi:10.1007/s41237-023-00205-2>.
Supports a structured approach
    for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also
    contains helper functions for enabling the modeler to follow best R
    practices (by appending the program name, figure name location, and
    draft status to each plot). In addition, it enables the modeler to
    follow best graphical practices (by providing a theme that
    reduces chart ink, and by providing time-scale, log-scale, and
    reverse-log-transform-scale functions for more readable axes).
    Finally, it provides some data checking and summarizing functions for
    rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.
Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes 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. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.
Computes robust association measures that do not 
    presuppose linearity. The xi correlation (xicor) is based
    on cross correlation between ranked increments.
    The reference for the methods implemented here is 
    Chatterjee, Sourav (2020) <arXiv:1909.10140>
    This package includes the Galton peas example.
Provides a simple XML tree parser/generator. It includes
        functions to read XML files into R objects, get information out of
        and into nodes, and write R objects back to XML code. It's not as
        powerful as the 'XML' package and doesn't aim to be, but for simple
        XML handling it could be useful. It was originally developed for
        the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin
        development easier.
Provides custom geoms and themes to create charts and graphics in 
    the distinctive, hand-drawn 'XKCD' webcomic style using the 'ggplot2' framework. 
    The package utilizes custom layers for jittered lines, segments, circles, and 
    figures, and includes a theme that supports the necessary 'XKCD' font.
The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.
Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.
An R interface to the 'OpenPyXL' 'Python' library to create 
  native 'Excel' charts and work with Microsoft 'Excel' files.
Provides comprehensive functionality to read, write and format Excel data.
Parse entire folders of non-rectangular 'xlsx' files into a single
  rectangular and tidy 'data.frame' based on a custom template file defining the 
  column names of the output.
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Blein--Nicolas et al (2024) <DOI:10.1093/jrsssc/qlae012>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
A high-level interface for creating and exporting summary tables to
  'Excel'. Built on 'dplyr' and 'openxlsx', it provides tools for generating
  one-way to n-way tables, and summarizing multiple response questions 
  and question blocks. Tables are exported with native 'Excel' formatting, 
  including titles, footnotes, and basic styling options.
Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.
Converts an 'XLSForm' (survey in 'Excel') into a well-structured 'Word' document, including sections, skip logic, options, and question labels.
    Designed to support survey documentation, training materials, and data collection workflows.
    The package was developed based on field experience with 'XLSForm' and humanitarian operations, aiming to streamline documentation and enhance training efficiency.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
Reading and writing sheets of a single 'Excel' file into and from a list of data frames. Eases I/O of tabular data in bioinformatics while keeping them in a human readable format.
The xlsxjars package collects all the external jars
  required for the xlxs package. This release corresponds to POI
  3.13.
Provides support for transformations of numeric aggregates
   between statistical classifications (e.g. occupation or industry categorisations) using the 'Crossmaps' framework. 
   Implements classes for representing transformations between a source and target classification
   as graph structures, and methods for validating and applying crossmaps to transform
   data collected under the source classification into data indexed using the target classification codes.
   Documentation about the 'Crossmaps' framework is provided in the included vignettes
   and in Huang (2024, <doi:10.48550/arXiv.2406.14163>).
A toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.
Many approaches for both reading and
        creating XML (and HTML) documents (including DTDs), both local
        and accessible via HTTP or FTP.  Also offers access to an
        'XPath' "interpreter".
Bindings to 'libxml2' for working with XML data using a simple, 
    consistent interface based on 'XPath' expressions. Also supports XML schema
    validation; for 'XSLT' transformations see the 'xslt' package.
Helpers for transforming
    XML content into number of tables while preserving parent to child
    relationships.
Import an XML document with nested object structures and convert
    it into a relational data model. The result is a set of R dataframes 
    with foreign key relationships. The data model and the data can be exported as
    SQL code of different SQL flavors.
Converts XML documents to R dataframes and dataframes to XML documents.
  A wide variety of options allows for different XML formats and flexible control
  of the conversion process. Results can be exported to CSV and Excel, if desired. 
  Also converts XML data to R lists.
Convert the output of 'utils::getParseData()' to an 'XML'
    tree, that one can search via 'XPath', and easier to manipulate in
    general.
'XML' package for creating and reading and manipulating 'XML', with an object model based on 'Reference Classes'.
The 'XML-RPC' is a remote procedure call protocol 
    based on 'XML'. The 'xmlrpc2' package is inspired by the 'XMLRPC'
	package but uses the 'curl' and 'xml2' packages instead 'RCurl' and 'XML'.
Provides a fast and elegant interface for generating XML
 fragments and documents. It can be used in companion with R packages 'XML'
 or 'xml2' to generate XML documents. The fast XML generation is implemented using the
 'Rcpp' package.
Edit 'XMP' metadata <https://en.wikipedia.org/wiki/Extensible_Metadata_Platform> 
    in a variety of media file formats as well as 
    edit bookmarks (aka outline aka table of contents) and documentation info entries in 'pdf' files.
    Can detect and use a variety of command-line tools to perform these operations such as
    'exiftool' <https://exiftool.org/>, 'ghostscript' <https://www.ghostscript.com/>, 
    and/or 'pdftk' <https://gitlab.com/pdftk-java/pdftk>.
XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.
Fit a two-step kernel ridge regression model for
        predicting edges in networks, and carry out cross-validation
        using shortcuts for swift and accurate performance assessment
        (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.
Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model. See, for example,
    Broman and Weber (2000) <doi:10.1086/302923>.
Cross platform solution to open files, directories or 'URLs'
    with their associated programs.
Implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.
Helps systematize and ease the process of 
    building unit tests with the 'testthat' package by providing 
    tools for generating expectations.
Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The 'xplain' interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.
Provides a consistent interface for common feature importance
    methods as described in Ewald et al. (2024)
    <doi:10.1007/978-3-031-63797-1_22>, including permutation feature
    importance (PFI), conditional and relative feature importance (CFI,
    RFI), leave one covariate out (LOCO), and Shapley additive global
    importance (SAGE), as well as feature sampling mechanisms to support
    conditional importance methods.
Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.
Provides a set of functions devoted to multivariate exploratory statistics on textual data. Classical methods such as correspondence analysis and agglomerative hierarchical clustering are available. Chronologically constrained agglomerative hierarchical clustering enriched with labelled-by-words trees is offered. Given a division of the corpus into parts, their characteristic words and documents are identified. Further, accessing to 'FactoMineR' functions is very easy. Two of them are relevant in textual domain. MFA() addresses multiple lexical table allowing applications such as dealing with multilingual corpora as well as simultaneously analyzing both open-ended and closed questions in surveys. See <http://xplortext.unileon.es> for examples.
Tools to build CDISC compliant data sets and check for CDISC
    compliance.
Diagnostics for non-linear mixed-effects (population) 
    models from 'NONMEM' <https://www.iconplc.com/solutions/technologies/nonmem/>. 
    'xpose' facilitates data import, creation of numerical run summary 
    and provide 'ggplot2'-based graphics for data exploration and model 
    diagnostics.
Extension to 'xpose' to support 'nlmixr2'. Provides functions to import 'nlmixr2' fit data into an 'xpose' data object, allowing the use of 'xpose' for 'nlmixr2' model diagnostics.
Adding some at-present missing functionality, or functions 
    unlikely to be added to the base 'xpose' package.  This includes some 
    diagnostic plots that have been missing in translation from 'xpose4', 
    but also some useful features that truly extend the capabilities of what 
    can be done with 'xpose'. These extensions include the concept of a set of
    'xpose' objects, and diagnostics for likelihood-based models.
A model building aid for nonlinear mixed-effects 
    (population) model analysis using NONMEM, facilitating data set 
    checkout, exploration and visualization, model diagnostics, candidate 
    covariate identification and model comparison. The methods are described 
    in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) 
    <doi:10.1016/s0169-2607(98)00067-4>.
There is limited native support for external pointers in the R interface.
 This package provides some basic tools to verify, create and modify 'externalptr' objects.
Support for interfaces from R to other languages,
    built around a class for evaluators and a combination of functions, classes and
    methods for communication. Will be used through a specific language interface
    package. Described in the book "Extending R".
Tools to analyze datasets previous to any statistical modeling.
    Has various functions designed to find inconsistencies and understanding the distribution of the data.
An implementation of the RuleFit algorithm as described in
    Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient
    Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to
    fit a sparse linear model on the raw and rule features. The result is
    a model that learns similarly to a tree ensemble, while often offering
    improved interpretability and achieving improved scoring runtime in
    live applications. Several algorithms for reducing rule complexity are
    provided, most notably hyperrectangle de-overlapping. All algorithms
    scale to several million rows and support sparse representations to
    handle tens of thousands of dimensions.
Contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.
Fits hierarchical regularized regression models
    to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. 
    Utilizes coordinate descent to efficiently fit regularized regression models both with and without
    external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). 
    Support for standard R matrices, sparse matrices and big.matrix objects.
This is a set of statistical quality control functions,  that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.
An extension for the 'xml2' package to transform XML documents
    by applying an 'xslt' style-sheet.
The circadian period of a time series data is predicted and the statistical significance of the periodicity are calculated using the chi-square periodogram.
Tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.
Implements the bootstrap slope heterogeneity test for panel data 
    based on Blomquist and Westerlund (2015) <doi:10.1007/s00181-015-0978-z>.
    Tests the null hypothesis that slope coefficients are homogeneous across
    cross-sectional units. Provides both standard and adjusted Delta statistics
    with bootstrap p-values. Supports partialling out of control variables and
    cross-sectional averages for dealing with cross-sectional dependence.
Implements panel cointegration tests allowing for structural breaks
    and cross-section dependence following the methodology of Banerjee and
    Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides
    iterative factor-break estimation, individual ADF tests on defactored
    residuals, standardized panel test statistics, and the Bai and Ng (2004)
    <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common
    stochastic trends. Supports five model specifications with varying
    deterministic components and break structures.
Implements the Cross-Sectionally Augmented Panel Quantile 
    Autoregressive Distributed Lag (CS-PQARDL) model and the Quantile 
    Common Correlated Effects Mean Group (QCCEMG) estimator for panel data 
    with cross-sectional dependence. The package handles unobserved common 
    factors through cross-sectional averages following Pesaran (2006) 
    <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) 
    <doi:10.1016/j.jeconom.2015.03.007>. Quantile regression for dynamic 
    panels follows Harding, Lamarche, and Pesaran (2018) 
    <doi:10.1016/j.jeconom.2018.07.010>. The ARDL approach to cointegration 
    testing is based on Pesaran, Shin, and Smith (2001) 
    <doi:10.1002/jae.616>.
Implements the Durbin-Hausman panel cointegration tests of 
    Westerlund (2008) <doi:10.1002/jae.963>. The tests are robust to 
    cross-sectional dependence through common factor extraction using 
    principal components. Provides both group-mean (DHg) and panel (DHp) 
    test statistics with automatic factor number selection via information 
    criteria.
The 'xtdml' package implements partially linear panel regression (PLPR) models with high-dimensional confounding variables and an exogenous treatment variable within the double machine learning framework. The package is used to estimate the structural parameter (treatment effect) in static panel data models with fixed effects using the approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. 'xtdml' is built on the object-oriented package 'DoubleML' (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the 'mlr3' ecosystem.
Estimation of Panel Quantile Autoregressive Distributed Lag 
    (PQARDL) models that combine panel ARDL methodology with quantile 
    regression. Supports Pooled Mean Group (PMG), Mean Group (MG), and 
    Dynamic Fixed Effects (DFE) estimators across multiple quantiles. 
    Computes long-run cointegrating parameters, error correction term speed 
    of adjustment, half-life of adjustment, and performs Wald tests for 
    parameter equality across quantiles. Based on the econometric frameworks 
    of Pesaran, Shin, and Smith (1999) <doi:10.1080/01621459.1999.10474156>, 
    Cho, Kim, and Shin (2015) <doi:10.1016/j.jeconom.2015.02.030>, and 
    Bildirici and Kayikci (2022) <doi:10.1016/j.energy.2022.124303>.
There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Based on 'STATA' xtsum command, it is used to compute summary statistics for a panel data set. It generates overall, between-group, and within-group statistics for specified variables in a panel data set, as presented in S. Porter (2023) <https://stephenporter.org/files/xtsum_handout.pdf>, StataCorp (2023) <https://www.stata.com/manuals/xtxtsum.pdf>.
Implements an iterative mean-variance panel regression estimator
    that allows both the mean and variance of the dependent variable to be 
    functions of covariates. The method alternates between estimating a mean 
    equation (using generalized linear models with Gaussian family) and a 
    variance equation (using generalized linear models with Gamma family on 
    squared within-group residuals) until convergence. Based on the methodology 
    in Mooi-Reci and Liao (2025) <doi:10.1093/esr/jcae052>.
This is a collection of some useful functions when dealing with text data. 
    Currently it only contains a very efficient function of decoding HTML entities
    in character vectors by 'Rcpp' routine.
Calculates a number of valuation adjustments including CVA, DVA,
    FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For
    the KVA calculation four regulatory frameworks are supported: CEM, (simplified) SA-CCR, OEM
	and IMM. The probability of default is implied through the credit spreads curve.
    The package supports an exposure calculation based on SA-CCR which includes several trade types
    and a simulated path which is currently available only for Interest Rate Swaps. The latest regulatory capital charge methodologies
    have been implementing including BA-CVA & SA-CVA.
Provides tools for reading, parsing and visualizing simulation data stored in 'xvg'/'xpm' file
  formats (commonly generated by 'GROMACS' molecular dynamics software). Streamlines post-processing
  and analysis of molecular dynamics ('MD') simulation outputs, enabling efficient exploration of
  molecular stability and conformational changes. Supports import of trajectory metrics ('RMSD',
  energy, temperature) and creation of publication-ready visualizations through integration with
  'ggplot2'.
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.
Institutional performance assessment remains a key challenge to a multitude of stakeholders. Existing indicators such as h-type indicators, g-type indicators, and many others do not reflect expertise of institutions that defines their research portfolio. The package offers functionality to compute and visualise two novel indices: the x-index and the xd-index. The x-index evaluates an institution's scholarly expertise within a specific discipline or field, while the xd-index provides a broader assessment of overall scholarly expertise considering an institution's publication pattern and strengths across coarse thematic areas. These indices offer a nuanced understanding of institutional research capabilities, aiding stakeholders in research management and resource allocation decisions. Lathabai, H.H., Nandy, A., and Singh, V.K. (2021) <doi:10.1007/s11192-021-04188-3>. Nandy, A., Lathabai, H.H., and Singh, V.K. (2023) <doi:10.5281/zenodo.8305585>. This package provides the h-, g-, x-, xd-indices, and their variants for use with standard format of Web of Science (WoS) scrapped datasets.
Extremely fast hashing of R objects using 'xxHash'.  R objects are hashed via
    the standard serialization mechanism in R.  Raw byte vectors and strings
    can be handled directly for compatibility with hashes created on 
    other systems.  This implementation is a wrapper around the 'xxHash' 'C'
    library which is available from <https://github.com/Cyan4973/xxHash>.
Tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the 'limma' method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with 'clusterProfiler' <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using 'igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.
An alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods.  A method for generating helio plots is also included.
Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. In case of computational heavy load, the procedures can run in parallel.
Another implementation of general regression neural network in R
    based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the 
    functional approximation or the classification.
Obtain historical and near real time data related to stocks, index 
    and currencies from the Yahoo Finance API. This package is community maintained 
    and is not officially supported by 'Yahoo'. The accuracy of data is only as 
    correct as provided on <https://finance.yahoo.com/>.
Performs nearest neighbor-based imputation using one or more alternative 
    approaches to processing multivariate data. These include methods based on canonical 
    correlation: analysis, canonical correspondence analysis, and a multivariate adaptation 
    of the random forest classification and regression techniques of Leo Breiman and Adele 
    Cutler. Additional methods are also offered. The package includes functions for 
    comparing the results from running alternative techniques, detecting imputation targets 
    that are notably distant from reference observations, detecting and correcting 
    for bias, bootstrapping and building ensemble imputations, and mapping results.
This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The big.read.table() function provided here may be
    useful for large files when only a subset is needed (but please
    see the note in the help page for this function).
Implements the 'libyaml' 'YAML' 1.1 parser and emitter
    (<https://pyyaml.org/wiki/LibYAML>) for R.
A fast, correct, safe, and ergonomic 'YAML' 1.2 parser and
    generator written in 'Rust'. Convert between 'YAML' and simple 'R'
    objects with full support for multi-document streams, tags, anchors,
    and aliases. Offers opt-in handlers for custom tag behavior and
    round-trips common 'R' data structures. Implements the 'YAML' 1.2.2
    specification from the 'YAML' Language Development Team (2021)
    <https://yaml.org/spec/1.2.2/>. Proudly supported by Posit.
A YAML-based
 mechanism for working with table metadata. Supports
 compact syntax for creating, modifying, viewing, exporting,
 importing, displaying, and plotting metadata coded as column 
 attributes. The 'yamlet' dialect is valid 'YAML' with
 defaults and conventions chosen to improve readability. 
 See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.
Setting layout through 'YAML' headers in 'R-Markdown' documents,
    enabling their automatic generation.
    Functions and methods may summarize 'R' objects in automatic reports, for
    instance check-lists and further reports applied to the packages 'taxlist'
    and 'vegtable'.
The generalized construction methods for magic squares, inspired by the ancient Chinese mathematician Yang Hui's classical work "Xu Gu Zhai Qi Suan Fa". These methods can construct 4n-order magic squares and 2(2n+1)-order magic squares.
Tidy tools for quantifying how well model fits to a data set
    such as confusion matrices, class probability curve summaries, and
    regression metrics (e.g., RMSE).
Contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://bookdown.org/ndphillips/YaRrr/>.
A collection of string functions designed for writing 
    compact and expressive R code. 'yasp' (Yet Another String Package) is simple,
    fast, dependency-free, and written in pure R. The package provides: a
    coherent set of abbreviations for paste() from package 'base' with a
    variety of defaults, such as p() for "paste" and pcc() for "paste and
    collapse with commas"; wrap(), bracket(), and others for wrapping a
    string in flanking characters; unwrap() for removing pairs of characters
    (at any position in a string); and sentence() for cleaning whitespace
    around punctuation and capitalization appropriate for prose sentences.
Provides functions to manage taxonomy when lineages are
    described with strings and ranks separated with special patterns like
    "|*__" or ";*__".
Test of linearity originally proposed by Yatchew (1997) <doi:10.1016/S0165-1765(97)00218-8> and improved by de Chaisemartin & D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> to be robust under heteroskedasticity.
Determines the sum of squares of the (2^n)-1 factorial effects in a 2^n factorial experiment using Yates' algorithm.
Nonparametric estimation of discount functions and yield curves from 
    transaction data of coupon paying bonds. 
    Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> 
    describe an application of this package using the Center for Research in 
    Security Prices (CRSP) Bond Data and document its implementation.
Analyze data from behavioral experiments conducted using 'MED-PC' software developed by Med Associates Inc. 
	Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for 
	inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.
Asks Yes-No questions with variable or custom responses.
Simple and efficient access to Yahoo Finance's historical data API <https://finance.yahoo.com/> for querying and retrieval of financial data. The core functionality of the 'yfhist' package abstracts the complexities of interacting with Yahoo Finance APIs, such as session management, crumb and cookie handling, query construction, date validation, and interval management. This abstraction allows users to focus on retrieving data rather than managing API details. Use cases include historical data across a range of security types including equities & ETFs, indices, and other tickers. The package supports flexible query capabilities, including customizable date ranges, multiple time intervals, and automatic data validation. It automatically manages interval-specific limitations, such as lookback periods for intraday data and maximum date ranges for minute-level intervals. The implementation leverages standard HTTP libraries to handle API interactions efficiently and provides support for both R and 'Python' to ensure accessibility for a broad audience.
Download financial market data, company information, financial statements, options data, and more from the unofficial 'Yahoo Finance' API.
Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, 
 a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system
 and support for parallel computation.
Simple and efficient access to Yahoo Finance's 'screener' API <https://finance.yahoo.com/research-hub/screener/> for querying and retrieval of financial data. The core functionality abstracts the complexities of interacting with Yahoo Finance APIs, such as session management, crumb and cookie handling, query construction, pagination, and JSON payload generation. This abstraction allows users to focus on filtering and retrieving data rather than managing API details. Use cases include screening across a range of security types including equities, mutual funds, ETFs, indices, and futures. The package supports advanced query capabilities, including logical operators, nested filters, and customizable payloads. It automatically handles pagination to ensure efficient retrieval of large datasets by fetching results in batches of up to 250 entries per request. Filters can be dynamically defined to accommodate a wide range of screening needs. The implementation leverages standard HTTP libraries to handle API interactions efficiently and provides support for both R and Python to ensure accessibility for a broad audience.
The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.
Modelling the yield curve with some parametric models.
        The models implemented are: 
		Nelson, C.R., and A.F. Siegel (1987) <doi: 10.1086/296409>, 
		Diebold, F.X. and Li, C. (2006) <doi: 10.1016/j.jeconom.2005.03.005> 
		and Svensson, L.E. (1994) <doi: 10.3386/w4871>. 
		The package also includes the data of the term structure of interest rate of Federal Reserve Bank and European Central Bank.
Compute the standard expected years of life lost (YLL),
    as developed by the Global Burden of Disease Study
    (Murray, C.J., Lopez, A.D. and World Health Organization, 1996).
    The YLL is based on comparing the age of death to an external standard life
    expectancy curve. It also computes the average YLL, which highlights premature
    causes of death and brings attention to preventable deaths
    (Aragon et al., 2008).
Convert 'YMD' format number or string to Date efficiently, using Rust's
    standard library. It also provides helper functions to handle Date, e.g., quick
    finding the beginning or end of the given period, adding months to Date, etc.
The Author's personal R Package that contains miscellaneous functions. 
    The current version of package contains miscellaneous functions for brain data 
    to compute Asymmetry Index (AI) and bilateral (L+R) measures and reshape the data.
Write 'YAML' front matter for R Markdown and related
    documents. Work with 'YAML' objects more naturally and write the
    resulting 'YAML' to your clipboard or to 'YAML' files related to your
    project.
Provides a collection of lightweight helper functions (imps) both
  for interactive use and for inclusion within other packages. These include
  functions for minimal input assertions, visualising colour palettes,
  quoting user input, searching rows of a data frame and capturing string
  tokens.
Provides helper functions to perform Bayesian model averaging
    using Markov chain Monte Carlo samples from separate models. Calculates
    weights and obtains draws from the model-averaged posterior for quantities
    of interest specified by the user. Weight calculations can be done using
    marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay,
    R. (2010) <doi:10.1016/j.ijforecast.2009.08.001>.
Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <https://cricsheet.org/>.
Dataset from the young elite swimmers study.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
For any spending function specified by the user, this 
    package provides corresponding boundaries for interim testing using
    the adaptively weighted log-rank test developed by Yang and Prentice
    (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). 
    The package uses a re-sampling method to obtain stopping boundaries 
    at the interim looks.The output consists of stopping boundaries 
    and observed values of the test statistics at the interim looks, 
    along with nominal p-values defined as the probability of the test 
    exceeding the specific observed test statistic value or critical 
    value, regardless of the test behavior at other looks. 
    The asymptotic validity of the stopping boundaries is established
    in Yang (2018 <doi:10.1002/sim.7958>).
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
Provides covariate-adjusted comparison of two groups of right 
    censored data, where the binary group variable has separate short-term 
    and long-term effects on the hazard function, while effects of covariates 
    such as age, blood pressure, etc. are proportional on the hazard. The model 
    was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends 
    the two sample version of the short-term and long-term hazard ratio model
    proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model 
    extends the usual Cox proportional hazards model to allow more flexible 
    hazard ratio patterns, such as gradual onset of effect, diminishing effect, 
    and crossing hazard or survival functions. This package provides 
    the following: 1) point estimates and confidence intervals for model 
    parameters; 2) point estimate and confidence interval of the average hazard 
    ratio; and 3) plots of estimated hazard ratio function with point-wise and 
    simultaneous confidence bands.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
An implementation of equilibrium-based yield per recruit
    methods.  Yield per recruit methods can used to estimate the optimal
    yield for a fish population as described by Walters and Martell (2004)
    <isbn:0-691-11544-3>.  The yield can be based on the number of fish
    caught (or harvested) or biomass caught for all fish or just large
    (trophy) individuals.
Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
Provides with parametric risk neutral densities and cumulative densities for futures prices on fixed-income products. It relies on options on Short Term Interest Rate futures contracts prices or options on bond futures contracts prices. It models the price of the underlying asset as a mixture of either two or three lognormal densities. It also brings new functions which provide with risk neutral densities and cumulative densities of the money market rate or the bond yield inferred from the futures contract's price, using the density of the futures price. The package leverages on the works of Melick, W. R. and Thomas, C. P. (1997) <doi:10.2307/2331318> and B. Bahra (1998) <doi:10.2139/ssrn.77429>.
Simplify working with the 'YouTube Analytics' API <https://developers.google.com/youtube/analytics>. Collect 
    data for your channel including geography, traffic sources, time period, etc.
Simulation and Inference for SDEs and Other Stochastic Processes.
Provides a graphical user interface for the 'yuima' package.
Miscellaneous functions commonly used by 'YuLab-SMU'.
Provides a number of functions to facilitate
  extracting information in 'YAML' fragments from one or 
  multiple files, optionally structuring the information
  in a 'data.tree'. 'YAML' (recursive acronym for "YAML ain't
  Markup Language") is a convention for specifying structured
  data in a format that is both machine- and human-readable.
  'YAML' therefore lends itself well for embedding (meta)data
  in plain text files, such as Markdown files. This principle
  is implemented in 'yum' with minimal dependencies (i.e. only
  the 'yaml' packages, and the 'data.tree' package can be
  used to enable additional functionality).
A fast 'JSON' parser, generator and validator which converts 'JSON', 
    'NDJSON' (Newline Delimited 'JSON') and 'GeoJSON' (Geographic 'JSON') data 
    to/from R objects.  The standard R data types are 
    supported (e.g. logical, numeric, integer) with configurable handling of NULL 
    and NA values. Data frames, atomic vectors and lists are all supported as data 
    containers translated to/from 'JSON'.  'GeoJSON' data is read in as
    'simple features' objects.
    This implementation wraps the 'yyjson' 'C' library which 
    is available from <https://github.com/ibireme/yyjson>.
Provides simple statistics from instruments and observations at sites in the NEON network, and acts as a simple interface for v0 of the National Ecological Observatory Network (NEON) API.
  Statistics are generated for meteorologic and soil-based observations, and are presented for daily, annual, and one-time observations at all available NEON sites.
  Users can also retrieve any dataset publicly hosted by NEON. Metadata for NEON sites and data products can be returned, as well as information on data product availability by site and date. 
  For more information on NEON, please visit <https://www.neonscience.org>. For detailed data product information, please see the NEON data product catalog at <https://data.neonscience.org/data-product-catalog>.
Provides fast and easy access to German census grid data
    from the 2011 and 2022 censuses <https://www.zensus2022.de/>, including a
    wide range of socio-economic indicators at multiple spatial resolutions
    (100m, 1km, 10km). Enables efficient download, processing, and analysis
    of large census datasets covering population, households, families,
    dwellings, and buildings. Harmonized data structures allow direct
    comparison with the 2011 census, supporting temporal and spatial analyses.
    Facilitates conversion of data into common formats for spatial analysis and
    mapping ('terra', 'sf', 'ggplot2').
A suite of statistics for identifying areas of the genome under selective pressure. See Jacobs, Sluckin and Kivisild (2016) <doi:10.1534/genetics.115.185900>.
The 'Zarr' specification is widely used to build libraries for the storage and retrieval of n-dimensional array data from data stores ranging from local file systems to the cloud. This package is a native 'Zarr' implementation in R with support for all required features of 'Zarr' version 3. It is designed to be extensible such that new stores, codecs and extensions can be added easily.
Principled methods for the imputation of zeros, left-censored and missing data in
    compositional data sets (Palarea-Albaladejo and Martin-Fernandez (2015) <doi:10.1016/j.chemolab.2015.02.019>).
Contains the US Census Bureau's 2020 ZCTA to County Relationship 
    File, as well as convenience functions to translate between States, Counties
    and ZIP Code Tabulation Areas (ZCTAs).
An implementation of z-curves - a method for estimating expected discovery 
    and replicability rates on the bases of test-statistics of published studies. The package 
    provides functions for fitting the density, EM, and censored EM version (Bartoš & Schimmack, 2022,
    <doi:10.15626/MP.2021.2720>; Schimmack & Bartoš, 2023, <doi: 10.1371/journal.pone.0290084>),
    as well as the original density z-curve (Brunner & Schimmack, 2020, <doi:10.15626/MP.2018.874>). 
    Furthermore, the package provides summarizing and plotting functions for the fitted z-curve objects. 
    See the aforementioned articles for more information about the z-curves, expected discovery 
    and replicability rates, validation studies, and limitations.
Facilitates making a connection to the 
  'Zendesk' API and executing various queries. You can use it to
  get ticket, ticket metrics, and user data. The 'Zendesk' documentation is 
  available at <https://developer.zendesk.com/rest_api
  /docs/support/introduction>. This package is not supported by 
  'Zendesk' (owner of the software).
Provides a %<-% operator to perform multiple,
    unpacking, and destructuring assignment in R. The
    operator unpacks the right-hand side of an assignment
    into multiple values and assigns these values to
    variables on the left-hand side of the assignment.
Implements the estimation of local (and global) association measures: Lewontin's D, Ducher's Z, pointwise mutual information, normalized pointwise mutual information and chi-squared residuals. The significance of local (and global) association is accessed using p-values estimated by permutations.
Use behavioural variables to compute period, rhythmicity and other circadian parameters.
  Methods include computation of chi square periodograms (Sokolove and Bushell (1978) <DOI:10.1016/0022-5193(78)90022-X>),
  Lomb-Scargle periodograms (Lomb (1976) <DOI:10.1007/BF00648343>, Scargle (1982) <DOI:10.1086/160554>, Ruf (1999) <DOI:10.1076/brhm.30.2.178.1422>),
  and autocorrelation-based periodograms.
Provides an Interface to 'Zenodo' (<https://zenodo.org>) REST API, 
  including management of depositions, attribution of DOIs by 'Zenodo' and 
  upload and download of files.
This package provides an R wrapper for the Zendesk API
Access, download and locally cache files deposited on 'Zenodo' <https://zenodo.org>.
Graphical tools for visualizing high-dimensional data along a
    path of alternating one- and two-dimensional plots. Includes optional
    interactive graphics via 'loon' (which uses 'tcltk' from base R).
    Support is provided for constructing graph structures and, when
    available, plotting them with Bioconductor packages (e.g., 'graph',
    'Rgraphviz'); these are optional and examples/vignettes are skipped if
    they are not installed. For algorithms and further details, see
    <doi:10.18637/jss.v095.i04>.
Fetch statistics about views, downloads and data volume from 'Zenodo' deposits. The package collects a 'Zenodo' (<https://zenodo.org>) deposit file information, respecting the website scrapping policies.
Procedures for calculation, plotting, animation, and approximation of the outputs for fuzzy numbers (see A.I. Ban, L. Coroianu, P. Grzegorzewski "Fuzzy Numbers: Approximations, Ranking and Applications" (2015)) based on the Zadeh's Extension Principle (see de Barros, L.C., Bassanezi, R.C., Lodwick, W.A. (2017) <doi:10.1007/978-3-662-53324-6_2>).
Provides a structured framework for consistent user
    communication and configuration management for package developers.
Uses bootstrap to test zero order correlation being equal to a 
    partial or semi-partial correlation (one or two tailed). Confidence 
    intervals for the parameter (zero order minus partial) can also be
    determined. Implements the bias-corrected and accelerated bootstrap
    method as described in "An Introduction to the Bootstrap" Efron (1983) 
    <0-412-04231-2>.
Implementation of new statistical distributions in (0, 1) interval. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters using Generalized Additive
    Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos
    (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.
Functions to compute compositional turnover using zeta-diversity,
    the number of species shared by multiple assemblages. The package includes
    functions to compute zeta-diversity for a specific number of
    assemblages and to compute zeta-diversity for a range of numbers of
    assemblages. It also includes functions to explain how zeta-diversity
    varies with distance and with differences in environmental variables
    between assemblages, using generalised linear models, linear models
    with negative constraints, generalised additive models,shape
    constrained additive models, and I-splines.
The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
Performs Zoom-Focus Algorithm (ZFA) to optimize testing regions for rare variant association tests in exome sequencing data.
Improve the usage of model fitting functions within a piped
    work flow.
A two-part zero-inflated Beta regression model with random 
    effects (ZIBR) for testing the association between microbial abundance 
    and clinical covariates for longitudinal microbiome data. Eric Z. Chen 
    and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.
Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.
Parameter estimation for zero-inflated discrete Weibull (ZIDW) regression models, the univariate setting, distribution functions, functions to generate randomized quantile residuals a pseudo R2, and plotting of rootograms. For more details, see Kalktawi (2017)  <https://bura.brunel.ac.uk/handle/2438/14476>, Taconeli and Rodrigues de Lara (2022) <doi:10.1080/00949655.2021.2005597>, and Yeh and Young (2025) <doi:10.1080/03610918.2025.2464076>.
The 'Ziggurat' pseudo-random number generator (or PRNG),
 introduced by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>) and
 further improved by Leong et al (2005, <doi:10.18637/jss.v012.i07>), offers
 a lightweight and very fast PRNG for the normal, exponential, and uniform
 distributions. It is provided here in a small zero-dependency package. It can
 be used from R as well as from 'C/C++' code in other packages as is demonstrated
 by four included sample packages using four distinct methods to use the PRNG
 presented here in client package. The implementation is influenced by our
 package 'RcppZiggurat' which offers a comparison among multiple alternative
 implementations but presented here in a lighter-weight implementation that is
 easier to use by other packages. The PRNGs provided are generally faster than
 the ones in base R: on our machine, the relative gains for normal, exponential
 and uniform are on the order of 7.4, 5.2 and 4.7 times faster than base R.
 However, these generators are of potentially lesser quality and shorter period
 so if in doubt use of the base R functions remains the general recommendation.
Provides tools for estimating Zero-Inflated INAR(1) 
    (ZI-INAR(1)) and Hurdle INAR(1) (H-INAR(1)) models using 'Stan'. 
    It allows users to simulate time series data for these models, 
    estimate parameters, and evaluate model fit using various criteria. 
    Functions include model estimation, simulation, and likelihood-based metrics.
Analyze count time series with excess zeros. 
    Two types of statistical models are supported: Markov regression by Yang et al.
    (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. 
    (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and 
    parameter-driven models respectively in the time series literature. The functions used for 
    Markov regression or observation-driven models can also be used to fit ordinary regression models 
    with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) 
    assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, 
    quantile, and generate random numbers from ZIP and ZINB distributions.
Gene‐based association tests to model count data with excessive zeros and rare variants using zero-inflated Poisson/zero-inflated negative Binomial regression framework. This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 <doi:10.1002/gepi.22438>.
Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) <doi:10.1007/978-3-030-82110-4_2>.
Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) <doi:10.1080/00949655.2020.1754819>.
Cross-Platform 'zip' Compression Library. A replacement for
    the 'zip' function, that does not require any additional external
    tools on any platform.
Implementation of zero-inflated Poisson models under Bayesian framework using data augmentation as discussed in Chapter 5 of Zhang (2020) <https://hdl.handle.net/10012/16378>. This package is constructed in accommodating four different scenarios: the general scenario, the scenario with measurement error in responses, the external validation scenario, and the internal validation scenario.
Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. 
             Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating
             ZIP codes to Census data, geocoding & distance calculations.
Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. 
    It provides estimates of coefficients in a new type of zero-inflated regression. 
    It provides a cross-validation method to determine the potential rank of the data in the ZIPFA 
    and conducts zero-inflated Poisson factor analysis based on the determined rank.
Implementation of four extensions of the Zipf distribution: the Marshall-Olkin 
  Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE), the 
  Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. 
  In log-log scale, the two first extensions allow for top-concavity 
  and top-convexity while the third one only allows for top-concavity. 
  All the extensions maintain the linearity associated with the Zipf model in the tail.
Statistical models and utilities for the analysis of word frequency distributions.
	The utilities include functions for loading, manipulating and visualizing word frequency
	data and vocabulary growth curves.  The package also implements several statistical 
	models for the distribution of word frequencies in a population.  (The name of this package 
	derives from the most famous word frequency distribution, Zipf's law.)
We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) <doi:10.1080/01621459.2022.2151447>.
Provides a set of functions for working with American postal codes,
   which are known as ZIP Codes. These include accessing ZIP Code to ZIP Code 
   Tabulation Area (ZCTA) crosswalks, retrieving demographic data for ZCTAs, and 
   tabulating demographic data for three-digit ZCTAs.
Implements Python-style zip for R. Is a more flexible version of cbind.
Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: <https://hal.archives-ouvertes.fr/hal-02936779v3>.
This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018)<https://repository.ipb.ac.id/handle/123456789/92308>. For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015)<doi:10.1016/j.csda.2015.07.013> and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016)<doi:10.1515/jos-2016-0051>.
Assesses evidence for Zipf's Law of Abbreviation in animal vocalisation using IDs, note class and note duration. The package also provides a web plot function for visualisation.
The 'zlib' package for R aims to offer an R-based equivalent of 'Python's' built-in 'zlib' module for data compression and decompression. This package provides a suite of functions for working with 'zlib' compression, including utilities for compressing and decompressing data streams, manipulating compressed files, and working with 'gzip', 'zlib', and 'deflate' formats.
Transformation of laboratory measurements into z or z(log)-value
    based on given or empirical reference limits as proposed in
    Hoffmann et al. 2017 <doi:10.1515/labmed-2016-0087>.
A collection of utility functions that facilitate looking up
    vector values from a lookup table, annotate values in at table for 
    clearer viewing, and support a safer approach to vector sampling, 
    sequence generation, and aggregation.
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included.
'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results
    in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time
    series forecasts for prediction challenges of interest to the modeling community. This package provides functions
    for working with the 'Zoltar' API, including connecting and authenticating, getting meta information (projects,
    models, and forecasts, and truth), and uploading, downloading, and deleting forecast and truth data.
Functions, documentation and example data to help divide
    geographic space into discrete polygons (zones).
    The package supports new zoning systems that are documented in the
    accompanying paper,
    "ClockBoard: A zoning system for urban analysis",
    by Lovelace et al. (2022) <doi:10.5311/JOSIS.2022.24.172>.
    The functions are motivated by research into the merits of different zoning systems
    (Openshaw, 1977) <doi:10.1068/a090169>. A flexible ClockBoard zoning system is
    provided, which breaks-up space by concentric rings
    and radial lines emanating from a central point.
    By default, the diameter of the rings grow according to the triangular number sequence
    (Ross & Knott, 2019) <doi:10.1080/26375451.2019.1598687> with the first 4 doughnuts
    (or annuli) measuring 1, 3, 6, and 10 km wide.
    These annuli are subdivided into equal segments (12 by default), creating the
    visual impression of a dartboard. Zones are labelled according to
    distance to the centre and angular distance from North, creating a simple
    geographic zoning and labelling system useful for visualising geographic
    phenomena with a clearly demarcated central location such as cities.
Computes a zonohedron from real vector generators.  The package also computes zonogons (2D zonotopes) and zonosegs (1D zonotopes).  An elementary S3 class for matroids is included, which supports matroids with rank 3, 2, and 1.  Optimization methods are taken from Heckbert (1985) <https://www.cs.cmu.edu/~ph/zono.ps.gz>.
An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.
This tool provides functions to load, segment and classify zooplankton images. 
  The image processing algorithms and the machine learning classifiers in this package are (will be, since these have not been added yet) direct ports of 
  an early 'python' implementation that can be found at <https://github.com/arickGrootveld/ZooID>. The model weights and datasets (also not added yet) 
  that are a part of this package can also be found at Arick Grootveld, Eva R. Kozak, Carmen Franco-Gordo (2023) <doi:10.5281/zenodo.7979996>.
A free (open source) solution for analyzing digital
  images of plankton. In combination with ImageJ, a free image analysis
  system, it processes digital images, measures individuals, trains for
  automatic classification of taxa, and finally, measures plankton samples
  (abundances, total and partial size spectra or biomasses, etc.).
Includes functions and reference data to 
    generate and manipulate log-ratios (also known as log size index (LSI)
    values) from measurements obtained on zooarchaeological material. Log
    ratios are used to compare the relative (rather than the absolute)
    dimensions of animals from archaeological contexts 
    (Meadow 1999, ISBN: 9783896463883).
    zoolog is also able to seamlessly integrate data and references 
    with heterogeneous nomenclature, which is internally managed by a 
    zoolog thesaurus. 
    A preliminary version of the zoolog methods was first used by 
    Trentacoste, Nieto-Espinet, and Valenzuela-Lamas (2018) 
    <doi:10.1371/journal.pone.0208109>.
You can call zm(), when displaying any active plot to enter an
    interactive session to zoom/navigate any plot. The development
    version, as well as binary releases can be found at
    <https://github.com/cbarbu/R-package-zoom>.
Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage.  The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) <doi:10.1145/997817.997857>, and Broder (1998) <doi:10.1109/SEQUEN.1997.666900> to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time.
Implements a grid search algorithm with an adaptive zooming strategy 
    for global optimisation problems with multiple local optima. 
    The method recursively refines the search region around promising grid points, 
    providing reliable initial values for subsequent optimisation procedures. 
    The algorithm is computationally efficient in moderate- to high-dimensional settings.
Provides utilities for processing and analyzing the files that are exported from a recorded 'Zoom' Meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.
Facilitates making a connection to the 'Zoom' API and executing
  various queries. You can use it to get data on 'Zoom' webinars and 'Zoom'
  meetings. The 'Zoom' documentation is available at 
  <https://developers.zoom.us/docs/api/>. This package is 
  not supported by 'Zoom' (owner of the software).
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
A tool for calculating z-scores and centiles for weight-for-age, 
    length/height-for-age, weight-for-length/height, BMI-for-age, 
    head circumference-for-age, age circumference-for-age, 
    subscapular skinfold-for-age, triceps skinfold-for-age based on the 
    WHO Child Growth Standards.
Generates well-known integer sequences. 'gmp' package is adopted for computing with arbitrarily large numbers. Every function has hyperlink to its corresponding item in OEIS (The On-Line Encyclopedia of Integer Sequences) in the function help page. For interested readers, see Sloane and Plouffe (1995, ISBN:978-0125586306).
Makes zebra-striped tables (tables with alternating row colors)
    in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
    glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
Utilities for simplifying common statistical operations including
 probability density functions, cumulative distribution functions, 
 Kolmogorov-Smirnov tests, principal component analysis plots, and prediction plots.
Functions for obtaining the density, random variates
             and maximum likelihood estimates of the Zero-truncated Poisson lognormal
             distribution and their mixture distribution.
Read '.xls' and '.sbj' files which are written by the
   Microsoft Windows program 'z-Tree'. The latter is a software for
   developing and carrying out economic experiments
   (see <http://www.ztree.uzh.ch/> for more information).
How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels.
Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2023 <doi:10.1214/22-BA1328>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2022 <doi:10.1093/biomet/asab036>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.
An efficient implementation of the slope method described by Sen (1968) <doi:10.1080/01621459.1968.10480934> plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) <doi:10.1080/07055900.2000.9649654> and Yue, Pilon, Phinney, and Cavadias (2002) <doi:10.1002/hyp.1095>.
A minor collection of HTTP wrappers for the 'Zamzar File Conversion'
    API. The wrappers makes it easy to utilize the API and thus convert
    between more than 100 different file formats (ranging from audio files,
    images, movie formats, etc., etc.) through an R session.  
    For specifics regarding the API, please see <https://developers.zamzar.com/>.
Functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Application" by A. C. Davison and
D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Various functions for classification, including k-nearest
neighbour, Learning Vector Quantization and Self-Organizing Maps.
Methods for Cluster analysis.  Much extended the original from
Peter Rousseeuw, Anja Struyf and Mia Hubert,
based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Reading and writing data stored by some versions of
'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
and for reading and writing some 'dBase' files.
Functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
A rich hierarchy of sparse and dense matrix classes,
including general, symmetric, triangular, and diagonal matrices
with numeric, logical, or pattern entries.  Efficient methods for
operating on such matrices, often wrapping the 'BLAS', 'LAPACK',
and 'SuiteSparse' libraries.
Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Cross Validation and similar, or using iterated nested Laplace
approximation for fully Bayesian inference. See Wood (2025)
<doi:10.1146/annurev-statistics-112723-034249> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Recursive partitioning for classification,
regression and survival trees.  An implementation of most of the
functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.
Please send bug reports to ismrp@grp.ism.ac.jp.
Please send questions and comments regarding pbdR to
RBigData@gmail.com
Please send questions and comments to wccsnow@gmail.com
Please send questions and comments about Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding RInside to
rcpp-devel@lists.r-forge.r-project.org
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Lauren L. Hulsman Hanna <Lauren.Hanna@ndsu.edu>
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Euloge C. Kenne Pagui <kenne@stat.unipd.it>
Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
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A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
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Krishna Harsha K H <khkrishnaharsha123@gmail.com>
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Olivier Percie du Sert <olivier.perciedusert@mail.mcgill.ca>
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Mark A. van de Wiel <mark.vdwiel@amsterdamumc.nl>
Hannah De los Santos <hdelossantos653@gmail.com>
Sebastian J. Teran Hidalgo <sebastianteranhidalgo@gmail.com>
Eliot J B McIntire <eliot.mcintire@canada.ca>
Don van den Bergh <d.vandenbergh@uva.nl>
Juan A. Arias Lopez <iguanamarina@protonmail.com>
Nelson de O. Quesado Filho <nquesado@gmail.com>
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João Pedro Melani Saraiva <jpsaraiva2002@gmail.com>
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Pedro Nascimento de Lima <plima@rand.org>
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Matthias Braun FKA Nistler <m_nistler@web.de>
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Bruno do Rosario Petrucci <petrucci@iastate.edu>
F. Marta L. Di Lascio <marta.dilascio@unibz.it>
"Australian e-Health Research Centre, CSIRO" <pathling@csiro.au>
Juan Pablo Vargas Perez <j.pablovargas340@gmail.com>
Luna Luisa Sanchez Reyes <sanchez.reyes.luna@gmail.com>
Wouter van der Bijl <wouter@zoology.ubc.ca>
Rodrigo Augusto Santinelo Pereira <raspereira@usp.br>
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Joaquin Bienvenido Ordieres Mere <j.ordieres@upm.es>
Dr. Himadri Shekhar Roy <himadriiasri@gmail.com>
Marieke van der Maaten-Theunissen <marieke.theunissen@tu-dresden.de>
Wessel N. van Wieringen <w.vanwieringen@vumc.nl>
Iago Augusto de Carvalho <iago.carvalho@unifal-mg.edu.br>
Paul van der Laken <paulvanderlaken@gmail.com>
Mijael Rodrigo Vargas Godoy <mirovago@gmail.com>
Thaís de Bessa Gontijo de Oliveira <thais.bgo@gmail.com>
Juan Carlos Gonçalves Dosantos <juan.carlos.goncalves@udc.es>
Blondeau Da Silva Stephane <blondeaudasilva@xlim.fr>
Pedro H. C. Sant'Anna <pedro.h.santanna@vanderbilt.edu>
Robbie C.M. van Aert <R.C.M.vanAert@tilburguniversity.edu>
Carlos J. Gil Bellosta <gilbellosta@gmail.com>
Jose Sergio Case de Oliveira <js_cdo@hotmail.com>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
Wagner Santos de Lima <wagnersantos.ufs@hotmail.com>
Maria de los Angeles Resa <maria@stat.columbia.edu>
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Tharso Augustus Rossiter Araújo Monteiro <tharso.augustus@ufpe.br>
1. School of Mathematical & Statistical Sciences, Clemson
University, Clemson, South Carolina, USA 2. Department of
Mathematics & Statistics, University of Agriculture Faisalabad,
Faisalabad, Pakistan
Package 'rjags' requires JAGS executabe file (compatiable
version) should be already installed.
The optmatch package, which is useful for running many of the
provided functions, may be downloaded from Github at
<https://github.com/markmfredrickson/optmatch> if not available
on CRAN. The rrelaxiv package, which provides an alternative
solver for the underlying network flow problems, carries an
academic license and is not available on CRAN, but may be
downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
Maintainers are not available to give advice on using a package
they did not author.
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
This is the CRAN version of the package.  Due to limitations of
that platform it does not support the changepoint analysis of
the spacing.  The full definitive version of the package is
available at
https://www.primachvis.com/data/Dimodal_latest.tar.gz
One minimum cost flow problem may have several or many solutions
that are equivalent in providing the same minimum total or mean
cost. Minor differences between computers or implementations
may have the minor consequence of altering which equivalent
solution is produced. The optmatch package, which is useful for
running many of the provided functions, may be downloaded from
Github at <https://github.com/markmfredrickson/optmatch> if not
available on CRAN. The rrelaxiv package, which provides an
alternative solver for the underlying network flow problems,
carries an academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002/0004 with the American Institutes for
Research. Mention of trade names, commercial products, or
organizations does not imply endorsement by the U.S.
Government.
School of Mathematical and Statistical Sciences, Clemson
University, Clemson, South Carolina, USA.
Ver. 1.0: The author obtained financial resources as part of
financing the doctoral scholarship from the Polish National
Science Centre (DEC-2018/28/T/HS4/00095). Ver. 2.0: The
research was supported by the program ''Excellence Initiative -
Research University (IDUB)'' through the project
BOB-IDUB-622-89/2021 - Nowe Idee POB III IDUB, 01/IDUB/2019/94
at the University of Warsaw, Poland.
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Pakistan.
Package includes self-contained 'llama.cpp' implementation
(~56MB) for complete functionality without external
dependencies.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with the American Institutes for Research.
Mention of trade names, commercial products, or organizations
does not imply endorsement by the U.S. Government.
This update fixes bugs noted because of global variables not
being recognised as global variables in some environments.
This update was performed to align with a planned ggplot2 update
Research funded by the Polish National Science Centre grant under
the contract number DEC-2015/19/N/HS4/00205.
Few parts are still preliminary and might be changed in the near
future. And more functions will be add as easier tools to
higher efficiency in analyzing.
While reporting issues on github start subject line with
[cran-package]
The authors are grateful to Mikhail Kanevski, Federico Amato and
Luciano Telesca for many fruitful discussions about the use and
the application of Fisher-Shannon method.
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
The package is also based on the discussion
https://stats.stackexchange.com/questions/168964/building-a-regression-tree-with-r-from-scratch/168967#168967
research product of the RISCOPE project (ANR, project
No.16CE04-0011) <https://perso.math.univ-toulouse.fr/riscope/>.
Development followed an iterative human-machine collaboration
where all algorithmic design, statistical methodologies, and
biological validation logic were conceptualized, tested, and
iteratively refined by Richard A. Feiss through repeated cycles
of running experimental data, evaluating analytical outputs,
and selecting among candidate algorithms and approaches. AI
systems ('Anthropic Claude' and 'OpenAI GPT') served as coding
assistants and analytical sounding boards under continuous
human direction. The selection of statistical methods,
evaluation of biological plausibility, and all final
methodology decisions were made by the human author. AI systems
did not independently originate algorithms, statistical
approaches, or scientific methodologies.
Department of Genetics and Plant Breeding, University of
Agricultural Sciecnes, Dharwad.
Package 'R2jags' requires JAGS executable file installed
The authors are grateful to Mikhail Kanevski, Michael
Leuenberger, Carmen D. Vega Orozco and Fabian Guignard for many
fruitful discussions about the use of intrinsic dimension in
data mining.
Because IPEDS requirements may change from year to year, having
the most recent version of this package is highly recommended.
Old versions can be found as GitHub branches. The package can
also be used to convert any correctly-prepared data into a
key-value pair format for any survey (IPEDS or non-IPEDS).
This package includes code from https://github.com/kudelalab/PSD
by kudelalab licensed under the MIT License.
Please, cite this package as: Marina Saez (2021). KarsTS: An
Interface For Microclimate Time Series Analysis. R package
version 2.4.1.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan
The originale code was in matlab, see details below.
The developer version of 'keras' should be used, rather than the
CRAN version. The latter will cause tests to fail on an initial
run, but work on subsequent tries. To avoid this, use
devtools::install_github("rstudio/keras"). The user also must
have an installation of 'Python 3'.