Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of 
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of the results of Genome Wide Association Studies.
Provides functions for the joint analysis of Q sets of
    p-values obtained for the same list of items. This joint analysis is
    performed by querying a composite hypothesis, i.e. an arbitrary
    complex combination of simple hypotheses, as described in Mary-Huard
    et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
    al.(2025) <doi: 10.1093/nargab/lqaf118>. In this approach, the Q-uplet
    of p-values associated with each item is distributed as a multivariate
    mixture, where each of the 2^Q components corresponds to a specific
    combination of simple hypotheses. The dependence between the p-value
    series is considered using a Gaussian copula function. A p-value for
    the composite hypothesis test is derived from the posterior
    probabilities.
Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.
Implements the Quantile Composite-based Path Modeling approach 
	(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; 
	 Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
Univariate and multivariate SQC tools that completes and increases
    the SQC techniques available in R. Apart from integrating different R packages 
    devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly 
    useful when Gaussian assumption is not met. 
    This package computes standard univariate control charts for individual measurements, 
    'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it 
    includes functions to perform multivariate control charts such as 'Hotelling T2', 
    'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric 
    alternatives based on data depth are implemented in this package: 'r', 'Q' and 
    'S' control charts. In addition, Phase I and II control charts for functional 
    data are included. This package also allows the estimation of the most complete 
    set of capability indices from first to fourth generation, covering the nonparametric 
    alternatives, and performing the corresponding capability analysis graphical outputs,
    including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.
An R implementation of quality control–based robust LOESS(local
    polynomial regression fitting) signal correction for metabolomics data
    analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011)
    <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter
    using generalized cross-validation (GCV) is provided as an option. In
    addition to signal correction, 'qcrlscR' includes some utility functions
    like batch shifting and data filtering.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
The letters 'qe' in the package title stand for "quick and
   easy," alluding to the convenience goal of the package. We bring
   together a variety of machine learning (ML) tools from standard R
   packages, providing wrappers with a simple, convenient, 
   and uniform interface.
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. 
 Spline quantile regression (SQR) for regression coefficient estimation.
 References:
    [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical
        Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>.
    [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154>
    [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric
        estimation of quantile spectra," <doi:10.48550/arXiv.2211.05844>.
    [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression
         estimation," <doi:10.48550/arXiv.2412.02513>.
    [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum,"
        <doi:10.48550/arXiv.2412.17163>.
    [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," 
        <doi:10.48550/arXiv.2501.03883>.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led to the development of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
An implementation of Quantitative Fatty Acid Signature
    Analysis (QFASA) in R.  QFASA is a method of estimating the diet
    composition of predators.  The fundamental unit of information in
    QFASA is a fatty acid signature (signature), which is a vector of
    proportions describing the composition of fatty acids within lipids.
    Signature data from at least one predator and from samples of all
    potential prey types are required.  Calibration coefficients, which
    adjust for the differential metabolism of individual fatty acids by
    predators, are also required. Given those data inputs, a predator
    signature is modeled as a mixture of prey signatures and its diet
    estimate is obtained as the mixture that minimizes a measure of
    distance between the observed and modeled signatures.  A variety of
    estimation options and simulation capabilities are implemented.
    Please refer to the vignette for additional details and references.
Evaluates moments of ratios (and products) of quadratic forms
    in normal variables, specifically using recursive algorithms developed by
    Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al.
    (2014) <doi:10.1017/S0266466613000364>. Also provides distribution,
    quantile, and probability density functions of simple ratios of quadratic
    forms in normal variables with several algorithms. Originally developed as
    a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for
    evaluating average evolvability measures in evolutionary quantitative
    genetics, but can be used for a broader class of statistics. Generating 
    functions for these moments are also closely related to the top-order zonal
    and invariant polynomials of matrix arguments.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra  (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
    'quantreg', the smoothing parameters are estimated automatically by marginal
    loss minimization, while the regression coefficients are estimated using either
    PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
    credible intervals of the estimated effects have approximately the correct
    coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
    fits non-parametric quantile regression models.
General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
G-computation for a set of time-fixed exposures with
    quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures. Works with continuous, binary, and right-censored
    time-to-event outcomes.  Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. Effect measure modification in this method is a way
    to assess how the effect of the mixture varies by a binary, categorical or continuous variable.  
    Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Provides seamless access to the 'QGIS'
    (<https://qgis.org>) processing toolbox using the standalone
    'qgis_process' command-line utility.  Both native and third-party
    (plugin) processing providers are supported.  Beside referring data
    sources from file, also common objects from 'sf', 'terra' and 'stars'
    are supported. The native processing algorithms are documented by QGIS.org 
    (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Nomograms are constructed to predict the cumulative incidence
    rate which is calculated after adjusting for competing causes to the event of interest. 
    K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index.
    Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003).
The quantity-intensity (Q/I) relationships, first introduced by Beckett
  (1964), can be employed to assess the K supplying capacity of different soils 
  based on solid-solution exchange equilibria. Such relationships describe the 
  changes in K+ concentration in the soil solution (or the intensity factor) in 
  relation to the corresponding changes in K+ at exchange sites of the soil 
  (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg 
  is generally used as the variable denoting the intensity, whereas, change 
  in exchangeable K is used to denote the quantity factor.
Functions for making run charts 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic
    Shewhart control charts [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>] for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts
    are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
    in the form of minor to moderate persistent shifts in data over time is
    identified by the Anhoej rules for unusually long runs and unusually few
    crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
Select optimal functional regression or dichotomized
       quantile predictors for survival/logistic/numeric outcome
       and perform optimistic bias correction for any optimally
       dichotomized numeric predictor(s), as in Yi, et. al.
       (2023) <doi:10.1016/j.labinv.2023.100158>.
'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').
Functionality to read, recode, and transcode data as used in 
  quantitative language comparison, specifically to deal with multilingual 
  orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) 
  and with the recoding of nominal data.
Extension of the functionality of the 'Matrix' package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison.
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Manages, builds and computes statistics and datasets for the 
   construction of quarterly (sub-annual) life tables by exploiting micro-data from
   either a general or an insured population.
   References: 
   Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
   Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
   Pavía and Lledó (2025) <doi:10.1371/journal.pone.0315937>.
   Acknowledgements:
   The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
A multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2022), On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics, 16, 2206-2251.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options.
  Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.
Produces quality scores for each of the US companies from 
    the Russell 3000, following the approach described in 
    "Quality Minus Junk" (Asness, Frazzini, & Pedersen, 2013) 
    <http://www.aqr.com/library/working-papers/quality-minus-junk>. 
    The package includes datasets for users who wish to view the most recently 
    uploaded quality scores. It also provides tools to automatically gather 
    relevant financials and stock price information, allowing users to update 
    their data and customize their universe for further analysis.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data.
    Usage of the package is described in
    Polzehl and Tabelow (2023),
    "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series.
    <doi:10.1007/978-3-031-38949-8>.
    J. Polzehl and K. Tabelow (2023), "Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data."
    <doi:10.20347/WIAS.DATA.6> provides extensive example code and data.
Basic functions for building parsers, with an application to PC-AXIS format files.
The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.
The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.
The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm>, tabulation functions which can create any table in different styles <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
Methods for statistical analysis of count data and quantal data.
    For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).
    For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated.
    All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.
Sigmoidal model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR), according to Ritz & Spiess (2008) <doi:10.1093/bioinformatics/btn227> and Spiess et al. (2008) <doi:10.1186/1471-2105-9-221>.
Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase 
    chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression 
    values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation
    of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: 
    Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ library <https://qpdf.sourceforge.io/> and does not require any command 
    line utilities. Note that 'qpdf' does not read actual content from PDF files: 
    to extract text and data you need the 'pdftools' package.
Provides functions for estimating ploidy levels and detecting aneuploidy in individuals using 
             allele intensities or allele count data from high-throughput genotyping platforms, including 
             single nucleotide polymorphism (SNP) arrays and sequencing-based technologies. Implements an extended version of the 'PennCNV' 
             signal standardization method by Wang et al. (2007) <doi:10.1101/gr.6861907> for higher ploidy 
             levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns.
Computes noncompartmental pharmacokinetic parameters
 for drug concentration profiles.  For each profile, data
 imputations and adjustments are made as necessary and
 basic parameters are estimated. Supports single dose, multi-dose,
 and multi-subject data.  Supports steady-state calculations
 and various routes of drug administration. See ?qpNCA and vignettes.
 Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), 
 Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
 Gibaldi and Perrier (1982, ISBN:978-0824710422).
Supports risk assessors in performing the entry step of the
    quantitative Pest Risk Assessment. It allows the estimation of the
    amount of a plant pest entering a risk assessment area (in terms of
    founder populations) through the calculation of the imported
    commodities that could be potential pathways of pest entry, and the
    development of a pathway model. Two 'Shiny' apps based on the
    functionalities of the package are included, that simplify the process
    of assessing the risk of entry of plant pests. The approach is based
    on the work of the European Food Safety Authority (EFSA PLH Panel et
    al., 2018) <doi:10.2903/j.efsa.2018.5350>.
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution <doi:10.18637/jss.v106.i10>.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Extensions of 'ggplot2' Q-Q plot functionalities.
A collection of routines for finding reference limits using, where 
    appropriate, QQ methodology.  All use a data vector X of cases from the 
    reference population. The default is to get the central 95% reference range 
    of the population, namely the 2.5 and 97.5 percentile, with optional 
    adjustment of the range.  Along with the reference limits, we want 
    confidence intervals which, for historical reasons, are typically at 90% 
    confidence.  A full analysis provides six numbers:
      – the upper and the lower reference limits, and 
      - each of their confidence intervals.
    For application details, see Hawkins and Esquivel (2024) 
    <doi:10.1093/jalm/jfad109>.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users' Guide". Third Edition. SIAM.
Methods for detecting structural breaks, determining the
    number of breaks, and estimating break locations in linear quantile
    regression, using one or multiple quantiles, based on Qu (2008) and
    Oka and Qu (2011).  Applicable to both time series and repeated
    cross-sectional data. The main function is rq.break().

        References for detailed theoretical and empirical explanations:

        (1) Qu, Z. (2008).  "Testing for Structural Change in Regression
    Quantiles."  Journal of Econometrics, 146(1), 170-184
    <doi:10.1016/j.jeconom.2008.08.006>

        (2) Oka, T., and Qu, Z. (2011).  "Estimating Structural Changes in
    Regression Quantiles."  Journal of Econometrics, 162(2), 248-267
    <doi:10.1016/j.jeconom.2011.01.005>.
Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Create static QR codes in R. The content of the QR code is
    exactly what the user defines. We don't add a redirect URL, making it
    impossible for us to track the usage of the QR code. This allows to
    generate fast, free to use and privacy friendly QR codes.
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
Provides statistical components, tables, and graphs
    that are useful in 'Quarto' and 'RMarkdown' reports and that produce 'Quarto'
    elements for special formatting such as tabs and marginal notes and graphs.
    Some of the functions produce entire report sections with tabs, e.g.,
    the missing data report created by missChk().  Functions for inserting
		variables and tables inside 'graphviz' and 'mermaid' diagrams are included,
		and so are special clinical trial graphics for adverse event reporting.
The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individual’s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
A no-frills open-source solution for designing plot labels affixed with QR 
    codes. It features 'EasyQrlabelr', a 'BrAPI-compliant' 'shiny' app that 
    simplifies the process of plot label design for non-R users. It builds on the 
    methods described by Wu 'et al.' (2020) <doi:10.1111/2041-210X.13405>.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Provides functions/methods to accompany the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference result
             is 
             for the fixed-effects and variance components.
             It also provides prediction and graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Execute multi-step 'SQL' workflows by
    leveraging specially formatted comments to define and control
    execution. This enables users to mix queries, commands, and metadata
    within a single script. Results are returned as named objects for use
    in downstream workflows.
Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.
Molecular descriptors and outcomes for several public domain data sets
Computes feature-specific R-squared (R2) contributions for
    boosting tree models using a Shapley-value-based decomposition of the
    total R-squared in polynomial time. Supports models fitted with
    'XGBoost' and 'LightGBM', and provides efficient parallel
    implementations suitable for large-scale problems. Multiple
    visualization tools are included for interpreting and communicating
    feature contributions. The methodology is described in Jiang, Zhang,
    and Zhang (2025) <doi:10.48550/arXiv.2407.03515>.
A quantum computer simulator framework with up to 24 qubits. It allows to
    define general single qubit gates and general controlled single
    qubit gates. For convenience, it currently provides the
    most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or
    CCNOT, Fredkin or CSWAP). 'qsimulatR' also implements noise models.
    'qsimulatR' supports plotting of circuits and is able to
    export circuits to 'Qiskit' <https://qiskit.org/>, a python package
    which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Implementations of the quantile slice sampler of Heiner et al. (2024+, in preparation) as well as other popular slice samplers are provided. 
    Helper functions for specifying pseudo-target distributions are included, both for diagnostics and for tuning the quantile slice sampler. 
    Other implemented methods include the generalized elliptical slice sampler of Nishihara et al. (2014)<https://jmlr.org/papers/v15/nishihara14a.html},
    latent slice sampler of Li and Walker (2023)<doi:10.1016/j.csda.2022.107652>, and stepping-out slice sampler of Neal (2003)<doi:10.1214/aos/1056562461>,
    and independence Metropolis-Hastings sampler.
Provides routines to create some quaternions splines:
    Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
    algorithm. The implementations are based on the Python library
    'splines'. Quaternions splines allow to construct spherical curves. 
    References: Barry and Goldman <doi:10.1145/54852.378511>, 
    Kochanek and Bartels <doi:10.1145/800031.808575>.
Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Support package for the textbook "An Introduction to
    Quantitative Text Analysis for Linguists: Reproducible Research Using
    R" (Francom, 2024) <doi:10.4324/9781003393764>. Includes functions to
    acquire, clean, and analyze text data as well as functions to document
    and share the results of text analysis. The package is designed to be
    used in conjunction with the book, but can also be used as a standalone
    package for text analysis.
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.
Provides a set of tools to perform quantitative
    trait locus (QTL) analysis in experimental crosses. It is a
    reimplementation of the 'R/qtl' package to better handle
    high-dimensional data and complex cross designs.
    Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.
Functions to plot QTL (quantitative trait loci) analysis
    results and related diagnostics.
    Part of 'qtl2', an upgrade of the 'qtl'
    package to better handle high-dimensional data and complex cross
    designs.
Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL)
    mapping over a chromosome.
    Useful in crosses with more than two alleles to identify how sets of alleles,
    genetically different strands at the same locus, have different response levels.
    Plots show profiles over a chromosome.
    Can handle multiple traits together.
    See <https://github.com/byandell/qtl2pattern>.
We implement an
    adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing
    the null hypothesis of pleiotropy against the alternative hypothesis,
    two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> 
    and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in
    that our test accommodates multiparental populations.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
    Broman (2015) <doi:10.1534/genetics.114.172742>.
Design of QTL (quantitative trait locus) experiments
  involves choosing which strains to cross, the type of cross,
  genotyping strategies, phenotyping strategies, and the number of
  progeny to raise and phenotype. This package provides tools to help
  make such choices.
  Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, directional quantile classification, and quantile
	ratio regression.
	A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Provides three 'Quarto' website templates as an R project, which are commonly used by academics.
    Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.
A tool that can be customized to aid in the clean up of ecological data 
    collected using quadrats and can crop quadrats to ensure comparability between
    quadrats collected under different methodologies.
A range of quadratic forms are evaluated, using efficient methods.
 Unnecessary transposes are not performed.  Complex values are handled
 consistently.
A set of functions of increasing complexity allows users to
  (1) convert QuadKey-identified datasets, 
  based on 'Microsoft's Bing Maps Tile System', into Simple Features data frames,
  (2) transform Simple Features data frames into rasters, and 
  (3) process multiple 'Meta' ('Facebook') QuadKey-identified human mobility files
  directly into raster files.
  For more details, see D’Andrea et al. (2024) <doi:10.21105/joss.06500>.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
  absolute value constraints and absolute values in the objective function.
Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) 
              to guess interactively the mean and the mean deviation. Thereafter,
              using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) 
              and visualize the standard deviation (SD). 
              For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, 
              Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.
It includes test for multivariate normality, test for uniformity on the d-dimensional 
    Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
    kernel-based density and clustering algorithm for spherical data. For more information see
    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
    <doi:10.48550/arXiv.2402.02290>
    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
It will assist the user to find simple quadratic roots from any quadratic equation.
Fits classical sparse regression models with
    efficient active set algorithms by solving quadratic problems as described by 
    Grandvalet, Chiquet and Ambroise (2017) <doi:10.48550/arXiv.1210.2077>. Also provides a few
    methods for model selection purpose (cross-validation, stability selection).
Estimate quadratic vector autoregression models with the
    strong hierarchy using the Regularization Algorithm under Marginality
    Principle (RAMP) by Hao et al. (2018)
    <doi:10.1080/01621459.2016.1264956>, compare the performance with
    linear models, and construct networks with partial derivatives.
An easy framework to set a quality control workflow on a dataset.
  Includes a various range of functions that allow to establish an adaptable 
  data quality control.
Quality of care is compared across accountable entities, including hospitals, provider groups, and insurance plans, using standardized quality measures. However, observed variations in quality measure performance might be the result of chance sampling or measurement errors. Contains functions for estimating the reliability of unadjusted and risk-standardized quality measures.
Tools for AI-assisted qualitative data coding using large language
    models ('LLMs') via the 'ellmer' package, supporting providers including
    'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'.
    Provides a 'codebook'-based workflow for defining coding instructions and
    applying them to texts, images, and other data. Includes built-in 'codebooks'
    for common applications such as sentiment analysis and policy coding, and
    functions for creating custom 'codebooks' for specific research questions.
    Supports systematic replication across models and settings, computing
    inter-coder reliability statistics including Krippendorff's alpha
    (Krippendorff 2019, <doi:10.4135/9781071878781>) and Fleiss' kappa
    (Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation
    metrics including accuracy, precision, recall, and F1 scores following
    Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit
    trail functionality for documenting coding workflows following Lincoln and
    Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness
    in qualitative research.
Companion package to 'quallmer' providing an interactive 'shiny'
    application for manual coding, reviewing large language model (LLM)
    generated annotations, and computing inter-rater reliability metrics.
    Supports three modes: blind manual coding, LLM output validation, and
    agreement calculation. Computes standard reliability metrics including
    Krippendorff's alpha (Krippendorff 2019 <doi:10.4135/9781071878781>),
    Cohen's kappa, Fleiss' kappa (Fleiss 1971 <doi:10.1037/h0031619>),
    intraclass correlation coefficient (ICC), and percent agreement for
    nominal, ordinal, interval, and ratio data. Also computes gold-standard
    validation metrics including accuracy, precision, recall, and F1 scores
    following Sokolova and Lapalme (2009 <doi:10.1016/j.ipm.2009.03.002>).
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of maximally distinct qualitative color palettes,
    optionally tailored to color deficiency. A set of colors or a subspace of a
    color space is used as input and a final palette of specified size is
    generated by picking colors that maximize the minimum pairwise difference
    among the chosen colors. Adaptations to color vision deficiency,
    background colors, and white points are supported.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
Implements quantile-based discriminant analysis (QuanDA) for
    imbalanced classification in high-dimensional, low-sample-size settings.
    The method fits penalized quantile regression directly on discrete class labels and
    tunes the quantile level to reflect class imbalance.
Implements the Quantification Evidence Standard algorithm for computing
    Bayesian evidence sufficiency from binary evidence matrices. It provides
    posterior estimates, credible intervals, percentiles, and optional visual
    summaries. The method is universal, reproducible, and independent of
    any specific clinical or rule based framework. For details see The Quantitative Omics Epidemiology Group et al. (2025) <doi:10.64898/2025.12.02.25341503>.
Values different types of assets and calibrates discount curves 
    for quantitative financial analysis. It covers fixed coupon assets, 
    floating note assets, interest and cross currency swaps with different 
    payment frequencies. Enables the calibration of spot, instantaneous forward 
    and basis curves, making it a powerful tool for accurate and flexible bond 
    valuation and curve generation. The valuation and calibration techniques 
    presented here are consistent with industry standards and incorporates 
    author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4).
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
An implementation of dimension reduction techniques
    for conditional quantiles. Nonparametric estimation of 
    conditional quantiles is also available.
A fast, flexible, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and n-grams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
Scaling models and classifiers for sparse matrix objects representing 
    textual data in the form of a document-feature matrix.  Includes original 
    implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
    'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, 
    and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Plotting functions for visualising textual data.  Extends 'quanteda' and 
   related packages with plot methods designed specifically for text data, textual statistics, 
   and models fit to textual data. Plot types include word clouds, lexical dispersion plots, 
   scaling plots, network visualisations, and word 'keyness' plots.
Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.
Enables 'tidyverse' operations on 'quanteda' corpus objects by extending 'dplyr' verbs to work directly with corpus objects and their document-level variables ('docvars'). Implements row operations for 'subsetting' and reordering documents; column operations for managing document variables; grouped operations; and two-table verbs for merging external data. For more on 'quanteda' see 'Benoit et al.' (2018) <doi:10.21105/joss.00774>. For 'dplyr' see 'Wickham et al.' (2023) <doi:10.32614/CRAN.package.dplyr>.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Code for centroid, median and quantile classifiers.
Functions for simulation, estimation, and model selection of
        finite mixtures of Tukey g-and-h distributions.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>. QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions.
Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 <doi:10.48550/arXiv.2506.12920>), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 <doi:10.3982/ECTA21048>).
Estimation and inference methods for the cross-quantilogram.
    The cross-quantilogram is a measure of nonlinear dependence between
    two variables, based on either unconditional or conditional quantile
    functions.  It can be considered an extension of the correlogram,
    which is a correlation function over multiple lag periods that mainly
    focuses on linear dependency.  One can use the cross-quantilogram to
    detect the presence of directional predictability from one time series
    to another.  This package provides a statistical inference method
    based on the stationary bootstrap.  For detailed theoretical and
    empirical explanations, see Linton and Whang (2007) for univariate
    time series analysis and Han, Linton, Oka and Whang (2016) for
    multivariate time series analysis.  The full references for these key
    publications are as follows: (1) Linton, O., and Whang, Y. J. (2007).
    The quantilogram: with an application to evaluating directional
    predictability.  Journal of Econometrics, 141(1), 250-282
    <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T.,
    and Whang, Y. J. (2016).  The cross-quantilogram: measuring quantile
    dependence and testing directional predictability between time series.
    Journal of Econometrics, 193(1), 251-270
    <doi:10.1016/j.jeconom.2016.03.001>.
Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.
A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.
Specify, build, trade, and analyse quantitative financial trading strategies.
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
Contains tools useful for data screening, testing
        moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, 
        mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> 
        and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.
Estimation and inference methods for models for conditional quantile functions: 
  Linear and nonlinear parametric and non-parametric (total variation penalized) models 
  for conditional quantiles of a univariate response and several methods for handling
  censored survival data.  Portfolio selection methods based on expected shortfall
  risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
  <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, 
  CRC Press, <doi:10.1201/9781315120256>.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying
  coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded
  in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter 
  selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by
  HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies.
  (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties.  
    Random intercepts and variable selection are allowed via the lasso penalties.
    The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity 
    constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, 
    and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
A sigmoidal quantile function estimator based on a newly defined 
    generalized expectile function. The generalized sigmoidal quantile function 
    can estimate quantiles beyond the range of the data, which is important for 
    certain applications given smaller sample sizes. The package is based on the 
    method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction <DOI:10.1103/physrevlett.77.793>, Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and a variational quantum classifier (Schuld 2018) <arXiv:1804.00633>. Can be used with the gridsynth algorithm <arXiv:1212.6253> to perform decomposition into the Clifford+T set.
Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Dynamically generate tabset panels
    <https://quarto.org/docs/output-formats/html-basics.html#tabsets> in
    'Quarto' HTML documents using a data frame as input.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress
  (1986), Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of binary trees.
In the spirit of Anscombe's quartet, this package includes datasets
   that demonstrate the importance of visualizing your data, the importance of 
   not relying on statistical summary measures alone, and why additional 
   assumptions about the data generating mechanism are needed when estimating 
   causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
   D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, 
   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
Converts R scripts (.R) into 'Quarto' markdown documents (.qmd) with
    automatic formatting. Recognizes 'RStudio' code sections, preserves comments as
    narrative text, extracts metadata from special comments, and provides both
    programmatic functions and an interactive 'RStudio' add-in for easy conversion.
Convert R Markdown documents and 'Jupyter' notebooks to a
    variety of output formats using 'Quarto'.
Provides helper functions to work programmatically within a quarto document. It allows 
    the user to create section headers, tabsets, divs, and spans, and formats these objects
    into quarto syntax when printed into a document.
The approach is based on the closed testing procedure to control familywise error rate in a strong sense.
  The local tests implemented are Wald-type and rank-score. 
  The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.
Functions for estimating the potential dispersal of tree species 
  using regeneration densities and dispersal distances to nearest seed trees. 
  A quantile regression is implemented to determine the dispersal potential.
  Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.
Syntax for defining complex filtering expressions in a programmatic way.
  A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'.
  What's more, it's possible to convert such configuration to a valid expression that can be applied to 
  popular 'dplyr' package operations.
Adds an LLM-powered chatbot to your 'shiny' app, that can
    turn your users' natural language questions into SQL queries that run
    against your data, and return the result as a reactive data frame. Use
    it to drive reactive calculations, visualizations, downloads, and
    more.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Retrieve protein information from
    the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.
A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
Property based testing, inspired by 
    the original 'QuickCheck'. This package builds on 
    the property based testing framework provided by 
    'hedgehog' and is designed to seamlessly integrate with 
    'testthat'.
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
An 'R' interface to the 'QuickJS' portable 'JavaScript'
    engine.  The engine and all 'R' to 'JavaScript' interoperability is bundled
    within the package, requiring no dependencies beyond a 'C' compiler.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".
Implements a suite of tools for outlier detection and treatment 
    in data mining. It includes univariate methods (Z-score, Interquartile Range), 
    multivariate detection using Mahalanobis distance, and density-based 
    detection (Local Outlier Factor) via the 'dbscan' package. It also 
    provides functions for visualization using 'ggplot2' and data cleaning 
    via Winsorization.
A high-level plotting system, compatible with `ggplot2` objects, 
    maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the 
    'grid' package. The objective of the package is to provide a plotting system 
    that is built for speed and modularity. This is useful for quick visualizations 
    when testing code and for plotting multiple figures to the same device from 
    independent sources that may be independent of one another (i.e., different 
    function or modules the create the visualizations).
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A collection of functions for constructing large pairwised comparisons 
             and rating them using Elo rating system with supporting parallel processing.
             The method of random sample pairs is based on Reservoir Sampling proposed by 
             JVitter (1985) <doi:10.1145/3147.3165>.
Compile R functions annotated with type and shape
    declarations for extremely fast performance and robust runtime
    type checking. Supports both just-in-time (JIT) and ahead-of-time
    (AOT) compilation. Compilation is performed by lowering R code to
    Fortran.
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.
A high-level pipeline that simplifies text classification into three streamlined steps: 
    preprocessing, model training, and standardized prediction. 
    It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', 
    'xgboost', and 'naivebayes') and memory-efficient sparse matrix vectorization 
    methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from 
    raw text to a fully evaluated sentiment model, complete with ROC-optimized 
    thresholds, in just a few function calls. The resulting model artifact 
    automatically aligns the vocabulary of new datasets during the prediction phase, 
    safely appending predicted classes and probability matrices directly to the 
    user's original dataframe to preserve metadata.
A toolkit for analysis and visualization of data from fluorophore-assisted seed amplification assays, such as Real-Time Quaking-Induced Conversion (RT-QuIC) and Fluorophore-Assisted Protein Misfolding Cyclic Amplification (PMCA). 'QuICSeedR' addresses limitations in existing software by automating data processing, supporting large-scale analysis, and enabling comparative studies of analysis methods. It incorporates methods described in Henderson et al. (2015) <doi:10.1099/vir.0.069906-0>, Li et al. (2020) <doi:10.1038/s41598-021-96127-8>, Rowden et al. (2023) <doi:10.3390/pathogens12020309>, Haley et al. (2013) <doi:10.1371/journal.pone.0081488>, and Mair and Wilcox (2020) <doi:10.3758/s13428-019-01246-w>. Please refer to the original publications for details.
Test whether equality and order constraints hold for all 
    individuals simultaneously by comparing Bayesian mixed models through Bayes 
    factors. A tutorial style vignette and a quickstart guide are available, via
    vignette("manual", "quid"), and vignette("quickstart", "quid") respectively.
    See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder
    (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.
Provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters."
Simplifies output suppression logic in R packages, as it's common
    to develop some form of it in R. 'quietR' intends to simplify that problem 
    and allow a set of simple toggle functions to be used to suppress console 
    output.
Functions to convert text data for labelling into format appropriate for importing into Qualtrics. Supports multiple language, including right-to-left scripts as well as different response types. Outputs an Advance Format .txt file that read into Qualtrics.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial. More information in Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016) <doi:10.3758/s13428-015-0594-z>.
Functions to compute Euclidean minimum spanning trees using single-,
    sesqui-, and dual-tree Boruvka algorithms.  Thanks to K-d trees, they are
    fast in spaces of low intrinsic dimensionality.  Mutual reachability
    distances (used in the definition of the 'HDBSCAN*' algorithm)
    are supported too.  The package also includes relatively fast fallback
    minimum spanning tree and nearest-neighbours algorithms for spaces of
    higher dimensionality.  The 'Python' version of 'quitefastmst' is available
    via 'PyPI'.
To construct a model in 2-D space from 2-D nonlinear
    dimension reduction data and then lift it to the high-dimensional
    space. Additionally, provides tools to visualise the model overlay the
    data in 2-D and high-dimensional space. Furthermore, provides
    summaries and diagnostics to evaluate the nonlinear dimension
    reduction layout.
Collect  your data on digital marketing campaigns from 'Quora' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.
High-throughput analysis of growth curves and fluorescence
    data using three methods: linear regression, growth model fitting, and
    smooth spline fit. Analysis of dose-response relationships via
    smoothing splines or dose-response models. Complete data analysis
    workflows can be executed in a single step via user-friendly wrapper
    functions. The results of these workflows are summarized in detailed
    reports as well as intuitively navigable 'R' data containers. A 'shiny'
    application provides access to all features without
    requiring any programming knowledge. The package is described in further
    detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.
Provide a variety of Q-matrix validation methods for the generalized
    cognitive diagnosis models, including the method based on the generalized
    deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011)
    <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by
    de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method
    by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method
    (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the
    multiple logistic regression‑based Q‑matrix validation method (the MLR-B method)
    by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on
    signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and
    Q-matrix validation based on relative fit index by Chen et al. (2013)
    <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative
    procedures during Q-matrix validating are available
    <DOI:10.3758/s13428-024-02547-5>.
Finding hidden clusters in structured data can be hindered
  by the presence of masking variables. If not detected,
  masking variables are used to calculate the overall similarities between units, 
  and therefore the cluster attribution is more imprecise.
  The algorithm q-vars implements an optimization method to find the variables
  that most separate units between clusters. In this way, masking variables can be 
  discarded from the data frame and the clustering is more accurate.
  Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.
Functions to compute quasi variances and associated measures of approximation error.
Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package 'qsimulatR' (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.
Functions and tools for creating, visualizing, and investigating properties of continuous-time quantum walks, including efficient calculation of matrices such as the mixing matrix, average mixing matrix, and spectral decomposition of the Hamiltonian. E. Farhi (1997): <arXiv:quant-ph/9706062v2>; C. Godsil (2011) <arXiv:1103.2578v3>.
The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference.
A collection of (wrapper) functions the creator found useful
    for quickly placing data summaries and formatted regression results into
    '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics,
    such as receiver operating curves or Bland-Altman plots, are also provided
    by 'qwraps2'.  'qwraps2' is an updated version of a package 'qwraps'. The
    original version 'qwraps' was never submitted to CRAN but can be found at
    <https://github.com/dewittpe/qwraps/>. The implementation and limited scope
    of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is
    fundamentally different from 'qwraps'.
Generalized eigenvalues and eigenvectors
        use QZ decomposition (generalized Schur decomposition).
        The decomposition needs an N-by-N non-symmetric
        matrix A or paired matrices (A,B) with eigenvalues reordering
        mechanism. The decomposition functions are mainly based Fortran
        subroutines in complex*16 and double precision of LAPACK
        library (version 3.10.0 or later).
A collection of personal functions designed to simplify and
    streamline common R programming tasks. This package provides reusable
    tools and shortcuts for frequently used calculations and workflows.
Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
The use of proxies is required in certain network
    environments.  Despite the power of system level software, it is still
    inconvenient to switch proxy networks at random in R's console. This
    package is designed to provide one-click switching between proxy and
    non-proxy states.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
An integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, lexical summary, terms
    co-occurrences and documents similarity measures, graphs of terms,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
This is a companion package of the book "R Programming: Zero to Pro"  <https://r02pro.github.io/>. It contains the datasets used in the book and provides interactive exercises corresponding to the book. It covers a wide range of topics including visualization, data transformation, tidying data, data input and output.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.
A series of functions to call 'AD Model Builder' (i.e.,
    compile and run models) from within R, read the results back
    into R as 'admb' objects, and provide standard accessors (i.e.
    coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.
Obtain information about countries around the globe. Information for names, states, languages, time, capitals, currency and many more. Data source are 'Wikipedia' <https://www.wikipedia.org>, 'TimeAndDate' <https://www.timeanddate.com> and 'CountryCode' <https://countrycode.org>.
Generic functions to analyze the distribution of two continuous
  variables: 'conf2d' to calculate a smooth empirical confidence region, and
  'freq2d' to calculate a frequency distribution.
Implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.1007/s11222-025-10667-x>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in 'rstan'. Please cite the paper if you use these codes.
Suite of tools for using 'D3', a library for producing dynamic, interactive data
  visualizations. Supports translating objects into 'D3' friendly data structures, rendering
  'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating
  interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R
  packages.
Despite the predominant use of R for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use R for other purposes. In doing this seemlessly, further tools are needed users to easily and freely write in R for all kinds of purposes. The r2dictionary introduces a means for users to directly search for definitions of terms within the R environment.
These tools help you to assess if a corporate lending
    portfolio aligns with climate goals. They summarize key climate
    indicators attributed to the portfolio (e.g. production, emission
    factors), and calculate alignment targets based on climate scenarios.
    They implement in R the last step of the free software 'PACTA' (Paris
    Agreement Capital Transition Assessment;
    <https://www.transitionmonitor.com/>). Financial institutions use 'PACTA'
    to study how their capital allocation decisions align with climate
    change mitigation goals.
These datasets support the implementation in R of the
    software 'PACTA' (Paris Agreement Capital Transition Assessment),
    which is a free tool that calculates the alignment between corporate
    lending portfolios and climate scenarios
    (<https://www.transitionmonitor.com/>). Financial institutions use
    'PACTA' to study how their capital allocation decisions align with
    climate change mitigation goals. Because both financial institutions
    and market data providers keep their data private, this package
    provides fake, public data to enable the development and use of
    'PACTA' in R.
These tools implement in R a fundamental part of the software
    'PACTA' (Paris Agreement Capital Transition Assessment), which is a
    free tool that calculates the alignment between financial portfolios
    and climate scenarios (<https://www.transitionmonitor.com/>). Financial
    institutions use 'PACTA' to study how their capital allocation
    decisions align with climate change mitigation goals. This package
    matches data from corporate lending portfolios to asset level data
    from market-intelligence databases (e.g. power plant capacities,
    emission factors, etc.). This is the first step to assess if a
    financial portfolio aligns with climate goals.
Create plots to visualize the alignment of a corporate
    lending financial portfolio to climate change scenarios based on
    climate indicators (production and emission intensities) across key
    climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital 
    Transition Assessment; <https://www.transitionmonitor.com/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.
Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow the display of fireworks on a document. Toolkit to easily incorporate celebratory splashes in 'Rmarkdown' and 'shiny' apps.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Includes HTML function and methods to write in an HTML
        file. Thus, making HTML reports is easy. Includes a function
        that allows redirection on the fly, which appears to be very
        useful for teaching purpose, as the student can keep a copy of
        the produced output to keep all that he did during the course.
        Package comes with a vignette describing how to write HTML
        reports for statistical analysis. Finally, a driver for 'Sweave'
        allows to parse HTML flat files containing R code and to
        automatically write the corresponding outputs (tables and
        graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Generates both total- and level-specific R-squared measures from
    Rights and Sterba’s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel
    models with random intercepts and/or slopes, which is based on a complete
    decomposition of variance. Additionally generates graphical 
    representations of these R-squared measures to allow visualizing and 
    interpreting all measures in the framework together as an integrated set.
    This framework subsumes 10 previously-developed R-squared measures for 
    multilevel models as special cases of 5 measures from the framework, and it
    also includes several newly-developed measures. Measures in the framework 
    can be used to compute R-squared differences when comparing multilevel 
    models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>).
    Bootstrapped confidence intervals can also be calculated. To use the 
    confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R wrapper for the 'JPMML-R' library <https://github.com/jpmml/jpmml-r>,
    which converts R models to Predictive Model Markup Language ('PMML').
Provides a simple set of wrappers to easily use 
         RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.
Provides a friendly, object oriented API for creating PowerPoint
    slide decks in R.
Implementation of hash tables (hash sets and hash maps) in R, 
	featuring arbitrary R objects as keys, 
	arbitrary hash and key-comparison functions, 
	and customizable behaviour upon queries of missing keys.
R2 statistic for significance test. Variance and covariance of R2 values used to assess the 95% CI and p-value of the R2 difference.
Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly 'shiny' applications, 'markdown' reports, and 'quarto' documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.
Area under the receiver operating characteristic curves (AUC) statistic for significance test. Variance and covariance of AUC values used to assess the 95% Confidence interval (CI) and p-value of the AUC difference for both nested and non-nested model.
Create production-ready Rich Text Format (RTF) tables and figures
    with flexible format.
The routine twosample_test() in this package runs the
    two sample test using various test statistic. The p values are
    found via permutation or large sample theory. The routine twosample_power() allows the
    calculation of the power in various cases, and plot_power() draws the corresponding power graphs.
    The routine run.studies allows a user to quickly study the power of a new method and 
    how it compares to some of the standard ones.
When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to 'Shiny' applications or dashboards and 'Rmarkdown' documents.Sharing capability on social media platforms including 'Facebook' <https://www.facebook.com>, 'Linkedin' <https://www.linkedin.com>, 'X/Twitter' <https://x.com>, 'Tumblr' <https://www.tumblr.com>, 'Pinterest' <https://www.pinterest.com>, 'Whatsapp' <https://www.whatsapp.com>, 'Reddit' <https://www.reddit.com>, 'Baidu' <https://www.baidu.com>, 'Blogger' <https://www.blogger.com>, 'Weibo' <https://www.weibo.com>, 'Instagram' <https://www.instagram.com>, 'Telegram' <https://www.telegram.me>, 'Youtube' <https://www.youtube.com>.
Create plots and LaTeX tables that look like SPSS output for use in teaching materials.  Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr.  Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots).
Converts data to STL (stereolithography) files
    that can be used to feed a 3-dimensional printer.  The
    3-dimensional output from a function can be materialized
    into a solid surface in a plastic material, therefore allowing
    more detailed examination. There are many possible uses for
    this new tool, such as to examine mathematical expressions
    with very irregular shapes, to aid teaching people with
    impaired vision, to create raised relief maps from digital
    elevation maps (DEMs), to bridge the gap between mathematical
    tools and rapid prototyping, and many more.  Ian Walker created
    the function r2stl() and Jose' Gama assembled the package.
Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.
Direct insertion of over 1000 symbols (e.g. currencies, letters, emojis, arrows, mathematical symbols and so on) into 'Rmarkdown' documents and 'Shiny' applications by incorporating 'HTML' hex codes.
Performs goodness-of-fit tests for capture-recapture models
    as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>.
    Also contains several functions to process capture-recapture data.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
  The suggested package 'BRugs' (only needed for function openbugs()) is only available from the CRAN archives, 
  see <https://cran.r-project.org/package=BRugs>.
Access and handle APIs that use the international 'open311'
    'GeoReport v2' standard for civic issue tracking
    <https://wiki.open311.org/GeoReport_v2/>. Retrieve civic service types
    and request data. Select and add available 'open311' endpoints and
    jurisdictions. Implicitly supports custom queries and 'open311'
    extensions.  Requires a minimal number of hard dependencies while
    still allowing the integration in common R formats ('xml2', 'tibble',
    'sf').
Create rich and fully interactive 3D visualizations of molecular data.
    Visualizations can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API
    to manipulate the visualization after creation, and supports getting data out of
    the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.
Provides R and 'JavaScript' functions to allow 'WebGL'-based 3D plotting
    using the 'three.js' 'JavaScript' library. Interactivity through roll-over 
    highlighting and toggle buttons is also supported.
Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.
Create and combine HTML and PDF reports from within R.
    Possibility to design tables and listings for reporting and also include R plots.
Provides a collection of functions for basic
    database and document management operations such as add, get, list access
    or delete. Every cdbFunction() gets and returns a list() containing the
    connection setup. Such a list can be generated by cdbIni().
When assigned "R for Data Science" (Wickham, Çetinkaya-Rundel, 
  and Grolemund (2023, ISBN: 1492097402)), students should read the book and
  type in all the associated R commands themselves. Sadly, that never happens.
  These tutorials allow students to demonstrate (and their instructors
  to be sure) that all work has been completed. See Kane (2023)
  <https://ppbds.github.io/tutorial.helpers/articles/instructions.html> from
  the 'tutorial.helpers' package for a background discussion.
Make optimal decisions for your personal or
    household finances. Use tools and methods that are selected carefully
    to align with academic consensus, bridging the gap between theoretical
    knowledge and practical application. They help you find
    your own personalized optimal discretionary spending or
    optimal asset allocation, and prepare you for retirement 
    or financial independence.
    The optimal solution to this problems is extremely complex,
    and we only have a single lifetime to get it right.
    Fortunately, we now have the user-friendly tools implemented,
    that integrate life-cycle models 
    with single-period net-worth mean-variance optimization models. 
    Those tools can be used by anyone who wants to see 
    what highly-personalized optimal decisions can look like.
    For more details see: 
    Idzorek T., Kaplan P. (2024, ISBN:9781952927379),
    Haghani V., White J. (2023, ISBN:9781119747918).
Interface for the 'Google Ads API'. 'Google Ads' is an online
    advertising service that enables advertisers to display advertising to web
    users (see <https://developers.google.com/google-ads/> for more information).
A collection of datasets that accompany the forthcoming book "R for Health Care Research".
Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.
Datasets and utility functions to support the book
    "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease,
    assessing spatial patterns, and modeling plant disease epidemics based on weather predictors.
    These tools are intended for teaching and research in plant disease epidemiology. Several functions
    are based on classical and contemporary methods, including those discussed in Laurence V. Madden,
    Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D. Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
    This version of '{r4ss}' is compatible with Stock Synthesis versions
    3.24 through 3.30 (specifically version 3.30.19.01, from April
    2022).
The 'r4sub' package is a meta-package that installs and loads
    core packages of the R4SUB (R for Regulatory Submission) clinical
    submission readiness ecosystem. Loading 'r4sub' attaches 'r4subcore',
    'r4subtrace', 'r4subscore', 'r4subrisk', 'r4subdata', and
    'r4subprofile'.
Foundational package in the R4SUB (R for Regulatory Submission)
    ecosystem. Defines the core evidence table schema, parsers, indicator
    abstractions, and scoring primitives needed to quantify clinical submission
    readiness. Provides a standardized contract for ingesting heterogeneous
    sources (validation outputs, metadata, traceability) into a single
    evidence framework.
Provides realistic synthetic example datasets for the R4SUB
    (R for Regulatory Submission) ecosystem. Includes a pharma study evidence
    table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model)
    metadata following CDISC (Clinical Data Interchange Standards Consortium)
    conventions (<https://www.cdisc.org>), traceability mappings, a risk
    register based on ICH (International Council for Harmonisation) Q9
    quality risk management principles
    (<https://www.ich.org/page/quality-guidelines>), and regulatory indicator
    definitions. Designed for demos, vignettes, and package testing.
Defines submission profiles per regulatory authority with
    authority-specific pillar weights, decision thresholds, indicator
    requirements, and risk configuration. Supports the U.S. Food and Drug
    Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and
    Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods
    Administration (TGA), and Medicines and Healthcare products Regulatory
    Agency (MHRA). Integrates with 'r4subscore' and 'r4subrisk' configuration
    systems.
Quantifies submission risk using a Failure Modes and Effects
    Analysis (FMEA)-inspired framework (probability, impact, detectability).
    Builds risk registers from evidence, computes Risk Priority Numbers (RPN),
    classifies risk levels, and emits standardized R4SUB (R for Regulatory
    Submission) evidence table rows via 'r4subcore'. Supports risk mitigation
    tracking and trend analysis across submission milestones.
Converts standardized R4SUB (R for Regulatory Submission) evidence
    into indicator scores, pillar scores, and a Submission Confidence Index
    (SCI). Provides sensitivity analysis, explainability tables, and decision
    band classification to answer the question: are we ready for regulatory
    submission.
Quantifies and explains end-to-end traceability between clinical
    submission artifacts (ADaM (Analysis Data Model) outputs, derivations,
    SDTM (Study Data Tabulation Model) sources, specs, code). Builds trace
    models from metadata and mapping sheets, computes trace levels, and emits
    standardized R4SUB (R for Regulatory Submission) evidence table rows via
    'r4subcore'.
Rapid realistic routing on multimodal transport networks
    (walk, bike, public transport and car) using 'R5', the Rapid Realistic
    Routing on Real-world and Reimagined networks engine
    <https://github.com/conveyal/r5>. The package allows users to generate
    detailed routing analysis or calculate travel time and monetary cost matrices 
    using seamless parallel computing on top of the R5 Java machine.  While R5
    is developed by Conveyal, the package r5r is independently developed
    by a team at the Institute for Applied Economic Research (Ipea) with
    contributions from collaborators. Apart from the documentation in this
    package, users will find additional information on R5 documentation at
    <https://docs.conveyal.com/>. Although we try to keep new releases of
    r5r in synchrony with R5, the development of R5 follows Conveyal's
    independent update process. Hence, users should confirm the R5 version
    implied by the Conveyal user manual (see
    <https://docs.conveyal.com/changelog>) corresponds with the R5 version
    that r5r depends on. This version of r5r depends on R5 v7.1.
Interactively build and explore public transit routes with
    'r5r' package via a graphical user interface in a 'shiny' app. The
    underlying routing methods are described in Pereira et al. (2021)
    <doi:10.32866/001c.21262>.
Creates classes with reference semantics, similar to R's
    built-in reference classes. Compared to reference classes, R6 classes
    are simpler and lighter-weight, and they are not built on S4 classes
    so they do not require the methods package. These classes allow public
    and private members, and they support inheritance, even when the
    classes are defined in different packages.
After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.
The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
Provides reference classes implementing some useful data structures.
    The package implements these data structures by using the reference class R6.
    Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference.
    The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree.
    See for example <https://en.wikipedia.org/wiki/Data_structure> for more information about the data structures.
Generate boilerplate code for R6 classes. Given R6 class create getters 
    and/or setters for selected class fields or use RStudio addins to insert methods 
    straight into class definition.
Build robust and maintainable software with object-oriented design 
    patterns in R. Design patterns abstract and present in neat, well-defined 
    components and interfaces the experience of many software designers and 
    architects over many years of solving similar problems. These are solutions 
    that have withstood the test of time with respect to re-usability,
    flexibility, and maintainability. 'R6P' provides abstract base classes with 
    examples for a few known design patterns. The patterns were selected by 
    their applicability to analytic projects in R. Using these patterns in R 
    projects have proven effective in dealing with the complexity that 
    data-driven applications possess.
A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.
Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.
Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2021) <doi:10.1002/sim.9104>.
Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.
Provide function for work with 'AcademyOcean API'
    <https://academyocean.com/api>.
Provides a flexible framework for implementing hierarchical access 
    control in 'shiny' applications. Features include user permission management 
    through a two-tier system of access panels and units, pluggable 'shiny' 
    module for administrative interfaces, and support for multiple storage 
    backends (local, 'AWS S3', 'Posit Connect'). The system enables fine-grained
    control over application features, with built-in audit trails and user 
    management capabilities. Integrates seamlessly with 'Posit Connect's 
    authentication system.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).
Implements a computational framework for a pattern-based, 
    zoneless analysis, and visualization of (ethno)racial topography 
    (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>).
    It is a reimagined
    approach for analyzing residential segregation and racial diversity based on 
    the concept of 'landscape’ used in the domain of landscape ecology.
Contains functions useful for reading in Licor 6800 files,
    correcting and analyzing rapid A/Ci response (RACiR) data. Requires some
    user interaction to adjust the calibration (empty chamber) data file to 
    a useable range. Calibration uses a 1st to 5th order polynomial as 
    suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>.
    Data can be processed individually or batch processed for all files paired
    with a given calibration file. RACiR is a trademark of LI-COR Biosciences,
    and used with permission.
A toolkit for making antigenic maps from immunological assay data,
    in order to quantify and visualize antigenic differences between different
    pathogen strains as described in
    Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World
    Health Organization influenza vaccine strain selection process. Additional
    functions allow for the diagnostic evaluation of antigenic maps and an
    interactive viewer is provided to explore antigenic relationships amongst
    several strains and incorporate the visualization of associated genetic
    information.
A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.
Interface for loading data from 'ActiveCampaign API v3' 
    <https://developers.activecampaign.com/reference>. Provide functions
    for getting data by deals, contacts, accounts, campaigns and messages.
Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.
Implementation of the MaxRank normalization method, which enables 
     standardization of Rank Abundance Distributions (RADs) to a specified 
     number of ranks. Rank abundance distributions are widely used in biology 
     and ecology to describe species abundances, and are mathematically 
     equivalent to complementary cumulative distribution functions (CCDFs) used 
     in physics, linguistics, sociology, and other fields. The method is 
     described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Creates the radar-boxplot, a plot that was created by the 
    author during his Ph.D. in forest resources. 
    The radar-boxplot is a visualization feature suited for  
    multivariate classification/clustering. It provides an intuitive 
    deep understanding of the data.
Create interactive radar charts using the 'Chart.js' 'JavaScript' library
    and the 'htmlwidgets' package. 'Chart.js' <http://www.chartjs.org/> is a 
    lightweight library that supports several types of simple chart using the 'HTML5' 
    canvas element. This package provides an R interface specifically to the 
    radar chart, sometimes called a spider chart, for visualising multivariate data.
Nuclear Decay Data for Dosimetric Calculations from the 
    International Commission on Radiological Protection from ICRP 
    Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 
    <doi:10.1016/j.icrp.2008.10.004> 
    <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. 
    This is a database of the physical data needed in calculations of 
    radionuclide-specific protection and operational quantities. The 
    data is prescribed by the ICRP, the international authority on 
    radiation dose standards, for estimating dose from the intake of or 
    exposure to radionuclides in the workplace and the environment. 
    The database contains information on the half-lives, decay chains, 
    and yields and energies of radiations emitted in nuclear transformations 
    of 1252 radionuclides of 97 elements.
A differential abundance method for the analysis of microbiome data. 'radEmu' estimates fold-differences in the abundance of taxa across samples relative to "typical" fold-differences. Notably, it does not require pseudocounts, nor choosing a denominator taxon. For more details, see Clausen et al. (2026) <doi:10.1093/biomet/asag009>.
A straightforward model to estimate soil migration rates across various soil contexts. 
	Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) 
	<doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>.
	'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived 
	from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. 
	The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated 
	from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. 
	The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.
Shiny-based interactive gadgets of radial visualization methods and extensions thereof.
A platform-independent browser-based interface for business
    analytics in R, based on the shiny package. The application combines the
    functionality of 'radiant.data', 'radiant.design', 'radiant.basics',
    'radiant.model', and 'radiant.multivariate'.
The Radiant Basics menu includes interfaces for probability 
    calculation, central limit theorem simulation, comparing means and proportions, 
    goodness-of-fit testing, cross-tabs, and correlation. The application extends 
    the functionality in 'radiant.data'.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in 'radiant.data'.
The Radiant Model menu includes interfaces for linear and logistic
    regression, naive Bayes, neural networks, classification and regression trees,
    model evaluation, collaborative filtering, decision analysis, and simulation.
    The application extends the functionality in 'radiant.data'.
The Radiant Multivariate menu includes interfaces for perceptual
    mapping, factor analysis, cluster analysis, and conjoint analysis. The
    application extends the functionality in 'radiant.data'.
A
        collection of programs for 
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument). The format of this plot with companion lines to
	assess atmospheric stability are both standard  in meteorology
	and difficult to create from basic graphics functions. Hence
	this package. One novel feature is being able add several
	profiles to the same plot for comparison. 
	Use "help(ExampleSonde)" for an explanation of the variables
	needed and how they should be named in a data frame. See
	<https://github.com/dnychka/Radiosonde> for the package home page.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Generate random user data from the Random User Generator API.
    For more information, see <https://randomuser.me/>.
Provides functions for radiation safety, also known as
    "radiation protection" and "radiological control". The science of 
    radiation protection is called "health physics" and its engineering 
    functions are called "radiological engineering". Functions in this 
    package cover many of the computations needed by radiation safety 
    professionals. Examples include: obtaining updated calibration and
    source check values for radiation monitors to account for radioactive 
    decay in a reference source, simulating instrument readings to better
    understand measurement uncertainty, correcting instrument readings 
    for geometry and ambient atmospheric conditions. Many of these 
    functions are described in Johnson and Kirby (2011, ISBN-13:  
    978-1609134198). Utilities are also included for developing inputs 
    and processing outputs with radiation transport codes, such as MCNP, 
    a general-purpose Monte Carlo N-Particle code that can be used for 
    neutron, photon, electron, or coupled neutron/photon/electron transport
    (Werner et. al. (2018) <doi:10.2172/1419730>).
Offers a handful of useful wrapper functions which streamline
    the reading, analyzing, and visualizing of variant call format (vcf) files in R.
    This package was designed to facilitate an explicit pipeline for optimizing
    Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>)
    parameters during de novo (without a reference genome) assembly and variant
    calling of restriction-enzyme associated DNA sequence (RADseq) data. The
    pipeline implemented here is based on the 2017 paper "Lost in Parameter Space"
    (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which
    establishes clear recommendations for optimizing the parameters
    'm', 'M', and 'n', during the process of assembling loci.
An implementation of the radviz projection in R. It enables the visualization of
    multidimensional data while maintaining the relation to the original dimensions.
    This package provides functions to create and plot radviz projections, and a number of summary
    plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) 
    (<doi:10.1145/331770.331775>) for original implementation, 
    see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), 
    for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) 
    (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Creating 3D radial visualizations of multivariate data.
    The package extends traditional radial coordinate visualization (RadViz) techniques
    to three-dimensional space, enabling enhanced exploration and analysis of
    high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022)
    <doi:10.1080/10618600.2021.2020129>.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
A model of single-layer groundwater flow in steady-state
    under the Dupuit-Forchheimer assumption can be created by placing
    elements such as wells, area-sinks and line-sinks at arbitrary
    locations in the flow field. Output variables include hydraulic head
    and the discharge vector. Particle traces can be computed numerically
    in three dimensions. The underlying theory is described in Haitjema
    (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references
    therein.
A series of shortcuts for routine tasks originally developed by
    Rafael A. Irizarry to facilitate data exploration.
A cross-validated minimal-optimal feature selection algorithm.
 It utilises popularity counting, hierarchical clustering with feature dissimilarity measures,
 and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.
Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). 
              It calculates the final sorted rankings based on a decision matrix where rows represent alternatives 
              and columns represent criteria. The method uses:
              - A numeric vector of weights for each criterion (the sum of weights must be 1).
              - A numeric vector of ideal values for each criterion.
              - A numeric vector of anti-ideal values for each criterion.
              - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, 
                and the extent to which the anti-ideal value is considered worse.
              The function standardizes the decision matrix, normalizes the data, applies weights, and returns the 
              final sorted rankings.
Functions for calculating life history metrics using matrix
    population models ('MPMs'). Described in Jones et al. (2021)
    <doi:10.1101/2021.04.26.441330>.
Enables Retrieval-Augmented Generation (RAG) workflows in R by combining
    local vector search using 'DuckDB' with optional web search via the 'Tavily' API.
    Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW'
    (Hierarchical Navigable Small World) indexing, and modular large language model
    (LLM) invocation. Designed for advanced question-answering, chat-based
    applications, and production-ready AI pipelines. This package is the R
    equivalent of the 'python' package 'RAGFlowChain' available at
    <https://pypi.org/project/RAGFlowChain/>.
Anti-Grain Geometry (AGG) is a high-quality and
    high-performance 2D drawing library. The 'ragg' package provides a set
    of graphic devices based on AGG to use as alternative to the raster
    devices provided through the 'grDevices' package.
Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.
Provides tools for implementing Retrieval-Augmented
    Generation (RAG) workflows with Large Language Models (LLM). Includes
    functions for document processing, text chunking, embedding
    generation, storage management, and content retrieval. Supports
    various document types and embedding providers ('Ollama', 'OpenAI'),
    with 'DuckDB' as the default storage backend. Integrates with the
    'ellmer' package to equip chat objects with retrieval capabilities.
    Designed to offer both sensible defaults and customization options
    with transparent access to intermediate outputs.  For a review of
    retrieval-augmented generation methods, see Gao et al. (2023)
    "Retrieval-Augmented Generation for Large Language Models: A Survey"
    <doi:10.48550/arXiv.2312.10997>.
Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) 
  <doi:10.18637/jss.v102.i04> and associated publications.
Algorithms to price American and European
    equity options, convertible bonds and a
    variety of other financial derivatives. It uses an
    extension of the usual Black-Scholes model in which
    jump to default may occur at a probability specified
    by a power-law link between stock price and hazard
    rate as found in the paper by Takahashi, Kobayashi,
    and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>.  We
    use ideas and techniques from Andersen and
    Buffum (2002) <doi:10.2139/ssrn.355308> and
    Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
A modified implementation of stepwise regression that greedily searches 
    the space of interactions among features in order to build polynomial regression models.
    Furthermore, the hypothesis tests conducted are valid-post model selection
    due to the use of a revisiting procedure that implements an alpha-investing
    rule. As a result, the set of rejected sequential hypotheses is proven to 
    control the marginal false discover rate. When not searching for polynomials,
    the package provides a statistically valid algorithm
    to run and terminate stepwise regression. For more information, see 
    Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Visualizing functional data and identifying functional outliers.
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Various statistical and mathematical ranking and rating methods with incomplete information are included. This package is initially designed for the scoring system in a high school project showcase to rank student research projects, where each judge can only evaluate a set of projects in a limited time period. See Langville, A. N. and Meyer, C. D. (2012), Who is Number 1: The Science of Rating and Ranking, Princeton University Press <doi:10.1515/9781400841677>, and Gou, J. and Wu, S. (2020), A Judging System for Project Showcase: Rating and Ranking with Incomplete Information, Technical Report.
Calculates I30 (maximum 30-minute rainfall intensity) and EI30
    (erosivity index) from rainfall breakpoint data. Supports multiple
    storm events, rainfall validation, and visualization for soil erosion
    modeling and hydrological analysis. Methods are based on
    Brown and Foster (1987) <doi:10.13031/2013.30422>,
    Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses:
    A Guide to Conservation Planning" <doi:10.22004/ag.econ.171903>,
    and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to
    Conservation Planning with the Revised Universal Soil Loss Equation
    (RUSLE)" (USDA Agriculture Handbook No. 703).
An R implementation of the Reinert text clustering method. For more
    details about the algorithm see the included vignettes or Reinert (1990)
    <doi:10.1177/075910639002600103>.
Determination of rainfall-runoff erosivity factor.
An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.
Efficient CRUD interface for the 'Airtable' API <https://airtable.com/developers/web/api>, supporting batch requests and parallel encoding of large data sets.
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
Functions for performing spatial microsimulation ('raking')
    in R.
The goal of 'ralger' is to facilitate web scraping in R.
Download, prepare and analyze data from large-scale assessments and
    surveys with complex sampling and assessment design (see 'Rutkowski',
    2010 <doi:10.3102/0013189X10363170>). Such studies are, for example,
    international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical
    interface is available for the non-technical user.The package includes
    functions to covert the original data from 'SPSS' into 'R' data sets
    keeping the user-defined missing values, merge data from different
    respondents and/or countries, generate variable dictionaries, modify
    data, produce descriptive statistics (percentages, means, percentiles,
    benchmarks) and multivariate statistics (correlations, linear
    regression, binary logistic regression). The number of supported
    studies and analysis types will increase in future. For a general
    presentation of the package, see 'Mirazchiyski', 2021a
    (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of
    the package, see 'Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Provides an R interface for using 'AmCharts' Library. Based on
    'htmlwidgets', it provides a global architecture to generate 'JavaScript' source
    code for charts. Most of classes in the library have their equivalent in R
    with S4 classes; for those classes, not all properties have been referenced but
    can easily be added in the constructors. Complex properties (e.g. 'JavaScript'
    object) can be passed as named list. See examples at 
    <https://datastorm-open.github.io/introduction_ramcharts/> 
    and <https://www.amcharts.com/> for
    more information about the library. The package includes the free version
    of 'AmCharts' Library. Its only limitation is a small link to the web site
    displayed on your charts. If you enjoy this library, do not hesitate to refer
    to this page <https://www.amcharts.com/online-store/> to purchase a licence,
    and thus support its creators and get a period of Priority Support. See also
    <https://www.amcharts.com/about/> for more information about 'AmCharts' company.
Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart.
It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arXiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2022) <arXiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.
Function for adapting the shape of the random walk Metropolis proposal
    as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. 
    The package also includes fast functions for rank-one Cholesky update and downdate.
    These functions can be used directly from R or the corresponding C++ header files 
    can be easily linked to other R packages.
Use R to interface with the 'TD Ameritrade' API <https://developer.tdameritrade.com/>.
    Functions include authentication, trading, price requests, account information, and option 
    chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README 
    for authentication process and examples.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
Value-calibrated color ramps can be useful to emphasize patterns in data from complex distributions. Colors can be tied to specific values, and the association can be expanded into full color ramps that also include the relationship between colors and values. Such ramps can be used in a variety of cases when heatmap-type plots are necessary, including the visualization of vector and raster spatial data, such as topographies.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a
    reparameterized and marginalized posterior sampling (RAMPS) algorithm
    designed to lower autocorrelation in MCMC samples.  Package performance is
    tuned for large spatial datasets.
R-based access to mass-spectrometry (MS) data. While many packages 
  exist to process MS data, many of these make it difficult to 
  access the underlying mass-to-charge ratio (m/z), intensity, and 
  retention time of the files 
  themselves. This package is designed to format MS data in a tidy fashion and 
  allows the user perform the plotting and analysis.
Provides a solution path for Reinforced Angle-based Multicategory 
    Support Vector Machines, with linear learning, polynomial learning, and 
    Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) 
    <doi:10.1080/10618600.2015.1043010>.
Provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.
Generate random positions (latitude/longitude), 
    Well-known text ('WKT') points or polygons, or 'GeoJSON' points or 
    polygons.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.
Provides random number generating functions that
    are much more context aware than the built-in functions. The
    functions are also much safer, as they check for incompatible 
    values, and more reproducible.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
A set of functions to create random Analysis Data Model
    (ADaM) datasets and cached dataset.  ADaM dataset specifications are
    described by the Clinical Data Interchange Standards Consortium
    (CDISC) Analysis Data Model Team.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) <doi:10.1080/01621459.2021.1950003>.
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Functions for assessing variable relations and associations 
    prior to modeling with a Random Forest algorithm (although these are 
    relevant for any predictive model).
    Metrics such as partial correlations and variance inflation factors
    are tabulated as well as plotted for the user. A function is available
    for tuning the main Random Forest hyper-parameter based on model performance 
    and variable importance metrics. This grid-search technique provides
    tables and plots showing the effect of the main hyper-parameter on each 
    of the assessment metrics. It also returns each of the evaluated models 
    to the user. The package also provides superior variable importance plots 
    for individual models. All of the plots are developed so that the 
    user has the ability to edit and improve further upon the 
    plots. Derivations and methodology are described in Bladen (2022) 
    <https://digitalcommons.usu.edu/etd/8587/>.
Provides an accessible and efficient implementation of a randomized
    feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method
    combines stratified bootstrap resampling with random feature subsampling and
    aggregates predictions via posterior averaging. Support is provided for
    mixed-type predictors and parallel computation. Methods are described in
    Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging
    with Gaussian naive Bayes and the R package RandomGaussianNB for big-data
    classification".
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in 
   Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. The significance is traditionally determined by randomizing
  the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for
  each category. We explore here the novel alternative of randomizing the GO database rather than
  the gene list.
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of 
   sequences (treatment sequences).
   If a blocksize is given, the randomization will be done within blocks.
   The randomization may be controlled by a Wald-Wolfowitz runs test.
   Functions to obtain the p-value of that test are included.
   The package is mainly intended for randomization of bioequivalence studies
   but may be used also for other clinical crossover studies.
   Contains two helper functions sequences() and williams() to get the sequences 
   of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
    based on sound scientific criteria. It comprises the generation of
    randomization sequences as well the assessment of randomization procedures
    based on carefully selected criteria. Furthermore, 'randomizeR' provides a
    function for the comparison of randomization procedures.
Generates random assignments for common experimental designs and 
	    random samples for common sampling designs.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
Performs the random projection test (Lopes et al., (2011) <doi:10.48550/arXiv.1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
Based on data of real user-agent strings, we can set filtering conditions
    and randomly sample user-agent strings from the user-agent string pool.
Generates random walks of various types by providing a set of functions
    that are compatible with the 'tidyverse'. The functions provided in the package
    make it simple to create random walks with a variety of properties, such as
    how many simulations to run, how many steps to take, and the distribution of
    random walk itself.
Randomization lists are an integral component of randomized clinical trials. 'randotools' provides tools to easily create such lists.
Carry out principal component analysis (PCA) of very large pedigrees such as found
    in breeding populations! This package exploits sparse matrices and randomised
    linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD)
    (and eigen decomposition).
Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Provides several non parametric randomness tests for numeric sequences.
Provides (1) pseudo random generators - general linear congruential generators, 
    multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister
    algorithm (<doi:10.1007/978-3-540-74496-2_36>) and WELL (<doi:10.1145/1132973.1132974>) 
    generators); (2) quasi random generators - the Torus algorithm, the
    Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some
    generator tests - the gap test, the serial test, the poker test, see, e.g., 
    Gentle (2003) <doi:10.1007/b97336>. 
    Take a look at the Distribution task view of types and tests of random number generators.
    The package can be provided without the 'rngWELL' dependency on demand.
    Package in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
Streamline the calculation of conditional probabilities for various numeric ranges in an R DataFrame. It automates the need to convert numerical data into categorical data for conditional probability calculation, making it ideal for quick and preliminary data analysis.
Tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.
Range Modeling Metadata Standards (RMMS) address three challenges: 
  they (i) are designed for convenience to encourage use, (ii) accommodate a wide 
  variety of applications, and (iii) are extensible to allow the community of range 
  modelers to steer it as needed. RMMS are based on a data dictionary that specifies 
  a hierarchical structure to catalog different aspects of the range modeling process. 
  The dictionary balances a constrained, minimalist vocabulary to improve 
  standardization with flexibility for users to provide their own values. 
  Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in 
  more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from
  <https://github.com/ecospat/ecospat>.
Used for generating randomized community matrices under
             strict range cohesion. The package can handle data where species occurrence
             are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.
Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation.
A fast implementation of Random Forests, particularly suited for high
          dimensional data. Ensembles of classification, regression, survival and
          probability prediction trees are supported. Data from genome-wide association
          studies can be analyzed efficiently. In addition to data frames, datasets of
          class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.
Displays palette of 5 colors based on photos depicting the unique 
             and vibrant culture of Punjab in Northern India. Since Punjab translates 
             to ``Land of 5 Rivers'' there are 5 colors per palette. If users need 
             more than 5 colors, they can merge 2 to 3 palettes to create their own 
             color-combination, or they can cherry-pick their own custom colors. 
             Users can view up to 3 palettes together. Users can also list all the 
             palette choices. And last but not least, users can see the photo that 
             inspired a particular palette.
Integrates population dynamics and dispersal into a
    mechanistic virtual species simulator. The package can be used to
    study the effects of environmental change on population growth and
    range shifts. It allows for simple and straightforward definition of
    population dynamics (including positive density dependence), extensive
    possibilities for defining dispersal kernels, and the ability to
    generate virtual ecologist data. Learn more about the 'rangr' at
    <https://docs.ropensci.org/rangr/>. This work was supported by 
    the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and 
    the Poznań Supercomputing and Networking Centre (grant no. pl0090-01).
Provides a flexible alternative to the built-in rank() function called smartrank(). 
    Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. 
    smartrank() is suitable for both numerical and categorical data.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
Easily compute an aggregate ranking (also called a median ranking or a
             consensus ranking) according to the axiomatic approach presented
             by Cook et al. (2007). This approach minimises the number of violations
             between all candidate consensus rankings and all input (partial) rankings,
             and draws on a branch and bound algorithm and a heuristic algorithm to
             drastically improve speed. The package also provides an option to bootstrap
             a consensus ranking based on resampling input rankings (with 
             replacement). Input rankings can be either incomplete (partial) or complete.
             Reference: Cook, W.D., Golany, B., Penn, M. and Raviv, T. (2007) <doi:10.1016/j.cor.2005.05.030>.
Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.
Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Estimates the total, between-, and within-cluster Spearman rank correlations for continuous and ordinal clustered data. See Tu et al. (2024) <DOI:10.1002/sim.10326> for details.
Implements Kornbrot's rank difference test as described in 
    <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified 
    Wilcoxon signed-rank test which produces consistent and meaningful 
    results for ordinal or monotonically-transformed data.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
	     Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.
Functions to generate plots and tables for comparing independently-
    sampled populations. Companion package to "A Primer on Visualizations
    for Comparing Populations, Including the Issue of Overlapping Confidence
    Intervals" by Wright, Klein, and Wieczorek (2019)
    <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an
    Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020)
    <DOI:10.1111/rssc.12402>.
A supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.
Provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.
Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.
Methods for ranking responses of a single response question or a 
  multiple response question are described in the two papers: 
  1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of 
  Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 
  2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple 
  Response Questions. Journal of the Royal Statistical Society: Series A 
  (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze  biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
Finds the k nearest neighbours for every point in a
    given dataset in O(N log N) time using Arya and Mount's ANN library
    (v1.1.3). There is support for approximate as well as exact searches,
    fixed radius searches and 'bd' as well as 'kd' trees. The distance is
    computed using the L2 (Euclidean) metric. Please see package 'RANN.L1'
    for the same functionality using the L1 (Manhattan, taxicab) metric.
Provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.
Implements reversal association pattern analysis for categorical data. Detects sub-tables exhibiting reversal associations in contingency tables, provides visualization tools, and supports simulation-based validation for complex I × J tables.
Interface for multiple data sources, such as the 
    `EDDS` API <https://evds2.tcmb.gov.tr/index.php?/evds/userDocs> of the 
    Central Bank of the Republic of Türkiye and the 
    `FRED` API <https://fred.stlouisfed.org/docs/api/fred/> of the Federal Reserve Bank. 
    Both data providers require API keys for access, which users can easily obtain 
    by creating accounts on their respective websites. 
    The package provides caching ability with the selection of periods to increase the 
    speed and efficiency of requests. 
    It combines datasets requested from different sources, 
    helping users when the data has common frequencies. 
    While combining data frames whenever possible, it also keeps all requested data
    available as separate data frames to increase efficiency.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Access to the C-level R date and 'datetime' code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
Extracts machine-readable variables from natural language text using
    AI APIs. Optimized for speed and cost efficiency through parallel processing
    and direct CSV-formatted responses from language models. Supports multiple AI
    providers with robust error handling and automatic retry mechanisms for
    failed extractions.
Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' <https://github.com/rapidfuzz/rapidfuzz-cpp>. This package integrates the 'C++' implementation, allowing 'R' users to access cutting-edge algorithms for fuzzy matching and text analysis.
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'RapiDoc' documentation from an 'OpenAPI' Specification:
   <https://mrin9.github.io/RapiDoc/>.
Rapidly estimates tree-topology from large allele frequency
    data using Root Distances Method, under a Brownian Motion Model. See
    Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
A 'Java' implementation of the RAKE algorithm ('Rose', S., 'Engel', D., 
  'Cramer', N. and 'Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can 
  be used to extract keywords from documents without any training data.
Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) <doi:10.3758/s13423-024-02597-y> for details.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
Access to the internal R serialization code is provided for
 use by other packages at the C function level by using the registration of
 native function mechanism. Client packages simply include a single header
 file RApiSerializeAPI.h provided by this package. This packages builds on
 the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a
 (partial) copy of the file src/main/serialize.c from R itself. The R Core
 group is the original author of the serialization code made available by
 this package.
Run simple 'R' scripts as command line applications, with
  automatic robust and convenient support for command line arguments.
  This package provides 'Rapp', an alternative 'R' front-end similar to 
  'Rscript', that enables this.
Bindings to kernel methods for enforcing security restrictions. 
    AppArmor can apply mandatory access control (MAC) policies on a given task 
    (process) via security profiles with detailed ACL definitions. In addition
    this package implements bindings for setting process resource limits (rlimit),
    uid, gid, affinity and priority. The high level R function 'eval.secure' 
    builds on these methods to perform dynamic sandboxing: it evaluates a single
    R expression within a temporary fork which acts as a sandbox by enforcing 
    fine grained restrictions without affecting the main R process. A portable 
    version of this function is now available in the 'unix' package.
An easy way to determine which directories on the users
    computer you should use to save data, caches and logs. A port of
    Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to R.
Provides functions to load and manage data from Apple Ads 
    accounts using the 'Apple Ads Campaign Management API' 
    <https://developer.apple.com/documentation/apple_ads>.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
Loading data from AppsFlyer Pull API
    <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.
Provides access to 'Rangeland Analysis Platform' (RAP) products <https://rangelands.app/products> for arbitrary extents via 'GDAL' virtual file system.
The Agricultural Production Systems sIMulator ('APSIM') is a widely
    used to simulate the agricultural systems for multiple crops. This package 
    is designed to create, modify and run 'apsimx' files in the 'APSIM' Next 
    Generation <https://www.apsim.info/>.
Assessing the comparative performance of two logistic regression models or results of such models or classification models. Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) <doi:10.1373/clinchem.2011.167965> and Pencina et al. (2008) <doi:10.1002/sim.2929>.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software (obtained from
    <https://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
Offers functions for fetching JSON data from the US EPA Air Quality
             System (AQS) API with options to comply with the API rate limits.
             See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             details of the AQS API.
Retrieve air monitoring data and associated metadata from the US
  Environmental Protection Agency's Air Quality System service using functions.
  See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details about
  the US EPA Data Mart API.
Generate a table of cumulative water influx into hydrocarbon reservoirs over time using un-steady and pseudo-steady state models. Van Everdingen, A. F. and Hurst, W. (1949) <doi:10.2118/949305-G>. Fetkovich, M. J. (1971) <doi:10.2118/2603-PA>. Yildiz, T. and Khosravi, A. (2007) <doi:10.2118/103283-PA>.
Perform risk-adjusted regression and sensitivity analysis as
    developed in "Mitigating Omitted- and Included-Variable Bias in Estimates of
    Disparate Impact" Jung et al. (2024) <arXiv:1809.05651>.
Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.
A custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.
Variational flow-based methods for modeling rare events using 
    Kullback–Leibler (KL) divergence, normalizing flows, Girsanov change of 
    measure, and Freidlin–Wentzell action functionals. The package provides 
    tools for rare-event inference, minimum-action paths, and quasi-potential 
    computation in stochastic dynamical systems. Methods are based on 
    Rezende and Mohamed (2015) <doi:10.48550/arXiv.1505.05770>, 
    Girsanov (1960) <doi:10.1137/1105027>, 
    and Freidlin and Wentzell (2012, ISBN:978-0387955477).
Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) <doi:10.1890/14-1261.1>.
Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.
Provides functions and command-line user interface to generate allocation sequence 
             by response-adaptive randomization for clinical trials. The package currently supports 
             two families of frequentist response-adaptive randomization procedures, Doubly Adaptive 
             Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for 
             binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square 
             (or 'ANOVA') hypothesis testing methods are also available in the package to  facilitate 
             the inference for treatment effect. Additionally, the package provides comprehensive and 
             efficient tools to allow one to evaluate and  compare the performance of randomization 
             procedures and tests based on various criteria. For example, plots for relationship among 
             assumed treatment effects,  sample size, and power are provided. Five allocation functions 
             for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations 
             such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) 
             <doi:10.1111/j.0006-341X.2001.00909.x> and 'Urn' allocations.
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 <doi:10.1111/j.1752-4598.2011.00148.x>; Leroy et al. 2013, Divers. Distrib. 19:794-803 <doi:10.1111/ddi.12040>).
Interface to easily access data via the United States Department of Agriculture (USDA)'s Agricultural Resource Management Survey (ARMS) 
  Data API <https://www.ers.usda.gov/developer/data-apis/arms-data-api/>. 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 inquery.
Previously an R wrapper of the 'ARPACK' library
    <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
    R package 'RSpectra', an R interface to the 'Spectra' library
    <http://yixuan.cos.name/spectra/> for solving large scale
    eigenvalue/vector problems. The current version of 'rARPACK'
    simply imports and exports the functions provided by 'RSpectra'.
    New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Inspired by Karl Broman`s reader on using 'knitr'
    with 'asciidoc'
    (<https://kbroman.org/knitr_knutshell/pages/asciidoc.html>), this is
    merely a wrapper to 'knitr' and 'asciidoc'.
We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.
Implements sample size and power calculation methods with a focus on balance and fairness in study design, inspired by the Zoroastrian deity Rashnu, the judge who weighs truth. Supports survival analysis and various hypothesis testing frameworks.
Algorithms for the spatial stratification of landscapes, sampling and modeling of 
    spatially-varying phenomena. These algorithms offer a simple framework for the stratification 
    of geographic space based on raster layers representing landscape factors and/or factor scales. 
    The stratification process follows a hierarchical approach, which is based on first level units 
    (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric 
    techniques allow to measure the correspondence between the geographic space and the landscape 
    configuration represented by the units. These correspondence metrics are useful to define 
    sampling schemes and to model the spatial variability of environmental phenomena. The 
    theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). 
    <doi:10.32614/RJ-2022-036>.
Description of the tables, both grouped and not grouped, with some associated data management actions, 
             such as sorting the terms of the variables and deleting terms with zero numbers.
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.
R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers
    are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR)
    with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from
    Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized
    Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018.
    Users supply a polygon or point location in the province of BC, and 'rasterbc' will download the overlapping raster tiles
    hosted at FRDR, merging them as needed and returning the result in R as a 'SpatRaster' object. Metadata associated with these
    layers, and code for downloading them from their original sources can be found in the 'github' repository
    <https://github.com/deankoch/rasterbc_src>.
Provides methods for calculating diversity indices on numerical
    matrices, based on information theory, following Rocchini, Marcantonio
    and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini et al.
    (2021) <doi:10.1101/2021.01.23.427872>.
Fast alternatives to several relatively slow 'raster' package
  functions. For large rasters, the functions run from 5 to
  approximately 100 times faster than the 'raster' package functions
  they replace. The 'fasterize' package, on which one function in this
  package depends, includes an implementation of the scan line
  algorithm attributed to Wylie et al. (1967)
  <doi:10.1145/1465611.1465619>.
This is a wrapper function for image(), which makes reasonable
    raster plots with nice axis and other useful features.
Provides R functions to selectively rasterize components
             of 'grid' output.
Performs kernel based estimates on in-memory raster images 
  from the raster package.  These kernel estimates include local means
  variances, modes, and quantiles.  All results are in the form of 
  raster images, preserving original resolution and projection attributes.
A S4 class has been created such that complex operations can be
    executed on each cell of a raster map. The raster of objects contains a raster map with the addition of a list of generic objects: one
    object for each raster cells. It allows to write few lines of R code for complex
    map algebra. Two environmental applications about frequency analysis of raster
    map of precipitation and creation of a raster map of soil water retention curves
    have been presented.
The ability to plot raster graphics in PDF files can be useful
    when one needs multi-page documents, but the plots contain so many
    individual elements that (the usual) use of vector graphics results in
    inconveniently large file sizes. Internally, the package plots each
    individual page as a PNG, and then combines them in one PDF file.
Generate 'SpatRaster' objects, as defined by the 'terra'
    package, from digital images, using a specified spatial object as a
    geographical reference.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.
A simplified version of the Portal Project Database designed for
    teaching. It provides a real world example of life-history, population, and
    ecological data, with sufficient complexity to teach many aspects of data
    analysis and management, but with many complexities removed to allow
    students to focus on the core ideas and skills being taught. The full
    database (which should be used for research) is available at
    <https://github.com/weecology/PortalData>.
Allows to limit the rate at which one or more functions can be called.
The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.
Fit statistical models based on the Dawid-Skene model - Dawid
    and Skene (1979) <doi:10.2307/2346806> - to repeated categorical
    rating data.  Full Bayesian inference for these models is supported
    through the Stan modelling language. 'rater' also allows the user to
    extract and plot key parameters of these models.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
Computes confidence intervals for binomial or Poisson rates and
    their differences or ratios. Including the rate (or risk)
    difference ('RD') or rate ratio (or relative risk, 'RR') for binomial
    proportions or Poisson rates, and odds ratio ('OR', binomial only).
    Also confidence intervals for RD, RR or OR for paired binomial data, 
    and estimation of a proportion from clustered binomial data.  
    Includes skewness-corrected asymptotic score ('SCAS') methods, 
    which have been developed in Laud (2017) <doi:10.1002/pst.1813> 
    from Miettinen and Nurminen (1985) <doi:10.1002/sim.4780040211> 
    and Gart and Nam (1988) <doi:10.2307/2531848>, and in 
    Laud (2025, under review) for paired proportions. The same score produces 
    hypothesis tests that are improved versions of the non-inferiority test 
    for binomial RD and RR by Farrington and Manning (1990) 
    <doi:10.1002/sim.4780091208>, or a generalisation of the McNemar test for 
    paired data. The package also includes MOVER methods (Method Of Variance
    Estimates Recovery) for all contrasts, derived from the Newcombe
    method but with options to use equal-tailed intervals in place of the
    Wilson score method, and generalised for Bayesian applications
    incorporating prior information. So-called 'exact' methods for
    strictly conservative coverage are approximated using continuity
    adjustments, and the amount of adjustment can be selected to avoid
    over-conservative coverage.  Also includes methods for stratified
    calculations (e.g. meta-analysis), either with fixed effect assumption
    (matching the CMH test) or incorporating stratum heterogeneity.
A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.
Designed to be compatible with the R package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this 'Python' 'Boto3' Software Development Kit ('SDK')
    <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.
Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list.
We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.
Functions to manipulate rational functions, including basic
    arithmetic operators, derivatives, and integrals with EXPLICIT forms.
Calculation of ratios between two data sets containing environmental data like
    element concentrations by different methods. Additionally plant element 
    concentrations can be corrected for adhering particles (soil, airborne dust).
Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.
An R interface to United States Environmental Protection Agency (EPA)  
    Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System 
    ('ATTAINS') data. 'ATTAINS' is the EPA database used to track information 
    provided by states about water quality assessments conducted under federal 
    Clean Water Act requirements. ATTAINS information and API information is available at <https://www.epa.gov/waterdata/attains>.
The R Analytic Tool To Learn Easily (Rattle) provides a
  collection of utilities functions for the data scientist. This
  package (v5.6.0) supports the companion graphical interface with
  the aim to provide a simple and intuitive introduction to R
  for data science, allowing a user to quickly load data from a CSV file
  transform and explore the data, and to build and evaluate models.
  A key aspect of the GUI is that all R commands are logged and
  commented through the log tab.
  This can be saved as a standalone R script file and as
  an aid for the user to
  learn R or to copy-and-paste directly into R itself. If you
  want to use the older Rattle implementing the GUI in RGtk2
  (which is no longer available from CRAN) then please install
  the Rattle package v5.5.1.
  See rattle.togaware.com for instructions on installing
  the modern Rattle graphical user interface.
Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).
Defines storage standard for Read, process, and analyze 
    intracranial electroencephalography and deep-brain stimulation in 'RAVE', 
    a reproducible framework for analysis and visualization of iEEG
    by Magnotti, Wang, and Beauchamp, (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>). 
    Supports brain imaging data structure (BIDS) 
    <https://bids.neuroimaging.io> and native file structure to
    ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), 
    European data format (EDF), BrainVision core data format (BVCDF), or 
    BlackRock Microsystem (NEV/NSx); process images in 
    Neuroimaging informatics technology initiative (NIfTI) and 
    'FreeSurfer' formats, providing brain imaging normalization to template 
    brain, facilitating 'threeBrain' package for comprehensive electrode 
    localization via 'YAEL' (your advanced electrode localizer) by Wang, 
    Magnotti, Zhang, and Beauchamp 
    (2023, <doi:10.1523/ENEURO.0328-23.2023>).
Provides access to the 'Ravelry' API <https://www.ravelry.com/groups/ravelry-api>. An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id.
Provides an I/O interface between R data.frames and
    Raven DataFrames. Defines functions to both read and write DataFrame
    files, as well as serialize/deserialize data.frames/DataFrames.
Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. 
    The 'RavenR' package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>.
    The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.
Defines the underlying pipeline structure for reproducible 
    neuroscience, adopted by 'RAVE' (reproducible analysis and visualization 
    of intracranial electroencephalography); provides high-level class 
    definition to build, compile, set, execute, and share analysis pipelines. 
    Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard 
    templates for extending and building customized pipelines. See the full 
    documentations at <https://rave.wiki>; to cite us, 
    check out our paper by Magnotti, Wang, and Beauchamp (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>), or run 
    citation("ravepipeline") for details.
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
Implemented fast and memory-efficient Notch-filter, 
    Welch-periodogram, discrete wavelet spectrogram for minutes of 
    high-resolution signals, fast 3D convolution, image registration,
    3D mesh manipulation; providing fundamental toolbox for intracranial 
    Electroencephalography (iEEG) pipelines. 
    Documentation and examples about 'RAVE' project are provided at 
    <https://rave.wiki>, and the paper by John F. Magnotti, 
    Zhengjia Wang, Michael S. Beauchamp (2020) 
    <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for 
    details.
In order to facilitate R instruction for actuaries, we have organized several 
  sets of publicly available data of interest to non-life actuaries. In addition, we suggest 
  a set of packages, which most practicing actuaries will use routinely. Finally, there is 
  an R markdown skeleton for basic reserve analysis.
Assess LC–MS system performance by visualizing instrument log
    files and monitoring raw quality control samples within a project.
The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for 
    credit risk scoring models. This package calculates the K–S statistic and 
    plots the true-positive rate and false-positive rate to measure the model strength. 
    This package was written with the credit marketer, who uses risk models in 
    conjunction with his campaigns. The users could read more details from 
    Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.
This is an R wrapper from the AWS Command Line Interface that 
  provides methods to manage the user configuration on Amazon Web Service. You 
  can create as many profiles as you want, manage them, and delete them. The 
  profiles created with this tool work with all AWS products such as S3, 
  Glacier, and EC2. It also provides a function to automatically install 
  AWS CLI, but you can download it and install it manually if you prefer.
Generates polygon straight skeletons and 3D models.  
    Provides functions to create and visualize interior polygon offsets, 
    3D beveled polygons, and 3D roof models.
Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images.
Downloads and parses 'SDF' (Structural Description Format) and 'PDB' (Protein Database) files for 3D rendering.
Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.
Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format.
Rossby wave ray paths are traced from 
             a determined source, specified wavenumber, and direction
             of propagation. "raytracing" also works with a set of 
             experiments changing these parameters, making possible the
             identification of Rossby wave sources automatically. 
             The theory used here is based on classical studies, 
             such as Hoskins and Karoly (1981) <doi:10.1175/1520-0469(1981)038%3C1179:TSLROA%3E2.0.CO;2>,
             Karoly (1983) <doi:10.1016/0377-0265(83)90013-1>, 
             Hoskins and Ambrizzi (1993) <doi:10.1175/1520-0469(1993)050%3C1661:RWPOAR%3E2.0.CO;2>,
             and Yang and Hoskins (1996) <doi:10.1175/1520-0469(1996)053%3C2365:PORWON%3E2.0.CO;2>.
Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.
Download and parse public files released by B3 and convert them
    into useful formats and data structures common to data analysis
    practitioners.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011).
Simulation of phenotype / genotype data under assortative mating. 
    Includes functions for generating Bahadur order-2 multivariate Bernoulli 
    variables with general and diagonal-plus-low-rank correlation structures. 
    Further details are provided in: Border and Malik (2022) 
    <doi:10.1101/2022.10.13.512132>.
An interface to the 'BaM' (Bayesian Modeling) engine, 
    a 'Fortran'-based executable aimed at estimating a model 
    with a Bayesian approach and using it for prediction,
    with a particular focus on uncertainty quantification.
    Classes are defined for the various building blocks of 
    'BaM' inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions).
    The typical usage is as follows:
    (1) specify the model to be estimated; 
    (2) specify the inference setting (dataset, parameters, error models...);
    (3) perform Bayesian-MCMC inference;
    (4) read, analyse and use MCMC samples;
    (5) perform prediction experiments.
    Technical details are available (in French) in 
    Renard (2017) <https://hal.science/hal-02606929v1>.
    Examples of applications include 
    Mansanarez et al. (2019) <doi:10.1029/2018WR023389>,
    Le Coz et al. (2021) <doi:10.1002/hyp.14169>,
    Perret et al. (2021) <doi:10.1029/2020WR027745>,
    Darienzo et al. (2021) <doi:10.1029/2020WR028607> and
    Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.
'BaseX' <https://basex.org> is a XML database engine and a compliant 'XQuery 3.1' processor with full support of 'W3C Update Facility'. This package is a full client-implementation of the client/server protocol for 'BaseX' and provides functionalities to create, manipulate and query on XML-data.
A Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
A novel bias-bound approach for non-parametric inference is introduced, 
    focusing on both density and conditional expectation estimation. 
    It constructs valid confidence intervals that account for the presence of 
    a non-negligible bias and thus make it possible to perform inference with 
    optimal mean squared error minimizing bandwidths. 
    This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.
Tools for regression-based Boolean rule inference in
    artificial intelligence studies.  The package fits ridge regression
    models on conjunction expansions and composes interpretable rule sets.
    Parallel execution is supported for multi-CPU environments.
Flexible statistical modelling using a modular framework for
    regression, in which groups of transformations are composed together
    and act on probability distributions.
The Brazilian Central Bank API delivers many datasets which regard economic
  activity, regional economy, international economy, public finances, credit
  indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>.
  These datasets can be accessed through 'rbcb' functions and can be obtained in
  different data structures common to R ('tibble', 'data.frame', 'xts', ...).
Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.
Issues RPC-JSON calls to 'bitcoind', the daemon of Bitcoin Cash (BCH), to
    extract transaction data from the blockchain. BCH is a fork of Bitcoin that permits
    a greater number of transactions per second. A BCH daemon is available under an MIT
    license from the Bitcoin Unlimited website <https://www.bitcoinunlimited.info>.
Implementing the BDAT tree taper Fortran routines, which were
  developed for the German National Forest Inventory (NFI), to calculate 
  diameters, volume, assortments, double bark thickness and biomass for
  different tree species based on tree characteristics and sorting information.
  See Kublin (2003) <doi:10.1046/j.1439-0337.2003.00183.x> for details.
Provide estimation and data generation tools for the quantile generalized beta 
        regression model. For details, see Bourguignon, Gallardo and Saulo <arXiv:2110.04428>
        The package also provides tools to perform covariates selection.
Provides plotting helpers, table-formatting utilities, and
    report templates for econometrics, model development, and applied data
    analysis. Includes University of Cincinnati branded themes for
    'ggplot2', 'modelsummary', 'flextable', 'rmarkdown', 'bookdown', and
    'quarto'.
BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
Implements an interface to Minecraft (Bedrock Edition) worlds. Supports the analysis and management of these worlds and game saves.
rbenchmark is inspired by the Perl module Benchmark, and
        is intended to facilitate benchmarking of arbitrary R code. The
        library consists of just one function, benchmark, which is a
        simple wrapper around system.time.  Given a specification of
        the benchmarking process (counts of replications, evaluation
        environment) and an arbitrary number of expressions, benchmark
        evaluates each of the expressions in the specified environment,
        replicating the evaluation as many times as specified, and
        returning the results conveniently wrapped into a data frame.
An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted."
Tool-set to support Bayesian evidence synthesis.  This
    includes meta-analysis, (robust) prior derivation from historical
    data, operating characteristics and analysis (1 and 2 sample
    cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19>
    for details on applying this package while Neuenschwander et al. (2010)
    <doi:10.1177/1740774509356002> and Schmidli et al. (2014)
    <doi:10.1111/biom.12242> explain details on the methodology.
Contains functions to generate random numbers
        from the beta distribution and random vectors from the
        Dirichlet distribution.
A robust backfitting algorithm for additive models based on (robust) local polynomial 
             kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and
             it computes predictions for points outside the training set if desired. See
             Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and 
             Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.
Facilities for working with Atlantis box-geometry model (BGM) 
 files. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model. 
 Functions are provided to read from BGM files directly, preserving their 
 internal topology, as well as helper functions to generate spatial data from these
 mesh forms. This functionality aims to simplify the creation and modification of box 
 and geometry as well as the ability to integrate with other data sources.
Provides a complete interface to 'LibBi', a library for Bayesian
    inference (see <https://libbi.org> and Murray, 2015
    <doi:10.18637/jss.v067.i10> for more information). This includes functions
    for manipulating 'LibBi' models, for reading and writing 'LibBi'
    input/output files, for converting 'LibBi' output to provide traces for use
    with the coda package, and for running 'LibBi' to conduct inference.
Contains a collection of helper functions to use with 'rbi', the R
  interface to 'LibBi', described in
  Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to
  adapt the proposal distribution and number of particles in
  particle Markov-Chain Monte Carlo, as well as calculating the
  Deviance Information Criterion (DIC) and converting between times in 'LibBi'
  results and R time/dates.
Implementation of the algorithms (with minor modifications)
    to correct bias in quantitative DNA methylation analyses as described
    by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Read and write 'Bibtex' files. Convert between bibliography
    formats, including 'Bibtex', 'Biblatex', 'PubMed', 'Endnote', and
    'Bibentry'.  Includes a port of the 'bibutils' utilities by Chris
    Putnam <https://sourceforge.net/projects/bibutils/>. Supports all
    bibliography formats and character encodings implemented in
    'bibutils'.
Manually bin data using weight of evidence and information value. Includes other binning 
    methods such as equal length, quantile and winsorized. Options for combining levels of categorical
    data are also available. Dummy variables can be generated based on the bins created using any of 
    the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
Provides bioaccumulation factors from a toxicokinetic model 
    fitted to accumulation-depuration data. It is designed to fulfil the requirements
    of regulators when examining applications for market authorization of active
    substances.
Currently fully supports Enrichr, JASPAR, miEAA, PANTHER,
    Reactome, STRING, and UniProt! The goal of rbioapi is to provide a
    user-friendly and consistent interface to biological databases and
    services. In a way that insulates the user from the technicalities of
    using web services API and creates a unified and easy-to-use interface
    to biological and medical web services. This is an ongoing project; New
    databases and services will be added periodically. Feel free to
    suggest any databases or services you often use.
A toolkit for working with Biological Observation Matrix ('BIOM') files.
    Read/write all 'BIOM' formats. Compute rarefaction, alpha diversity, and 
    beta diversity (including 'UniFrac'). Summarize counts by taxonomic level. 
    Subset based on metadata. Generate visualizations and statistical analyses. 
    CPU intensive operations are coded in C for speed.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
A simple implementation of Binary Indexed Tree by R. The BinaryIndexedTree class supports construction of Binary Indexed Tree from a vector, update of a value in the vector and query for the sum of a interval of the vector.
R interface to access prices and market data with the 
    'Bloomberg Data License' service from 
    <https://www.bloomberg.com/professional/product/data-license/>. 
    As a prerequisite, a valid Data License from 'Bloomberg' is needed 
    together with the corresponding SFTP credentials and whitelisting 
    of the IP from which accessing the service. 
    This software and its author are in no way affiliated, 
    endorsed, or approved by 'Bloomberg' or any of its affiliates.
    'Bloomberg' is a registered trademark.
Provides an interface to 'Blimp' software for Bayesian latent variable modeling,
    missing data analysis, and multiple imputation. The package generates 'Blimp' syntax,
    executes 'Blimp' models, and imports results back into 'R' as structured objects with
    methods for visualization and analysis. Requires 'Blimp' software (freely available
    at <https://www.appliedmissingdata.com/blimp>) to be installed separately.
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
An R-shiny application to visualize bio-loggers time series at a microsecond precision as Acceleration, Temperature, Pressure, Light intensity. It is possible to link behavioral labels extracted
  from 'BORIS' software <http://www.boris.unito.it> or manually written in a csv file.
BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a user's search query.
    This package provides a light wrapper around the 'BM25' 'rust' crate for Okapi BM25 text search.
    For more information, see Robertson et al. (1994) <https://trec.nist.gov/pubs/trec3/t3_proceedings.html>.
Provides four boolean matrix factorization (BMF) methods.
             BMF has many applications like data mining and categorical data 
             analysis. BMF is also known as boolean matrix decomposition (BMD) 
             and was found to be an NP-hard (non-deterministic polynomial-time)
             problem. Currently implemented methods are 
             'Asso' Miettinen, Pauli and others (2008) 
                    <doi:10.1109/TKDE.2008.53>,
             'GreConD' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'GreConDPlus' R. Belohlavek, V. Vychodil (2010) 
                      <doi:10.1016/j.jcss.2009.05.002> ,
             'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) 
                       <arXiv:1903.10326>.
Implements standard and reference based multiple imputation methods for continuous
    longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
    this package supports deterministic conditional mean imputation and jackknifing as described
    in Wolbers et al.  (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
    in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
    likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Provides utility functions that extend the capabilities of the 
    reference-based multiple imputation package 'rbmi'. It supports clinical 
    trial analysis workflows with functions for managing imputed datasets, 
    applying analysis methods across imputations, and tidying results for 
    reporting.
Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...
Provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.
Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect.  Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.
The RBP curve is a visual tool to assess the
    performance of prediction models.
A tool for building projects that are visually consistent,
    accessible, and easy to maintain. It provides functions for managing
    branding assets, applying organization-wide themes using 'brand.yml',
    and setting up new projects with accessibility features and correct
    branding. It supports 'quarto', 'shiny', and 'rmarkdown' projects, and
    integrates with 'ggplot2'. The accessibility features are based
    on the Web Content Accessibility Guidelines
    <https://www.w3.org/WAI/WCAG22/quickref/?versions=2.1>
    and Accessible Rich Internet Applications (ARIA) specifications
    <https://www.w3.org/WAI/ARIA/apg/>. The branding framework implements
    the 'brand.yml' specification <https://posit-dev.github.io/brand-yml/>.
Methods for detecting structural breaks and estimating break
    locations for linear multiple regression models under general linear
    restrictions on the coefficient vector. Restrictions can be within
    regimes, across regimes, or both, and are supported in two forms: an
    affine parameterization (Form A: delta = S*theta + s) and explicit
    linear constraints (Form B: R*delta = r). Provides break date
    estimation with confidence intervals, a restricted sup-F test for the
    null of no structural change, simulation of critical values by Monte
    Carlo, and a bootstrap restart procedure to reduce the risk of
    convergence to spurious local optima. Also implements a generalized
    regression tree (linear model tree) procedure where each leaf contains
    a linear regression model rather than a local average. Reference:
    Perron, P., and Qu, Z. (2006). 'Estimating Restricted Structural
    Change Models.' Journal of Econometrics, 134(2), 373-399.
    <doi:10.1016/j.jeconom.2005.06.030>.
The goal of the 'rbrsa' package is to provide automated access to banking 
    sector data from the Turkish Banking Regulation and Supervision 
    Agency (BRSA, known as BDDK in Turkish). The package retrieves tables from 
    two distinct publication portals maintained by the BRSA: The Monthly 
    Bulletin Portal <https://www.bddk.org.tr/bultenaylik> and The FinTurk Data 
    System <https://www.bddk.org.tr/BultenFinturk>.
Provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the 'StatXact' 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.
Implementation of the RPC-JSON API for Bitcoin and utility functions for address creation and content analysis of the blockchain.
The regression-based (RB) approach is a method to test the missing data mechanism.
			This package contains two functions that test the type of missing data (Missing Completely 
			At Random vs Missing At Random) on the basis of the RB approach. The first function applies 
			the RB approach independently on each variable with missing data, using the completely 
			observed variables only. The second function tests the missing data mechanism globally 
			(on all variables with missing data) with the use of all available information. The 
			algorithm is adapted both to continuous and categorical data.
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
Residual balancing is a robust method of constructing weights for
  marginal structural models, which can be used to estimate (a) the average treatment effect in 
  a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
  analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
  <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(),
  that produce residual balancing weights for estimating (a), (b), (c), respectively.
Data for the examples and exercises in the book "R by Example".  
    Jim Albert and Maria Rizzo (2012, ISBN 978-1-4614-1365-3).
Relational Class Analysis (RCA) is a method for detecting
        heterogeneity in attitudinal data (as described in Goldberg
        A., 2011, Am. J. Soc, 116(5)).
Tools for preprocessing and processing canopy photographs with 
      support for raw data reading. Provides methods to address variability in 
      sky brightness and to mitigate errors from image acquisition in 
      non-diffuse light. Works with all types of fish-eye lenses, and some 
      methods also apply to conventional lenses.
Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2020a) <doi:10.1093/biomet/asz059>, Tan (2020b) <doi:10.1214/19-AOS1824> and Sun and Tan (2020) <arXiv:2009.09286>.
Calculate the flow of particles between polygons by two integration methods:
  integration by a cubature method and integration on a grid of points.
  Annie Bouvier, Kien Kieu, Kasia Adamczyk and Herve Monod (2009)
  <doi:10.1016/j.envsoft.2008.11.006>.
Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) <https://camels.cr2.cl/>, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.
Tools for basic and advance cancer statistics and graphics.
	Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and
    time, such as age-specific trends, bar chart and period-cohort trends.
Estimation of abundance and other demographic parameters for closed 
             populations, open populations and the robust design in capture-recapture  
             experiments using loglinear models.
Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.
Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).
Provides color schemes for maps and other graphics
    designed by 'CARTO' as described at <https://carto.com/carto-colors/>.
    It includes four types of palettes: aggregation, diverging, qualitative, 
    and quantitative.
Calculate 22 summary statistics coded in C on time-series vectors to enable 
    pattern detection, classification, and regression applications in the 
    feature space as proposed by <doi:10.1007/s10618-019-00647-x>.
Accesses the California Academy of Sciences Eschmeyer's Catalog of Fishes in R using web requests. The Catalog of fishes is the leading authority in fish taxonomy. Functions in the package allow users to search for fish taxa and valid names, retrieve taxonomic references, retrieve monthly taxonomic changes, obtain natural history collection information, and see the number of species by taxonomic group. For more information on the Catalog: Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2025. ESCHMEYER'S CATALOG OF FISHES <https://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp>.
Generates tile maps for the East Caucasian language family, inspired by the Typological Atlas of the Languages of Daghestan (TALD, <https://lingconlab.ru/tald/>). It leverages 'ggplot2' to create visually informative maps, displaying rectangles for each language and allowing for color-coding based on linguistic features. The package includes a built-in dataset of 56 languages and the template for their distribution and provides flexibility to customize the tile map's appearance. The default template can be modified via the ability to hide or rename languages.  It's designed to be used with external data tables containing language information and features, offering a tool for visualizing the geographic distribution and linguistic characteristics of East Caucasian languages.
CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.
Scalable methods for learning causal graphical models from mixed data, including continuous, discrete, and censored variables. The package implements CausalMGM, which combines a convex, score-based approach for learning an initial moralized graph with a producer-consumer scheme that enables efficient parallel conditional independence testing in constraint-based causal discovery algorithms. The implementation supports high-dimensional datasets and provides individual access to core components of the workflow, including MGM and the PC-Stable and FCI-Stable causal discovery algorithms. To support practical applications, the package includes multiple model selection strategies, including information criteria based on likelihood and model complexity, cross-validation for out-of-sample likelihood estimation, and stability-based approaches that assess graph robustness across subsamples.
Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.
Provides implementations of a classifier based on the
    "Classification Based on Associations" (CBA). It can be used for building
    classification models from association rules. Rules are pruned in the order of
    precedence given by the sort criteria and a default rule is added. The final
    classifier labels provided instances. CBA was originally proposed by Liu,
    B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule
    Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).
Tools for large, sparse optimal matching of treated units
	and control units in observational studies.  Provisions are
	made for refined covariate balance constraints, which include
	fine and near-fine balance as special cases.  Matches are 
	optimal in the sense that they are computed as solutions to
	network optimization problems rather than greedy algorithms.
	See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> 
	and Pimentel (2016), Obs. Studies 2(1):4-23. 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/>.
A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.
Nonparametric maximum likelihood estimation methods
    for random coefficient binary response  models and some related
    functionality for sequential processing of hyperplane arrangements.
    See J. Gu and R. Koenker (2020) <DOI:10.1080/01621459.2020.1802284>.
Tools for optimal subset matching of treated units
	and control units in observational studies, with support
	for refined covariate balance constraints, (including
	fine and near-fine balance as special cases). A close 
	relative is the 'rcbalance' package.  See Pimentel, 
	et al.(2015) <doi:10.1080/01621459.2014.997879>
	and Pimentel and Kelz (2020) 
	<doi:10.1080/01621459.2020.1720693>. 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/>.
Fit Class Cover Catch Digraph Classification models that can be 
    used in machine learning. Pure and proper and random walk approaches are 
    available. Methods are explained in Priebe et al. (2001) 
    <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) 
    <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) 
    <doi:10.48550/arXiv.1904.04564>.
The handling of an API key (misnomer for password) for protected
    data can be difficult. This package provides secure convenience functions for 
    entering / handling API keys and pulling data directly into memory. By default
    it will load from REDCap instances, but other sources are injectable via
    inversion of control.
The RCC_PCA criterion is a tool to determine the optimal number of components to retain in PCA;See Alshammri (2021).
Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.
R interface to (some of) cddlib
    (<https://github.com/cddlib/cddlib>).
    Converts back and forth between two representations of a convex polytope:
    as solution of a set of linear equalities and inequalities and as
    convex hull of set of points and rays.
    Also does linear programming and redundant generator elimination
    (for example, convex hull in n dimensions).  All functions can use exact
    infinite-precision rational arithmetic.
With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), 
             Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. 
             The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>. 
             The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>.
             Besides we provide graphs to help with the choice of m. 
             We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units.
Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions.
Allows the user to access functionality in the
    'CDK', a Java framework for cheminformatics. This allows the user to load
    molecules, evaluate fingerprints, calculate molecular descriptors and so on.
    In addition, the 'CDK' API allows the user to view structures in 2D.
An R interface to the Chemistry Development Kit, a Java library
    for chemoinformatics. Given the size of the library itself, this package is
    not expected to change very frequently. To make use of the CDK within R, it is
    suggested that you use the 'rcdk' package. Note that it is possible to directly
    interact with the CDK using 'rJava'. However 'rcdk' exposes functionality in a more
    idiomatic way. The CDK library itself is released as LGPL and the sources can be
    obtained from <https://github.com/cdk/cdk>.
Provides a translation layer between 'R' and 'CDO' operators. Each 
  operator is it's own function with documentation. Nested or piped functions
  will be translated into 'CDO' chains.
An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions.
Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the  testing phase, with two options of fixed length intervals and random lengths.
The key function 'get_vintage_data()' returns a dataframe and is 
    the window into the Census Bureau API requiring just a dataset name, 
    vintage(year), and vector of variable names for survey estimates/percentages. 
    Other functions assist in searching for available datasets, geographies, 
    group/variable concepts of interest.  Also provided are functions to access 
    and layer (via standard piping) displayable geometries for the US, states, 
    counties, blocks/tracts, roads, landmarks, places, and bodies of water. 
    Joining survey data with many of the geometry functions is built-in to 
    produce choropleth maps.
To facilitate using 'cereal' with R via 'cpp11' or 'Rcpp'. 'cereal'
    is a header-only C++11 serialization library. 'cereal' takes arbitrary data
    types and reversibly turns them into different representations, such as
    compact binary encodings, 'XML', or 'JSON'. 'cereal' was designed to be
    fast, light-weight, and easy to extend - it has no external dependencies and
    can be easily bundled with other code or used standalone. Please see
    <https://uscilab.github.io/cereal/> for more information.
Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.
Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.
A simple data science challenge system using R Markdown and 'Dropbox' <https://www.dropbox.com/>.
    It requires no network configuration, does not depend on external platforms
    like e.g. 'Kaggle' <https://www.kaggle.com/> and can be easily installed on a personal computer.
An R implementation of ChASM (Chromosomal Aneuploidy Screening Methodology): a statistically rigorous Bayesian approach for screening data sets for autosomal and sex chromosomal aneuploidies. This package takes as input the number of (deduplicated) reads mapping to chromosomes 1-22 and the X and Y chromosomes, and models these using a Dirichlet-multinomial distribution. From this, This package returns posterior probabilities of sex chromosomal karyotypes (XX, XY, XXY, XYY, XXX and X) and full autosomal aneuploidies (trisomy 13, trisomy 18 and trisomy 21). This package also returns two diagnostic statistics: (i) a posterior probability addressing whether contamination between XX and XY may explain the observed sex chromosomal aneuploidy, and (ii) a chi-squared statistic measuring whether the observed read counts are too divergent from the underlying distribution (and may represent abnormal sequencing/quality issues).
Interface to the 'ChEA3' transcription factor enrichment API.
    'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature
    resources to prioritize transcription factors regulating a given set
    of genes. This package provides convenient R functions to query the API,
    retrieve ranked results across collections (including integrated scores),
    and standardize output for downstream analysis in R/Bioconductor workflows.
    See <https://maayanlab.cloud/chea3/> or Keenan (2019) <doi:10.1093/nar/gkz446>
    for further details.
Data exploration and prediction with focus on high dimensional data and chemometrics.
    The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data.
    The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.
    Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).
Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.
Provides algorithms to locate multiple
    distributional change-points in piecewise stationary time series. The
    algorithms are provably consistent, even in the presence of long-range
    dependencies. Knowledge of the number of change-points is not
    required. The code is written in Go and interfaced with R.
A set of functions to generate, access and analyze standard data products from archival tagging data.
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.
Collection of portable choice dialog widgets.
Client for 'ChromaDB', a vector database for storing and querying embeddings. This package provides a convenient interface to interact with the REST API of 'ChromaDB' <https://docs.trychroma.com>.
The concept of reliable and clinically significant change
    (Jacobson & Truax, 1991) helps you answer the following questions for a
    sample with two measurements at different points in time (pre & post):
    Which proportion of my sample has a (considering the reliability of the
    instrument) probably not-just-by-chance difference in pre- vs. post-scores?
    Which proportion of my sample does not only change in a statistically
    significant way (see question one), but also in a clinically significant way
    (e.g. change from a test score regarded "dysfunctional" to a score regarded
    "functional")?
    This package allows you to very easily create a scatterplot of your sample
    in which the x-axis maps to the pre-scores, the y-axis maps to the
    post-scores and several graphical elements (lines, colors) allow you to gain
    a quick overview about reliable changes in these scores.
    An example of this kind of plot is Figure 2 of Jacobson & Truax (1991).
    Referenced article:
    Jacobson, N. S., & Truax, P. (1991) <doi:10.1037/0022-006X.59.1.12>.
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
Implements the Clustering-Informed Shared-Structure Variational Autoencoder ('CISS-VAE'), a deep learning framework for missing data imputation introduced in Khadem Charvadeh et al. (2025) <doi:10.1002/sim.70335>. The model accommodates all three types of missing data mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). While it is particularly well-suited to MNAR scenarios, where missingness patterns carry informative signals, 'CISS-VAE' also functions effectively under MAR assumptions.
A programmatic interface to the Species+ <https://speciesplus.net/> database via the Species+/CITES Checklist API <https://api.speciesplus.net/>.
Functions to assist manipulation of matrix 
             row and column labels for all types of matrix mathematics
             where row and column labels are to be respected.
Cross-Linguistic Data Format (CLDF) is a framework for storing 
    cross-linguistic data, ensuring compatibility and ease of data exchange between
    different linguistic datasets see Forkel et al. (2018) <doi:10.1038/sdata.2018.205>. 
    The 'rcldf' package is designed to facilitate the manipulation and analysis of 
    these datasets by simplifying the loading, querying, and visualisation of CLDF
    datasets making it easier to conduct comparative linguistic analyses, manage 
    language data, and apply statistical methods directly within R.
'Yandex Clickhouse' (<https://clickhouse.com/>) is a high-performance relational column-store database to enable
    big data exploration and 'analytics' scaling to petabytes of data. Methods are
    provided that enable working with 'Yandex Clickhouse' databases via
    'DBI' methods and using 'dplyr'/'dbplyr' idioms.
'Climacell' is a weather platform that provides hyper-local forecasts and weather 
    data. This package enables the user to query the core layers of the 
    time line interface of the 'Climacell' v4 API <https://www.climacell.co/weather-api/>. 
    This package requires a valid API key. See vignettes for instructions on use.
Leverages the functionality of 'clipboard.js', a JavaScript library
    for HMTL5-based copy to clipboard from web pages (see <https://clipboardjs.com>
    for more information), and provides a reactive copy-to-clipboard UI button 
    component, called 'rclipButton', and a a reactive copy-to-clipboard UI link 
    component, called 'rclipLink', for 'shiny' R applications.
Provides an R interface to 'rclone' <https://rclone.org>, a
    command-line program for managing files on cloud storage. 'rclone' supports
    over 40 cloud storage providers including 'S3'-compatible services
    ('Amazon S3', 'MinIO', 'Ceph'), 'Google Cloud Storage', 'Azure Blob
    Storage', and many others. This package downloads and manages the 'rclone'
    binary automatically and wraps its commands as R functions, returning
    results as data frames where appropriate.
Convex Least Squares Programming (CLSP) is a two-step estimator 
    for solving underdetermined, ill-posed, or structurally constrained 
    least-squares problems. It combines pseudoinverse-based estimation with 
    convex-programming correction methods inspired by Lasso, Ridge, and 
    Elastic Net to ensure numerical stability, constraint enforcement, and 
    interpretability. The package also provides numerical stability analysis 
    and CLSP-specific diagnostics, including partial R^2, normalized RMSE 
    (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based 
    confidence bands.
Run 'R CMD check' from 'R' and capture the results
    of the individual checks. Supports running checks in the background,
    timeouts, pretty printing and comparing check results.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().
Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394>.
Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.
Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.
We provide an Rcmdr plug-in based on the depthTools 
        package, which implements different robust statistical tools 
        for the description and analysis of gene expression data based 
        on the Modified Band Depth, namely, the scale curves for 
        visualizing the dispersion of one or various groups of samples
        (e.g. types of tumors), a rank test to decide whether two
        groups of samples come from a single distribution and two 
        methods of supervised classification techniques, the DS and 
        TAD methods.
Provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
Export Rcmdr output to LaTeX or HTML code. The
        plug-in was originally intended to facilitate exporting Rcmdr
        output to formats other than ASCII text and to provide R
        novices with an easy-to-use, easy-to-access reference on
        exporting R objects to formats suited for printed output. The
        package documentation contains several pointers on creating
        reports, either by using conventional word processors or
        LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.
Rcmdr Plugin for the 'FactoMineR' package.
Rcmdr menu support for many of the functions in the HH package.
        The focus is on menu items for functions we use in our introductory
        courses.
A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram,
    Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
    bar chart, contour plot, and distribution plot.
Extends R Commander with a unified menu of new and pre-existing 
        statistical functions related to public management and policy analysis 
        statistics. Functions and menus have been renamed according to the 
        usage in PMGT 630 in the Master of Public Administration program at
        Brigham Young University.
An R Commander "plug-in" extending functionality of linear models
    and providing an interface to Partial Least Squares Regression and Linear and
    Quadratic Discriminant analysis. Several statistical summaries are extended,
    predictions are offered for additional types of analyses, and extra plots, tests
    and mixed models are available.
A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems.
The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method.
R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
Rcmdr interface to the 'sos' package. The plug-in renders
  the 'sos' searching functionality easily accessible via the Rcmdr
  menus. It also simplifies the task of performing multiple searches and 
  subsequently obtaining the union or the intersection of the results.
An R Commander plug-in for the survival
  package, with dialogs for Cox models, parametric survival regression models,
  estimation of survival curves, and testing for differences in survival
  curves, along with data-management facilities and a variety of tests, 
  diagnostics and graphs.
Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.
R Commander plugin for teaching statistical methods. 
    It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
An 'R Commander' plug-in providing an integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, vocabulary tables, terms
    co-occurrences and documents similarity measures, time series analysis,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Some extensions to Rcmdr (R Commander), randomness test, 
 variance test for one normal sample and predictions using active model, 
 made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
An R Commander plug-in for the 'WorldFlora' package. It was mainly developed to show work flows and scripts for first-time users.
Data sets, and functions for simulating and fitting nonlinear time series with minification and nonparametric models.
Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).
Detects copy number alteration events in targeted exon sequencing 
    data for tumor samples without matched normal controls. The advantage of 
    this method is that it can be applied to smaller sequencing panels including 
    evaluations of exon, transcript, gene, or even user specified genetic
    regions of interest. Functions in the package include steps for GC-content
    correction, calculation of quantile based normal karyotype ranges,
    and calculation of feature score.  Cutoffs for "normal" quantile and score
    are user-adjustable.
Functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).
Automatically apply different strategies to optimize R code. 
    'rco' functions take R code as input, and returns R code as output.
A data structure and toolkit for documenting and recoding
    categorical data that can be shared in other statistical software.
Simplifies integration with 'Amazon Cognito' (<https://aws.amazon.com/cognito/>) for R developers, enabling easy management of user authentication, registration, and password flows.
The 'Coinbase Advanced Trade API' <https://docs.cdp.coinbase.com/api-reference/advanced-trade-api/rest-api/introduction> lets you manage orders, portfolios, products, and fees with the new v3 endpoints.
Provides functionality to group lines that form naturally
    continuous lines in a spatial network. The algorithm implemented is based
    on the Continuity in Street Networks (COINS) method from Tripathy et al.
    (2021) <doi:10.1177/2399808320967680>, which identifies continuous
    "strokes" in the network as the line strings that maximize the angles
    between consecutive segments.
Get your data (forms, structures, answers) from Coletum 
    <https://coletum.com> to handle and analyse.
A collection of data sets relating to ADHD (Attention Deficit 
    Hyperactivity Disorder) which have been sourced from other packages on CRAN
    or from publications on other websites such as Kaggle
    <http://www.kaggle.com/>.The package also includes some simple functions for
    analysing data sets. The data sets and descriptions of the data sets may
    differ from what is on CRAN or other source websites. The aim of this
    package is to bring together data sets from a variety of ADHD research
    publications. This package would be useful for those interested in finding
    out what research has been done on the topic of ADHD, or those interested in
    comparing the results from different existing works. I started this project
    because I wanted to put together a collection of the data sets relevant to
    ADHD research, which I have a personal interest in. This work was conducted
    with the support of my mentor within the Global Talent Mentoring platform.
    <https://globaltalentmentoring.org/>.
Provides color schemes for maps (and other graphics)
        designed by Cynthia Brewer as described at http://colorbrewer2.org.
Provides a collection of palettes designed to integrate with 'ggplot', reflecting the color schemes associated with 'ConesaLab'.
'NCL' (NCAR Command Language) is one of the most popular spatial 
    data mapping tools in meteorology studies, due to its beautiful output 
    figures with plenty of color palettes designed by experts 
    <https://www.ncl.ucar.edu/index.shtml>. 
    Here we translate all 'NCL' color palettes into R hexadecimal RGB colors and 
    provide color selection function, which will help users make a beautiful figure.
Utility functions for interacting with the 'COMPADRE' and
    'COMADRE' databases of matrix population models. Described in Jones et
    al. (2021) <doi:10.1101/2021.04.26.441330>.
Functions and datasets to support Summary and Analysis of
             Extension Program Evaluation in R, and An R
             Companion for the Handbook of Biological Statistics. 
             Vignettes are available at <https://rcompanion.org>.
Makes easier the creation of R package or research compendium 
    (i.e. a predefined files/folders structure) so that users can focus on the 
    code/analysis instead of wasting time organizing files. A full 
    ready-to-work structure is set up with some additional features: version 
    control, remote repository creation, CI/CD configuration (check package 
    integrity under several OS, test code with 'testthat', and build and deploy 
    website using 'pkgdown'). This package heavily relies on the R packages 
    'devtools' and 'usethis' and follows recommendations made by Wickham H. 
    (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) 
    <doi:10.7287/peerj.preprints.3192v2>.
Minimal and lightweight configuration tool that provides basic support for 'YAML' configuration files without requiring additional package dependencies. It offers a simple method for loading and parsing configuration settings, making it ideal for quick prototypes and lightweight projects.
Configuration management using files (YAML, JSON, INI, TXT),
  JSON strings, and command line arguments. Command line arguments
  can be used to override configuration. Period-separated command line
  flags are parsed as hierarchical lists. Environment variables, R global variables,
  and configuration values can be substituted.
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
'TROLL' is coded in C++ and it typically simulates hundreds of
    thousands of individuals over hundreds of years. The 'rcontroll' R package 
    is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs 
    for simulations and run simulations. Finally, it is possible to analyse 
    the 'TROLL' outputs through tables, figures, and maps taking advantage of 
    other R visualisation packages. 'rcontroll' also offers the possibility to 
    generate a virtual LiDAR point cloud that corresponds to a snapshot of 
    the simulated forest.
Convert text into target classifications (e.g., ISO 3166-1) using a
    JSON mapping with regular expressions. Provides helpers to return the full
    mapping and associated metadata.
Researchers across disciplines often face biased regression
    model estimates due to endogenous regressors correlated with the error
    term. Traditional solutions require instrumental variables (IVs), which
    are often difficult to find and validate. This package provides
    flexible, alternative IV-free methods using copulas, as described in
    the practical guide to endogeneity correction using copulas (Yi Qian,
    Tony Koschmann, and Hui Xie 2025) <doi:10.1177/00222429251410844>. The
    current version implements the two-stage copula endogeneity correction
    (2sCOPE) method to fit models with continuous endogenous regressors and
    both continuous and discrete exogenous regressors, as described in Fan
    Yang, Yi Qian, and Hui Xie (2024)
    <doi:10.1177/00222437241296453>. Using this method, users can address
    regressor endogeneity problems in nonexperimental data without
    requiring IVs.
A collection of small text corpora of interesting data.
    It contains all data sets from 'dariusk/corpora'. Some examples:
    names of animals: birds, dinosaurs, dogs; foods: beer categories,
    pizza toppings; geography: English towns, rivers, oceans;
    humans: authors, US presidents, occupations; science: elements,
    planets; words: adjectives, verbs, proverbs, US president quotes.
Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.
Bundles the datasets and functions featured in Philip H. 
    Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.
R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.
The 'Rcpp' package provides R functions as well as C++ classes which
 offer a seamless integration of R and C++. Many R data types and objects can be
 mapped back and forth to C++ equivalents which facilitates both writing of new
 code as well as easier integration of third-party libraries. Documentation
 about 'Rcpp' is provided by several vignettes included in this package, via the
 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and
 Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013,
 <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018,
 <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
Provides optimized functions and flexible iterators implemented in
    C++ for solving problems in combinatorics and computational mathematics.
    Handles various combinatorial objects including combinations, permutations,
    integer partitions and compositions, Cartesian products, unordered
    Cartesian products, and partition of groups. Utilizes the RMatrix class
    from 'RcppParallel' for thread safety. The combination and permutation
    functions contain constraint parameters that allow for generation of all
    results of a vector meeting specific criteria (e.g. finding all
    combinations such that the sum is between two bounds). Capable of
    ranking/unranking combinatorial objects efficiently (e.g. retrieve only the
    nth lexicographical result) which sets up nicely for parallelization as
    well as random sampling. Gmp support permits exploration where the total
    number of results is large (e.g. comboSample(10000, 500, n = 4)).
    Additionally, there are several high performance number theoretic
    functions that are useful for problems common in computational mathematics.
    Some of these functions make use of the fast integer division library
    'libdivide'. The primeSieve function is based on the segmented sieve of
    Eratosthenes implementation by Kim Walisch. It is also efficient for large
    numbers by using the cache friendly improvements originally developed by
    Tomás Oliveira. Finally, there is a prime counting function that implements
    Legendre's formula based on the work of Kim Walisch.
'Annoy' is a small C++ library for Approximate Nearest Neighbors 
 written for efficient memory usage as well an ability to load from / save to
 disk. This package provides an R interface by relying on the 'Rcpp' package,
 exposing the same interface as the original Python wrapper to 'Annoy'. See
 <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
 under Version 2.0 of the Apache License. Also included is a small Windows
 port of 'mmap' which is released under the MIT license.
The 'APT Package Management System' provides Debian and
 Debian-derived Linux systems with a powerful system to resolve package
 dependencies. This package offers access directly from R.  This can
 only work on a system with a suitable 'libapt-pkg-dev' installation
 so functionality is curtailed if such a library is not found.
'Armadillo' is a templated C++ linear algebra library aiming towards
 a good balance between speed and ease of use. It provides high-level syntax and
 functionality deliberately similar to Matlab. It is useful for algorithm development
 directly in C++, or quick conversion of research code into production environments.
 It provides efficient classes for vectors, matrices and cubes where dense and sparse
 matrices are supported. Integer, floating point and complex numbers are supported.
 A sophisticated expression evaluator (based on template meta-programming) automatically
 combines several operations to increase speed and efficiency. Dynamic evaluation
 automatically chooses optimal code paths based on detected matrix structures.
 Matrix decompositions are provided through integration with LAPACK, or one of its
 high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can
 automatically use 'OpenMP' multi-threading (parallelisation) to speed up
 computationally expensive operations.

   The 'RcppArmadillo' package includes the header files from the 'Armadillo' library;
 users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'.
 Starting from release 15.0.0, the minimum compilation standard is C++14 so 'Armadillo'
 version 14.6.3 is included as a fallback when an R package forces the C++11 standard.
 Package authors should set a '#define' to select the 'current' version, or select the
 'legacy' version (also chosen as default) if they must. See 'GitHub issue #475' for
 details.

   Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous
 releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3
 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed
 under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Interoperability between 'Rcpp' and the 'C++11' array and tuple
    types. Linking to this package allows fixed-length 'std::array' objects to
    be converted to and from equivalent R vectors, and 'std::tuple' objects
    converted to lists, via the as() and wrap() functions. There is also
    experimental support for 'std::span' from 'C++20'.
Access to Boost Date_Time functionality for dates,
 durations (both for days and date time objects), time zones, and 
 posix time ('ptime') is provided by using 'Rcpp modules'. The 
 posix time implementation can support high-resolution of up to 
 nano-second  precision by using 96 bits (instead of 64 with R)
 to present a 'ptime' object (but this needs recompilation with
 a #define set).
Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.
Features the multiple polynomial quadratic sieve (MPQS) algorithm
    for factoring large integers and a vectorized factoring function that
    returns the complete factorization of an integer. The MPQS is based off of
    the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17>
    along with the modification of multiple polynomials introduced by Peter
    Montgomery and J. Davis as outlined by Robert D. Silverman (1987)
    <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library
    GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple
    Elliptic Curve algorithm is attempted followed by a constrained version of 
    Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm
    used by the factorize function in the 'gmp' package.
Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. 
    With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and 
    ease of use of a domain-specific language with HPC-grade performance, making it one of the most 
    intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files 
    from the 'Blaze' library with disabling some functionalities related to link to the thread and system
    libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 
    'Blaze'.
'Rcpp' access to the 'CCTZ' timezone library is provided. 'CCTZ' is
 a C++ library for translating between absolute and civil times using the rules
 of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License,
 is included in this package. See <https://github.com/google/cctz> for more
 details.
Creates a header only package to link to the 'CGAL' 
  (Computational Geometry Algorithms Library)
  header files in 'Rcpp'. There are a variety of potential uses for 
  the software such as Hilbert sorting, K-D Tree nearest neighbors, 
  and convex hull algorithms. For more information about how to use the header files, 
  see the 'CGAL' documentation at <https://www.cgal.org>. Currently
  downloads version 6.1 of the 'CGAL' header files.
The 'RcppClassic' package provides a deprecated C++ library which
 facilitates the integration of R and C++. New projects should use the new 'Rcpp'
 'API' in the 'Rcpp' package.
The 'Rcpp' package contains a C++ library that facilitates the
 integration of R and C++ in various ways via a rich API. This API was
 preceded by an earlier version which has been deprecated since 2010 (but is
 still supported to provide backwards compatibility in the package
 'RcppClassic').  This package 'RcppClassicExamples' provides usage examples for
 the older, deprecated API. There is also a corresponding package
 'RcppExamples' with examples for the newer, current API which we
 strongly recommend as the basis for all new development.
The 'cnpy' library written by Carl Rogers provides read and write
 facilities for files created with (or for) the 'NumPy' extension for 'Python'.
 Vectors and matrices of numeric types can be read or written to and from
 files as well as compressed files. Support for integer files is available if
 the package has been built with as C++11 which should be the default on
 all platforms since the release of R 3.3.0.
In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'.
Provides 'C++' header files to deal with color conversion
    from some color spaces to hexadecimal with 'Rcpp', and exports some
    color mapping functions for usage in R. Also exports functions to
    convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is
    a human-friendly alternative to HSL.
'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query 
  engine to efficiently analyze large corpora (<https://cwb.sourceforge.io>). 'RcppCWB' is licensed
  under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/Licenses/GPL-3>).
  The 'CWB' relies on 'pcre2' (BSD license, see <https://github.com/PCRE2Project/pcre2/blob/master/LICENCE.md>)
  and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en.html>).
  See the file LICENSE.note for further information. The package includes modified code of the
  'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors
  of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this
  package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code
  is the approach used by 'RcppCWB'.
A header-only C++ library is provided with support
 for dates, time zones, ISO weeks, Julian dates, and Islamic dates.
 'date' offers extensive date and time functionality for the C++11,
 C++14 and C++17 standards and was written by Howard Hinnant and released 
 under the MIT license. A slightly modified version has been accepted
 (along with 'tz.h') as part of C++20. This package regroups all
 header files from the upstream repository by Howard Hinnant so that
 other R packages can use them in their C++ code. At present, few of
 the types have explicit 'Rcpp' wrappers though these may be added as
 needed.
An efficient C++ based implementation of the 'DEoptim'
 function which performs global optimization by differential evolution.
 Its creation was motivated by trying to see if the old approximation "easier,
 shorter, faster: pick any two" could in fact be extended to achieving all
 three goals while moving the code from plain old C to modern C++.  The
 initial version did in fact do so, but a good part of the gain was due to
 an implicit code review which eliminated a few inefficiencies which have
 since been eliminated in 'DEoptim'.
The 'Rcpp' package provides a C++ library to make it easier
    to use C++ with R. R and 'Rcpp' provide functions for a variety of
    statistical distributions. Several R packages make functions
    available to R for additional statistical distributions. However,
    to access these functions from C++ code, a costly call to the R
    functions must be made. 'RcppDist' provides a header-only C++ library
    with functions for additional statistical distributions that can be
    called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'.
    Functions are available that return a 'NumericVector' as well as
    doubles, and for multivariate or matrix distributions, 'Armadillo'
    vectors and matrices. 'RcppDist' provides functions for the following
    distributions: the four parameter beta distribution; the location-
    scale t distribution; the truncated normal distribution; the
    truncated t distribution; a truncated location-scale t distribution;
    the triangle distribution; the multivariate normal distribution*;
    the multivariate t distribution*; the Wishart distribution*; and
    the inverse Wishart distribution*. Distributions marked with an
    asterisk rely on 'RcppArmadillo'.
'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.
Dynamic Programming implemented in 'Rcpp'.  Includes example partition and out of sample fitting applications.  Also supplies additional custom coders for the 'vtreat' package.
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices, vectors,
 numerical solvers and related algorithms.  It supports dense and sparse
 matrices on integer, floating point and complex numbers, decompositions of
 such matrices, and solutions of linear systems. Its performance on many
 algorithms is comparable with some of the best implementations based on
 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header
 files from the 'Eigen' C++ template library. Thus users do not need to
 install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1,
 'Eigen' is licensed under the Mozilla Public License (version 2); earlier
 version were licensed under the GNU LGPL version 3 or later. 'RcppEigen'
 (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL
 version 2 or later, as is the rest of 'Rcpp'.
Compiles 'C++' code using 'Rcpp' <doi:10.18637/jss.v040.i08>, 'Eigen' <doi:10.18637/jss.v052.i05> and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions.
'Ensmallen' is a templated C++ mathematical optimization library 
 (by the 'MLPACK' team) that provides a simple set of abstractions for writing an
 objective function to optimize. Provided within are various standard and
 cutting-edge optimizers that include full-batch gradient descent techniques, 
 small-batch techniques, gradient-free optimizers, and constrained optimization.
 The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library
 and pairs the appropriate header files from 'armadillo' through the 
 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor
 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 
 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2,
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under 
 the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under
 similar terms. Note that 'Ensmallen' requires a compiler that supports 
 'C++14' and 'Armadillo' 10.8.2 or later.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.
 Note that the documentation in this package currently does not cover all the
 features in the package. The site <https://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
The Google 'FarmHash' family of hash functions is used by
 the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT'
 function. This package permits to calculate these hash digest
 fingerprints directly from R, and uses the included 'FarmHash'
 files written by G. Pike and copyrighted by Google, Inc.
The header-only 'C++' template library 'FastAD' for automatic
 differentiation <https://github.com/JamesYang007/FastAD> is provided by
 this package, along with a few illustrative examples that can all be called
 from R.
Converting ascii text into (floating-point) numeric values is a
 very common problem. The 'fast_float' header-only C++ library by Daniel Lemire
 does it very well and very fast at up to or over to 1 gigabyte per second as
 described in more detail in <doi:10.1002/spe.2984>. 'fast_float' is
 licensed under the Apache 2.0 license and provided here for use by other R
 packages via a simple 'LinkingTo:' statement.
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
'Hnswlib' is a C++ library for Approximate Nearest Neighbors.
    This package provides a minimal R interface by relying on the 'Rcpp'
    package. See <https://github.com/nmslib/hnswlib> for more on
    'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache
    License.
Header library and R functions to solve minimum cost bipartite matching problem 
 using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>;
 Kuhn (1955) <doi:10.1002/nav.3800020109>). 
 This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
'Int64' values can be created and accessed via the 'bit64' package and its 'integer64'
 class which package the 'int64' representation cleverly into a 'double'. The 'nanotime' packages
 builds on this to support nanosecond-resolution timestamps. This packages helps conversions between
 'R' and 'C++' via several helper functions provided via a single header file.  A complete example
 client package is included as an illustration.
A wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) <arXiv:2305.19045>. Jagger uses patterns derived from morphological dictionaries and training data sets and applies them from the beginning of the input. This simultaneous and deterministic process enables it to effectively perform tokenization, POS tagging, and lemmatization.
The header-only modern 'C++' template library 'Magic Enum' for static reflection
 of 'enums' (to string, from string, iteration) is provided by this package. More
 information about the underlying library can be found at its repository at
 <https://github.com/Neargye/magic_enum>.
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. 
    The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
    This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
    For installation, please refer to README.md file.
Fast machine learning algorithms including matrix factorization 
    and divisive clustering for large sparse and dense matrices.
A Minimal Example Package which demonstrates 'mlpack' use via C++ Code from R.
'MsgPack' header files are provided for use by R packages, along 
 with the ability to access, create and alter 'MsgPack' objects directly from R.
 'MsgPack' is an efficient binary serialization format. It lets you exchange
 data among multiple languages like 'JSON' but it is faster and smaller.
 Small integers are encoded into a single byte, and typical short strings
 require only one extra byte in addition to the strings themselves. This
 package provides headers from the 'msgpack-c' implementation for C and
 C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers
 are licensed under the Boost Software License (Version 1.0); the code added
 by this package as well the R integration are licensed under the GPL (>= 2).
 See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of  copyright holders
 and contributors to 'msgpack-c'.
An example package which shows use of 'NLopt' functionality from
 C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks
 to the exporting API added there by Jelmer Ypma. This package is a fully
 functioning, updated, and expanded version of the initial example by
 Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample>
 also containing a large earlier pull request of mine.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
'Rcpp' bindings for 'PLANC', a highly parallel
             and extensible NMF/NTF  (Non-negative Matrix/Tensor Factorization) library.
             Wraps algorithms described in
             Kannan et. al (2018) <doi:10.1109/TKDE.2017.2767592> and
             Eswar et. al (2021) <doi:10.1145/3432185>.
             Implements algorithms described in
             Welch et al. (2019) <doi:10.1016/j.cell.2019.05.006>,
             Gao et al. (2021) <doi:10.1038/s41587-021-00867-x>, and
             Kriebel & Welch (2022) <doi:10.1038/s41467-022-28431-4>.
Allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
'QuantLib' bindings are provided for R using 'Rcpp' via an updated
 variant of the header-only 'Quantuccia' project (put together initially by Peter
 Caspers) offering an essential subset of 'QuantLib' (and now maintained separately
 for the calendaring subset). See the included file 'AUTHORS' for a full list of
 contributors to both 'QuantLib' and 'Quantuccia'. Note that this package provided
 an initial viability proof, current work is done (via approximately quarterly releases
 tracking 'QuantLib') in the smaller package 'qlcal' which is generally preferred.
Connection to the 'Redis' (or 'Valkey') key/value store
 using the C-language client library 'hiredis' (included as a fallback)
 with 'MsgPack' encoding provided via 'RcppMsgPack' headers. It now
 also includes the pub/sub functions from the 'rredis' package.
Provides fast and efficient routines for
    common rolling / windowed operations. Routines for the
    efficient computation of windowed mean, median,
    sum, product, minimum, maximum, standard deviation
    and variance are provided.
The 'JSON' format is ubiquitous for data interchange, and the
 'simdjson' library written by Daniel Lemire (and many contributors) provides a
 high-performance parser for these files which by relying on parallel 'SIMD'
 instruction manages to parse these files as faster than disk speed. See the
 <doi:10.48550/arXiv.1902.08318> paper for more details about 'simdjson'.  This
 package parses 'JSON' from string, file, or remote URLs under a variety of
 settings.
R access to the Sequential Monte Carlo Template Classes
 by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
 additional examples have been added, and the first example from the JSS
 paper has been extended. Further integration and extensions are planned.
The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides
 many desirable features. This package bundles these header files for easy use by R packages
 from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also
 see the 'spdl' package which enhanced this package with a consistent R and C++ interface.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R. See Nagler (2021) <doi:10.18637/jss.v097.c01>.
Provides 'Rcpp' bindings for 'cpptimer', a simple tic-toc timer class for benchmarking 'C++' code <https://github.com/BerriJ/cpptimer>. It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism <https://www.openmp.org/>. It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame.
R-level and C++-level functionality
    to generate random deviates from and calculate moments of a
    Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>.
    In addition to RNG, functions for calculating moments, densities,
    and entropies are provided at both levels.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format
 (described at <https://toml.io/en/>) suitable for both human editing
 as well as the common uses of a machine-readable format. This package
 uses 'Rcpp' to connect to the 'toml++' parser written by Mark Gillard
 to R.
'Tskit' enables efficient storage, manipulation, and analysis
    of ancestral recombination graphs (ARGs) using succinct tree sequence
    encoding. The tree sequence encoding of an ARG is described in Wong et
    al. (2024) <doi:10.1093/genetics/iyae100>, while `tskit` project is
    described in Jeffrey et al. (2026) <doi:10.48550/arXiv.2602.09649>.
    See also <https://tskit.dev> for project news, documentation, and
    tutorials. 'Tskit' provides 'Python', 'C', and 'Rust' application
    programming interfaces (APIs). The 'Python' API can be called from 'R' via
    the 'reticulate' package to load and analyse tree sequences as
    described at <https://tskit.dev/tutorials/tskitr.html>. 'RcppTskit'
    provides 'R' access to the 'tskit C' API for cases where the
    'reticulate' option is not optimal; for example, high-performance or
    low-level work with tree sequences. Currently, 'RcppTskit' provides a
    limited set of 'R' functions because the 'Python' API and 'reticulate'
    already covers most needs.
Using the efficient implementation in the Boost C++ library, functions are
 provided to generate vectors of 'Universally Unique Identifiers (UUID)' from R supporting
 random (version 4), name (version 5) and time (version 7) 'UUIDs'. The initial repository
 was at <https://gitlab.com/artemklevtsov/rcppuuid>.
Provides the means to compile user-supplied C++ functions with
  'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.
This header-only library provides modern, portable C++ wrappers for SIMD
    intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512).
    By placing this library in this package, we offer an efficient distribution system for
    Xsimd <https://github.com/xtensor-stack/xsimd> for R packages using CRAN.
Access to some of the C level functions of the 'xts' package.  
 In its current state, the package is mostly a proof-of-concept to support
 adding useful functions, and does not yet add any of its own.
The Ziggurat generator for normally distributed random
 numbers, originally proposed by Marsaglia and Tsang (2000,
 <doi:10.18637/jss.v005.i08>) has been improved upon a few times
 starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>).
 This package provides an aggregation in order to compare different
 implementations in order to provide a 'faster but good enough'
 alternative for use with R and C++ code. See the 'zigg' package
 for a lighter implementation for much easier use in other packages.
Simplify the process of extracting and processing Clinical Practice
  Research Datalink (CPRD) data in order to build datasets ready for statistical
  analysis. This process is difficult in 'R', as the raw data is very large and 
  cannot be read into the R workspace. 'rcprd' utilises 'RSQLite' to create 
  'SQLite' databases which are stored on the hard disk. These are then  queried 
  to extract the required information for a cohort of interest, and create 
  datasets ready for statistical analysis. The  processes follow closely that 
  from the 'rEHR' package, see Springate et al., (2017) 
  <doi:10.1371/journal.pone.0171784>.
An implementation of functions for the analysis of crime incident or records
  management system data. The package implements analysis algorithms scaled for city
  or regional crime analysis units. The package provides functions for kernel density
  estimation for crime heat maps, geocoding using the 'Google Maps' API, identification 
  of repeat crime incidents, spatio-temporal map comparison across time intervals, 
  time series analysis (forecasting and decomposition), detection of optimal parameters 
  for the identification of near repeat incidents, and near repeat analysis with crime 
  network linkage.
Provides tools to automate the morphological delineation of
    riverside urban areas based on a method introduced in Forgaci (2018)
    <doi:10.7480/abe.2018.31>. Delineation entails the identification of
    corridor boundaries, segmentation of the corridor, and delineation of
    the river space using two-dimensional spatial information from street
    network data and digital elevation data in a projected CRS. The
    resulting delineation can be used to characterise spatial phenomena
    that can be related to the river as a central element.
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.
'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed.
Client for various 'CrossRef' 'APIs', including 'metadata' search
    with their old and newer search 'APIs', get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground 
    points filtering algorithm  which is based on cloth simulation. It tries to simulate the interactions
    between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be
    determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.
Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U,
    L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures.
    rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta".
    The Restricted Cubic Splines method were based on 
    Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>,
    and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).
Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.
Assists in the whole process of designing and evaluating Randomized Control Trials.
    Robust treatment assignment by strata/blocks, that handles misfits; 
    Power calculations of the minimum detectable treatment effect or minimum populations;
    Balance tables of T-test of covariates; 
    Balance Regression: (treatment ~ all x variables) with F-test of null model; 
    Impact_evaluation: Impact evaluation regressions. This function
    gives you the option to include control_vars, fixed effect variables,
    cluster variables (for robust SE), multiple endogenous variables and
    multiple heterogeneous variables (to test treatment effect heterogeneity)
    summary_statistics: Function that creates a summary statistics table with statistics 
    rank observations in n groups: Creates a factor variable with n groups. Each group has 
    a min and max label attach to each category.
    Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.
Provides various statistical methods for designing and analyzing two-stage randomized controlled 
	trials using the methods developed by Imai, Jiang, and Malani (2021) <doi:10.1080/01621459.2020.1775612> and 
	(2022+) <doi:10.48550/arXiv.2011.07677>.  The package enables the estimation of direct and spillover effects, 
	conduct hypotheses tests, and conduct sample size calculation for two-stage randomized controlled trials.
Predict fish year-class strength by calibration
  regression analysis of multiple recruitment index series.
Accurate prediction of subject recruitment for Randomized Clinical
    Trials (RCT) remains an ongoing challenge. Many previous prediction models rely
    on parametric assumptions. We present functions for non-parametric RCT
    recruitment prediction under several scenarios.
Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.
Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. 
  The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. 
  This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
  The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
  with their loadings. The number of groups and factors can be estimated if they are unknown.
Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example 'OpenGL').
A wrapper for 'libcurl' <https://curl.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al.,  <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al.,  <doi:10.2174/1875397301004010057>) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
Defines classes and methods to process text-based
  cytogenetics using the CytoGPS web site, then import the results
  into R for further analysis and graphing.
Administrative regions and other spatial objects of the Czech Republic.
Provides estimation and inference procedures for boundary regression discontinuity (RD) designs
    using local polynomial methods, based on either bivariate coordinates or distance-based approaches.
    Methods are developed in Cattaneo, Titiunik, and Yu (2025) 
    <https://mdcattaneo.github.io/papers/Cattaneo-Titiunik-Yu_2025_BoundaryRD.pdf>.
Native R only allows PDF exports of reference manuals.
    The 'Rd2md' package converts the package documentation files into
    markdown files and combines them into a markdown version of the package
    reference manual.
Functions to convert Rd to 'roxygen' documentation. It can parse an
    Rd file to a list, create the 'roxygen' documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities that can help developers
    build packages using 'roxygen' more easily. The 'formatR' package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Creates interactive analytic graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as 'D3.js' graphs embedded in a web page ready for its interactive analysis and exploration.
Provides functions implementing the shrunken centroids regularized discriminant analysis for
        classification purpose in high dimensional data. The method is described in 
        Guo at al. (2013) <doi:10.1093/biostatistics/kxj035>.
This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13: 782-788 <DOI:10.1111/2041-210X.13800>.
Connect, execute, and parse results from the Daisi Microservice Platform <https://www.daisi.io/>. The rdaisi client
  includes a set of functionality that allows remote execution of microservices directly from R.
  Daisis allow R users to access a wide variety of Python functionality and interact with them natively.
Client for the web service methods provided by 'DataCite'
    (<https://www.datacite.org/>), including functions to interface with
    their 'RESTful' search API. The API is backed by 'Elasticsearch',
    allowing expressive queries, including faceting.
R implementation of the 'FAIR Data Pipeline API'. The 'FAIR Data 
    Pipeline' is intended to enable tracking of provenance of FAIR (findable, 
    accessible and interoperable) data used in epidemiological modelling.
Provides an R interface to the Data Retriever
    <https://retriever.readthedocs.io/en/latest/> via the Data Retriever's
    command line interface. The Data Retriever automates the
    tasks of finding, downloading, and cleaning public datasets,
    and then stores them in a local database.
Download and access datasets from the Rdatasets archive
    (<https://vincentarelbundock.github.io/Rdatasets/>). The package provides
    functions to search, download, and view documentation for thousands of
    datasets from various R packages, available in both CSV and Parquet
    formats for efficient access.
A simple 'WebDAV' client that provides functions to fetch and send
  files or folders to servers using the 'WebDAV' protocol
  (see 'RFC' 4918 <https://www.rfc-editor.org/rfc/rfc4918>).
  Only a subset of the protocol is implemented (e.g. file locks are not yet
  supported).
R access to hundreds of millions data series from DBnomics API
    (<https://db.nomics.world/>).
Many packages in the 'r-dcm' family take similar arguments,
    which are checked for expected structures and values. Rather than
    duplicating code across several packages, commonly used check
    functions are included here.  This package can then be imported to
    access the check functions in other packages.
The rank distance correlation <doi:10.1080/01621459.2020.1782223> is computed. Included also is a function to perform permutation based testing.
Estimation of both single- and multiple-assignment Regression Discontinuity Designs 
    (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
    both sharp and fuzzy designs, along with power analysis and assumption checks. 
    Introductions to the underlying logic and analysis of RDDs are in 
    Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and
    Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.
A direct interface to the underlying XML representation of DDI Codebook 2.5 with flexible API creation.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Allows caching of raw data directly in R code. This allows R
  scripts and R Notebooks to be shared and re-run on a machine without access
  to the original data. Cached data is encoded into an ASCII string that can
  be pasted into R code. When the code is run, the data is automatically
  loaded from the cached version if the original data file is unavailable.
  Works best for small datasets (a few hundred observations).
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
Classes and functions for modelling health care interventions 
  using decision trees and semi-Markov models. Mechanisms are provided for 
  associating an uncertainty distribution with each source variable and for
  ensuring transparency of the mathematical relationships between variables.
  The package terminology follows Briggs "Decision Modelling for Health
  Economic Evaluation" (2006, ISBN:978-0-19-852662-9).
Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).
Screens all '.R', '.Rmd', and '.qmd' files to extract the name of
    packages used in a project. This package detects packages called with 
    'library(foo)', 'require(foo)', 'foo::bar()' and 'use("foo", "bar")' and 
    adds these dependencies in the DESCRIPTION file in the sections Depends, 
    Imports, and Suggests.
The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.
Honest and nearly-optimal confidence intervals in fuzzy and sharp
    regression discontinuity designs and for inference at a point based on local
    linear regression. The implementation is based on Armstrong and Kolesár (2018)
    <doi:10.3982/ECTA14434>, and Kolesár and Rothe (2018)
    <doi:10.1257/aer.20160945>. Supports covariates, clustering, and weighting.
Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.
Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <https://rdpackages.github.io/references/Calonico-Cattaneo-Farrell-Palomba-Titiunik_2025_HTERD.pdf>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: 'rdhte' to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); 'rdbwhte', which implements automatic bandwidth selection methods; and 'rdhte_lincom' to test linear combinations of parameters.
Methods for calculation and visualization of the Repertoire
    Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) 
    <doi:10.1186/s12859-017-1556-5>.
Functions and methods for manipulating 'SNOMED CT'
    concepts. The package contains functions for loading the 'SNOMED CT'
    release into a convenient R environment, selecting 'SNOMED CT'
    concepts using regular expressions, and navigating the 'SNOMED CT'
    ontology. It provides the 'SNOMEDconcept' S3 class for a vector of
    'SNOMED CT' concepts (stored as 64-bit integers) and the
    'SNOMEDcodelist' S3 class for a table of concepts IDs with
    descriptions. The package can be used to construct sets of
    'SNOMED CT' concepts for research (<doi:10.1093/jamia/ocac158>).
    For more information about 'SNOMED CT' visit
    <https://www.snomed.org/>.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library code is included.
We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
	For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.
A common framework for calculating distance matrices.
Distance-sampling (<doi:10.1007/978-3-319-19219-2>) 
  is a field survey and analytical method that estimates density and 
  abundance of survey targets (e.g., animals) when 
  detection probability declines with observation distance. 
  Distance-sampling is popular in ecology, 
  especially when survey targets are observed from aerial platforms (e.g., 
  airplane or drone), surface vessels (e.g., boat or truck), or along 
  walking transects. Analysis involves fitting smooth (parametric) curves to 
  histograms of observation distances and using those functions to 
  adjust density estimates for missed targets.  Routines included here 
  fit curves to observation distance histograms, estimate effective 
  sampling area, density of targets in surveyed areas, and the abundance 
  of targets in a surrounding study area. Confidence interval estimation
  uses built-in bootstrap resampling. Help files are extensive and have been 
  vetted by multiple authors. Many tutorials are available on the package's 
  website (URL below).
Provides a framework for the measurement and partitioning of
    the (similarity-sensitive) biodiversity of a metacommunity and its
    constituent subcommunities. Richard Reeve, et al. (2016) 
    <arXiv:1404.6520v3>.
A task-oriented R interface to the 'RDKit' <https://www.rdkit.org>
    library through its 'Python' API via 'reticulate'. The package offers
    high-level cheminformatics functionality, including molecule parsing,
    descriptor calculation, and fingerprint generation without replicating the 
    native structure of 'RDKit'.
Implements safe policy learning under regression discontinuity designs
    with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>.
    The learned cutoffs are guaranteed  to perform no worse than the existing
    cutoffs in terms of overall outcomes. The 'rdlearn' package also includes
    features for visualizing the learned cutoffs relative to the baseline and
    conducting sensitivity analyses.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. 
  Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas.
  It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages, as described in Rodiger et al. (2017) <doi:10.1093/bioinformatics/btx528>.
    Contains a dendrogram visualization for the structure of RDML object and GUI for RDML editing.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <https://www.dnb.de/EN/Home/home_node.html>. 
    The German National Library is the German central archival library, 
    collecting, archiving, bibliographically classifying all German and 
    German-language publications, foreign publications about Germany, 
    translations of German works, and the works of German-speaking emigrants 
    published abroad between 1933 and 1945.
Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.
Dump source code, documentation and vignettes of an 'R'
    package into a single file. Supports installed packages, tar.gz
    archives, and package source directories. If the package is not
    installed, only its source is automatically downloaded from CRAN for
    processing. The output is a single plain text file or a character
    vector, which is useful to ingest complete package documentation and
    source into a large language model (LLM) or pass it further to other
    tools, such as 'ragnar' <https://github.com/tidyverse/ragnar> to
    create a Retrieval-Augmented Generation (RAG) workflow.
Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,
    Virustotal (which provides access to lots of services) <https://www.virustotal.com/>,
    Alexa <https://aws.amazon.com/awis/>, DMOZ <https://curlie.org/>, University Domain list
    <https://github.com/Hipo/university-domains-list> or validated machine learning
    classifiers based on Shallalist data to learn about the kind of content hosted by a domain.
An R API Client for Valve's Dota2. RDota2 can be easily used 
    to connect to the Steam API and retrieve data for Valve's popular video 
    game Dota2. You can find out more about Dota2 at 
    <http://store.steampowered.com/app/570/>.
Create doxygen documentation for source code in R packages. 
  Includes a RStudio Addin, that allows to trigger the doxygenize process.
Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve.
    Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>.
    David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.
Functions for manipulation of R documentation objects,
    including functions reprompt() and ereprompt() for updating 'Rd'
    documentation for functions, methods and classes; 'Rd' macros for
    citations and import of references from 'bibtex' files for use in
    'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and
    inserting snippets of 'R' code and the results of its evaluation or
    creating graphics on the fly; and many functions for manipulation of
    references and Rd files.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.
Provide an interface for 'Drama Corpora Project' ('DraCor') API: <https://dracor.org/documentation/api>.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) <arXiv:2005.08049>.
Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of
    data underlying scientific publications.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, 
    Gile and Handcock (2015) <doi:10.1111/rssa.12091> and
    Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. 'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book.
Provides functionality for carrying out sample size estimation and power calculation in Respondent-Driven Sampling.
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.
Defines functions that can be used to collect provenance as
  an 'R' script executes or during a console session. The output is a text 
  file in 'PROV-JSON' format.
Bundles the 'duckhts' 'DuckDB' extension for reading High Throughput
    Sequencing file formats with 'DuckDB'. The 'DuckDB' C extension API
    <https://duckdb.org/docs/stable/clients/c/api> and its 'htslib' dependency are
    compiled from vendored sources during package installation. James K Bonfield and co-authors (2021) <doi:10.1093/gigascience/giab007>.
Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.
Easily interact with the 'Arduino Iot Cloud API' <https://www.arduino.cc/reference/en/iot/api/>, managing devices, things, properties and data.
Enables the use of color palettes inspired by the 'Dune' movies. These palettes are compatible with 'ggplot2'. See Wickham (2016) <doi:10.1007/978-3-319-24277-4> for more details on 'ggplot2'.
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see 
             <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information).
             Choose observational time series from meteorological stations with 'selectDWD()'.
             Find raster data from radar and interpolation according to <https://brry.github.io/rdwd/raster-data.html>.
             Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'.
             Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Compute spatially explicit land-use metrics for stream survey sites in GRASS GIS and R as an open-source implementation of IDW-PLUS (Inverse Distance Weighted Percent Land Use for Streams). The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) <doi:10.1111/j.1365-2427.2010.02507.x> and previously implemented by Peterson and Pearse (2017) <doi:10.1111/1752-1688.12558> in ArcGIS-Python as IDW-PLUS.
A comprehensive set of regular expression functions based on those found in 'Python'
    without relying on 'reticulate'. It provides functions that intend to (1) make it easier for
    users familiar with 'Python' to work with regular expressions, (2) reduce the complexity often
    associated with regular expressions code, (3) and enable users to write more readable and
    maintainable code that relies on regular expression-based pattern matching.
Pattern matching, extraction, replacement and other string
  processing operations using Google's RE2 <https://github.com/google/re2>
  regular-expression engine. Consistent interface (similar to 'stringr').
  RE2 uses finite-automata based techniques, and offers a
  fast and safe alternative to backtracking regular-expression engines
  like those used in 'stringr', 'stringi' and other PCRE implementations.
Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.
Tools to help with 'shiny' reactivity. The 'react' object offers
    an alternative way to call reactive expressions to better identify them
    in the server code.
Interactive data tables for R, based on the 'React Table'
    JavaScript library. Provides an HTML widget that can be used in 'R Markdown'
    or 'Quarto' documents, 'Shiny' applications, or viewed from an R console.
Enhanced functionality for 'reactable' in 'shiny' applications, offering interactive and dynamic data table capabilities with ease.
    With 'reactable.extras', easily integrate a range of functions and components to enrich your 'shiny' apps and facilitate user-friendly data exploration.
Provides a checkbox group input for usage in a 'Shiny'
    application. The checkbox group has a head checkbox allowing to check
    or uncheck all the checkboxes in the group. The checkboxes are
    customizable.
Building interactive web applications with R is incredibly easy
  with 'shiny'. Behind the scenes, 'shiny' builds a reactive graph that can
  quickly become intertwined and difficult to debug. 'reactlog'
  (Schloerke 2019) <doi:10.5281/zenodo.2591517> provides a visual insight into
  that black box of 'shiny' reactivity by constructing a directed dependency
  graph of the application's reactive state at any time point in a reactive
  recording.
Make it easy to use 'React' in R with 'htmlwidget' scaffolds,
              helper dependency functions, an embedded 'Babel' 'transpiler',
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
You can easily share url pages using 'React Router' in 'shiny' applications
  and 'Quarto' documents. The package wraps the 'react-router-dom' 'React' library and
  provides access to hash routing to navigate on multiple url pages.
A robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.
Download and import agricultural data from the Australian
    Bureau of Agricultural and Resource Economics and Sciences (ABARES)
    <https://www.agriculture.gov.au/abares> and Australian Bureau of
    Statistics (ABS) <https://www.abs.gov.au>. Data types serviced include
    spreadsheets, comma separated value (CSV) files, geospatial data
    including shape files and geotiffs covering topics including broadacre
    crops, livestock, soil data, commodities and more. Unifies field names
    and formats for data interoperability making analysis easier by
    standardising names between data formats. Also simplifies importing
    geospatial data as well as correcting issues in the geospatial data
    upon import.
Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at <https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.
Implements a high performance C++ parser 
    for 'ActiGraph' 'GT3X'/'GT3X+' data format (with extension '.gt3x') 
    for 'accelerometer' samples. Activity samples can be easily read into a
    matrix or data.frame.  This allows for storing the raw 'accelerometer' 
    samples in the original binary format to reserve space.
Loads Axon Binary Files (both 'ABF' and 'ABF2') created by Axon Instruments/Molecular Devices software such as 'pClamp'.
Downloads, imports, and tidies time series data from the 
    Australian Bureau of Statistics <https://www.abs.gov.au/>.
Provides clean, tidy access to Australian Electoral Commission
    (AEC) federal election data. Includes results for the House of
    Representatives and Senate from 2007 onwards, at both division and polling
    place level. Data is downloaded directly from the AEC
    <https://results.aec.gov.au> on first use and cached locally for
    subsequent calls.
Download the latest data from the Australian Prudential Regulation
    Authority <https://www.apra.gov.au/> and import it into R as a tidy data 
    frame.
Identifies and reads Windows BMP, JPEG, PNG, and TIFF format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <https://osdoc.cogsci.nl/>.
It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.
Provides clean, tidy access to statistical data published by the
    'European Central Bank' ('ECB') via the 'ECB Data Portal' API
    <https://data.ecb.europa.eu>. Covers policy interest rates, 'EURIBOR',
    euro exchange rates, harmonised consumer price inflation ('HICP'), euro area
    yield curves, the euro short-term rate ('ESTR'), monetary aggregates (M1,
    M2, M3), mortgage and lending rates, GDP, unemployment, and government
    debt-to-GDP. Each dataset has a dedicated function that abstracts away the
    underlying 'SDMX' key structure, so users do not need to know series codes.
    A generic fetcher is also provided for direct access to any of the 'ECB'
    100-plus dataflows. Data is downloaded on first use and cached locally for
    subsequent calls.
Import Data from Relational Database Management Systems
    (RDBMS) and Health Information Systems ('HIS'). The current version of the
    package supports importing data from RDBMS including 'MS SQL', 'MySQL',
    'PostGRESQL', and 'SQLite', as well as from two HIS platforms: 'DHIS2' and
    'SORMAS'.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Efficient reading of raw markdown tables into tibbles. Designed to
    accept content from strings, files, and URLs with the ability to extract
    and read multiple tables from markdown for analysis.
Convert README.md to vignettes when installing packages without vignettes.
Contains tools for reading and writing data from or to files in the formats: akterm, dmna, Scintec Format-1, and Campbell Scientific TOA5.
Clinical care data from 130 U.S. hospitals in the years 1999-2008
    adapted from the study Strack et al. (2014) <doi:10.1155/2014/781670>. 
    Each row describes an "encounter" with a patient with diabetes, including 
    variables on demographics, medications, patient history, diagnostics, 
    payment, and readmission.
Contains tools for working with and analyzing hospital readmissions data. The package provides utilities for components of the Hospital Readmissions Reduction Program (HRRP), including program timeline functions, Hospital-Specific Report (HSR) helpers, and general importing tools for the Provider Data Catalog (PDC).
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
Tools to read various file types into one list of data structures, usually, but not limited to, data frames.
  Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal
  separators are determined automatically.
Functions for reading mass spectrometry data in mzXML format.
Provides clean, tidy access to climate and weather data from the
    'National Oceanic and Atmospheric Administration' ('NOAA') via the 'National
    Centers for Environmental Information' ('NCEI') Data Service API
    <https://www.ncei.noaa.gov/access/services/data/v1>. Covers daily weather
    observations, monthly and annual summaries, and 30-year climate normals from
    over 100,000 stations across 180 countries. No API key is required.
    Dedicated functions handle the most common datasets, while a generic fetcher
    provides access to all 'NCEI' datasets. Station discovery functions help
    users find stations by location or name. Data is downloaded on first use
    and cached locally for subsequent calls. This package is not endorsed or
    certified by 'NOAA'.
Loads 'Blackrock' <https://blackrockneurotech.com> neural signal 
    data files into the memory, provides utility tools to extract the data into 
    common formats such as plain-text 'tsv' and 'HDF5'.
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.
Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.
Provides clean, tidy access to key economic indicators published
    by the 'Organisation for Economic Co-operation and Development' ('OECD'),
    covering GDP, CPI inflation, unemployment, tax revenue, government deficit,
    health expenditure, education expenditure, income inequality, labour
    productivity, and current account balance across all
    38 'OECD' member countries. Data is downloaded from the 'OECD Data Explorer'
    API <https://data-explorer.oecd.org> on first use and cached locally for
    subsequent calls. Returns tidy long-format data frames ready for analysis
    and visualisation.
Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.
Imports log and data files from "Eosense" ecosystem gas flux chambers into dataframes that can directly be used with "fluxible" by Gaudard et al (2025) <doi:10.1111/2041-210X.70161>.
The goal of 'readr' is to provide a fast and friendly way to
    read rectangular data (like 'csv', 'tsv', and 'fwf').  It is designed
    to flexibly parse many types of data found in the wild, while still
    cleanly failing when data unexpectedly changes.
Download up-to-date data from the Reserve Bank of Australia 
    in a tidy data frame. Package includes functions to download current and 
    historical statistical tables 
    (<https://www.rba.gov.au/statistics/tables/>) and forecasts 
    (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data
    includes a broad range of Australian macroeconomic and financial time
    series.
A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.
Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <https://rapidxml.sourceforge.net/> is used to parse the XML data.
The goal of 'readsdr' is to bridge the design capabilities from
    specialised System Dynamics software with the powerful numerical tools 
    offered by 'R' libraries. The package accomplishes this goal by parsing 
    'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct 
    networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate
    via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
Read and write labelled sparse matrices in text format as used by
    software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM',
    and others. Supports labelled data for regression, classification (binary, multi-class, multi-label),
    and ranking (with 'qid' field), and can handle header metadata and comments in files.
Function to read and write the 'Stata' file format.
Imports data from the 'SurveyXact' commercial service 
    <https://rambollxact.com>, adds variable labels, and converts value 
    labels to factors in the same order as exported from 'SurveyXact'.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
'Praat' <https://www.fon.hum.uva.nl/praat/> is a widely 
    used tool for manipulating, annotating and analyzing speech and 
    acoustic data. It stores annotation data in a format called a 
    'TextGrid'. This package provides a way to read these 
    files into R.
Import excel files into R. Supports '.xls' via the embedded
    'libxls' C library <https://github.com/libxls/libxls> and '.xlsx' via
    the embedded 'RapidXML' C++ library <https://rapidxml.sourceforge.net/>.
    Works on Windows, Mac and Linux without external dependencies.
A template model module, tools to help find model modules
    derived from this template and a programming syntax to use these
    modules in health economic analyses.  These elements are the
    foundation for a prototype software framework for developing living
    and transferable models and using those models in reproducible health
    economic analyses. The software framework is extended by other R
    libraries.  For detailed documentation about the framework and how to
    use it visit <https://www.ready4-dev.com/>. For a background to the
    methodological issues that the framework is attempting to help solve,
    see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.
Provides a flexible and streamlined pipeline for formatting, analyzing, and 
    visualizing omics data, regardless of omics type (e.g. transcriptomics, proteomics, 
    metabolomics). The package includes tools for shaping input data into analysis-ready 
    structures, fitting linear or mixed-effect models, extracting key contrasts, and 
    generating a rich variety of ready-to-use publication-quality plots. 
    Designed for transparency and reproducibility across a wide range of study designs, 
    with customizable components for statistical modeling.
Provides tools for simulating synthetic survival data using a variety of
    methods, including kernel density estimation, parametric distribution fitting,
    and bootstrap resampling techniques for a desired sample size.
A framework for unit testing for realistic minimalists,
    where we distinguish between expected, acceptable, current, fallback,
    ideal, or regressive behaviour. It can also be used for monitoring
    third-party software projects for changes.
Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
Calculate the probability density functions (PDFs) for two threshold evidence 
    accumulation models (EAMs). These are defined using the following Stochastic 
    Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is
    the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is 
    the noise scale, and W is the standard Wiener process. The boundary conditions 
    of this process are the upper and lower decision thresholds, represented by b_u(t) 
    and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. 
    The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We 
    represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as 
    a ratio of the initial threshold location. This package generates the PDF using
    the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes
    (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the 
    forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2,
    then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, 
    it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx 
    evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) 
    or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, 
    specifically its PDF. We discuss further details of this approach in this package 
    and 'PyBEAM' publications. Additionally, one can calculate the cumulative distribution 
    functions of and sampling from the EAMs.
Takes user-provided baseline data from groups of randomised controlled data and  
    assesses whether the observed distribution of baseline p-values, numbers of participants 
    in each group, or categorical variables are consistent with the expected distribution, as 
    an aid to the assessment of integrity concerns in published randomised controlled trials. 
    References (citations in PubMed format in details of each function):
    Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>.
    Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) 
    <doi:10.1016/j.jclinepi.2019.05.006>.
    Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>.
    Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>.
    Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) 
    <doi:10.1016/j.jclinepi.2020.11.012>.
    Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>.
    Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) 
    <doi:10.1016/j.jclinepi.2022.12.018>.
    Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.
    Carlisle JB. (2017) <doi:10.1111/anae.13938>.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Arrange data by a set of methods. Use rearrangers to reorder
    data points and mutators to change their values. From basic utilities,
    to centering the greatest value, to swirling in 3-dimensional space,
    'rearrr' enables creativity when plotting and experimenting with data.
Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).
Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.  See Koenker and Gu (2017) REBayes: An R Package for Empirical
    Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, 
    <DOI:10.18637/jss.v082.i08>.
Authors working with 'LaTeX' articles use the built-in bibliography
    options and 'BibTeX' files. While this might work with 'LaTeX', it does not
    function well with Web articles. As a way out, 'rebib' offers tools to
    convert and combine bibliographies from both sources.
A programmatic client for the eBird database 
    (<https://ebird.org/home>), including functions for searching for bird 
    observations by geographic location (latitude, longitude), eBird 
    hotspots, location identifiers, by notable sightings, by region, and by 
    taxonomic name.
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.
Tools are provided for estimating, testing, and simulating
    abundance in a two-event (Petersen) mark-recapture experiment. Functions are
    given to calculate the Petersen, Chapman, and Bailey estimators and associated
    variances. However, the principal utility is a set of functions to simulate
    random draws from these estimators, and use these to conduct hypothesis
    tests and power calculations. Additionally, a set of functions are provided
    for generating confidence intervals via bootstrapping. Functions are also
    provided to test abundance estimator consistency under complete or partial
    stratification, and to calculate stratified or partially stratified estimators.
    Functions are also provided to calculate recommended sample sizes.
    Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>, 
    Bailey (1951) <DOI:10.2307/2332575>, 
    Bailey (1952) <DOI:10.2307/1913>, 
    Chapman (1951) NAID:20001644490, 
    Cohen (1988) ISBN:0-12-179060-6, 
    Darroch (1961) <DOI:10.2307/2332748>, and
    Robson and Regier (1964) <ISSN:1548-8659>.
Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.
A set of functions to see and interactively adjust a distribution
    of lessons by day, aiming at homogenizing individual distributions (for
    each class and teacher).
Sends texts to the <https://www.receptiviti.com> API to be scored,
    and facilitates the creation of custom norms and local results databases.
A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
A tool designed to analyze recurrent events when dealing with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). It extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).
HydroBudget is a spatially distributed
  groundwater recharge model that computes a superficial water
  budget on grid cells with outputs aggregated into monthly time steps.
  It was developed as an accessible and computationally affordable model
  to simulate groundwater recharge over large areas (thousands of km2,
  regional-scale watersheds) and for long time periods (decades), in cold
  and humid climates. Model algorithms are based on the research of
  Dubois, E. et al. (2021a) <doi:10.5683/SP3/EUDV3H> and 
  Dubois, E. et al. (2021b) <doi:10.5194/hess-25-6567-2021>.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
A recipe prepares your data for modeling. We provide an
    extensible framework for pipeable sequences of feature engineering
    steps provides preprocessing tools to be applied to data. Statistical
    parameters for the steps can be estimated from an initial data set and
    then applied to other data sets. The resulting processed output can
    then be used as inputs for statistical or machine learning models.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities
    (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, 
    T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), 
    "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0),
    forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage. Focus is on memory, CPU performance and flexibility.
The analysis of different aspects of biodiversity requires specific algorithms. For example, in regionalisation analyses, the high frequency of ties and zero values in dissimilarity matrices produced by Beta-diversity turnover produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, visualisation of biogeographical regionalisation can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to visualise and analyse patterns of biodiversity and to improve occurrence data for cryptic taxa.
Implements the RecMap MP2 construction heuristic
  <doi:10.1109/INFVIS.2004.57>.
  This algorithm draws maps according to a given statistical
  value, e.g., election results, population, or epidemiological data.
  The basic idea of the RecMap algorithm is that each map region,
  e.g., different countries, is represented by a rectangle.
  The area of each rectangle represents the statistical value provided
  as input to maintain zero cartographic error.
  Computationally intensive tasks are implemented in C++.
  The included vignette documents recmap algorithm usage.
Modern results of psychometric theory are implemented to provide users with a way of evaluating the internal structure of a set of items guided by theory. These methods are discussed in detail in VanderWeele and Padgett (2024) <doi:10.31234/osf.io/rnbk5>. The relative excess correlation matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. The positive deviations of the relative excess correlation matrix entries help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for rotations or decisions concerning the number of factors. A goal similar to exploratory/confirmatory factor analysis, but 'recmetrics' uses novel methods that do not rely on assumptions of latent variables or latent variable structures.
The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).
Contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. 
    Providing 'htmlwidgets' bindings to the 'recogito' <https://github.com/recogito/recogito-js> and 'annotorious' <https://github.com/recogito/annotorious> libraries.
Automatic, semi-automatic, and manual functions for
    generating color maps from images. The idea is to simplify
    the colors of an image according to a metric that is useful for
    the user, using deterministic methods whenever possible.
    Many images will be clustered well using the out-of-the-box
    functions, but the package also includes a toolbox of functions
    for making manual adjustments (layer merging/isolation, blurring,
    fitting to provided color clusters or those from another image, etc).
    Also includes export methods for other color/pattern analysis packages
    (pavo, patternize, colordistance).
Turns nested lists into data.frames in an orderly manner.
Calculates evaluation metrics for implicit-feedback recommender systems
  that are based on low-rank matrix factorization models, given the fitted model
  matrices and data, thus allowing to compare models from a variety of libraries.
  Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k),
  AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k),
  Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from
  which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the
  receiver-operating characteristic curve), and PR-AUC (area under the
  precision-recall curve).
  These are calculated on a per-user basis according to the ranking of items induced
  by the model, using efficient multi-threaded routines. Also provides functions
  for creating train-test splits for model fitting and evaluation.
Provides a research infrastructure to develop and evaluate
    collaborative filtering recommender algorithms. This includes a sparse 
    representation for user-item matrices, many popular algorithms, top-N recommendations,
    and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.
Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
The Rearrangement Correlation Coefficient is an adjusted version of Pearson's 
    correlation coefficient that accurately measures monotonic dependence 
    relationships, including both linear and nonlinear associations. This 
    method addresses the underestimation problem of classical correlation 
    coefficients in nonlinear monotonic scenarios through improved statistical 
    bounds derived from rearrangement inequalities. For more details, 
    see Ai (2024) <doi:10.52202/079017-1180>.
A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.
Provides functions for linking and deduplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain. For details, 
  see our paper "The RecordLinkage Package: Detecting Errors in Data" 
  Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.
Functions for generating k-record values and k-record
        times
Statistical tools based on the probabilistic properties of the 
  record occurrence in a sequence of independent and identically distributed 
  continuous random variables. In particular, tools to prepare a time series 
  as well as distribution-free trend and change-point tests and graphical 
  tools to study the record occurrence. Details about the implemented tools 
  can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> 
  and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.
R wrapper of the 'libmf' library
    <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Rectangle packing is a packing problem where rectangles are placed
    into a larger rectangular region (without overlapping) in order to maximise
    the use space.  Rectangles are packed using the skyline heuristic as discussed 
    in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip
    Packing Problem' <doi:10.1007/978-3-642-21827-9_29>.  A function is also 
    included for determining a good small-sized box for containing a given
    set of rectangles.
Computation of one-, two- and three-dimensional 
  pseudo-observations based on recurrent events and terminal events. 
  Generalised linear models are fitted using generalised estimating 
  equations. Technical details on the bivariate procedure can be found 
  in "Bivariate pseudo-observations for recurrent event analysis with 
  terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>.
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <https://iucn.org/> for more information) red list criteria.
Contains implementations of recurrent event data analysis routines
    including (1) survival and recurrent event data simulation from
    stochastic process point of view by the thinning method
    proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304>
    and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976),
    (2) the mean cumulative function (MCF) estimation by the
    Nelson-Aalen estimator of the cumulative hazard rate function,
    (3) two-sample recurrent event responses comparison with the pseudo-score
    tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>,
    (4) gamma frailty model with spline rate function following
    Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.
The aim of this package is to manipulate relational
   data models in R.
   It provides functions to create, modify and export data models
   in json format.
   It also allows importing models created
   with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>).
   These functions are accessible through a graphical user
   interface made with 'shiny'.
   Constraints such as types, keys, uniqueness and mandatory fields are
   automatically checked and corrected when editing a model.
   Finally, real data can be confronted to a model to check their compatibility.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library.
    The full context of this project and details about the implementation are
    available in <doi:10.1017/dap.2025.4> (Open Access).
Provides an API to work with 'Redatam' (see <https://redatam.org>) 
  databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 
  'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' 
  core and provides functions to open/close a database (redatam_open()/redatam_close()), 
  list entities and variables from the database (redatam_entities(), redatam_variables()) 
  and execute a 'SPC' program and gets the results as data frames 
  (redatam_query(), redatam_run()).
Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) <doi:10.15381/rpb.v13i2.1782>. This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset.
Access data stored in 'REDCap' databases using the Application
    Programming Interface (API).  'REDCap' (Research Electronic Data CAPture;
    <https://projectredcap.org>, Harris, et al. (2009) <doi:10.1016/j.jbi.2008.08.010>, 
    Harris, et al. (2019) <doi:10.1016/j.jbi.2019.103208>) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically. The 'redcapAPI' package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Casting metadata for REDCap database creation and handling of 
    castellated data using repeated instruments and longitudinal projects in 
    'REDCap'. Keeps a focused data export approach, by allowing to only export 
    required data from the database. Also for casting new REDCap databases based 
    on datasets from other sources.
    Originally forked from the R part of 'REDCapRITS' by Paul Egeler. 
    See <https://github.com/pegeler/REDCapRITS>.
    'REDCap' (Research Electronic Data Capture) is a secure, web-based software
    platform designed to support data capture for research studies, providing
    1) an intuitive interface for validated data capture; 2) audit trails for
    tracking data manipulation and export procedures; 3) automated export
    procedures for seamless data downloads to common statistical packages; and
    4) procedures for data integration and interoperability with external 
    sources (Harris et al (2009) <doi:10.1016/j.jbi.2008.08.010>; 
    Harris et al (2019) <doi:10.1016/j.jbi.2019.103208>).
REDCap Data Management - 'REDCap' (Research Electronic Data CAPture; <https://projectredcap.org>) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries.
Export all data, including metadata, from a REDCap (Research
    Electronic Data Capture) Project via the REDCap API
    <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>.
    The exported (meta)data will be processed and formatted into a stand alone R
    data package which can be installed and shared between researchers.  Several
    default reports are generated as vignettes in the resulting package.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms.
'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides a specialized function for calling the 'REDUCE' feature for solving systems of equations, returning the output as an 'R' object designed for the purpose. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents, e.g. using 'Sweave'.
Collect  your data on digital marketing campaigns from reddit Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
A collection of tools for extracting structured data from <https://www.reddit.com/>.
Applies quality control to daily precipitation observations; 
    reconstructs the original series by estimating precipitation in missing values; and 
    creates gridded datasets of daily precipitation.
Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Extension to 'REddyProc' that allows reading data from netCDF files.
Provides tools to download, process, and analyze real-time 
    meteorological radar images from Simepar (Paraná, Brazil)
    <https://www.simepar.br/simepar/radar_msc>. Designed to support the
    'Rede Agropesquisa' hydrological monitoring, it includes functions to
    detect rainfall intensity based on Red, Green, and Blue (RGB) color
    values within predefined circular study areas. Features automated
    integration with the 'Telegram Bot API' 
    <https://core.telegram.org/bots/api> to send spatialized image alerts and 
    an interactive 'shiny' dashboard for easy configuration and continuous 
    weather tracking.
Implementation of the Robust Exponential Decreasing Index (REDI),
    proposed in the article by Issa Moussa, Arthur Leroy et al. (2019)
    <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>.
    The REDI represents a measure of cumulated workload, robust to missing data,
    providing control of the decreasing influence of workload over time. 
    Various functions are provided to format data, compute REDI, and 
    visualise results in a simple and convenient way.
Biologically relevant, yet mathematically sound constraints are used
    to compute the propensity and thence infer the dominant direction of reactions
    of a generic biochemical network. The reactions must be unique and their 
    number must exceed that of the reactants,i.e., reactions >= reactants + 2.  
            'ReDirection', computes the null space of a user-defined stoichiometry
    matrix. The spanning non-zero and unique reaction vectors (RVs) are 
    combinatorially summed to generate one or more subspaces recursively.  
            Every reaction is represented as a sequence of identical components 
    across all RVs of a particular subspace. The terms are evaluated with 
    (biologically relevant bounds, linear maps, tests of convergence, descriptive
    statistics, vector norms) and the terms are classified into forward-, reverse- 
    and equivalent-subsets. Since, these are mutually exclusive the probability 
    of occurrence is binary (all, 1; none, 0).
            The combined propensity of a reaction is the p1-norm of the 
    sub-propensities, i.e., sum of the products of the probability and maximum
    numeric value of a subset (least upper bound, greatest lower bound). This, 
    if strictly positive is the probable rate constant, is used to infer dominant
    direction and annotate a reaction as "Forward (f)", "Reverse (b)" or 
    "Equivalent (e)".
            The inherent computational complexity (NP-hard) per iteration suggests
    that a suitable value for the number of reactions is around 20.
            Three functions comprise ReDirection. These are check_matrix() and 
    reaction_vector() which are internal, and calculate_reaction_vector()
            which is external.
Providing the container for the DockerParallel package.
An optimized method for identifying mutually 
    exclusive genomic events.  Its main contribution is a
    statistical analysis based on the Poisson-Binomial 
    distribution that takes into account that some samples
    are more mutated than others. See [Canisius, Sander, John WM Martens,
    and  Lodewyk FA Wessels. (2016)  "A novel independence test for
    somatic alterations in cancer shows that 
    biology drives mutual exclusivity but chance explains
    most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>].
    The mutations matrices are sparse matrices. The method developed takes 
    advantage of the advantages of this type of matrix to save 
    time and computing resources.
Enables researchers to sample redistricting plans from a pre-specified
    target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo
    algorithms. The package allows for the implementation of various constraints in
    the redistricting process such as geographic compactness and population parity
    requirements. Tools for analysis such as computation of various summary statistics
    and plotting functionality are also included. The package implements the SMC 
    algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration 
    algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>,
    the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>,
    the Merge-split/Recombination algorithms of Carter et al. (2019) <doi:10.48550/arXiv.1911.01503> 
    and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst 
    optimization algorithm of Cannon et al. (2020) <doi:10.48550/arXiv.2011.02288>.
Reliable and flexible tools for scoring redistricting plans using 
  common measures and metrics. These functions provide key direct access to 
  tools useful for non-simulation analyses of redistricting plans, such as for 
  measuring compactness or partisan fairness. Tools are designed to work with 
  the 'redist' package seamlessly.
Easy installation, loading, and control of packages for redistricting
    data downloading, spatial data processing, simulation, analysis, and 
    visualization. This package makes it easy to install and load multiple 
    'redistverse' packages at once. The 'redistverse' is developed and maintained 
    by the Algorithm-Assisted Redistricting Methodology (ALARM) Project.
    For more details see <https://alarm-redist.org>.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <https://www.w3.org/TR/rdf-primer/>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <https://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
Provides an interface to access data from the International Union 
    for Conservation of Nature (IUCN) Red List <https://api.iucnredlist.org/api-docs/index.html>. It allows users to retrieve up-to-date 
    information on species' conservation status, supporting biodiversity research 
    and conservation efforts.
A toolbox created by members of the International Union for
    Conservation of Nature (IUCN) Red List of Ecosystems Committee for
    Scientific Standards. Primarily, it is a set of tools suitable for
    calculating the metrics required for making assessments of species and
    ecosystems against the IUCN Red List of Threatened Species and the
    IUCN Red List of Ecosystems categories and criteria. See the IUCN
    website for detailed guidelines, the criteria, publications and other
    information.
An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO').  The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library.  It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.
Provide color schemes for maps (and other graphics) based on the
    color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'Redoc' documentation from an 'OpenAPI' Specification:
   <https://redocly.com/redoc/>.
Transfer 'REDCap' (Research Electronic Data Capture) data to a database,
    specifically optimized for 'DuckDB'. 
    Processes data in chunks to handle large datasets without exceeding available memory. 
    Features include data labeling, coded value conversion, and hearing a "quack" sound on success.
A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' API.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.
A data mining approach for longitudinal and clustered data, 
        which combines the structure of mixed effects model with tree-based 
        estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM 
        trees: a data mining approach for longitudinal and clustered data 
        <doi:10.1007/s10994-011-5258-3>.
Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach for modeling spatially 
    correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.
A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.
Imputation of missing numerical outcomes for a longitudinal trial 
    with protocol deviations.
    The package uses distinct treatment arm-based assumptions for the unobserved data, following 
    the general algorithm of Carpenter, Roger, and Kenward (2013) <doi:10.1080/10543406.2013.834911>, and the causal model
    of White, Royes and Best (2020) <doi:10.1080/10543406.2019.1684308>.
    Sensitivity analyses to departures from these assumptions can be done by the 
    Delta method of Roger.
    The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, 
    with additional coding for the causal model and delta method.
    The reference-based methods are jump to reference (J2R), 
    copy increments in reference (CIR), copy reference (CR),
    and the causal model, all of which must specify the reference treatment arm.
    Other methods are
    missing at random (MAR) and the last mean carried forward (LMCF). 
    Individual-specific imputation methods
    (and their reference groups) can be specified.
Reference database manager offering a set of functions
    to import, organize, clean, filter, audit and export reference genetic
    data. Provide functions to download sequence data from
    NCBI GenBank <https://www.ncbi.nlm.nih.gov/genbank/>.
    Designed as an environment for semi-automatic and assisted
    construction of reference databases and to improve standardization
    and repeatability in barcoding and metabarcoding studies.
Allows users to easily create references to R objects then
    'dereference' when needed or modify in place without using reference 
    classes, environments, or active bindings as workarounds. Users can 
    also create expression references that allow subsets of any object to 
    be referenced or expressions containing references to multiple 
    objects.
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
A suite of methods to fit and predict case count data using 
    a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) 
    model, based on an assumed specification of the effective reproduction 
    number. The significance of this approach is that it relates epidemic 
    progression to the average number of contacts of infected individuals, 
    which decays as a function of the total susceptible fraction remaining 
    in the population. The main functions are pred.curve(), which computes 
    the epidemic curve for a set of parameters, and estimate.mle(), which 
    finds the best fitting curve to observed data. The easiest way to pass 
    arguments to the functions is via a config file, which contains input 
    settings required for prediction, and the package offers two methods, 
    navigate_to_config()  which points the user to the configuration file, 
    and re_predict() for starting the fit-predict process. The main model was published in
    Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.
Indirect method for the estimation of reference intervals (RIs)
    using Real-World Data ('RWD') and methods for comparing and verifying RIs.
    Estimates RIs by applying advanced statistical methods to routine
    diagnostic test measurements, which include both pathological and
    non-pathological samples, to model the distribution of non-pathological
    samples. This distribution is then used to derive reference intervals
    and support RI verification, i.e., deciding if a specific RI is suitable
    for the local population. The package also provides functions for
    printing and plotting algorithm results. See ?refineR for a detailed
    description of features. Version 1.0 of the algorithm is described in
    'Ammer et al. (2021)' <doi:10.1038/s41598-021-95301-2>. Additional
    guidance is in 'Ammer et al. (2023)' <doi:10.1093/jalm/jfac101>. The
    verification method is described in 'Beck et al. (2025)'
    <doi:10.1515/cclm-2025-0728>.
These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <https://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://openrefine.org/docs/technical-reference/clustering-in-depth>.
Reallocating the respective lessons by hours (respecting the 
    constraints induced by the existence of coupled lessons) so that 
    the total number of gaps is as small as possible.
Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".
Automatic coding of open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology that assess the tendency to override an initial intuitive (but incorrect) answer and engage in reflection to reach a correct solution. The package standardizes CRT response coding across datasets in cognitive psychology, decision-making, and related fields. Automated coding reduces manual effort and improves reproducibility by limiting variability from subjective interpretation of open-ended responses. The package supports automatic coding and machine scoring for the original English-language CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, CRT4 and CRT7 (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, CRT-long (Primi et al., 2016) <doi:10.1002/bdm.1883>, and CRT-2 (Thomson & Oppenheimer, 2016) <doi:10.1017/s1930297500007622>.
Uses an indirect method based on truncated quantile-quantile plots to estimate reference limits from routine laboratory data: Georg Hoffmann and colleagues (2024) <doi: 10.3390/jcm13154397>. The principle of the method was developed by Robert G Hoffmann (1963) <doi:10.1001/jama.1963.03060110068020> and modified by Georg Hoffmann and colleagues (2015) <doi:10.1515/labmed-2015-0104>, and Frank Klawonn and colleagues (2020) <doi:10.1515/labmed-2020-0005>, (2022) <doi:10.1007/978-3-031-15509-3_31>.
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
Takes formulas including random-effects components (formatted as in 'lme4', 'glmmTMB', etc.) and processes them. Includes various helper functions.
An R package for estimating conditional multivariate reference regions. 
    The reference region is non parametrically estimated using a kernel density estimator.
    Covariates effects on the multivariate response means vector and variance-covariance
    matrix, thus on the region shape, are estimated by flexible additive predictors. 
    Continuous covariates non linear effects might be estimated using penalized splines smoothers.
    Confidence intervals for the covariates estimated effects might be derived from
    bootstrap resampling. Kernel density bandwidth can be estimated with different methods, including
    a method that optimize the region coverage. Numerical, and graphical, summaries
    can be obtained by the user in order to evaluate reference region performance with real data.
    Full mathematical details can be found in <doi:10.1002/sim.9163> and <doi:10.1007/s00477-020-01901-1>.
Connects dataframes/tables with a remote data source. Raw data downloaded
  from the data source can be further processed and transformed using data preparation 
  code that is also baked into the dataframe/table. Refreshable dataframes can 
  be shared easily (e.g. as R data files). Their users do not need to care about
  the inner workings of the data update mechanisms.
Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to  
    extract information at the level of gene, mRNA or protein accessions.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Access the 'Refuge' API, a web-application for locating trans and 
    intersex-friendly restrooms, including unisex and accessible restrooms. 
    Includes data on the location of restrooms, along with directions, 
    comments, user ratings and amenities. Coverage is global, but data is 
    most comprehensive in the United States.
    See <https://www.refugerestrooms.org/api/docs/> for full API documentation.
The Refugee Population Statistics Database published by
    The Office of The United Nations High Commissioner for Refugees (UNHCR)
    contains information about forcibly displaced populations
    spanning more than 70 years of statistical activities.
    It covers displaced populations such as refugees, asylum-seekers and
    internally displaced people, including their demographics.
    Stateless people are also included, most of who have never been displaced.
    The database also reflects the different types of solutions
    for displaced populations such as repatriation or resettlement. 
    More information on the data and methodology can be found on
    the UNHCR Refugee Data Finder <https://www.unhcr.org/refugee-statistics/>.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Produces Shiny applications for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.
It streamlines the evaluation of regression model
    assumptions, enhancing result reliability. With integrated tools for
    assessing key aspects like linearity, homoscedasticity, and more. It's
    a valuable asset for researchers and analysts working with regression
    models.
Implements regression calibration methods for correcting 
    measurement error in regression models using external or internal 
    reliability studies. Methods are described in Carroll, Ruppert, 
    Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear 
    Models: A Modern Perspective" <doi:10.1201/9781420010138>.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.
We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.
Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.
Drift-Diffusion Model (DDM) has been widely used to model binary
    decision-making tasks, and many research studies the relationship between
    DDM parameters and other characteristics of the subject. This package uses
    'RStan' to perform generalized liner regression analysis over DDM parameters
    via a single Bayesian Hierarchical model. Compared to estimating DDM
    parameters followed by a separate regression model, 'RegDDM' reduces bias
    and improves statistical power.
Performs regularization of differential item functioning (DIF) parameters 
    in item response theory (IRT) models (Belzak & Bauer, 2020)
    <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization
    algorithm.
A novel generalized Random Forest method, that can improve on
    RFs by borrowing the strength of penalized parametric regression. Based on
    Zhang et al. (2019) <doi:10.48550/arXiv.1904.10416>.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.
Extracts information from text using lookup tables of regular
    expressions. Each text entry is compared against all patterns, and all
    matching patterns and their corresponding substrings are returned. If a
    text entry matches multiple patterns, multiple rows are generated to
    capture each match. This approach enables comprehensive pattern coverage
    when processing large or complex text datasets.
A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, and calculating
    simple slopes, standardized coefficients, regions of significance
    (Johnson & Neyman, 1936; cf. Spiller et al., 2012), etc. See the reghelper
    documentation for more information, documentation, and examples.
A unified framework for detecting regime changes (changepoints) in 
    time series data. Implements both frequentist methods including Cumulative Sum 
    (CUSUM, Page (1954) <doi:10.1093/biomet/41.1-2.100>), Pruned Exact Linear 
    Time (PELT, Killick, Fearnhead, and Eckley (2012) 
    <doi:10.1080/01621459.2012.737745>), Binary Segmentation, and Wild Binary 
    Segmentation, as well as Bayesian methods such as Bayesian Online Changepoint 
    Detection (BOCPD, Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742>
    and Shiryaev-Roberts. Supports offline analysis for retrospective detection 
    and online monitoring for real-time surveillance. Provides rigorous 
    uncertainty quantification through confidence intervals and posterior 
    distributions. Handles univariate and multivariate series with detection of 
    changes in mean, variance, trend, and distributional properties.
Calculates intra-regional and inter-regional similarities based on user-provided
    spatial vector objects (regions) and spatial raster objects (cells with values).
    Implemented metrics include inhomogeneity, isolation 
    (Haralick and Shapiro (1985) <doi:10.1016/S0734-189X(85)90153-7>, 
    Jasiewicz et al. (2018) <doi:10.1016/j.cageo.2018.06.003>), 
    and distinction (Nowosad (2021) <doi:10.1080/13658816.2021.1893324>).
Provides methods to calculate approximate regional
    consistency probabilities using Method 1 and Method 2 proposed by the Japanese
    Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods
    are useful for assessing regional consistency in multi-regional
    clinical trials.  The package can calculate unconditional, joint, and
    conditional regional consistency probabilities.  For technical
    details, please see Homma (2024) <doi:10.1002/pst.2358>.
A tool to conquer the difficulties to convert various region names and administration division codes of Chinese regions. The current version enables seamlessly converting Chinese regions' formal names, common-used names, and codes between each other at the city level from 1986 to 2019.
Validating sub-national statistical typologies, re-coding across 
    standard typologies of sub-national statistics, and making valid aggregate
    level imputation, re-aggregation, re-weighting and projection down to 
    lower hierarchical levels to create meaningful data panels and time series.
A method for performing joint registration and functional principal
    component analysis for curves (functional data) that are generated from exponential family distributions. This 
    mainly implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963> and further adapts them to potentially
    incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration 
    can be used to better understand patterns in functional data by separating curves into phase and amplitude variability.
    This software handles both binary and continuous functional data, and is
    especially applicable in accelerometry and wearable technology.
Provides a generic infrastructure for creating and using registries.
An algorithm is proposed to estimate regression kink model proposed by the paper, Lixiong Yang and Jen-Je Su (2018) <doi:10.1016/j.jimonfin.2018.06.002>.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).
Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).
The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson.
	Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031>
	Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.
Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
	Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response 
	are supported. Robust network-based methods are available for continuous and survival 
	responses.
A function to plot a regression nomogram of regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	can be animated to allow on-the-fly changes to distribution representation and to 
	enable outcome calculation.
Provides R6 classes, methods and utilities to construct,
    analyze, summarize, and visualize regression models.
Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.
The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.
The visualization tool offers a nuanced understanding of regression dynamics, going beyond traditional per-unit interpretation of continuous variables versus categorical ones. It highlights the impact of unit changes as well as larger shifts like interquartile changes, acknowledging the distribution of empirical data. Furthermore, it generates visualizations depicting alterations in Odds Ratios for predictors across minimum, first quartile, median, third quartile, and maximum values, aiding in comprehending predictor-outcome interplay within empirical data distributions, particularly in logistic regression frameworks.
An R6 class "Replacer" provided by the package simplifies working with
    regex patterns containing named groups. It allows easy retrieval of matched portions
    and targeted replacements by group name, improving both code clarity and maintainability.
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (best
        linear unbiased predictors, BLUPs).
        Clifford and McCullagh (2006)
        <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
Plot regression surfaces and marginal effects in three dimensions. The plots are 'plotly' objects and can be customized using functions and arguments from the 'plotly' package.
Simulate samples from populations with known covariate
    distributions, generate response variables according to common linear and
    generalized linear model families, draw from sampling distributions of
    regression estimates, and perform visual inference on diagnostics from model
    fits.
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional structural
    equation models. Also contains a function to perform exploratory mediation (XMed).
Computes linear Bayesian spectral estimates from multirate	data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
In repeated measures studies with extreme large or small values it is common 
  that the subjects measurements on average are closer to the mean of the basic population. 
  Interpreting possible changes in the mean in such situations can lead to biased results 
  since the values were not randomly selected, they come from truncated sampling.
  This method allows to estimate the range of means where treatment effects are likely to occur 
  when regression toward the mean is present. 
  Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>.
  Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.
Tools for linear, nonlinear and nonparametric regression
             and classification.  Novel graphical methods for assessment 
             of parametric models using nonparametric methods. One 
             vs. All and All vs. All multiclass classification, optional
             class probabilities adjustment.  Nonparametric regression 
             (k-NN) for general dimension, local-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity.  Utilities for converting time series
             to rectangular form.  Utilities for conversion between
             factors and indicator variables.  Some code related to
             "Statistical Regression and Classification: from Linear
             Models to Machine Learning", N. Matloff, 2017, CRC,
             ISBN 9781498710916.
Population genetic data such as 'Single Nucleotide
        Polymorphisms' (SNPs) is often used to identify genomic regions
        that have been under recent natural or artificial selection
        and might provide clues about the molecular mechanisms of adaptation. 
        One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), 
        introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to 
        several statistics designed for whole genome scans. 
        The package provides functions to compute three of these,
        namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
        detecting positive or 'Darwinian' selection within a single population as well as
        'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 
        'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
        at differential selection between two populations. 
        Various plotting functions are included to facilitate
        visualization and interpretation of these statistics.
Contains example data for the 'rehh' package.
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.
Search R files for not installed packages and run install.packages.
Application of reinsurance treaties to claims portfolios. 
            The package creates a class Claims whose objective is to 
            store claims and premiums, on which different treaties can be applied.
            A statistical analysis can then be applied to measure the impact of
            reinsurance, producing a table or graphical output. This package can
            be used for estimating the impact of reinsurance on several portfolios
            or for pricing treaties through statistical analysis. Documentation
            for the implemented methods can be found in "Reinsurance: Actuarial
            and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant,
            Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and 
            "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance"
            by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) <doi:10.2307/2346830> and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) <doi:10.1007/s10852-005-9022-1>.
According to the order of the loci on the chromosome, the loci can be
    connected according to the interrelationship between them and classified according
    to different locus types.
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
Parameters estimation and linear regression models for Reliability 
    distributions families reviewed by Almalki & Nadarajah (2014)
    <doi:10.1016/j.ress.2013.11.010> using Generalized Additive
    Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos
    (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.
Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. This package prepares these files and also updates the versions according to the branches. It relies heavily on the 'desc' packages.
Functions to obtain an important number of electoral indicators described in the package, which can be divided into two large sections: The first would be the one containing the indicators of electoral disproportionality, such as, Rae index, Loosemore–Hanby index, etc. The second group is intended to study the dimensions of the party system vote, through the indicators of electoral fragmentation, polarization, volatility, etc. Moreover, multiple seat allocation simulations can also be performed based on different allocation systems, such as the D'Hondt method, Sainte-Laguë, etc. Finally, some of these functions have been built so that, if the user wishes, the data provided by the Spanish Ministry of Home Office for different electoral processes held in Spain can be obtained automatically. All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226).
Calculates relevance and significance values for
  simple models and for many types of regression models.
  These are introduced in
  'Stahel, Werner A.' (2021)
  "Measuring Significance and Relevance instead of p-values."
  <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>.
  These notions are also applied to replication studies,
  as described in the manuscript
  'Stahel, Werner A.' (2022)
  "'Replicability': Terminology, Measuring Success, and Strategy"
  available in the documentation.
Tools to fit and simulate realizations from relational event models.
A method for fitting the entire regularization path of the
    reluctant generalized additive model (RGAM) for linear regression, logistic, 
    Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019)
    <arXiv:1912.01808> for details.
Checking the reliability of predictions via the CORP approach,
    which generates provably statistically 'C'onsistent, 'O'ptimally binned, and
    'R'eproducible reliability diagrams using the 'P'ool-adjacent-violators
    algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.
Perform structural reliability analysis, including computation and
    simulation with system signatures, Samaniego (2007)
    <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and
    Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally
    supports parametric and topological inference given system lifetime data,
    Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.
Calculates and compares various reliability coefficients for 
    unidimensional and multidimensional scales. Supported unidimensional 
    estimators include coefficient alpha, congeneric reliability, the 
    Gilmer-Feldt coefficient, Feldt's classical congeneric reliability, 
    Hancock's H, Heise-Bohrnstedt's omega, Kaiser-Caffrey's alpha, and Ten 
    Berge and Zegers' mu series. Multidimensional estimators include 
    stratified alpha, maximal reliability, correlated factors reliability, 
    second-order factor reliability, and bifactor reliability.
    See Cho (2021) <doi:10.1007/s11336-021-09801-1>,
    Cho (2024) <doi:10.1037/met0000475>,
    Cho (2025) <doi:10.1037/met0000525>.
Modeling and plotting functions for Reliability Growth Analysis (RGA). Models include the Duane (1962) <doi:10.1109/TA.1964.4319640>, Non-Homogeneous Poisson Process (NHPP) by Crow (1975) (No. AMSAATR138), Piecewise Weibull NHPP by Guo et al. (2010) <doi:10.1109/RAMS.2010.5448029>, and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) <https://cran.r-project.org/package=segmented>.
Learning modules for reliability analysis including modules for Reliability, 
  Availability, and Maintainability (RAM) Analysis, Life Data Analysis, and Reliability Testing.
Build interactive Reliability Probability Plots with 'plotly' by Carson Sievert (2020) <https://plotly.com/r/>, an interactive web-based graphing library.
Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools  such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.
An interactive web application for reliability analysis using the 'shiny' <https://shiny.posit.co/> framework. The app provides an easy-to-use interface for performing reliability analysis using 'WeibullR' <https://cran.r-project.org/package=WeibullR> and 'ReliaGrowR' <https://cran.r-project.org/package=ReliaGrowR>.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.
It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions.
  References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>; Ho, H. J., Lin, T. I., Chen, H. Y., & Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 <doi:10.1016/j.jspi.2011.06.006>; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 <doi:10.1007/s11222-022-10200-4>.
Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population 
  substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals' relationships rely on DNA mixtures, 
  and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented 
  by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). 
  The implementation and method is described in Dorum et al. (2017) <doi:10.1007/s00414-016-1526-x>, Hernandis et al. (2019)
  <doi:10.1016/j.fsigss.2019.09.085> and Kaur et al. (2016) <doi:10.1007/s00414-015-1276-1>.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
The rema package implements a permutation-based approach for binary 
	meta-analyses of 2x2 tables, founded on conditional logistic regression, 
	that provides more reliable statistical tests when heterogeneity is 
	observed in rare event data (Zabriskie et al. 2021 <doi:10.1002/sim.9142>). 
	To adjust for the effect of heterogeneity, this method conditions on the 
	sufficient statistic of a proxy for the heterogeneity effect as opposed to
	estimating the heterogeneity variance. While this results in the model not
	strictly falling under the random-effects framework, it is akin to a 
	random-effects approach in that it assumes differences in variability due 
	to treatment. Further, this method does not rely on large-sample 
	approximations or continuity corrections for rare event data. This method
	uses the permutational distribution of the test statistic instead of
	asymptotic approximations for inference. The number of observed events 
	drives the computation complexity for creating this permutational 
	distribution. Accordingly, for this method to be computationally feasible,
	it should only be applied to meta-analyses with a relatively low number of
	observed events. To create this permutational distribution, a network 
	algorithm, based on the work of Mehta et al. (1992) <doi:10.2307/1390598> 
	and Corcoran et al. (2001) <doi:10.1111/j.0006-341x.2001.00941.x>, is 
	employed using C++ and integrated into the package.
Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent.  Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.  Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) <doi:10.1016/j.ajhg.2009.11.001>, and random effects meta-analysis uses the method of Han, et al. <doi:10.1093/hmg/ddw049>.
Automatically creates separate regression models for different spatial 
    regions. The prediction surface is smoothed using a regional border smoothing 
    method. If regional models are continuous, the resulting prediction surface is 
    continuous across the spatial dimensions, even at region borders. Methodology 
    is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.
An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.
Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.
Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
Reference-based multiple imputation of ordinal and binary responses under Bayesian
          framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for 
          missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta 
          Adjustment which can generate tipping point analysis.
Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses 'Monolix' software (<https://monolixsuite.slp-software.com/>), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use.
Traditional latent variable models assume that the population
    is homogeneous, meaning that all individuals in the population are
    assumed to have the same latent structure. However, this assumption is
    often violated in practice given that individuals may differ in their
    age, gender, socioeconomic status, and other factors that can affect
    their latent structure. The robust expectation maximization (REM)
    algorithm is a statistical method for estimating the parameters of a
    latent variable model in the presence of population heterogeneity as recommended by 
    Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the
    expectation-maximization (EM) algorithm, but it allows for the case
    when all the data are generated by the assumed data generating model.
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.
An HTTP API client for 'Lemmy'
    (<https://github.com/LemmyNet/lemmy>) in R. Code and documentation are
    generated from the official 'JavaScript' client source
    (<https://github.com/LemmyNet/lemmy-js-client>).
Provides the function remode() for recursive modality 
    detection in ordinal data. 'remode' is an algorithm specifically designed to 
    estimate the number and location of modes in ordinal data while being robust to large sample sizes.
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
These tools were created to test map-scale hypotheses about trends in large
  remotely sensed data sets but any data with spatial and temporal variation
  can be analyzed. Tests are conducted using the PARTS method for analyzing spatially
  autocorrelated time series
  (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>).
  The method's unique approach can handle extremely large data sets that other
  spatiotemporal models cannot, while still appropriately accounting for
  spatial and temporal autocorrelation. This is done by partitioning the data
  into smaller chunks, analyzing chunks separately and then combining the
  separate analyses into a single, correlated test of the map-scale hypotheses.
Download and install R packages stored in 'GitHub', 'GitLab',
    'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git'
    repositories.  This package provides the 'install_*' functions in
    'devtools'. Indeed most of the code was copied over from 'devtools'.
Make your workflow faster and easier. Easily customizable
    plots (via 'ggplot2'), nice APA tables (following the style of the
    *American Psychological Association*) exportable to Word (via
    'flextable'), easily run statistical tests or check assumptions, and
    automatize various other tasks.
T (extent of the primary tumor), N (absence or presence and extent of regional lymph node metastasis) and M (absence or presence of distant metastasis) are three components to describe the anatomical tumor extent. TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) <https://www.austinpublishinggroup.com/biometrics/fulltext/biometrics-v2-id1014.php>.
Computes a variety of statistics for relational event models. Relational event models enable researchers to investigate both exogenous and endogenous factors influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.
A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization  (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.
Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 <doi:10.48550/arXiv.2403.19329>). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant  (758791).
Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.
ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016).
Provides the datasets in the book "Methods of Multivariate Analysis (3rd)", such as Table 6.27 Blood Pressure Data, for statistical analysis,especially MANOVA. The dataset names correspond to their numbering in the third edition of the book, such as table6.27. Based on the book by Rencher and Christensen (2012, ISBN:9780470178966).
Fits linear models with endogenous regressor using latent instrumental variable approaches. 
    The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as 
    Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The
    distribution for the excesses can be chosen, and heterogeneous data
    (including historical data or block data) can be used in a
    Maximum-Likelihood framework.
Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power.
Provides an R interface to the NCBI's 'EUtils' API, 
    allowing users to search databases like 'GenBank' 
    <https://www.ncbi.nlm.nih.gov/genbank/> and 'PubMed' 
    <https://pubmed.ncbi.nlm.nih.gov/>, process the 
    results of those searches and pull data into their R sessions.
A dependency management toolkit for R. Using 'renv', you can create
    and manage project-local R libraries, save the state of these libraries to
    a 'lockfile', and later restore your library as required. Together, these
    tools can help make your projects more isolated, portable, and reproducible.
Provides a general routine, envMU, which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model.  This package implements response envelopes,  partial response envelopes,  envelopes in the predictor space,  heteroscedastic envelopes,  simultaneous envelopes,  scaled response envelopes,  scaled envelopes in the predictor space,  groupwise envelopes, weighted envelopes,  envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares,  envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.
Provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.
Provides functions to compute Shannon entropy, Renyi entropy,
    Tsallis entropy, and related extropy measures for discrete probability
    distributions. Includes joint and conditional entropy, KL divergence,
    Jensen-Shannon divergence, cross-entropy, normalized entropy, and
    Renyi extropy (including the conditional and maximum forms). All
    measures use the natural logarithm (nats). Useful for information
    theory, statistics, and machine learning applications.
Contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization 
	methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the 
	ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation 
	of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential
	biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten 
	equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making 
	use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate
	the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.
The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS).
      It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. 
      The package also contains via quests enriched datasets on batteries,
      printer, mobiles, and tablets.
Set of utilities to facilitate the reproduction of analysis in R.
 It allow to make_structure(), clean_structure(), and run and log programs in a
 predefined order to allow secondary files, analysis and reports be constructed in
 an ordered and reproducible form.
The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.
Performs genome-wide association studies (GWAS) on individuals that are both related and have repeated measurements. For each Single Nucleotide Polymorphism (SNP), it computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. The computed p-values can be visualized in a Manhattan plot. For more details see Ronnegard et al. (2016) <doi:10.1111/2041-210X.12535> and for more examples see <https://github.com/larsronn/RepeatABEL_Tutorials>.
Various functions to fit models for non-normal repeated
    measurements, such as Binary Random Effects Models with Two Levels of Nesting,
    Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
    Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, 
    Discrete-time Hidden Markov Chain Models,
    Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
    generalized nonlinear autoregression models, multivariate Gaussian copula models,
    generalized non-linear mixed models with one random effect,  
    generalized non-linear mixed models using h-likelihood for one random effect, 
    Repeated Measurements Models for Counts with Frailty or Serial Dependence,
    Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
    Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
    contingency tables, correlated negative binomial models with Kalman update.
    References include Lindsey's text books, 
    JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.
A toolkit for the analysis of high-dimensional repeated measurements, providing functions 
    for outlier detection, differential expression analysis, gene-set tests, and binary random data generation.
Utilities for accessing RePEc (Research Papers in Economics)
    through a RESTful API. You can request a code and get detailed
    information at the following page: <https://ideas.repec.org/api.html>.
Creates reports from Trello, a collaborative, project organization 
             and list-making application. <https://trello.com/>
             Reports are created by comparing individual Trello board
             cards from two different points in time and documenting any changes made
             to the cards.
Estimation of Bayes and local Bayes false discovery rates for
      replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
Mimic the style of traditional reporting macros for clinical trials. The purpose is to generate tables, listings and figures that support clinical research. This package is well suited for firms or individuals who wish to incorporate R without changing their ways of working as it follows a traditional clinical research workflow. Invoke functions (instead of macros) to summarize data and produce formatted reports. This package differs from others in that it includes tools (wrappers) for both analyzing and reporting data.
Updates values within csv format data files using a custom, User-built
    csv format lookup file. Based on 'data.table' package.
Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.
For a multisite replication project, computes the consistency metric P_orig, which is
    the probability that the original study would observe an estimated effect size as extreme or more extreme
    than it actually did, if in fact the original study were statistically consistent with the replications. Other
    recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate
    the original study; and (2) the probability of a true effect of meaningful size in the direction opposite
    the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}.
    Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well
    as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>)
    for details.
Performs comparative bioavailability calculations for Average
    Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' /
    'Method B' and the detection of outliers. If the design allows, assessment
    of the empiric Type I Error and iteratively adjusting alpha to control the
    consumer risk. Average Bioequivalence - optionally with a tighter (narrow
    therapeutic index drugs) or wider acceptance range (South Africa: Cmax) -
    is implemented as well.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Provides utilities for the design and analysis of replication
    studies.  Features both traditional methods based on statistical
    significance and more recent methods such as the sceptical p-value;
    Held L. (2020) <doi:10.1111/rssa.12493>, Held et al. (2022)
    <doi:10.1214/21-AOAS1502>, Micheloud et al. (2023) <doi:10.1111/stan.12312>.
    Also provides related methods including the harmonic mean chi-squared
    test; Held, L. (2020) <doi:10.1111/rssc.12410>, and intrinsic
    credibility; Held, L. (2019) <doi:10.1098/rsos.181534>. Contains
    datasets from five large-scale replication projects.
Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
Tools for generating descriptives and report tables for different models,
    data.frames and tables and exporting them to different formats.
Generates a project and repo for easy initialization of a GitHub repo for R workshops. The repo includes a README with instructions to ensure that all users have the needed packages, an 'RStudio' project with the right directories and the proper data. The repo can then be used for hosting code taught during the workshop.
Fits linear models to repeated ordinal scores using GEE methodology.
The aim of the 'report' package is to bridge the gap between
    R’s output and the formatted results contained in your manuscript.
    This package converts statistical models and data frames into textual
    reports suited for publication, ensuring standardization and quality
    in results reporting.
Contains functions to create regulatory-style statistical reports.
    Originally designed to create tables, listings, and figures for the 
    pharmaceutical, biotechnology, and medical device industries, these
    reports are generalized enough that they could be used in any industry.
    Generates text, rich-text, PDF, HTML, and Microsoft Word file formats.  
    The package specializes 
    in printing wide and long tables with automatic page wrapping and splitting.  
    Reports can be produced with a minimum of function calls, and without 
    relying on other table packages.  The package supports titles, footnotes, 
    page header, page footers, spanning headers, page by variables, 
    and automatic page numbering.
Methods to easily build requests in the non-standard JSON
    schema required by the National Institute of Health (NIH)'s 'RePORTER
    Project API' <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>.
    Also retrieve and process result sets as either a ragged or flattened 'tibble'.
Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter 
    Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along 
    with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free 
    method that works well with all types of biomedical features, such as genes, chemical compounds, 
    and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental 
    designs, because of the included multi-group-compatible statistical methods.
Provides an infrastructure for handling multiple R Markdown
  reports, including automated curation and time-stamping of outputs,
  parameterisation and provision of helper functions to manage dependencies.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression from function 'coxph' of package 'survival';
    2. Conditional logistic regression from function 'clogit' of package 'survival';
    3. Ordered logistic regression from function 'polr' of package 'MASS';
    4. Binary logistic regression from function 'glm' of package 'stats';
    5. Linear regression from function 'lm' of package 'stats';
    6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression';
    7. Multilevel model from function 'lme' of package 'nlme'.
Streamlined statistical reporting in 'Rmarkdown' environments. 
    Facilitates the automated reporting of descriptive statistics, multiple 
    univariate models, multivariable models and tables combining these outputs. 
    Plotting functions include customisable survival curves, forest plots from 
    logistic and ordinal regression and bivariate comparison plots.
Provides an easy way to report the results of ROC analysis, including:
    1. an ROC curve. 
    2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy),
    SEN (sensitivity), SPE (specificity),
    PLR (positive likelihood ratio), NLR (negative likelihood ratio),
    PPV (positive predictive value), NPV (negative predictive value),
    PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance),
    KAPPA (kappa value).
Adds subtotal rows / sections (a la the 'SAS' 'Proc Tabulate' All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.
These functions are especially helpful when writing reports of data analysis using "Sweave".
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package.
Performs exploratory projection pursuit  via 'REPPlab' (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019) <doi:10.1080/03610918.2019.1626880>) using a Shiny app.
String and binary representations of objects for several formats /
    mime types.
Compute the values of various parameters evaluating how similar two multidimensional datasets' structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), <doi:10.1016/S0169-7439(98)00005-7>. The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets' centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets.
An implementation of Kaplan, Betancourt, Steorts (2022) <doi:10.1080/00031305.2022.2041482> that creates representative records for use in downstream tasks after entity resolution is performed. Multiple methods for creating the representative records (data sets) are provided.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
Convenience wrapper that uses the 'rmarkdown' package to
    render small snippets of code to target formats that include both code
    and output.  The goal is to encourage the sharing of small,
    reproducible, and runnable examples on code-oriented websites, such as
    <https://stackoverflow.com> and <https://github.com>, or in email. The
    user's clipboard is the default source of input code and the default
    target for rendered output. 'reprex' also extracts clean, runnable R
    code from various common formats, such as copy/paste from an R
    session.
Includes data analysis and meta-analysis functions (e.g., to 
    calculate effect sizes and 95% Confidence Intervals (CI) on Standardised 
    Effect Sizes (d) for AB/BA cross-over repeated-measures experimental 
    designs), data presentation functions (e.g., density curve overlaid on 
    histogram),and the data sets analyzed in different research papers in 
    software engineering (e.g., related to software defect prediction or multi-
    site experiment concerning the extent to which structured abstracts were
    clearer and more complete than conventional abstracts) to streamline
    reproducible research in software engineering.
A collection of high-level, machine- and OS-independent tools
    for making reproducible and reusable content in R.
    The two workhorse functions are 'Cache()' and 'prepInputs()'.
    'Cache()' allows for nested caching, is robust to environments and objects
    with environments (like functions), and deals with some classes of 
    file-backed R objects e.g., from 'terra' and 'raster' packages. 
    Both functions have been developed to be foundational components of data
    retrieval and processing in continuous workflow situations. In both functions,
    efforts are made to make the first and subsequent calls of functions have 
    the same result, but faster at subsequent times by way of checksums
    and digesting. Several features are still under development, including
    cloud storage of cached objects allowing for sharing between users. Several
    advanced options are available, see '?reproducibleOptions()'.
Provide reproducible R chunks in R Markdown document that automatically check computational results for reproducibility. This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, <doi:10.31234/osf.io/3zjvf>).
Transform coordinates from a specified source to a specified 
 target map projection. This uses the 'PROJ' library directly, by wrapping the 
 'PROJ' package which leverages 'libproj', otherwise the 'proj4' package. The 'reproj()' 
 function is generic, methods may be added to remove the need for an explicit 
 source definition. If 'proj4' is in use 'reproj()' handles the requirement for 
 conversion of angular units where necessary. This is for use primarily to 
 transform generic data formats and direct leverage of the underlying
 'PROJ' library. (There are transformations that aren't possible with 'PROJ' and
 that are provided by the 'GDAL' library, a limitation which users of this 
 package should be aware of.) The 'PROJ' library is available at 
 <https://proj.org/>.
Provides teaching datasets and helper functions to support the open
    educational resource Martinez (2026) "Reproducible Research Using R"
    <doi:10.5281/zenodo.19136755>. The package includes datasets used throughout
    the book and utilities to list and copy chapter scripts shipped with the package.
Compute price indices using various Hedonic and
    multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic
    Multilateral Time series re-estimation with splicing). The central function
    calculate_hedonic_index() offers a unified interface for running these methods
    on structured datasets. This package is designed to support index construction workflows across a wide range of domains 
    — including but not limited to real estate — where quality-adjusted price comparisons over time are essential. 
    The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands.
    The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024)
    <doi:10.1177/0282423X241246617>. For broader methodological context, see Eurostat
    (2013, ISBN:978-92-79-25984-5, <doi:10.2785/34007>).
Root Expected Proportion Squared Difference (REPSD) is a nonparametric
  differential item functioning (DIF) method that (a) allows practitioners 
  to explore for DIF related to small, fine-grained focal groups of examinees, 
  and (b) compares the focal group directly to the composite group that will be 
  used to develop the reported test score scale. Using your provided response 
  matrix with a column that identifies focal group membership, this package
  provides the REPSD values, a simulated null distribution of possible REPSD
  values, and the simulated p-values identifying items possibly displaying DIF 
  without requiring enormous sample sizes.
Provides a collection of methods for quantifying representational similarity between learned features or multivariate data. The package offers an efficient 'C++' backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) <doi:10.1145/3728458> for a comprehensive overview of the topic.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Provides easy access to 'The Reptile Database', a comprehensive catalogue of all living reptile species and their classification. This package includes taxonomic data for over 10,000 reptile species, approximately 2,800 of which are subspecies, covering all extant reptiles. The dataset features taxonomic names, synonyms, distribution data, type specimens, and literature references, making it ready for research and analysis. Data is sourced from 'The Reptile Database' <http://www.reptile-database.org/>.
Recursive lists in the form of R objects, 'JSON', and 'XML',
    for use in teaching and examples. Examples include color palettes,
    Game of Thrones characters, 'GitHub' users and repositories, music
    collections, and entities from the Star Wars universe. Data from the
    'gapminder' package is also included, as a simple data frame and in
    nested and split forms.
In order to facilitate parsing of http requests and creating
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
Contains the function run.eqs() which calls an EQS script file, executes the EQS estimation, and, finally, imports the results as R objects. These two steps can be performed separately: call.eqs() calls and executes EQS, whereas read.eqs() imports existing EQS outputs as objects into R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
A single key function, 'Require' that makes rerun-tolerant
    versions of 'install.packages' and `require` for CRAN packages, packages
    no longer on CRAN (i.e., archived), specific versions of packages, 
    and GitHub packages. This approach is developed to create reproducible 
    workflows that are flexible and fast enough to use while in development stages,
    while able to build snapshots once a stable package collection is found. 
    As with other functions in a reproducible workflow, this package 
    emphasizes functions that return the same result whether it is 
    the first or subsequent times running the function, with subsequent times being
    sufficiently fast that they can be run every time without undue waiting burden on 
    the user or developer.
Helper function to install packages for R using an external
    'requirements.txt' or a string containing diverse packages from
    several resources like Github or CRAN.
Modularizes source code. Keeps the global environment clean,
    explicifies interdependencies. Inspired by 'RequireJS'<http://requirejs.org/>.
Computes the power resulting from completely randomized and rerandomized experiments with two groups. Furthermore, computes the sample size necessary to obtain a desired level of power for completely randomized and rerandomized experiments.
General purpose R client for 'ERDDAP™' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP™' information: 
    <https://upwell.pfeg.noaa.gov/erddap/information.html>.
Contains three functions that access
    environmental data from any 'ERDDAP™' data web service. The rxtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    rxtracto_3D() function extracts data in a box. The rxtractogon() function
    extracts data in a polygon. All of those three function use the 'rerddap' package
    to extract the data, and should work with any 'ERDDAP™' server.
    There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
    package to simplify the creation of maps of the data.
A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.
Bootstrap, permutation tests, and jackknife,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.
Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1)
    "Mathematical Statistics with Resampling in R" (3rd Ed).
Maximum likelihood estimation for univariate reducible
 stochastic differential equation models. Discrete, possibly noisy
 observations, not necessarily evenly spaced in time. Can fit
 multiple individuals/units with global and local parameters, by
 fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019)
 "Estimating reducible stochastic differential equations by
 conversion to a least-squares problem", Computational Statistics
 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.
Functions for dissimilarity analysis and memory-based learning 
    (MBL, a.k.a local modeling) in complex spectral data sets. 
    Most of these functions are based on the methods presented in 
    Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
A simple user-friendly library based on the 'python' module 'reservoirpy'.
             It provides a flexible interface to implement efficient Reservoir
             Computing (RC) architectures with a particular focus on Echo State Networks
             (ESN). Some of its features are: offline and online training, parallel implementation, 
             sparse matrix computation, fast spectral initialization, advanced learning 
             rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create 
             complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, 
             and complex feedback loops. Moreover, graphical tools are included to easily 
             explore hyperparameters. Finally, it includes several tutorials exploring
             time series forecasting, classification and hyperparameter tuning. For more information
             about 'reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>.
             This package was developed in the framework of the University of Bordeaux’s IdEx
             "Investments for the Future" program / RRI PHDS.
Define distribution families and fit them to
    interval-censored and interval-truncated data, where the truncation
    bounds may depend on the individual observation. The defined
    distributions feature density, probability, sampling and fitting
    methods as well as efficient implementations of the log-density log
    f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
    neural networks via the 'tensorflow' package. Allows training
    parametric neural networks on interval-censored and interval-truncated
    data with flexible parameterization. Applications include Claims
    Development in Non-Life Insurance, e.g. modelling reporting delay
    distributions from incomplete data, see Bücher, Rosenstock (2022)
    <doi:10.1007/s13385-022-00314-4>.
Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. 
  Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for 
  single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.
Simulates individual-based models of agricultural pest management
    and the evolution of pesticide resistance. Management occurs on a spatially
    explicit landscape that is divided into an arbitrary number of farms that
    can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest
    genomes are modelled in a way that allows for any number of pest traits with
    an arbitrary covariance structure that is constructed using an evolutionary
    algorithm in the mine_gmatrix() function. Simulations are then run using the
    run_farm_sim() function. This package thereby allows for highly mechanistic
    social-ecological models of the evolution of pesticide resistance under
    different types of crop rotation and pesticide application regimes.
Flexibly restructure and aggregate data using 
  just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').
Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.
Developed to assist researchers with planning analysis,
    prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens.
    With functionality to export and import marginal distributions as well as synthesise data, both with
    and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA).
    Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence
    Sandercock et al. (2011) <doi:10.7488/ds/104>,
    Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.
Datasets with energy consumption data of different data measurement frequencies. 
    The data stems from several publicly funded research projects of the Chair of Information  
    Systems and Energy Efficient Systems at the University of Bamberg.
Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2024), Biostatistics 25(4): 1094-1111.
Contains various tools to estimate, analyze, and visualize Response Item Networks. 'ResIN' dummy-codes ordered and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.
An intuitive and explainable metric of Feature Importance for Classification Problems.
    Resolution Index measures the extent to which a Feature clusters different classes when data is 
    sorted on it. User provides a DataFrame, column name of the Class, sample size and number of 
    iterations used for calculation. Resolution Index for each Feature is returned, which can be 
    effectively used to rank Features and reduce Dimensionality of Training data. For more details
    on Feature Selection see Theng and Bhoyar (2023) <doi:10.1007/s10115-023-02010-5>.
Electrical properties of resistor networks using matrix methods.
A 'shiny' module to facilitate page layouts with resizable panes for page content based on 'split.js' 'JavaScript' library (<https://split.js.org>).
A pair of functions for calculating mean residual life (MRL)
    , median residual life, and percentile residual life using the outputs of 
    either the 'flexsurv' package or parameters
    provided by the user. Input information about the distribution, the given
    'life' value, the percentile, and the type of residual life, 
    and the function will return your desired values. For the 'flexsurv' option,
    the function allows the user to input their own data for making predictions. 
    This function is based on Jackson (2016) <doi:10.18637/jss.v070.i08>.
Implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues 'PROCEED', 'DEFER', or 'ABSTAIN' signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.
The Bayesian modelling of relative sea-level data
    using a comprehensive approach that incorporates
    various statistical models within a unifying framework. 
    Details regarding each statistical models; 
    linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, 
    change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, 
    integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, 
    temporal splines (Upton et al 2023) <arXiv:2301.09556>, 
    spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and
    generalised additive models (Upton et al 2023) <arXiv:2301.09556>.
    This package facilitates data loading, 
    model fitting and result summarisation.
    Notably, it accommodates the inherent measurement errors
    found in relative sea-level data across multiple dimensions,
    allowing for their inclusion in the statistical models.
Compress local and online images using the 'reSmush.it' API
    service <https://resmush.it/>.
Utility functions to download data from the 'RESOURCECODE'
    hindcast database of sea-states, time series of sea-state parameters
    and time series of 1D and 2D wave spectra.  See
    <https://resourcecode.ifremer.fr> for more details about the available
    data.  Also provides facilities to plot and analyse downloaded data,
    such as computing the sea-state parameters from both the 1D and 2D
    surface elevation variance spectral density.
Includes Resourcecode hindcast database (see
    <https://resourcecode.ifremer.fr>) configuration data: nodes locations
    for both the sea-state parameters and the spectra data; examples of
    time series of 1D and 2D surface elevation variance spectral density.
A resource represents some data or a computation unit. It is 
    described by a URL and credentials. This package proposes a Resource model
    with "resolver" and "client" classes to facilitate the access and the usage of the 
    resources.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
A collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) <doi:10.2307/2337638>. Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively.  In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size.
Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data,  calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) <doi:10.1007/s004249900135>), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' <doi:10.1103/PhysRevLett.89.068102>) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) <doi:10.1016/S0378-4371(02)01383-3>) were applied for the analysis of respiratory data.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Functions for
    analysis of resulting respirometry data are also provided. This package
    provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) <doi:10.7275/vyxb-gt24> for more information.
Provides a structural, reproducible workflow for the
    processing and analysis of respirometry data. It contains analytical
    functions and utilities for working with oxygen time-series to determine
    respiration or oxygen production rates, and to make it easier to report and
    share analyses. See Harianto et al. 2019 <doi:10.1111/2041-210X.13162>.
Calculate common survey data quality indicators for multi-item scales 
             and matrix questions. Currently supports the calculation of
             response style indicators and response distribution indicators.
             For an overview on response quality indicators see Bhaktha N,
             Henning S, Clemens L (2024). 'Characterizing response quality
             in surveys with multi-item scales: A unified framework'
             <https://osf.io/9gs67/>.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Eurostat is the statistical office of the European Union and provides high quality statistics for Europe.
             Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). 
             The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services 
             (<https://ec.europa.eu/eurostat/web/user-guides/data-browser/api-data-access/api-detailed-guidelines/sdmx2-1>) to search and download data from 
             the Eurostat database using the SDMX standard.
A RESTful API wrapper for accessing the main databases of
    Germany's Federal Statistical System. Supports data 
    search functions, credential management, result caching, and handling 
    remote background jobs for large datasets.
Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.
Download large sections of
    'GenBank' <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local
    SQL-based database. A user can then query this database using 'restez'
    functions or through 'rentrez' <https://CRAN.R-project.org/package=rentrez>
    wrappers.
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
Implementation of the RESTK algorithm based on Markov's Inequality from  Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco  J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).
Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 <doi:10.1111/1365-2664.13803>). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 <doi:10.1023/A:1008129329289>), or the integral index of connectivity (Pascual-Hortal & Saura 2006 <doi:10.1007/s10980-006-0013-z>). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the 'Choco-solver' software to perform optimization using constraint programming (CP) techniques (<https://choco-solver.org/>).
A random-effects stochastic model that allows quick detection of 
  clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. 
  The parameters of the base model, which are inferred using a maximum likelihood approach, 
  are assumed to be shared across the clones. Although this assumption makes inference easier, 
  in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. 
  Therefore we extended the base model by introducing random effects for the clones. 
  In this extended formulation the dynamic parameters are estimated using a tailor-made 
  expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.05.31.494100>.
Build reusable validators from small building blocks using the
    base pipe operator. Define runtime contracts once with 'restrict()' and
    enforce them anywhere in code. Validators compose naturally, support
    dependent rules via formulas, and produce clear, path-aware error
    messages. No DSL, no operator overloading, just idiomatic R.
Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.
Allows to easily create high-performance full featured HTTP APIs from R
  functions. Provides high-level classes such as 'Request', 'Response',
  'Application', 'Middleware' in order to streamline server side
  application development. Out of the box allows to serve requests using
  'Rserve' package, but flexible enough to integrate with other HTTP servers
  such as 'httpuv'.
Allows wrapping values in success() and failure() types to capture 
    the result of operations, along with any status codes. Risky expressions can be 
    wrapped in as_result() and functions wrapped in result() to catch errors
    and assign the relevant result types. Monadic functions can be bound together 
    as pipelines or transaction scripts using then_try(), to gracefully handle 
    errors at any step.
Database data model management utilities for R packages in the Observational Health Data Sciences and
    Informatics programme. 'ResultModelManager' provides utility functions to allow package
    maintainers to migrate existing SQL database models, export and import results in consistent patterns.
Using a CSV, LaTeX and R to easily build attractive resumes.
Permite obtener rápidamente una serie de medidas de resumen y gráficos para datos numéricos discretos o continuos en series simples. También permite obtener tablas de frecuencia clásicas y gráficos cuando se desea realizar un análisis de series agrupadas. Su objetivo es de aplicación didáctica para un curso introductorio de Bioestadística  utilizando el software R, para las carreras de grado las carreras de grado y otras ofertas educativas de la Facultad de Ciencias Agrarias de la UNJu / It generates summary measures and graphs for discrete or continuous numerical data in simple series. It also enables the creation of classic frequency tables and graphs when analyzing grouped series. Its purpose is for educational application in an introductory Biostatistics course using the R software, aimed at undergraduate programs and other educational offerings of the Faculty of Agricultural Sciences at the National University of Jujuy (UNJu).
Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>. 
             Implementation of Neural Networks, Extreme Gradient Boosting, 
             and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
Implements the regularized exponentially tilted empirical 
    likelihood method. Details of the method are given in Kim, MacEachern, and 
    Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by 
    the U.S. National Science Foundation under Grants No. SES-1921523 and 
    DMS-2015552.
Implementation of the race/ethnicity prediction method, described 
    in "rethnicity: An R package for predicting ethnicity from names" 
    by Fangzhou Xie (2022) <doi:10.1016/j.softx.2021.100965> and 
    "Rethnicity: Predicting Ethnicity from Names" 
    by Fangzhou Xie (2021) <doi:10.48550/arXiv.2109.09228>.
Interface to 'Python' modules, classes, and functions. When calling
    into 'Python', R data types are automatically converted to their equivalent 'Python'
    types. When values are returned from 'Python' to R they are converted back to R
    types. Compatible with all versions of 'Python' >= 2.7.
Retime speech signals with a native Waveform Similarity Overlap-Add (WSOLA) implementation translated from the 'TSM toolbox' by Driedger & Müller (2014) <https://www.audiolabs-erlangen.de/content/resources/MIR/TSMtoolbox/2014_DriedgerMueller_TSM-Toolbox_DAFX.pdf>. Design retimings and pitch (f0) transformations with tidy data and apply them via 'Praat' interface. Produce spectrograms, spectra, and amplitude envelopes. Includes implementation of vocalic speech envelope analysis (fft_spectrum) technique and example data (mm1) from Tilsen, S., & Johnson, K. (2008) <doi:10.1121/1.2947626>.
Reconstructs retinae by morphing a flat surface with cuts
    (a dissected flat-mount retina) onto a curvilinear surface (the
    standard retinal shape). It can estimate the position of a point
    on the intact adult retina to within 8 degrees of arc (3.6% of
    nasotemporal axis). The coordinates in reconstructed retinae can
    be transformed to visuotopic coordinates. For more details see
    Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D.
    (2013) <doi:10.1371/journal.pcbi.1002921>.
We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an 'rmarkdown' printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies").
Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.
Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi:10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi:10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.
A collection of tools to import and structure the (currently) single-season
    event, game-log, roster, and schedule data available from <https://www.retrosheet.org>.
    In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
    This package does the parsing on those files, returning the requested data in the most
    practical R structure to use for sabermetric or other analyses.
Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.
Simplified scenario testing and sensitivity analysis,
    redesigned to use packages 'future' and 'furrr'. Provides
    functions for generating function argument sets using
    one-factor-at-a-time (OFAT) and (sampled) permutations.
Provides an interface to the 'Vamp' audio analysis plugin system
    <https://www.vamp-plugins.org/> developed by Queen Mary University of London's 
    Centre for Digital Music. Enables loading and running 'Vamp' plugins for various audio analysis
    tasks including tempo detection, onset detection, spectral analysis, and
    audio feature extraction. Supports mono and stereo audio with automatic
    channel adaptation and domain conversion.
Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo ('MCMC') methods for posterior simulation.  In addition, there
    are functions for making inferences about the extremal index, using 
    the models for threshold inter-exceedance times of Suveges and Davison 
    (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) 
    <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for 
    the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') 
    distributions that deal appropriately with cases where the shape parameter 
    is very close to zero.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.
R Markdown format for 'reveal.js' presentations, a framework
    for easily creating beautiful presentations using HTML.
Facilitates making a connection to the 'Revenera' 
  API and executing various queries. You can use it to get 
  event data and metadata. The 'Revenera' documentation 
  is available at <https://rui-api.redoc.ly/>. This package is 
  not supported by 'Flexera' (owner of the software).
Tests linear regressions for significance reversal through leave-one(multiple)-out.
Reversion mutations are secondary mutations that reverse the deleterious effects of an original pathogenic mutation, partially or fully restoring the gene's function. The revert package detects reversion mutations for a specific pathogenic mutation from DNA-seq bam files.
Processes and visualizes the output of complex phylogenetic analyses from the 'RevBayes' phylogenetic graphical modeling software.
A portable Shiny tool to explore patient-level electronic health record data 
    and perform chart review in a single integrated framework. This tool supports 
    browsing clinical data in many different formats including multiple versions 
    of the 'OMOP' common data model as well as the 'MIMIC-III' data model. In 
    addition, chart review information is captured and stored securely via the 
    Shiny interface in a 'REDCap' (Research Electronic Data Capture) project 
    using the 'REDCap' API. See the 'ReviewR' website for additional information, 
    documentation, and examples.
Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.
Analyzes revisions in real-time time series vintages. The
    package converts between wide revision triangles and tidy long
    vintages, extracts selected releases, computes revision series,
    visualizes vintage paths, and summarizes revision properties such as
    bias, dispersion, autocorrelation, and news-noise diagnostics. It
    also identifies efficient releases and estimates state-space models
    for revision nowcasting. Methods are based on Howrey (1978)
    <doi:10.2307/1924972>, Jacobs and Van Norden (2011)
    <doi:10.1016/j.jeconom.2010.04.010>, and Kishor and Koenig (2012)
    <doi:10.1198/jbes.2010.08169>.
Tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.
We visualize the standard deviation of a data set as the radius of a cylinder whose volume equals the total volume of several cylinders made by revolving the empirical cumulative distribution function about the vertical line through the mean. For more details see Sarkar and Rashid (2016) <doi:10.1080/00031305.2016.1165734>.
Implements and enhances the estimation techniques described in
    Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the
    location and scale of very small samples.
Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.
Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.
Diagnostics and data preparation for random effects within estimator, random effects 
    within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model.
    Mundlak, Yair (1978) <doi:10.2307/1913646>.
    Hausman, Jeffrey (1978) <doi:10.2307/1913827>.
    Allison, Paul (2009) <doi:10.4135/9781412993869>.
    Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.
A friendly interface for the construction of regular
    expressions.
The main purpose of this package is to streamline the
    generation of exams that include random elements in exercises.
    Exercises can be defined in a table, based on text and figures, and
    may contain gaps to be filled with provided options. Exam documents
    can be generated in various formats. It allows us to generate a
    version for conducting the assessment and another version that
    facilitates correction, linked through a code.
Provides a user-friendly interface to NASA 'Exoplanets Archive API'
    <https://exoplanetarchive.ipac.caltech.edu/>, enabling retrieval and analysis of exoplanetary and stellar data. 
    Includes functions for querying, filtering, summarizing, and computing derived parameters from the 'Exoplanets' catalog.
Provides functions to compile and load Rust code from R, similar
    to how 'Rcpp' or 'cpp11' allow easy interfacing with C++ code. Also provides
    helper functions to create R packages that use Rust code. Under the hood,
    the Rust crate 'extendr' is used to do all the heavy lifting.
Provides an interface to the Facebook API.
Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
    Marketing API <https://developers.facebook.com/docs/marketing-api>.
The 'rfacts' package is an R interface to the
  Fixed and Adaptive Clinical Trial Simulator ('FACTS')
  on Unix-like systems. It programmatically invokes 'FACTS' to run clinical
  trial simulations, and it aggregates simulation output data
  into tidy data frames. These capabilities provide end-to-end
  automation for large-scale simulation pipelines, and
  they enhance computational reproducibility.
  For more information on 'FACTS' itself,
  please visit <https://www.berryconsultants.com/software/>.
Autoencoding Random Forests ('RFAE') provide a method to
    autoencode mixed-type tabular data using Random Forests ('RF'), which 
    involves projecting the data to a latent feature space of user-chosen 
    dimensionality (usually a lower dimension), and then decoding the latent 
    representations back into the input space. The encoding stage is useful for 
    feature engineering and data visualisation tasks, akin to how principal
    component analysis ('PCA') is used, and the decoding stage is useful
    for compression and denoising tasks. At its core, 'RFAE' is a
    post-processing pipeline on a trained random forest model. This means
    that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or
    'ARF'. Because of this, it inherits Random Forests' robust performance and 
    capacity to seamlessly handle mixed-type tabular data. For more details, see 
    Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.
Simulates the dynamics of exploited fish populations using the Jones modification of the Beverton-Holt 
             equilibrium yield equation to compute yield-per-recruit and dynamic pool models (Ricker 1975) 
             <https://publications.gc.ca/site/eng/480738/publication.html>. 
             Allows users to evaluate minimum, slot, and inverted length limits on exploited fisheries using 
             specified life history parameters. Users can simulate population under a variety of conditional fishing mortality 
             and conditional natural mortality. Calculated quantities include number of fish harvested and dying naturally, 
             mean weight and length of fish harvested, number of fish that reach specified lengths of interest, total number 
             of fish and biomass in the population, and stock density indices.
Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.
A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .
Methods for Resampling-based False Discovery Proportion control.
        A function is provided that provides simultaneous, multi-resolution
        False Discovery Exceedance (FDX) control as described in 
	Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.
An R package for multiple imputation using chained random forests.
    Implemented methods can handle missing data in mixed types of variables by
    using prediction-based or node-based conditional distributions constructed
    using random forests. For prediction-based imputation, the method based on
    the empirical distribution of out-of-bag prediction errors of random forests
    and the method based on normality assumption for prediction errors of random
    forests are provided for imputing continuous variables. And the method based
    on predicted probabilities is provided for imputing categorical variables.
    For node-based imputation, the method based on the conditional distribution
    formed by the predicting nodes of random forests, and the method based on
    proximity measures of random forests are provided. More details of the
    statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.
Provides functionality to interact with the 
  'FieldClimate' API <https://api.fieldclimate.com/v2/docs/>.
An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.
A programmatic interface to 'FishBase', re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to 'SeaLifeBase' data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
Rank-based (R) estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Provides functions for detecting spatial clusters using the flexible
    spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
    This package implements a wrapper for the 'C' routine used in the 'FleXScan' 3.1.2 
    <https://sites.google.com/site/flexscansoftware/home> developed by Takahashi,
    Yokoyama, and Tango. For details, see Otani et al. (2021) <doi:10.18637/jss.v099.i13>.
Sets a significance level for  Random Forest MDI (Mean Decrease in Impurity, Gini or
             sum of squares) variable importance scores, using an empirical Bayes approach.
             See Dunne et al. (2022)  <doi:10.1101/2022.04.06.487300>.
Data for the vignette and examples in 'RFlocalfdr'. Contains a dataset of 1103547 importance values,
       and the table of variables used in the random forest splits. The data is Chromosome 22 taken from Auton et al.
       (2015) <doi:10.1038/nature15393>. It also contains a 51 samples by 22283 genes data set taken from
       Spira et al. (2004) <doi:10.1165/rcmb.2004-0273OC>.
Provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).
Interface to the 'flsgen' neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to
  - Generate fractal terrain;
  - Generate landscape structures satisfying user targets over landscape indices;
  - Generate landscape raster from landscape structures.
Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
Fits cause-specific random survival forests for flexible
    multistate survival analysis with covariate-adjusted transition
    probabilities computed via product-integral. State transitions are 
    modeled by random forests. Subject-specific transition probability matrices 
    are assembled from predicted cumulative hazards using the product-integral formula.
    Also provides a standalone Aalen-Johansen nonparametric estimator as
    a covariate-free baseline. Supports arbitrary state spaces with any
    number of states (three or more) and any set of allowed transitions,
    applicable to clinical trials, disease progression, reliability
    engineering, and other domains where subjects move among discrete
    states over time. Provides per-transition feature importance,
    bias-variance diagnostics, and comprehensive visualizations. Handles
    right censoring and competing transitions. Methods are described in
    Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival
    forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate
    competing risks decomposition, and Aalen and Johansen (1978)
    <https://www.jstor.org/stable/4615704> for the nonparametric
    estimator.
Various tools for handling fuzzy measures, calculating Shapley value and interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment 
  is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following:
	[1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html>
	[2] G. Beliakov, H. Bustince, T. Calvo, 'A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3).
	[3] G. Beliakov, S. James, J-Z. Wu, 'Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Execute  'FOCAL' (<https://en.wikipedia.org/wiki/FOCAL_(programming_language)>) source code directly in 'R'. This is achieved by translating 'FOCAL' code into equivalent 'R' commands and controlling the sequence of execution.
Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into 
    the R folder while making additional checks.
The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.
Contains all the code examples in the book "R for Dummies" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.
Provides fast implementations of Random Forests, 
    Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
    and interpretability. Additionally contains methods for variable 
    importance, out-of-bag prediction, regression monotonicity, and
    several methods for missing data imputation.
A minimal R code formatter following base R style conventions.
    Formats R code with consistent spacing, indentation, and structure.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
A machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.
Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.
'FRACTRAN' is an obscure yet tantalizing programming language invented by John Conway of 'Game of Life' fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .
Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.
Methods and tools for implementing functional singular spectrum analysis and related techniques.
Set of functions that enable you to use the FUSION commands (Program available in: <http://forsys.sefs.uw.edu/fusion/fusionlatest.html>).
Provides and extends the 'Fuzzy Coco' algorithm by wrapping the
    'FuzzyCoCo' 'C++' Library, cf
    <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. 'Fuzzy Coco'
    constructs systems that predict the outcome of a human decision-making
    process while providing an understandable explanation of a possible
    reasoning leading to it.  The constructed fuzzy systems are composed
    of rules and linguistic variables.  This package provides a 'S3' classic
    interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip'
    interface, a custom engine with custom iteration stop criterion and
    progress bar support as well as a systematic implementation that do
    not rely on genetic programming but rather explore all possible
    combinations.
Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>. 
    The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
    This gain is aggregated for each split variable in a tree and averaged across trees.
Sequential permutation testing for statistical
  significance of predictors in random forests and other prediction methods. 
  The main function of the package is rfvimptest(), which allows to test for 
  the   statistical significance of predictors in random forests using 
  different (sequential) permutation test strategies [1]. The advantage 
  of sequential over conventional permutation tests is that they
  are computationally considerably less intensive, as the sequential
  procedure is stopped as soon as there is sufficient evidence
  for either the null or the alternative hypothesis.
  Reference:
  [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation
	  testing of variable importance measures by the example of random forests.
	  Computational Statistics & Data Analysis 181:107689, <doi:10.1016/j.csda.2022.107689>.
An interactive data visualization and exploration toolkit
    that implements Breiman and Cutler's original random forest Java based        
    visualization tools in R, for supervised and unsupervised classification and 
    regression within the algorithm random forest.
Analyze multi-level one-way
        experimental designs where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially 
    described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by 
    comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two
    neural networks play an adversarial minimax game.
    Built-in GAN models make the training of GANs in R possible in one line and make it easy to 
    experiment with different design choices (e.g. different network architectures, value functions, optimizers).
    The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. 
    Methods to post-process the output of GAN models to enhance the quality of samples are available.
The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility (GBIF;
    <https://www.gbif.org/developer/summary>). GBIF is a database
    of species occurrence records from sources all over the globe.
    rgbif includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, getting counts of occurrence records, and using the GBIF
    tile map service to make rasters summarizing huge amounts of data.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multi-block data analysis concerns the analysis of several
    sets of variables (blocks) observed on the same group of individuals.
    The main aims of the RGCCA package are: to study the relationships
    between blocks and to identify subsets of variables of each block
    which are active in their relationships with the other blocks. This
    package allows to (i) run R/SGCCA and related methods,
    (ii) help the user to find out the optimal parameters for R/SGCCA such
    as regularization parameters (tau or sparsity), (iii) evaluate the
    stability of the RGCCA results and their significance, (iv) build predictive
    models from the R/SGCCA. (v) Generic print()
    and plot() functions apply to all these functionalities.
Toolbox for chemometrics analysis of bidimensional gas 
    chromatography data. This package import data for common scientific data
    format (NetCDF) and fold it to 2D chromatogram. Then, it can perform
    preprocessing and multivariate analysis. In the preprocessing algorithms,
    baseline correction, smoothing, and peak alignment are available.
    While in multivariate analysis, multiway principal component analysis is
    incorporated.
Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.
Earth Engine <https://earthengine.google.com/> client library for R. All
  of the 'Earth Engine' API classes, modules, and functions are made available. Additional
  functions implemented include importing (exporting) of Earth Engine spatial objects, 
  extraction of time series, interactive map display, assets management interface, 
  and metadata display. See <https://r-spatial.github.io/rgee/> for further details.
Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: <https://geedim.readthedocs.io/>.
    Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>.
    By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .
Download the lyrics of your favorite songs in text and table formats. 
    Also search for related songs or song information.
    More information: <https://docs.genius.com/> .
A genetic algorithm plus derivative optimizer.
Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, 
    Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code 
    of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis.
    The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
A lightweight implementation of the geomorphon terrain
  form classification algorithm of Jasiewicz and Stepinski (2013)
  <doi:10.1016/j.geomorph.2012.11.005> based largely on the 
  'GRASS GIS' 'r.geomorphon' module. This implementation employs 
  a novel algorithm written in C++ and 'RcppParallel'.
Provides an interface to the Python package 'Geomstats' authored by Miolane et 
    al. (2020) <arXiv:2004.04667>.
Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <https://github.com/Nowosad/geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.
Create, read, and write 'GEXF' (Graph Exchange 'XML' Format) graph files (used in 'Gephi' and others). Using the 'XML' package, rgexf allows reading and writing GEXF files, including attributes, 'GEXF' visual attributes (such as color, size, and position), network dynamics (for both edges and nodes), and edges' weights. Users can build/handle graphs element-by-element or massively through data frames, visualize the graph on a web browser through 'gexf-js' (a 'javascript' library), and interact with the 'igraph' package.
Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.
Datasets from the 2021 Ghana Population and Housing Census Results. Users can access results as 'tidyverse' and 'sf'-Ready Data Frames.    The data in this package is scraped from pdf reports released by the Ghana Statistical Service website <https://census2021.statsghana.gov.gh/> . The package currently only contains datasets from the literacy and education reports. Namely, school attendance data for respondents aged 3 years and above.
Implementation of some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
Visualizing crystal structures and selected area electron diffraction (SAED) patterns.  It provides functions cry_demo() and dp_demo() to load a file in 'CIF' (Crystallographic Information Framework) formats and display crystal structures and electron diffraction patterns.  The function dp_demo() also performs simple simulation of powder X-ray diffraction (PXRD) patterns, and the results can be saved to a file in the working directory.  The package has been tested on several platforms, including Linux on 'Crostini' with a Core™ m3-8100Y Chromebook, I found that even on this low-powered platform, the performance was acceptable.
    T. Hanashima (2001) <https://www2.kek.jp/imss/pf/tools/sasaki/sinram/sinram.html>
    Todd Helmenstine (2019) <https://sciencenotes.org/molecule-atom-colors-cpk-colors/>
    Wikipedia contributors (2023) <https://en.wikipedia.org/w/index.php?title=Atomic_radius&oldid=1179864711>.
The 'glTF' file format is used to describe 3D models.  This package 
 provides read and write functions to work with it.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI)
    (<https://www.globalbioticinteractions.org/>). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name.
Provides programmatic access to Glottography, an online
    repository of geospatial speaker-area polygons for the world's
    languages. The package allows users to list available datasets,
    download and install them, and load speaker-area polygons as
    standard spatial 'sf' objects in R. Data are sourced from either
    the Glottography organization on GitHub
    <https://github.com/Glottography> or the Glottography community
    on Zenodo <https://zenodo.org/communities/glottography>.
    Based on Ranacher et al. (2026) <doi:10.5334/johd.459>.
R interface to the GNU Linear Programming Kit.
  'GLPK' is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming ('MILP') and other related problems.
Provides 3D plotting routines that facilitate the use of the 'rgl' package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.
Algorithms for estimating robustly the parameters of a Gaussian, Student, or Laplace Mixture Model.
Implementation of the Robust Gauss-Newton (RGN) algorithm,
    designed for solving optimization problems with a sum of least squares
    objective function. For algorithm details please refer to Qin et. al. (2018) 
    <doi:10.1029/2017WR022488>.
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
Parse scientific names using 'gnparser'
    (<https://github.com/gnames/gnparser>), written in Go. 'gnparser'
    parses scientific names into their component parts; it utilizes
    a Parsing Expression Grammar specifically for scientific names.
Routines that allow the user to run a large number of goodness-of-fit tests.  
     It allows for data to be continuous or discrete. It includes routines to estimate
     the power of the tests and display them as a power graph.
     The routine run.studies allows a user to quickly study the power of a new method and 
     how it compares to some of the standard ones.
Interface for loading data from 'Google Ads API', 
    see <https://developers.google.com/google-ads/api/docs/start>. 
    Package provide function for authorization and loading reports.
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
Serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
Interface to the 'ReebGraphPairing' program to compute critical
    points of Reeb graphs following Tu, Hajij, & Rosen (2019)
    <doi:10.1007/978-3-030-33720-9_8> via the 'rJava' package.
    Also store Reeb graphs in a minimal S3 class,
    convert between other network data structures,
    and post-process pairing data to obtain extended persistent homology
    following Carrière & Oudot (2018) <doi:10.1007/s10208-017-9370-z>.
Query functions to the GPlates <https://www.gplates.org/> Desktop Application and the GPlates Web Service <https://gws.gplates.org/> allow users to reconstruct past positions of geographic entities based on user-selected rotation models without leaving the R running environment. The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere <https://cran.r-project.org/package=chronosphere>. This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions).
Encode network data as strings of printable ASCII characters. Implemented 
    functions include encoding and decoding adjacency matrices, edgelists, igraph, and
    network objects to/from formats 'graph6', 'sparse6', and 'digraph6'. The formats and
    methods are described in McKay, B.D. and Piperno, A (2014)
    <doi:10.1016/j.jsc.2013.09.003>.
Data and Functions from the book R Graphics, Third Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interface to integrate 'igraph' and 'ggplot2' graphics in a normalized coordinate system. 'RGraphSpace' implements new geometric objects using 'ggplot2' prototypes, customized for side-by-side visualization of multiple graphs. By scaling shapes and graph elements, 'RGraphSpace' helps to create layered visualizations that stay coherent across multiple graphs.
An interface between the 'GRASS' geographical information system ('GIS') and 'R', based on starting 'R' from within the 'GRASS' 'GIS' environment, or running a free-standing 'R' session in a temporary 'GRASS' location; the package provides facilities for using all 'GRASS' commands from the 'R' command line. The original interface package for 'GRASS 5' (2000-2010) is described in Bivand (2000) <doi:10.1016/S0098-3004(00)00057-1> and Bivand (2001) <https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf>. This was succeeded by 'spgrass6' for 'GRASS 6' (2006-2016) and 'rgrass7' for 'GRASS 7' (2015-present). The 'rgrass' package modernizes the interface for 'GRASS 8' while still permitting the use of 'GRASS 7'.
Fully 'DBI'-compliant interface to 'Greenplum' <https://greenplum.org/>,
    an open-source parallel database. This is an extension of the 'RPostgres'
    package <https://github.com/r-dbi/RPostgres>.
The tools and utilities to estimate the model described in "Gremlin's in the
    Data: Identifying the Information Content of Research Subjects" (Howell et al.
    (2021) <doi:10.1177/0022243720965930>)  using conjoint analysis data such as
    that collected in Sawtooth Software's 'Lighthouse' or 'Discover' products.
    Additional utilities are included for formatting the input data.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).
Useful tools for determining whether two samples are from the same distribution. Utilizes a robust method to address the problematic structure of the similarity graph constructed from high-dimensional data. The method is provided in Yichuan Bai and Lynna Chu (2023) <arXiv:2307.12325>.
This is a library to access the current API of the web speed test service 'GTmetrix'.
    It provides a convenient wrapper to start tests, get reports, and access all kinds of meta data. 
    For more information about using the API please visit <https://gtmetrix.com/api/docs/2.0/>.
Automatic open data acquisition from resources of Polish Head Office
    of Geodesy and Cartography ('Główny Urząd Geodezji i Kartografii')
    (<https://www.gov.pl/web/gugik>).
    Available datasets include various types of numeric, raster and vector data,
    such as orthophotomaps, digital elevation models (digital terrain models,
    digital surface model, point clouds), state register of borders, spatial
    databases, geometries of cadastral parcels, 3D models of buildings, and more.
    It is also possible to geocode addresses or objects using the geocodePL_get()
    function.
Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.
Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.
DBI/RJDBC interface to h2 database. h2 version 2.3.232 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.
The renewal Hawkes (RHawkes) process (Wheatley,
    Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is
    an extension to the classical Hawkes self-exciting point process
    widely used in the modelling of clustered event sequence data.
    This package provides functions to simulate the RHawkes process
    with a given immigrant hazard rate function and offspring birth
    time density function, to compute the exact likelihood of a
    RHawkes process using the recursive algorithm proposed by Chen and
    Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the
    Rosenblatt residuals for goodness-of-fit assessment, and to
    predict future event times based on observed event times up to a
    given time. A function implementing the linear time RHawkes
    process likelihood approximation algorithm proposed in Stindl and
    Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.
The evaluation criteria of rangeland health, condition and landscape function analysis
             based on species diversity and functional diversity of rangeland plant communities.
Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.
Here we performs robust hierarchical co-clustering between row and column entities of a data matrix in 
             absence and presence of outlying observations. It can be used to explore important co-clusters consisting of  
             important samples and their regulatory significant features. Please see Hasan, Badsha and Mollah (2020) 
             <doi:10.1101/2020.05.13.094946>.
An implementation of the 'Heroicons' icon library for 'shiny'
    applications and other 'R' web-based projects. You can search, render,
    and customize icons without 'CSS' or 'JavaScript' dependencies.
Implements the hierarchical Bayesian analysis of populations structure (hierBAPS) 
  algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences 
  from multiple sequence alignments in FASTA format. 
  The implementation includes improved defaults and plotting capabilities 
  and unlike the original 'MATLAB' version removes singleton SNPs by default.
A framework that supports creating and extending enterprise Shiny applications using best practices.
Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.
Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.
A useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) <doi:10.1002/9781118497869.ch6> Fasoula V.A., and Tokatlidis I.S. (2012) <doi:10.1007/s13593-011-0034-0> Fasoulas A.C., and Fasoula V.A. (1995) <doi:10.1002/9780470650059.ch3> Tokatlidis I. (2016) <doi:10.1017/S0014479715000150> Tokatlidis I., and Vlachostergios D. (2016)  <doi:10.3390/d8040029>.
Rho is used to test the generalization of inter rater reliability
    (IRR) statistics. Calculating rho starts by generating a large number of
    simulated, fully-coded data sets: a sizable collection of hypothetical
    populations, all of which have a kappa value below a given threshold -- which
    indicates unacceptable agreement. Then kappa is calculated on a sample from
    each of those sets in the collection to see if it is equal to or higher than
    the kappa in then real sample. If less than five percent of the distribution
    of samples from the simulated data sets is greater than actual observed kappa,
    the null hypothesis is rejected and one can conclude that if the two raters had
    coded the rest of the data, we would have acceptable agreement (kappa above the
    threshold).
Higher-order spectra or polyspectra of time series, such as bispectrum and bicoherence, have been investigated in abundant literature and applied to problems of signal detection in a wide range of fields. This package aims to provide a simple API to estimate and analyze them. The current implementation is based on Brillinger and Irizarry (1998) <doi:10.1016/S0165-1684(97)00217-X> for estimating bispectrum or bicoherence, Lii and Helland (1981) <doi:10.1145/355958.355961> for cross-bispectrum, and Kim and Powers (1979) <doi:10.1109/TPS.1979.4317207> for cross-bicoherence.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
Methods to scan RR interval data for Premature Ventricular Complexes (PVCs) and parameterise and plot the resulting Heart Rate Turbulence (HRT). The methodology of HRT analysis is based on the original publication by Schmidt et al. <doi:10.1016/S0140-6736(98)08428-1> and extended with suggestions from <doi:10.1088/1361-6579/ab98b3>.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.
The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons.
    This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
R-hub v2 uses GitHub Actions to run 'R CMD check' and
    similar package checks. The 'rhub' package helps you set up
    R-hub v2 for your R package, and start running checks.
Tool for the analysis Mass Spectrometry (MS) data in the context of 
             immunopeptidomic analysis for the identification of hybrid 
             peptides and the predictions of binding affinity of all peptides 
             using 'netMHCpan' <doi:10.1093/nar/gkaa379> while providing a summary 
             of the netMHCpan output. 'RHybridFinder' (RHF) is destined for 
             researchers who are looking to analyze their MS data for the purpose 
             of identification of potential spliced peptides. This package, 
             developed mainly in base R, is based on the workflow published by 
             Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.
Wrapper for 'Datamuse' API to find rhyming and other associated words.
    This includes words of similar meaning, spelling, or other related words. Learn
    more about the 'Datamuse' API here <https://www.datamuse.com/api/>.
Create and manipulate hypergraph objects. This early version of
    rhype allows for the output of matrices associated with the hypergraphs 
    themselves. It also uses these matrices to calculate hypergraph spectra and
    perform spectral comparison. Functionality coming soon includes calculation
    of hyperpaths and hypergraph centrality measures.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI,
             nrrd and npy (numpy array) file formats.
             RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and
             geometry-based statistics. Almost all calculations are done using vectorized formulas to
             optimize run speeds. Calculation of several thousands of parameters only takes minutes
             on a single core of a conventional PC. Detailed methodology has been published: Kolossvary
             et al. Circ: Cardiovascular Imaging. 2017;10(12):e006843 <doi: 10.1161/CIRCIMAGING.117.006843>.
Calculates the Iberian Actuarial Climate Index and its components—including temperature, precipitation, wind power, and sea level data—to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.
Allows interaction with 'Interactive Brokers' 'Trader Workstation'
    <https://interactivebrokers.github.io/tws-api/>.
    Handles the connection over the network and the exchange of messages.
    Data is encoded and decoded between user and wire formats.
    Data structures and functionality closely mirror the official implementations.
Recursive algorithms for computing various relatedness
    coefficients, including pairwise kinship, kappa and identity
    coefficients. Both autosomal and X-linked coefficients are computed.
    Founders are allowed to be inbred, which enables construction of any
    given kappa coefficients, as described in Vigeland (2020)
    <doi:10.1007/s00285-020-01505-x>. In addition to the standard
    coefficients, 'ribd' also computes a range of lesser-known
    coefficients, including generalised kinship coefficients, multi-person
    coefficients and two-locus coefficients (Vigeland, 2023,
    <doi:10.1093/g3journal/jkac326>). Many features of 'ribd' are
    available through the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>; see Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>.
The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
	Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
	statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be 
	used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
	To include any indirect method, a custom wrapper function needs to be provided. 
	The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
	See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. 
	A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.
Provides functions to handle command-line arguments for R
    scripting. It enables building stand-alone R programs that accept and
    parse command-line options in 'BIOS' style.
    Zhang (2025) <https://github.com/bedapub/ribiosArg>.
Tools to manipulate and visualize graphs (networks) for computational biology in drug discovery, for instance functions for creating bipartite graphs and for interactive visualizations. Zhang (2025) <https://github.com/bedapub/ribiosGraph>.
Provides data structures and functions for file input/output in the 'ribios' software suite, supporting common bioinformatics and computational biology file formats, designed for fast loading and high performance with minimal dependencies.
Mathematical and statistical tools for computational biology
    in drug discovery. Functions are designed for high performance. Implements
    the hierarchical fuzzy multi-linkage partitioning method proposed by
    Huang et al. (2007) <doi:10.1186/gb-2007-8-9-r183>.
Provides data structures and functions for data transformation and visualization in computational biology in drug discovery as part of the 'ribios' software suite. Zhang (2025) <https://github.com/bedapub/ribiosPlot>.
Provides interface to the 'Bioinfo-C' (internal name: 'BIOS') library and utilities. 'ribiosUtils' is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.
Provides a compact 'R' interface for performing tensor calculations.
    This is achieved by allowing (upper and lower) index labeling of arrays
    and making use of Ricci calculus conventions to implicitly trigger
    contractions and diagonal subsetting.
    Explicit tensor operations, such as addition, subtraction and
    multiplication of tensors via the standard operators, raising and lowering
    indices, taking symmetric or antisymmetric tensor parts, as well as the
    Kronecker product are available. Common tensors like the Kronecker delta,
    Levi Civita epsilon, certain metric tensors, the Christoffel symbols,
    the Riemann as well as Ricci tensors are provided.
    The covariant derivative of tensor fields with respect to any metric
    tensor can be evaluated. An effort was made to provide the user with
    useful error messages.
Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). Supporting publication: Blaauw, M., Reimer, P.J., in press. An open-source toolkit for radiocarbon dating and calibration. Radiocarbon. The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package 'rintcal'.
Gene annotation of rice (Oryza Sativa L.spp.japonica).
    The package is based on the annotation file from the website <http://plants.ensembl.org/Oryza_sativa/Info/Index>. 
    Input gene's name then return some information, including the from position, the end position, 
    the position type and the chromosome number.
Convert one biological ID to another of rice (Oryza sativa).
    Rice(Oryza sativa) has more than one form gene ID for the genome. 
    The two main gene ID for rice genome are  the RAP (The Rice Annotation Project, <https://rapdb.dna.affrc.go.jp/>, 
    and the MSU(The Rice Genome Annotation Project, <http://rice.plantbiology.msu.edu/>.
    All RAP rice gene IDs are of the form Os##g####### as explained on the website <https://rapdb.dna.affrc.go.jp/>.
    All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website <http://rice.plantbiology.msu.edu/analyses_nomenclature.shtml>.
    All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, <https://www.ncbi.nlm.nih.gov/>.
    The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######.
    The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI.
    There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' <https://rapdb.dna.affrc.go.jp/tools/converter>.
    But it is difficult to convert super multiple IDs on these websites.
    The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Fast 'C++' agglomerative hierarchical clustering 
	algorithm packaged into easily callable R functions, designed 
	to help cluster biological terms based on how
    similar of genes are expressed in their activation.
Methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. 
    User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.
Provides a suite of tools to create tables that accompany maps. The
    tools create clean, informative tables for electoral outcomes, compactness, and
    other district-level quantities. Most tools are aimed at the redistricting
    context, but are broadly applicable to other electoral data.
Focused on (but not exclusive to) data sets hosted on PhysioNet
    (<https://physionet.org>), 'ricu' provides utilities for download, setup
    and access of intensive care unit (ICU) data sets. In addition to
    functions for running arbitrary queries against available data sets, a
    system for defining clinical concepts and encoding their representations
    in tabular ICU data is presented.
Provides efficient functions for detecting multiple change points in multidimensional time series. The models can be piecewise constant or polynomial. Adaptive threshold selection methods are available, see Fan and Wu (2024)	<arXiv:2403.00600>.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in
            <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
Implementation of a Principal Component Analysis (PCA) in the
    torus via density ridge estimation. The main function, ridge_pca(), obtains
    the relevant density ridge for bivariate sine von Mises and bivariate
    wrapped Cauchy distribution models and provides the associated scores and
    variance decomposition. Auxiliary functions for evaluating, fitting, and
    sampling these models are also provided. The package provides replicability
    to García-Portugués and Prieto-Tirado (2023) 
    <doi:10.1007/s11222-023-10273-9>.
It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.
An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.
Allows to get weather data from Automated Surface Observing
    System (ASOS) stations (airports) in the whole world thanks to the
    Iowa Environment Mesonet website.
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.
Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (<doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (<doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (<doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
Provides functions to compute recentered influence functions
    (RIF) of a distributional variable at the mean, quantiles, variance,
    gini or any custom functional of interest. The package allows to
    regress the RIF on any number of covariates. Generic print, plot and
    summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) <doi:10.3982/ECTA6822>. "Unconditional Quantile Regressions.".
Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.
Presentation-ready results tables for epidemiologists in an 
    automated, reproducible fashion. The user provides the final analytical 
    dataset and specifies the design of the table, with rows and/or columns 
    defined by exposure(s), effect modifier(s), and estimands as desired, 
    allowing to show descriptors and inferential estimates in one table -- 
    bridging the rift between epidemiologists and their data, one table at a 
    time. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.
The goal of Rigma is to provide a user friendly client to the
    'Figma' API <https://www.figma.com/developers/api>. It uses the latest
    `httr2` for a stable interface with the REST API. More than 20 methods
    are provided to interact with 'Figma' files, and teams. Get design
    data into R by reading published components and styles, converting and
    downloading images, getting access to the full 'Figma' file as a
    hierarchical data structure, and much more. Enhance your creativity
    and streamline the application development by automating the
    extraction, transformation, and loading of design data to your
    applications and 'HTML' documents.
A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.
Functionality to download stock prices,
    cryptocurrency data, and more from the 'Tiingo' API
    <https://api.tiingo.com/>.
Provides tools to access, search, and manipulate ILO's ilostat database,
  including bulk download of statistical data, dictionary lookups, and table of contents.
An interface to the powerful and fairly complete computer algebra system 'Maxima'.
    It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. 
    Results from 'Maxima' can be parsed and evaluated in R. 
    It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 
    2D and 3D plots can be displayed directly. 
    This package also registers a 'knitr'-engine enabling 'Maxima' code chunks 
    to be written in 'RMarkdown' documents.
Provides functions to read and write 'ImageJ' (<https://imagej.net>)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (<https://spatstat.org/>) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
Tools for manipulating, exploring, and visualising  multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.
A programmatic interface to the API provided by the 'iNaturalist' website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.
Relative, generalized, and Erreygers corrected concentration index; plot Lorenz curves; and decompose health
    inequalities into contributing factors. The package currently works with (generalized) linear models, survival models, complex survey models, and marginal effects probit models.
    originally forked by Brecht Devleesschauwer from the 'decomp' package  (no longer on CRAN), 'rineq' is now maintained by Kaspar Walter Meili. Compared to the earlier 'rineq' version on 'github' by Brecht Devleesschauwer (<https://github.com/brechtdv/rineq>), the regression tree functionality has been removed.
    Improvements compared to earlier versions include improved plotting of decomposition and concentration, added functionality to calculate the concentration index with different methods, calculation of robust standard errors, and support for the decomposition analysis using marginal effects probit regression models. The development version is available at <https://github.com/kdevkdev/rineq>.
Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" <doi:10.1016/j.jbi.2026.104980>.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Loading calls data from 'Ringostat API'. 
    See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.
C++ classes to embed R in C++ (and C) applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. Numerous examples are
 provided in the nine subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for
 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C
 interface and 'Ruby' illustration.  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual 
    and population niche width (Araujo's E, clustering and pairwise similarity 
    among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess 
    individual specialization based on data of resource use. Resource use can 
    be quantified by counts of categories, measures of mass or length, or 
    proportions. Monte Carlo resampling procedures are available for hypothesis 
    testing against multinomial null models.
    Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079>
    and associated references.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951, Stuiver & Suess 1966, Pearson & Stuiver 1986) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.
Allows the user to view an image in full screen when clicking on it in 'RMarkdown' documents and 'shiny' applications. 
  The package relies on the 'JavaScript' library 'intense-images'. See <https://tholman.com/intense-images/> for more information.
A wrapper for the 'Intro.js' library (For more info: <https://introjs.com/>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data format from the file extension, reasonable defaults are used for
    data import and export, web-based import is natively supported (including
    from SSL/HTTPS), compressed files can be read directly, and fast import
    packages are used where appropriate. An additional convenience function,
    'convert()', provides a simple method for converting between file types.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) <doi:10.1017/9781108887205>.
Utilities to access Integrated Food Security Phase Classification
    (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are
    available for all of the 'IPC-CH' Public API (<https://docs.api.ipcinfo.org>)
    simplified and advanced endpoints to easily download the data in a clean and
    tidy format.
Ports the Ripser <doi:10.48550/arXiv.1908.02518>
    and Cubical Ripser <doi:10.48550/arXiv.2005.12692>
    persistent homology calculation engines from C++.
    Can be used as a rapid calculation tool in topological data analysis
    pipelines.
The open sourced data management software 'Integrated
    Rule-Oriented Data System' ('iRODS') offers solutions for the whole
    data life cycle (<https://irods.org/>). The loosely constructed and
    highly configurable architecture of 'iRODS' frees the user from strict
    formatting constraints and single-vendor solutions. This package
    provides an interface to the 'iRODS' HTTP API, allowing you to manage
    your data and metadata in 'iRODS' with R. Storage of annotated files
    and R objects in 'iRODS' ensures findability, accessibility,
    interoperability, and reusability of data.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
A reliable and validated tool that captures detailed risk metrics 
    such as R 'CMD' check, test coverage, traceability matrix, documentation, dependencies, 
    reverse dependencies, suggested dependency analysis, repository data, 
    and enhanced reporting for R packages that are local or stored 
    on remote repositories such as GitHub, CRAN, and Bioconductor.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.
Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.
Calculates risk differences (or prevalence differences for 
    cross-sectional data) and Number Needed to Treat (NNT) using generalized 
    linear models with automatic link function selection. Provides robust 
    model fitting with fallback methods, support for stratification and 
    adjustment variables, inverse probability of treatment weighting (IPTW) 
    for causal inference with NNT calculations, and publication-ready output 
    formatting. Handles model convergence issues gracefully and provides 
    confidence intervals using multiple approaches.
    Methods are based on approaches described in Mark W. Donoghoe and 
    Ian C. Marschner (2018) "logbin: An R Package for Relative Risk 
    Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> 
    for robust GLM fitting, Peter C. Austin (2011) "An Introduction to 
    Propensity Score Methods for Reducing the Effects of Confounding in 
    Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
    and standard epidemiological methods for risk difference estimation as 
    described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash 
    (2008, ISBN:9780781755641) "Modern Epidemiology".
Geostatistical analysis of continuous and count data.
  Implements stationary Gaussian processes with Matérn correlation for spatial prediction,
  as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).
Facilities for assessing R packages against a number of metrics to 
    help quantify their robustness.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Risk ratios and risk differences are estimated using regression 
  models that allow for binary, categorical, and continuous exposures and 
  confounders. Implemented are marginal standardization after fitting logistic 
  models (g-computation) with delta-method and bootstrap standard errors, 
  Miettinen's case-duplication approach (Schouten et al. 1993, 
  <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical 
  variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting 
  values from Poisson models (Spiegelman and Hertzmark 2005, 
  <doi:10.1093/aje/kwi188>), and others.
Implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process.
A tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) <doi:10.1136/openhrt-2024-002755>. Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>. EDACS described in Mark DG, et al (2016) <doi:10.1016/j.jacc.2017.11.064>. GRACE described in Fox KA, et al (2006) <doi:10.1136/bmj.38985.646481.55>. HEART is described in Mahler SA, et al (2017) <doi:10.1016/j.clinbiochem.2017.01.003>. SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) <doi:10.1093/eurheartj/ehab309>. TIMI described in Antman EM, et al (2000) <doi:10.1001/jama.284.7.835>. SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) <doi:10.1093/eurheartj/ehab260>. SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) <doi:10.1093/eurjpc/zwac176>.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see <doi:10.3389/fpsyg.2020.567817>, for details).
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Automated performance of common transformations used to fulfill parametric
    assumptions of normality and identification of the best performing method 
    for the user. Output for various normality tests (Thode, 2002) corresponding 
    to the best performing method and a descriptive statistical report of the 
    input data in its original units (5-number summary and mathematical moments) 
    are also presented. Lastly, the Rankit, an empirical normal quantile transformation 
    (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard 
    use cases and facilitate adoption.  
    <DOI: 10.1201/9780203910894>.
    <DOI: 10.22237/jmasm/1257034080>.
A programmatic interface to the Web Service methods provided by ITALIC (<https://italic.units.it>).
    ITALIC is a database of lichen data in Italy and bordering European countries. 'ritalic' includes functions for retrieving information
    about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys.
    More information about the data is available at <https://italic.units.it/?procedure=base&t=59&c=60>.
    The API documentation is available at <https://italic.units.it/?procedure=api>.
Selected functions for simulation and regression of integrated Isothermal Titration Calorimetry (ITC) data with the most commonly used one-to-one binding model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008)  <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed
    for fisheries telemetry research, such as minimum home range, kernel density
    estimation, and clustering analysis using empirical k-functions with
    a bootstrap envelope.  Tools are also provided for editing the river
    networks, meaning there is no reliance on external software.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.
Seamless extraction of river networks from digital 
    elevation models data. The package allows analysis of digital 
	elevation models that can be either externally provided or
	downloaded from open source repositories (thus interfacing
	with the 'elevatr' package). Extraction is performed via the 
	'D8' flow direction algorithm of TauDEM (Terrain Analysis Using 
	Digital Elevation Models), thus interfacing with the 'traudem' 
	package. Resulting river networks are compatible with functions 
	from the 'OCNet' package. See Carraro (2023) 
	<doi:10.5194/hess-27-3733-2023> for a presentation of the package.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing) as described in
    Koohafkan MC, Younis BA (2015). "Open-channel computation with R."
    The R Journal, 7(2), 249–262. <doi: 10.32614/RJ-2015-034>.
Provides access to global river gauge data from a variety of national-level river agencies. The package interfaces with the national-level agency websites to provide access to river gauge locations, river discharge, and river stage. Currently, the package is available for the following countries: Australia, Brazil, Canada, Chile, France, Japan, South Africa, the United Kingdom, and the United States.
Simplifies the creation of reproducible data science environments
    using the 'Nix' package manager, as described in
    Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()`
    function generates a complete description of the environment as a
    `default.nix` file, which can then be built using 'Nix'. This results in
    project specific software environments with pinned versions of R, packages,
    linked system dependencies, and other tools or programming languages such
    as Python or Julia. Additional helpers make it easy to run R code in
    'Nix' software environments for testing and production.
Streamlines the creation of reproducible analytical pipelines using
  'default.nix' expressions generated via the 'rix' package for reproducibility. Define
  derivations in 'R', 'Python' or 'Julia', chain them into a composition of pure
  functions and build the resulting pipeline using 'Nix' as the underlying
  end-to-end build tool. Functions to plot the pipeline as a directed acyclic
  graph are included, as well as functions to load and inspect intermediary results
  for interactive analysis. User experience heavily inspired by the 'targets' package.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) <doi:10.48550/arXiv.2311.00577>. The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes.
Jalali calendar, or solar Hijri, is calendar of Iran and
    Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>).
    This package is designed to working with Jalali date. For this
    purpose, It defines JalaliDate class that is similar to Date class.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
Quickly install 'Java Development Kit (JDK)' without
    administrative privileges and set environment variables in current R
    session or project to solve common issues with 'Java' environment
    management in 'R'. Recommended to users of 'Java'/'rJava'-dependent
    'R' packages such as 'r5r', 'opentripplanner', 'xlsx', 'openNLP',
    'rWeka', 'RJDBC', 'tabulapdf', and many more. 'rJavaEnv' prevents
    common problems like 'Java' not found, 'Java' version conflicts,
    missing 'Java' installations, and the inability to install 'Java' due
    to lack of administrative privileges.  'rJavaEnv' automates the
    download, installation, and setup of the 'Java' on a per-project basis
    by setting the relevant 'JAVA_HOME' in the current 'R' session or the
    current working directory (via '.Rprofile', with the user's consent).
    Similar to what 'renv' does for 'R' packages, 'rJavaEnv' allows
    different 'Java' versions to be used across different projects, but
    can also be configured to allow multiple versions within the same
    project (e.g.  with the help of 'targets' package). Note: there are a
    few extra steps for 'Linux' users, who don't have any 'Java'
    previously installed in their system, and who prefer package
    installation from source, rather then installing binaries from 'Posit
    Package Manager'. See documentation for details.
Interface to 'JDemetra+' 3.x (<https://github.com/jdemetra>) 
    time series analysis software. It provides a variety of methods for 
    temporal disaggregation & interpolation, benchmarking, reconciliation 
    and calendarization. It incorporates statistical methods described in 
    the latest European Statistical System (ESS) guidelines on temporal 
    disaggregation, benchmarking, and reconciliation (2018 edition). The package 
    implements highly efficient algorithms for fast and reliable computation.
It provides external jars required for the 'rjdverse' (as 'rjd3toolkit', 'rjd3x13' and 'rjd3tramoseats').
Interface to 'JDemetra+' 3.x (<https://github.com/jdemetra>)
    time series analysis software. It offers full access to txt, csv, xml
    and spreadsheets files which are meant to be read by 'JDemetra+'
    Graphical User Interface.
R Interface to 'JDemetra+ 3.x'
    (<https://github.com/jdemetra>) time series analysis software.  It
    provides functions allowing to model time series (create outlier
    regressors, user-defined calendar regressors, Unobserved Components
    AutoRegressive Integrated Moving Average (UCARIMA) models...), to test
    the presence of trading days or seasonal effects and also to set
    specifications in pre-adjustment and benchmarking when using 'rjd3x13'
    or 'rjd3tramoseats'.
Interface to 'JDemetra+' 3.x (<https://github.com/jdemetra>)
    time series analysis software.  It offers full access to options and
    outputs of 'TRAMO-SEATS' (Time series Regression with ARIMA noise,
    Missing values and Outliers - Signal Extraction in ARIMA Time Series),
    including 'TRAMO' modelling (ARIMA model with outlier detection and
    trading days adjustment). ARIMA = AutoRegressive Integrated Moving Average.
R Interface to 'JDemetra+' 3.x
    (<https://github.com/jdemetra>) time series analysis software.  It
    offers full access to options and outputs of 'X-13', including Reg-ARIMA
    modelling (automatic AutoRegressive Integrated Moving Average (ARIMA) model 
    with outlier detection and trading days adjustment) and X-11 decomposition.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Functions to have nice 'rmarkdown' outputs of the 
  seasonal and trading day adjustment models made with 'RJDemetra'.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.
Set of tools to manipulate the 'JDemetra+' workspaces.  Based
    on the 'RJDemetra' package (which interfaces with version 2 of the
    'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal
    adjustment software officially recommended to the members of the
    European Statistical System (ESS) and the European System of Central
    Banks).  This package provides access to additional workspace
    manipulation functions such as metadata manipulation, raw paths and
    wrangling of several workspaces simultaneously.  These additional
    functionalities are useful as part of a CVS data production chain.
A series of functions in some way considered useful to the author.  These 
    include methods for subsetting tables and generating indices for arrays, 
    conditioning and intervening in probability distributions, generating 
    combinations, fast transformations, and more...
Implementation of the Johnson Quantile-Parameterised Distribution in R. 
  The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution
  system that is parameterised by a symmetric percentile triplet of quantile values
  (typically the 10th-50th-90th) along with known support bounds for the distribution.
  The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>.
  This package implements the density, quantile, CDF and random number generator functions.
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <https://sdmx.org>.
Converts R object into JSON objects and vice-versa.
Functions to query (filter or transform), pivot (convert
    from array-of-objects to object-of-arrays, for easy import as 'R'
    data frame), search, patch (edit), and validate (against 'JSON Schema')
    'JSON' and 'NDJSON' strings, files, or URLs. Query and
    pivot support 'JSONpointer', 'JSONpath' or 'JMESpath'
    expressions. The implementation uses the 'jsoncons'
    <https://danielaparker.github.io/jsoncons/> header-only library;
    the library is easily linked to other packages for direct access
    to 'C++' functionality not implemented here.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Handle 'JSON-stat' format (<https://json-stat.org>) in R.
    Not all features are supported, especially the extensive metadata
    features of 'JSON-stat'.
Create an 'R Journal' 'Rmarkdown' template article, that will
  generate html and pdf versions of your paper. Check that the paper folder 
  has all the required components needed for submission. 
  Examples of 'R Journal' publications can be found at 
  <https://journal.r-project.org>.
Provides an R interface to the 'JuliaBUGS.jl' package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the 'bayesplot' and 'posterior' packages.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
Easily download datasets from Kaggle <https://www.kaggle.com/> directly into your R environment using 'RKaggle'. Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see <http://www.keel.es/>.
Calculates the Kelly criterion (Kelly, J.L. (1956) <doi:10.1002/j.1538-7305.1956.tb03809.x>) for bets given quoted prices, model predictions and commissions.
    Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games.
Exchange rate for Kenya Shilling against other currencies, US DOLLAR, EURO, STERLING POUND, Tanzania Shilling, Uganda Shilling.
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database <https://www.kolada.se/>.
This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****.
A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
An 'Eigen'-based computationally efficient 'C++' implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Provides functions and datasets required for the ST 370 course at North Carolina State University.
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <https://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a 'LabKey' Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
Offers bathymetric interpolation using Inverse Distance Weighted and Ordinary Kriging via the 'gstat' and 'terra' packages. Other functions focus on quantifying physical aquatic habitats (e.g., littoral, epliminion, metalimnion, hypolimnion) from interpolated digital elevation models (DEMs). Functions were designed to calculate these metrics across water levels for use in reservoirs but can be applied to any DEM and will provide values for fixed conditions. Parameters like Secchi disk depth or estimated photic zone, thermocline depth, and water level fluctuation depth are included in most functions.
Provides access to a suite of geospatial data layers for wildfire management, fuel modeling, ecology, natural resource management, climate, conservation, etc., via the 'LANDFIRE' (<https://www.landfire.gov/>) Product Service ('LFPS') API.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://community.asprs.org/leadership-restricted/leadership-content/public-documents/standards>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.
Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Common 'base' and 'stats' methods for 'rle' objects, aiming to make it possible to treat them transparently as vectors.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the open-
    source JavaScript library 'Leaflet' (see <https://leafletjs.com/>). Maps can be
    rendered in a web browser or displayed in the HTML viewer pane of 'RStudio'.
    This package is designed to be easy to use and can create complex maps with
    vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
Allows easy access to the LEMON Graph Library set of algorithms, written in C++.
             See the LEMON project page at <https://lemon.cs.elte.hu/trac/lemon>.
             Current LEMON version is 1.3.1.
An implementation to compute an optimal dose escalation rule
    using deep reinforcement learning in phase I oncology trials
    (Matsuura et al. (2023) <doi:10.1080/10543406.2023.2170402>).
    The dose escalation rule can directly optimize the percentages of correct
    selection (PCS) of the maximum tolerated dose (MTD).
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.
Assists in the manipulation and processing of linear features with the help of the 'sf' package.
  Makes use of linear referencing to extract data from most shape files.
  Reference for this packages methods: Albeke, S.E. et al. (2010) <doi:10.1007/s10980-010-9528-4>.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Unlock the power of large-scale geospatial analysis, 
    quickly generate high-resolution kernel density visualizations, 
    supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. 
    Regardless of the size of your dataset, our library delivers efficient and accurate results.
    Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) <doi:10.1145/3555041.3589401>.
    Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) <doi:10.1145/3555041.3589711>.
    Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.1145/3514221.3517823>.
    Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3554821.3554855>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3503585.3503591>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3494124.3494135>.
    Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) <doi:10.14778/3476311.3476312>.
    Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) <doi:10.14778/3461535.3461540>.
    Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) <doi:10.1145/3318464.3380561>.
    Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) <doi:10.1109/TKDE.2020.3018376>.
    Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) <doi:10.1109/ICDE.2019.00055>.
Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should
    provide most standard Kriging / Gaussian process regression features
    (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages).
    'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 
    'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by 
    Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.
Performs Wavelet Lifting Transforms focusing on signal denoising 
    and functional data analysis (FDA). Implements a hybrid architecture with 
    a zero-allocation 'C++' core for high-performance processing. Features include 
    unified offline (batch) denoising, causal (real-time) filtering using a 
    ring buffer engine, and adaptive recursive thresholding.
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Get data from 'Linkedin Advertising API' <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>.
    You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and
    also you can load ad analytics data from your 'Linkedin' Ad account.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Listings are often part of the submission of clinical trial
    data in regulatory settings.  We provide a framework for the specific
    formatting features often used when displaying large datasets in that
    context.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
An implementation to compute an optimal adaptive allocation rule
    using deep reinforcement learning in a dose-response study
    (Matsuura et al. (2022) <doi:10.1002/sim.9247>).
    The adaptive allocation rule can directly optimize a performance metric,
    such as power, accuracy of the estimated target dose, or mean absolute error
    over the estimated dose-response curve.
The goal of 'rlowdb' is to provide a lightweight, file-based JSON database. 
    Inspired by 'LowDB' in 'JavaScript', it generates an intuitive interface for
    storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. 
    Ideal for prototyping, small-scale applications, and lightweight data management needs.
The Linear Programming via Regularized Least Squares (LPPinv) is a
             two-stage estimation method that reformulates linear programs as
             structured least-squares problems. Based on the Convex Least
             Squares Programming (CLSP) framework, LPPinv solves linear
             inequality, equality, and bound constraints by (1) constructing a
             canonical constraint system and computing a pseudoinverse
             projection, followed by (2) a convex-programming correction stage
             to refine the solution under additional regularization (e.g.,
             Lasso, Ridge, or Elastic Net). LPPinv is intended for
             underdetermined and ill-posed linear problems, for which standard
             solvers fail.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmerTest::lmer(), gamm4::gamm4() and
    mgcv::gamm().
Random forest with a variety of additional features for regression, classification and survival analysis. 
             The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable,
             based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, 
             tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence production in 
    dosimetric materials using Monte Carlo methods. Implemented are models for 
    delocalised transitions (e.g., Chen and McKeever (1997) <doi:10.1142/2781>), 
    localised transitions (e.g., Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>) 
    and tunnelling transitions (Jain et al. (2012) <doi:10.1088/0953-8984/24/38/385402> 
    and Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>). 
    Supported stimulation methods are thermal luminescence (TL), 
    continuous-wave optically stimulated luminescence (CW-OSL), 
    linearly-modulated optically stimulated luminescence (LM-OSL), 
    linearly-modulated infrared stimulated luminescence (LM-IRSL),
    and isothermal luminescence (ITL or ISO-TL).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
 'Luminescence'. These mainly, but not exclusively, include applications for
 plotting chronometric data from e.g. luminescence or radiocarbon dating. It
 further provides access to bootstraps tooltip and popover functionality and
 contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Work with the 'Macrostrat' (<https://macrostrat.org/>) Web Service 
    (v.2, <https://macrostrat.org/api/v2>) to fetch geological data relevant to
    the spatial and temporal distribution of sedimentary, igneous, and
    metamorphic rocks as well as data extracted from them.
Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>. The suggested 'FuzzyDBScan' package (which allows for fuzzy clustering) can be obtained from
    <https://github.com/henrifnk/FuzzyDBScan/> (or from CRAN's Archive <https://cran.r-project.org/src/contrib/Archive/FuzzyDBScan/>).
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial rankings, with or without ties.
        Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains), as proposed in Molina et al. (2010) <doi:10.1162/evco.2010.18.1.18102> and Molina et al. (2011) <doi:10.1007/s00500-010-0647-2>. Rmalschains is further discussed in Bergmeir et al. (2016) <doi:10.18637/jss.v075.i04>. Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
An interface to the 'Mangal' database - a collection of ecological networks. This package includes functions to work with the 'Mangal RESTful API' methods (<https://mangal-interactions.github.io/mangal-api/>).
Edit and simplify 'geojson', 'Spatial', and 'sf'
    objects.  This is wrapper around the 'mapshaper' 'JavaScript' library
    by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform
    topologically-aware polygon simplification, as well as other
    operations such as clipping, erasing, dissolving, and converting
    'multi-part' to 'single-part' geometries.
Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://www.mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
A port of the C++ routine for applying the marching cubes algorithm written by 
    Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. 
    The package supplies the contour3d() function, which takes a 3-dimensional array of voxel 
    data and calculates the vertices, vertex normals, and faces for a 3d mesh representing 
    the contour(s) at a given level.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a DBI-compliant interface to MariaDB
    (<https://mariadb.org/>) and MySQL (<https://www.mysql.com/>)
    databases.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.
Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subject effects, rendering its relationship with the Bayes factor less clear 
    in within-subject (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subject intervals in within-subject designs, 
    which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, 
    the Loftus-Masson (1994) <doi:10.3758/BF03210951>, 
    the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, 
    and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.
Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.
Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 <doi:10.1016/j.simpa.2025.100762>.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.
Functions for simulating Markov chains using the Barker proposal
    to compute Markov chain Monte Carlo (MCMC) estimates of expectations with
    respect to a target distribution on a real-valued vector space. The Barker
    proposal, described in Livingstone and Zanella (2022)
    <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the
    Barker accept-reject rule. It combines the robustness of simpler MCMC
    schemes, such as random-walk Metropolis, with the efficiency of
    gradient-based methods, such as the Metropolis adjusted Langevin algorithm. 
    The key function provided by the package is sample_chain(), which allows
    sampling a Markov chain with a specified target distribution as its
    stationary distribution. The chain is sampled by generating proposals and
    accepting or rejecting them using a Metropolis-Hasting acceptance rule.
    During an initial warm-up stage, the parameters of the proposal distribution
    can be adapted, with adapters available to both: tune the scale of the
    proposals by coercing the average acceptance rate to a target value; tune
    the shape of the proposals to match covariance estimates under the target 
    distribution. As well as the default Barker proposal, the package also
    provides implementations of alternative proposal distributions, such as
    (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's
    R interface <https://roualdes.us/bridgestan/latest/languages/r.html>,
    available on GitHub <https://github.com/roualdes/bridgestan>, is installed,
    then 'BridgeStan' can be used to specify the target distribution to sample
    from.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets. For more details, see the web documentation 
    <https://lmarusich.github.io/rmcorr/index.html> and the 
    original paper: Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. All models are implemented in 'Stan' (see Stan Development Team, 2019) <https://mc-stan.org/>. The package also implements demand forecasting (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare calculation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>) for policy simulation. 'Stan' models can be estimated using either the 'cmdstanr' (default) or 'rstan' backend. If using 'cmdstanr', then user will need to install 'cmdstanr' manually <https://mc-stan.org/cmdstanr/>.
Supports concordances in 'R Markdown' 
  documents.  This currently allows the original source
  location in the '.Rmd' file of errors detected by 'HTML tidy'
  to be found more easily, and potentially allows forward
  and reverse search in 'HTML' and 'LaTeX' documents
  produced from 'R Markdown'.  The 'LaTeX' support
  has been included in the most recent development 
  version of the 'patchDVI' package.
Download, inspect, reconcile, and summarize mammal taxonomic
    names with the Mammal Diversity Database (MDD). Supports accepted
    names, synonyms, original combinations, distribution summaries, and
    mapped outputs derived from packaged MDD releases. Designed for
    reproducible mammal name resolution workflows in 'R'.
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
Use 'rmarkdown' partials, also know as child documents in
		'knitr', so you can make components for HTML, PDF, and Word documents. 
		The package provides various helper functions to make certain functions easier. 
		You may want to use this package, if you want to flexibly summarise objects 
		using a combination of figures, tables, text, and HTML widgets. 
		Unlike HTML widgets, the output is Markdown and can hence be turn into other
		output formats than HTML. Currently does not play well with 'rmarkdown' notebooks,
		not tested with Quarto.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
Computes word, character, and non-whitespace character counts in R Markdown documents and Jupyter notebooks, with or without code chunks. Returns results as a data frame.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
Computes confidence intervals for nonlinear functions of model 
    parameters (e.g., product of k coefficients) in single-level and multilevel 
    structural equation models. Methods include the distribution of the product, 
    Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based 
    Constrained Optimization (MBCO) procedure for hypothesis testing of indirect 
    effects.
    References:
    Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation 
    analysis confidence intervals. Behavior Research Methods, 43, 692-700. 
    <doi:10.3758/s13428-011-0076-x>;
    Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. 
    Psychological Methods, 25(4), 496-515. <doi:10.1037/met0000259>;
    Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of 
    Indirect Effects. Frontiers in Psychology, 10, 2989. 
    <doi:10.3389/fpsyg.2019.02989>.
Conducts mediation analysis for structural equation models (SEM)
    estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements
    the Baron and Kenny (1986) <doi:10.1037/0022-3514.51.6.1173> and
    Zhao, Lynch & Chen (2010) <doi:10.1086/651257>
    approaches to determine the presence and type of mediation. Supports
    covariance-based SEM, partial least squares SEM, Bayesian SEM, and
    moderated mediation models. Reports indirect effects with standard
    errors from Sobel, Delta, Monte-Carlo, and bootstrap methods, along
    with effect size measures (RIT, RID).
Implementation of the MEthod based on the Removal Effects of Criteria - MEREC- a new objective weighting method for determining criteria weights for Multiple Criteria Decision Making problems, created by Mehdi Keshavarz-Ghorabaee (2021) <doi:10.3390/sym13040525>. Given a decision matrix, the function return the Merec´s weight vector and all intermediate matrix/vectors used to calculate it.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, <arXiv:2306.03883>). These procedures enable us to verify the overall hypothesis regarding equality, as well as hypotheses for pairwise comparisons (i.e., post hoc analysis) of mean functions corresponding to repeated experiments.
We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.
Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2025) <doi:10.48550/arXiv.2503.07286> for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a 'shiny' application for interactive use of the functions from the package.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>. This package is deprecated in favor of 'rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates.
Fits 'MIDAS' denoising autoencoder models for multiple
    imputation of missing data, generates multiply-imputed datasets,
    computes imputation means, and runs Rubin's rules regression analysis.
    Wraps the 'MIDAS2' 'Python' engine via a local 'FastAPI' server over
    'HTTP', so no 'reticulate' dependency is needed at runtime. Methods are
    described in Lall and Robinson (2022) <doi:10.1017/pan.2020.49> and
    Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.
Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.
Perform derivative-free optimization algorithms in R using C++.
    A wrapper interface is provided to call C function of the 'bobyqa' implementation
    (See <https://github.com/emmt/Algorithms/tree/master/bobyqa>).
Provides header files of 'mio', a cross-platform C++11 header-only 
    library for memory mapped file IO <https://github.com/mandreyel/mio>.
Contains many functions useful for data analysis
    and utility operations.
Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling <doi: 10.18637/jss.v067.i06>.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with
    heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm.
    It includes 8 models for real, categorical, counting, functional and ranking data.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management.
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility
    functions to facilitate the analysis of 'MixtComp' output.
Sundry discrete probability distributions and helper functions.
Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. This package enables a nomogram creation for any ML prediction models, which is conventionally limited to only a linear/logistic regression model. This nomogram may indicate the explainability value per feature, e.g., the Shapley additive explanation value, for each individual. However, this package only allows a nomogram creation for a model using categorical without or with single numerical predictors. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rmlnomogram/blob/master/doc/ml_nomogram_exemplar.html>.
R implementation of Maximum Likelihood Principal Component Analysis
    The main idea of this package is to have an alternative way of PCA for 
    subspace modeling that considers measurement errors.
    More details can be found in Peter D. Wentzell (2009) 
    <doi:10.1016/B978-0-444-64165-6.03029-9>.
The RMM  fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.
Connect R with MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>) to build classification models and
    regression models on streaming data or out-of-RAM data.
    Also streaming recommendation models are made available.
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.
Supports automated Markov chain Monte Carlo for arbitrarily structured correlation
    matrices. The user supplies data, a correlation matrix in symbolic form, the current state
    of the chain, a function that computes the log likelihood, and a list of prior distributions.
    The package's flagship function then carries out a parameter-at-a-time update of all correlation
    parameters, and returns the new state. The method is presented in Hughes (2023), in preparation.
Provides an intuitive and user-friendly interface for working with emojis in 'R'. It allows users to search, insert, and manage emojis by keyword, category, or through an interactive 'shiny'-based drop-down. The package enables integration of emojis into 'R' scripts, 'R Markdown', 'Quarto', 'shiny' apps, and 'ggplot2' plots. Also includes built-in mappings for commit messages, useful for version control. It builds on established emoji libraries and Unicode standards, adding expressiveness and visual cues to documentation, user interfaces, and reports. For more details see 'Emojipedia' (2024) <https://emojipedia.org> and GitHub Emoji Cheat Sheet <https://github.com/ikatyang/emoji-cheat-sheet/tree/master>.
Graphical visualization of the birds' molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).
The 'R' equivalent of 'nodemon'. Watches specified
    directories for file changes and reruns a designated 'R' script when
    changes are detected. It's designed to automate the process of
    reloading your 'R' applications during development, similar to
    'nodemon' for 'Node.js'.
Integrated tools to support rigorous and well documented data 
    harmonization based on Maelstrom Research guidelines. The package includes 
    functions to assess and prepare input elements, apply specified processing 
    rules to generate harmonized datasets, validate data processing and identify 
    processing errors, and document and summarize harmonized outputs. The 
    harmonization process is defined and structured by two key user-generated 
    documents: the DataSchema (specifying the list of harmonized variables to 
    generate across datasets) and the Data Processing Elements (specifying the 
    input elements and processing algorithms to generate harmonized variables 
    in DataSchema formats). The package was developed to address key  challenges 
    of retrospective data harmonization in epidemiology (as described in 
    Fortier I and al. (2017) <doi:10.1093/ije/dyw075>) but can be used for any 
    data harmonization initiative.
Encrypt R objects to a raw vector or file using modern cryptographic
    techniques.  Password-based key derivation is with 'Argon2' (<https://en.wikipedia.org/wiki/Argon2>).
    Objects are serialized and then encrypted using 'XChaCha20-Poly1305' (<https://en.wikipedia.org/wiki/ChaCha20-Poly1305>)
    which follows RFC 8439 for authenticated encryption (<https://en.wikipedia.org/wiki/Authenticated_encryption>).
    Cryptographic functions are provided by the included 'monocypher' 'C' library (<https://monocypher.org>).
The 'rmoo' package is a framework for multi- and many-objective
        optimization, which allows researchers and users versatility
        in parameter configuration, as well as tools for analysis, replication
        and visualization of results. The 'rmoo' package was built as a fork of
        the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and
        implementing the Non-Dominated Sorting Genetic Algorithms proposed
        by K. Deb's.
Provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) <doi:10.1137/080718061>).
This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.
Estimates Pareto-optimal solution for personnel selection
    with 3 objectives using Normal Boundary Intersection (NBI) algorithm
    introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>.
    Takes predictor intercorrelations and predictor-objective relations as
    input and generates a series of solutions containing predictor weights
    as output. Accepts between 3 and 10 selection predictors. Maximum 2
    objectives could be adverse impact objectives. Partially modeled after
    De Corte (2006) TROFSS Fortran program
    <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from
    'ParetoR' package described in Song et al. (2017)
    <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).
Performs univariate probability mass function estimation via Bayesian nonparametric mixtures of rounded kernels as in Canale and Dunson (2011) <doi:10.1198/jasa.2011.tm10552>.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, the package interfaces to
  the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the 'GMP' (GNU Multiple Precision) Library.
An interface (wrapper) to MPI. It also 
	     provides interactive R manager and worker environment.
Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) <arXiv:1604.08616> .
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics.  It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'.  The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) <https://www.jstor.org/stable/2347760>.
Implements Bayesian model averaging for settings with many
    candidate regressors relative to the available sample size, including
    cases where the number of regressors exceeds the number of observations.
    By restricting attention to models with at most M regressors, the package
    supports reduced model space inference, thereby preserving degrees of
    freedom for estimation. It provides posterior summaries, Extreme Bounds
    Analysis, model selection procedures, joint inclusion measures, and
    graphical tools for exploring model probabilities, model size
    distributions, and coefficient distributions. The methodological
    approach follows Doppelhofer and Weeks (2009) <doi:10.1002/jae.1046>.
A function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained.  The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. 
    The function is called the single core version in Wada & Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> and evaluated with other methods in Wada, Kawano & Tsubaki (2020) <doi:10.17713/ajs.v49i2.872>.
A parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package.  The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in 'RMSD' package.  See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.
Provides streamlined functions for summarising and visualising 
  regression models fitted with the 'rms' package, in the preferred format for medical journals. 
  The 'modelsummary_rms()' function produces concise summaries for linear, logistic, and 
  Cox regression models, including automatic handling of models containing restricted 
  cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into 
  publication-ready documents using the 'flextable' and 'officer' packages. The 'ggrmsMD()' 
  function creates clear and customizable plots ('ggplot2' objects) to visualise RCS terms.
'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.
Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust 
             multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of 
             the least trimmed square loss of the models in the ensembles under constraints  for the size of 
             the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and
             diversity of the robust ensemble are selected by cross-validation.
The parametric Bayes analysis for the 
    restricted mean survival time (RMST) with cluster effect,
    as described in Hanada and Kojima (2024) <doi:10.48550/arXiv.2406.06071>.
    Bayes estimation with random-effect and frailty-effect can be applied to 
    several parametric models useful in survival time analysis.
    The RMST under these parametric models can be computed from 
    the obtained posterior samples.
Performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold  timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014)<doi:10.1200/JCO.2014.55.2208>, Uno et al(2022)<https://CRAN.R-project.org/package=survRM2>, Ueno and Morita(2023)<doi:10.1007/s43441-022-00484-z>.
Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.
We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.
Provides two general frameworks to generate a multi-layer network. This also provides several methods to reveal the embedding of both nodes and layers. The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) <arXiv:2302.04437>.
Some basic features of 'MUMPS' (Multifrontal Massively Parallel
         sparse direct Solver) are wrapped in a class whose methods can be used
         for sequentially solving a sparse linear system (symmetric or not)
         with one or many right hand sides (dense or sparse).
         There is a possibility to do separately symbolic analysis,
         LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'.
         'MUMPS' method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <https://www.commanster.eu/rcode.html>).
Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents <https://academictorrents.com/collection/mvl-datasets>.
A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
    (1) effectively process large data, 
    (2) rapidly evaluate population structure, 
    (3) efficiently estimate variance components several algorithms, 
    (4) implement parallel-accelerated association tests of markers three methods, 
    (5) globally efficient design on GWAS process computing, 
    (6) enhance visualization of related information. 
    'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) 
    and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX 
    (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, 
    R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 
    'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression 
    (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
An integrated set of tools to allow data users to conduct 
    meteorological normalisation and counterfactual modelling for air quality 
    data. The meteorological normalisation technique uses predictive random 
    forest models to remove variation of pollutant concentrations so trends and 
    interventions can be explored in a robust way. For examples, see 
    Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and 
    Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random
    forest models can also be used for counterfactual or business as usual (BAU) 
    modelling by using the models to predict, from the model's perspective, the 
    future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.
Extend Rasch and Item Response Theory (IRT) analyses by providing  
 tools for post-processing the output from five major IRT packages  
 (i.e., 'eRm', 'psychotools', 'ltm', 'mirt', and 'TAM').            
 The current version provides the plotPIccc() function, which       
 extracts from the return object of the originating package all     
 information required to draw an extended Person-Item-Map (PIccc),  
 showing any combination of                                         
 * category characteristic curves (CCCs),                           
 * threshold characteristic curves (TCCs),                          
 * item characteristic curves (ICCs),                               
 * category information functions (CIFs),                           
 * item information functions (IIFs),                               
 * test information function (TIF), and the                         
 * standard error curve (S.E.).                                     
 for uni- and multidimensional models (as far as supported by each  
 package). It allows for selecting dimensions, items, and categories
 to plot and offers numerous options to adapt the output. The return
 object contains all calculated values for further processing.
Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code
    ported from S-PLUS. A modern 'MySQL' client written in 'C++' is available
    from the 'RMariaDB' package.
Allows work with 'MyTarget Statistics API v2' 
	<https://target.my.com/adv/api-marketing/doc/stat-v2> and 
	'MyTarget Statistics API v3' <https://target.my.com/adv/api-marketing/doc/stat-v2#statisticsv3>
	load data by ads, campaigns, agency clients and statistic from
	your ads account.
Reads, writes and validates 'mzQC' files. The 'mzQC' format is a 
  standardized file format for the exchange, transmission, and archiving of 
  quality metrics derived from biological mass spectrometry data, as defined 
  by the HUPO-PSI (Human Proteome Organisation - Proteomics Standards Initiative) 
  Quality Control working group. 
  See <https://hupo-psi.github.io/mzQC/> for details.
Analysis of RNA crosslinking data for RNA structure prediction. The package is suitable for the analysis of RNA structure cross-linking data and chemical probing data.
Recursive display of names and paths of all the items nested within sublists of a list object.
Performs RNA emulation and active learning proposed by Heo and Sung (2025) <doi:10.1080/00401706.2024.2376173> for multi-fidelity computer experiments. The RNA emulator is particularly useful when the simulations with different fidelity level are nonlinearly correlated. The hyperparameters in the model are estimated by maximum likelihood estimation.
Finds the k nearest neighbours for every point in a given dataset using Jose Luis' 'nanoflann' library. There is support for exact searches, fixed radius searches with 'kd' trees and two distances, the 'Euclidean' and 'Manhattan'. For more information see <https://github.com/jlblancoc/nanoflann>. Also, the 'nanoflann' library is exported and ready to be used via the linking to mechanism.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter. The method is fully described in
    <doi:10.1093/bioinformatics/btx819>.
Functions for semi-automated quality control of bulk RNA-seq data.
Interface to access data via the United States Department of 
  Agriculture's National Agricultural Statistical Service (NASS) 'Quick Stats' 
  web API <https://quickstats.nass.usda.gov/api/>. Convenience functions 
  facilitate building queries based on available parameters and valid parameter 
  values. This product uses the NASS API but is not endorsed or certified by NASS.
Facilitates mapping by making natural earth map data from <https://www.naturalearthdata.com/> more easily available to R users.
Vector map data from <https://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.
A collection of methods for estimating the basic reproduction
    number (R0) of infectious diseases. Features a web application to interface
    with the estimators. Uses the models from:
        Fisman et al. (2013) <DOI:10.1371/journal.pone.0083622>,
        Bettencourt and Ribeiro (2008) <DOI:10.1371/journal.pone.0002185>, and
        White and Pagano (2008) <DOI:10.1002/sim.3136>.
    Includes datasets for Canadian national and provincial COVID-19 case counts
    provided by Berry et al. (2021) <DOI:10.1038/s41597-021-00955-2>.
Image data used as examples in the loon R package.
Use the <https://api.nbp.pl/> API through R. Retrieve
    currency exchange rates and gold prices data published by the
    National Bank of Poland in form of convenient R objects.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html>) and NCEP/DOE Reanalysis II (<https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html>) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1038/s41437-020-00401-w>.
To enable quantitative trait loci mapping of neighbor effects, this package extends a single-marker regression to interval mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1093/g3journal/jkab017>.
An implementation of a method based on information theory devised 
    for the identification of genes showing a significant variation of expression
    across multiple conditions. Given expression estimates from any number of 
    RNA-Seq samples and conditions it identifies genes or transcripts with a 
    significant variation of expression across all the conditions studied, 
    together with the samples in which they are over- or under-expressed. 
    Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
Provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, <doi:10.1038/nature03288>).
An interface to the 'NetCDF' file formats designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  Most capabilities of 'NetCDF' version 4 are supported. Optional conversions
  of time units are enabled by 'UDUNITS' version 2, also from Unidata.
Interface to use and access Wilensky's 'NetLogo' (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the 'NetLogo' 'Mathematica' Link <https://github.com/NetLogo/Mathematica-Link>.
A collection of tools for measuring the similarity of text messages and tracing the flow of messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
Generation of univariate and multivariate data that follow the generalized 
  Poisson distribution. The details of the univariate part are explained in 
  Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is 
  an extension of the correlated Poisson data generation routine that was introduced in 
  Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.
Provides a set of functions for working with
    Random Number Generators (RNGs). In particular, a generic
    S4 framework is defined for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, 
  which were introduced in Panneton et al. (2006), ``Improved Long-Period 
  Generators Based on Linear Recurrences Modulo 2'', see <doi:10.1145/1132973.1132974>. 
  But this package is not intended to be used directly, you are strongly __encouraged__ 
  to use the 'randtoolbox' package, which depends on this package.
Tools for downloading and analyzing CDC NHANES data, with a focus
    on analytical laboratory data.
Provides very fast read and write access to images stored in the
    NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation
    of in-memory image objects between compiled C and interpreted R code. Also
    provides a simple image viewer, and a C/C++ API that can be used by other
    packages. Not to be confused with 'RNiftyReg', which performs image
    registration and applies spatial transformations.
Provides an 'R' interface to the 'NiftyReg' image registration tools
    <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration
    are supported, in two and three dimensions.
Allows the user to implement a dark/light toggle mode in 'shiny' using the 'Nightly' 'JavaScript' library. 
    The default mode is dark/light however the user can also specify other colours.
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al.) and vanilla RNN.
The Nearest Neighbor Descent method for finding approximate
    nearest neighbors by Dong and co-workers (2010)
    <doi:10.1145/1963405.1963487>. Based on the 'Python' package
    'PyNNDescent' <https://github.com/lmcinnes/pynndescent>.
A proof of concept implementation of regularized non-negative matrix factorization optimization.
    A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative
    matrices L and R of reduced rank. This package supports such factorizations with weighted objective and
    regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R,
    as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are
    a modification of the algorithm of Lee and Seung (2001)
    <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well
    as an additive update derived from that multiplicative update.  See also Pav (2004) <doi:10.48550/arXiv.2410.22698>.
Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.
Programmatic interface to the Web Service methods provided by
    the National 'Phenology' Network (<https://usanpn.org/>), which
    includes data on various life history events that occur at specific
    times.
Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.
Utility functions to retrieve 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>). The package
    contains R wrappers to the UK NRFA data temporary-API. There are
    functions to retrieve stations falling in a bounding box, to generate
    a map and extracting time series and general information. The package
    is fully described in Vitolo et al (2016) "rnrfa: An R package to
    Retrieve, Filter and Visualize Data from the UK National River Flow
    Archive"
    <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.
Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user.
Creates JavaScript charts with the 'nvd3' library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.
Provides an R interface to the ROCEEH Out of Africa Database (ROAD) (<https://www.roceeh.uni-tuebingen.de/roadweb/smarty_road_simple_search.php>), a comprehensive resource for archaeological, anthropological, paleoenvironmental and geographic data from Africa and Eurasia dating from 3,000,000 to 20,000 years BP. The package allows users to retrieve data from the online database at different levels of detail and customize search requests. Functions return `data frame` objects compatible with other R packages used in prehistoric and paleoenvironmental science, supporting reproducible workflows as an input provider.
This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
Iterative least cost path and minimum spanning tree methods for projecting 
    forest road networks. The methods connect a set of target points to an existing 
    road network using 'igraph' <https://igraph.org> to identify least cost routes.
    The cost of constructing a road segment between adjacent pixels is determined
    by a user supplied weight raster and a weight function; options include the
    average of adjacent weight raster values, and a function of the elevation 
    differences between adjacent cells that penalizes steep grades. These road
    network projection methods are intended for integration into R workflows and 
    modelling frameworks used for forecasting forest change, and can be applied 
    over multiple time-steps without rebuilding a graph at each time-step.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use. See the R 
    Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an 
    in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) 
    <arXiv:2103.08874> for details about the concept of depthgram.
Provide helper functions for package developers to create 
    active bindings that looks like data embedded in the package, but 
    are downloaded from remote sources.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
It enables the identification of sequentialexperimentation orders for factorial designs that jointly reduce bias and the number of level changes. The method used is that presented by Conto et al. (2025), known as the Assignment-Expansion method, which consists of adapting the linear programming assignment problem to generate balanced experimentation orders. The properties identified are then generalized to designs with a larger number of factors and levels using the expansion method proposed by Correa et al. (2009) and later generalized by Bhowmik et al. (2017). For more details see Conto et al. (2025) <doi:10.1016/j.cie.2024.110844>, Correa et al. (2009) <doi:10.1080/02664760802499337> and Bhowmik et al. (2017) <doi:10.1080/03610926.2016.1152490>.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
            currently used by package 'RobExtremes' only.
Implementation of a variety of methods to compute
    the robustness of ecological interaction networks with binary interactions 
    as described in <doi:10.1002/env.2709>. In particular, using the Stochastic 
    Block Model and its bipartite counterpart, the Latent Block Model to put a 
    parametric model on the network, allows the comparison of the robustness of 
    networks differing in species richness and number of interactions. It also
    deals with networks that are partially sampled and/or with missing values.
Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) <doi:10.48550/arXiv.2403.11954>. For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) <doi:10.1017/psy.2025.10066> and robust estimation of polyserial correlation (Welz, 2026 <doi:10.1017/psy.2026.10091>) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases.
Methods for analysis of compositional data including robust
    methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace 
    rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), 
    count zeros (<doi:10.1177/1471082X14535524>), 
    methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), 
    contingency (<doi:10.1080/03610926.2013.824980>) 
    and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 
    and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.
Gene-environment (G×E) interactions have important implications to elucidate the 
    etiology of complex diseases beyond the main genetic and environmental effects. 
    Outliers and data contamination in disease phenotypes of G×E studies have been commonly 
    encountered, leading to the development of a broad spectrum of robust penalization methods. 
    Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing 
    studies. We develop a robust Bayesian variable selection method for G×E interaction 
    studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and 
    outliers in the response variable while conducting variable selection by accounting for 
    structural sparsity. In particular, the spike-and-slab priors have been imposed on both 
    individual and group levels to identify important main and interaction effects. An efficient 
    Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo 
    algorithms of the proposed and alternative methods are efficiently implemented in C++.
Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and
            Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and
            \doi{10.1007/s00184-011-0366-4}.
Implementations for several robust procedures that allow for (online)
        extraction of the signal of univariate or multivariate time series by
        applying robust regression techniques to a moving time window are provided.
        Included are univariate filtering procedures based on repeated-median 
        regression as well as hybrid and trimmed filters derived from it; 
        see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive 
        online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> 
        and the slope comparing adaptive repeated median by Borowski and Fried (2013) 
        <doi:10.1007/s11222-013-9391-7> choose the width of the moving time 
        window adaptively. Multivariate versions are also provided; see  
        Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate 
        online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>  
        for a multivariate slope comparing adaptive repeated median. Furthermore, 
        a repeated-median based filter with automatic outlier replacement and 
        shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.
Functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression.
Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.
Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.
Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials.
    Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>.
    Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>.
    Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>.
    Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.1093/biomet/asaf029>.
    Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) <doi:10.48550/arXiv.2408.12541>.
Performs robust estimation and inference when using covariate adjustment
  and/or covariate-adaptive randomization in randomized controlled trials.
  This package is trimmed to reduce the dependencies and validated to be used across industry.
  See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>,
  Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>,
  Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>,
  Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>,
  and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.
Perform robust estimation and inference in platform trials and other master protocol trials.
  Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) <doi:10.48550/arXiv.2411.12944>.
Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Client for the Ocean Biodiversity Information System (<https://obis.org>).
Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.
Functions for the determination of optimally robust influence curves and
            estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005)
            <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).
A framework for estimating ensembles of meta-analytic, meta-regression, and 
    multilevel models (assuming either presence or absence of the effect, heterogeneity,
    publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to 
    combine the competing meta-analytic models into a model ensemble, weights 
    the posterior parameter distributions based on posterior model probabilities 
    and uses Bayes factors to test for the presence or absence of the
    individual components (e.g., effect vs. no effect; Bartoš et al., 2022, 
    <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, 
    <doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>). 
    Users can define a wide range of prior distributions for the effect size, heterogeneity, 
    publication bias (including selection models and PET-PEESE), and moderator components. 
    The package provides convenient functions for summary, visualizations, and fit diagnostics.
Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.1002/wics.70051>.
This companion package extends the package 'robmed' (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways.  Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data.
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine.
             This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation.
             The implemented algorithms includes:  CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. 
             Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>.
             NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>.
             Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>.
             Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.
Implementations of several robust nonparametric two-sample tests
    for location or scale differences. The test statistics are based on robust
    location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators
    as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>.
    The p-values can be computed via the permutation principle, the randomization principle, or by using
    the asymptotic distributions of the test statistics under the null hypothesis, which ensures
    (approximate) distribution independence of the test decision. To test for a difference in
    scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>.
    Random noise on a small range can be added to the original observations in order to
    hold the significance level on data from discrete distributions.
    The location tests assume homoscedasticity and the scale tests require the
    location parameters to be zero.
An implementation of Bayesian online changepoint detection 
  (Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742>) with an option for probability 
  based outlier detection and removal (Wendelberger et. al. (2021) <doi:10.48550/arXiv.2112.12899>). 
  Building on the independent multivariate constant mean model implemented in 
  the 'R' package 'ocp', this package models multivariate data as multivariate 
  normal about a linear trend, defined by user input covariates, with an 
  unstructured error covariance. Changepoints are identified based on a 
  probability threshold for windows of points.
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The 'robomit' package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the 'psacalc' command in 'Stata'. Additionally, 'robomit' offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>. Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: <doi:10.48550/arXiv.2206.02303>.
Provides the ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) framework for imbalanced 
    classification tasks. This package extends Mahalanobis distance-based 
    oversampling techniques by integrating robust covariance estimators to 
    better handle outliers and complex data distributions. The implemented 
    methodology builds upon and significantly expands the RM-SMOTE algorithm 
    originally proposed by Taban et al. (2025) <doi:10.1007/s10260-025-00819-8>.
Suite of utilities for accessing and manipulating
    data from the 'KoboToolbox' API. 'KoboToolbox' is a robust
    platform designed for field data collection in various disciplines.
    This package aims to simplify the process of fetching and handling
    data from the API. Detailed documentation for the 'KoboToolbox' API
    can be found at <https://support.kobotoolbox.org/api.html>.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.
Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.
Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021)<doi:10.17713/ajs.v50i1.994> and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained.
Three-step regression and inference for cellwise and casewise contamination.
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Robust methods for estimating the parameters of multivariate Gaussian linear models.
A framework for estimating ensembles of parametric survival models
    with different parametric families. The RoBSA framework uses Bayesian 
    model-averaging to combine the competing parametric survival models into 
    a model ensemble, weights the posterior parameter distributions based on 
    posterior model probabilities and uses Bayes factors to test for the 
    presence or absence of the individual predictors or preference for a 
    parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>).
    The user can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    fit diagnostics, and prior distribution calibration.
Estimates robust location and scale parameters using
    platform-specific Single Instruction, Multiple Data (SIMD) vectorization
    and Intel Threading Building Blocks (TBB) for parallel processing.
    Implements a novel variance-weighted ensemble estimator that adaptively
    combines all available statistics. Methods include logistic
    M-estimators, the estimators of Rousseeuw and Croux (1993), the Gini
    mean difference, the scaled Median Absolute Deviation (MAD), the scaled
    Interquartile Range (IQR), and unbiased standard deviations. Achieves
    substantial speedups over existing implementations through an 'Rcpp'
    backend with fused single-buffer algorithms that halve memory traffic
    for MAD and M-scale estimation, and a unified dispatcher that
    automatically selects the optimal estimator based on sample size.
An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.
Allows loading and displaying an Observable notebook (online JavaScript  
    notebooks powered by <https://observablehq.com>) as an HTML Widget in an R 
    session, 'shiny' application or 'rmarkdown' document.
Fast algorithms for the Theil-Sen estimator,
  Siegel's repeated median slope estimator, and Passing-Bablok regression.
  The implementation is based on algorithms by
  Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>.
  The implementations are detailed in 
  Raymaekers (2023) <doi:10.32614/RJ-2023-012> and 
  Raymaekers J., Dufey F. (2022) <doi:10.48550/arXiv.2202.08060>.
  All algorithms run in quasilinear time.
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Functions to perform robust stepwise split regularized regression. The approach first 
             uses a robust stepwise algorithm to split the variables into the models of an ensemble. 
             An adaptive robust regularized estimator is then applied to each subset of predictors
             in the models of an ensemble.
Robust (outlier-resistant) estimators of finite population
    characteristics like of means, totals, ratios, regression, etc. Available
    methods are M- and GM-estimators of regression, weight reduction,
    trimming, and winsorization. The package extends the 'survey'
    <https://CRAN.R-project.org/package=survey> package.
An implementation of Bayesian model-averaged t-tests that allows 
    users to draw inferences about the presence versus absence of an effect, 
    variance heterogeneity, and potential outliers. The 'RoBTT' package estimates 
    ensembles of models created by combining competing hypotheses and applies 
    Bayesian model averaging using posterior model probabilities. Users can 
    obtain model-averaged posterior distributions and inclusion Bayes factors, 
    accounting for uncertainty in the data-generating process 
    (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also 
    provides a truncated likelihood version of the model-averaged t-test, 
    enabling users to exclude potential outliers without introducing bias 
    (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify 
    a wide range of informative priors for all parameters of interest. 
    The package offers convenient functions for summary, visualization, 
    and fit diagnostics.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.
Systematic conservation prioritization with robust optimization techniques.
    This is important because
    conservation prioritizations typically only consider the most likely outcome
    associated with a conservation action (e.g., establishing a protected area
    will safeguard a threatened species population) and fail to consider other
    outcomes and their consequences for meeting conservation objectives. By
    extending the 'prioritizr' package, this package can be used to generate
    conservation prioritizations that account of uncertainty in the climate
    change scenario projections, species distribution models, ecosystem service
    models, and measurement errors. In particular, prioritizations can be
    generated to be fully robust to uncertainty by minimizing (or maximizing)
    objectives under the worst possible outcome. Since reducing the uncertainty
    associated with achieving conservation objectives may sacrifice other
    objectives (e.g., minimizing protected area implementation costs),
    prioritizations can also be generated to be partially robust based on a
    specified confidence level parameter. Partially robust prioritizations can
    be generated based on the chance constrained programming problem (Charnes &
    Cooper 1959, <doi:10.1287/mnsc.6.1.73>) and the conditional value-at-risk
    problem (Rockafellar & Uryasev 2000, <doi:10.21314/JOR.2000.038>).
An implementation of easy tools for outlier robust inference in
    two-stage least squares (2SLS) models. The user specifies a reference 
    distribution against which observations are classified as outliers or not. 
    After removing the outliers, adjusted standard errors are automatically 
    provided. Furthermore, several statistical tests for the false outlier 
    detection rate can be calculated. The outlier removing algorithm can be 
    iterated a fixed number of times or until the procedure converges. The 
    algorithms and robust inference are described in more detail in Jiao (2019) 
    <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
Provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.
Functions for fitting a linear regression model with ARIMA
  errors using a filtered tau-estimate.
  The methodology is described in Maronna et al (2017, ISBN:9781119214687).
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.
Robust estimators for the beta regression, useful for modeling 
  bounded continuous data. Currently, four types of robust estimators are supported. 
  They depend on a tuning constant which may be fixed or selected by a 
  data-driven algorithm also implemented in the package. Diagnostic tools 
  associated with the fitted model, such as the residuals and goodness-of-fit 
  statistics, are implemented. Robust Wald-type tests are available. More details
  about robust beta regression are described in Maluf et al. (2025) 
  <doi:10.1007/s00184-024-00949-1>.
Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.
Implements full Bayesian analysis for calibrating mathematical models with new   methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics.
Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, 
 the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented.
 The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. 
 All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is 
 asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
Outliers virtually exist in any datasets of any application field. 
  To avoid the impact of outliers, we need to use robust estimators. 
  Classical estimators of multivariate mean and covariance matrix are 
  the sample mean and the sample covariance matrix. Outliers will 
  affect the sample mean and the sample covariance matrix, and thus 
  they will affect the classical factor analysis which depends on 
  the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, 
  P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). 
  So it is necessary to use the robust estimators of the sample 
  mean and the sample covariance matrix. There are several robust 
  estimators in the literature: Minimum Covariance Determinant estimator, 
  Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, 
  M, S, and Stahel-Donoho. 
  The most direct way to make multivariate analysis more robust is to replace
  the sample mean and the sample covariance matrix of the classical estimators 
  to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) 
  <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) 
  <doi:10.18637/jss.v032.i03>), which is our choice of robust factor
  analysis. We created an object oriented solution for robust factor 
  analysis based on new S4 classes.
A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.
Robust parameter estimation and prediction of Gaussian stochastic 
 process emulators. It allows for robust parameter estimation and prediction using 
 Gaussian stochastic process emulator. It also implements the parallel partial 
 Gaussian stochastic process emulator for computer model with massive outputs 
 See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; 
 Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression. Specifically, the package implements robust least angle 
    regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), 
    (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; 
    <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares 
    regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).
Inference for the treatment effect with possibly invalid instrumental variables via TSHT ('Guo et al.' (2018) <doi:10.1111/rssb.12275>) and SearchingSampling ('Guo' (2023) <doi:10.1093/jrsssb/qkad049>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2018) <doi:10.1016/j.jeconom.2018.07.002>).
Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.
Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.
Implements the Robust Scoring Equations estimator to fit linear
    mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Robust covariance estimation for matrix-valued data and data with Kronecker-covariance structure using the Matrix Minimum Covariance Determinant (MMCD) estimators and outlier explanation using and Shapley values.
Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.
Calculates robust Matthews Correlation Coefficient (MCC) and robust F-Beta Scores, as introduced by Holzmann and Klar (2024) <doi:10.48550/arXiv.2404.07661>. 
    These performance metrics are designed for imbalanced classification problems.
    Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve.
Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.
Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
    genes. It implements the Robust Rank Aggregation Kolde et al (2012)
    <doi:10.1093/bioinformatics/btr709> and some other simple algorithms 
    for the task. RRA method uses a probabilistic model for aggregation that 
    is robust to noise and also facilitates the calculation of significance
    probabilities for all the elements in the final ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.
Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <doi:10.48550/arXiv.2107.00975>.
Implements a robust multivariate control-chart methodology for
    batch-based industrial processes with multiple correlated variables using the
    Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework.
    A robust compromise covariance matrix is constructed from Phase I batches with the
    Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is
    applied for anomaly detection in Phase II. The package includes functions to simulate
    clean and contaminated batches, to compute both robust and classical Hotelling T²
    control charts, to visualize results via robust biplots, and to launch an interactive
    'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility.
    See Lavit, Escoufier, Sabatier and Traissac (1994) <doi:10.1016/0167-9473(94)90134-1>
    for the original STATIS methodology, and Rousseeuw and Van Driessen (1999)
    <doi:10.1080/00401706.1999.10485670> for the MCD estimator.
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  Hence methods which are not well established,
  often related to methods in package 'robustbase'.  Amazingly, 'BACON()', originally by
  Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>
  has become established in places.  The "barrow wheel" `rbwheel()` is from
  Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.
Helps users in quickly visualizing risk-of-bias 
    assessments performed as part of a systematic review. It allows users to 
    create weighted bar-plots of the distribution of risk-of-bias judgments 
    within each bias domain, in addition to traffic-light plots of the 
    specific domain-level judgments for each study. The resulting figures are 
    of publication quality and are formatted according the risk-of-bias 
    assessment tool use to perform the assessments. Currently, the supported 
    tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019)  
    <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of 
    interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 
    (for diagnostic accuracy studies; Whiting et al (2011) 
    <doi:10.7326/0003-4819-155-8-201110180-00009>).
Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Aggregates multiple Receiver Operating Characteristic (ROC) curves 
    obtained from different sources into one global ROC. Additionally, it’s 
    also possible to calculate the aggregated precision-recall (PR) curve.
Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)<doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)<doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.
Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.
For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) <doi:10.48550/arXiv.2410.19523>.
The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution.
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. It was developed to be both
  human- and machine-readable, in the spirit of MarkDown and 'YAML'. The idea is
  that this makes it relatively easy to write other functions and packages
  to process 'ROCK' files. The 'rock' package contains functions for basic
  coding and analysis, such as collecting and showing coded fragments and
  prettifying sources, as well as a number of advanced analyses such as the
  Qualitative Network Approach and Qualitative/Unified Exploration of State
  Transitions. The 'ROCK' and this 'rock' package are described in the ROCK
  book (Zörgő & Peters, 2022; <https://rockbook.org>), in Zörgő & Peters
  (2024) <doi:10.1080/21642850.2022.2119144> and Peters, Zörgő and van der
  Maas (2022) <doi:10.31234/osf.io/cvf52>, and more information and
  tutorials are available at <https://rock.science>.
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    <https://pj.freefaculty.org/guides/>. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
'R6' class interface for handling relational database connections using 'DBI' package as backend.
  The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite.
  The purpose is having an intuitive object allowing straightforward handling of SQL databases.
A set of functions for receiver operating characteristic (ROC) curve 
    estimation and area under the curve (AUC) calculation.
    All functions are designed to work with aggregated data; 
    nevertheless, they can also handle raw samples. 
    In 'ROCket', we distinguish two types of ROC curve representations:
    1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) 
    are functions of a parameter (the score),
    2) functions - TPR is a function of FPR.
    There are several ROC curve estimation methods available. An introduction to 
    the mathematical background of the implemented methods (and much more) can be found in 
    de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
Connector to the REST API of a 'Rock' R server, to perform operations 
  on a remote R server session, or administration tasks. See 'Rock' documentation at
  <https://rockdoc.obiba.org/>.
Provides helper functions for authenticating and retrieving data from your 'ODK-X Sync Endpoint'. This is an early release intended for testing and feedback.
Implements ROC (Receiver Operating Characteristic)–Optimizing 
    Binary Classifiers, supporting both linear and kernel models. Both model 
    types provide a variety of surrogate loss functions. In addition, linear 
    models offer multiple regularization penalties, whereas kernel models 
    support a range of kernel functions. Scalability for large datasets is 
    achieved through approximation-based options, which accelerate training 
    and make fitting feasible on large data. Utilities are provided for model 
    training, prediction, and cross-validation. The implementation builds on 
    the ROC-Optimizing Support Vector Machines. For more information, see 
    Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>, 
    presented in the ROC Analysis in AI Workshop (ROCAI-2004).
Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code.
A toolkit for analyzing classifier performance by using receiver
    operating characteristic (ROC) curves. Performance may be assessed on
    a single classifier or multiple ones simultaneously, making it suitable for
    comparisons. In addition, different metrics allow the evaluation of local
    performance when working within restricted ranges of sensitivity and
    specificity. For details on the different implementations, see
    McClish D. K. (1989) <doi:10.1177/0272989X8900900307>,
    Vivo J.-M., Franco M. and Vicari D. (2018) <doi:10.1007/S11634-017-0295-9>,
    Jiang Y., et al (1996) <doi:10.1148/radiology.201.3.8939225>,
    Franco M. and Vivo J.-M. (2021) <doi:10.3390/math9212826> and
    Carrington, André M., et al (2020) <doi: 10.1186/s12911-019-1014-6>.
Non-inferiority test and diagnostic test are very important in clinical trails.
  This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.
A set of tools for working with Romanian personal numeric codes. 
    The core is a validation function which applies several verification
    criteria to assess the validity of numeric codes. This is accompanied by 
    functionality for extracting the different components of a personal numeric
    code. A personal numeric code is issued to all Romanian residents either at 
    birth or when they obtain a residence permit.
Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) <doi:10.1214/21-STS839>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) <doi:10.32614/RJ-2021-066> for more details.
Provides the robust gamma rank correlation coefficient as introduced by
	     Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
	     along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
R package for creating, manipulating and reading RO-Crates. Latest
    supported version of the specification: <https://w3id.org/ro/crate/1.2/>.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).
Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the proposed distributions. In addition, the volume under the ROC surface and true positive fractions values are evaluated by ROC surface analysis.
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Show physics, math and engineering students how an ODE solver
    is made and how effective R classes can be for the construction of
    the equations that describe natural phenomena. Inspiration for this work 
    comes from the book on "Computer Simulations in Physics" 
    by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. 
    Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Implements the robust functional analysis of variance (RoFANOVA), described in Centofanti et al. (2023) <doi:10.1093/jrsssc/qlad074>. 
    It allows testing mean differences among groups of  functional data by being robust against the presence of outliers.
Tools for grading the coding style and documentation of R
  scripts. This is the R component of Roger the Omni Grader, an
  automated grading system for computer programming projects based on
  Unix shell scripts; see <https://gitlab.com/roger-project>. The
  package also provides an R interface to the shell scripts. Inspired by
  the lintr package.
Rogue ("wildcard") taxa are leaves with uncertain phylogenetic
  position.
  Their position may vary from tree to tree under inference methods that yield a
  tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony).
  The presence of rogue taxa in a tree set can potentially remove all
  information from a consensus tree. The information content of a consensus
  tree - a function of its resolution and branch support values - can often be
  increased by removing rogue taxa. 
  'Rogue' provides an explicitly information-theoretic approach to rogue
  detection (Smith 2022) <doi:10.1093/sysbio/syab099>,
  and an interface to 'RogueNaRok' (Aberer et al. 2013)
  <doi:10.1093/sysbio/sys078>.
The R Optimization Infrastructure ('ROI') <doi:10.18637/jss.v094.i15>
	is a sophisticated framework for handling optimization problems in R.
	Additional information can be found on the 'ROI' homepage <https://roi.r-forge.r-project.org/>.
A collection of non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'clarabel' solver for solving convex cone problems.
         More information about 'clarabel' can be found at
         <https://oxfordcontrol.github.io/ClarabelDocs/stable/>.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 'CPLEX' commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure ('ROI') package
        	 with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is
        	 used for unconstrained optimization and 'DEoptimR' for
        	 constrained optimization.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering
	     the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP')
	     problems as well as all variants/combinations of 'LP', 'IP'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'HiGHS'. More information about
         'HiGHS' can be found at <https://highs.dev>.
Enhances the 'R' Optimization Infrastructure ('ROI') package 
    by registering the 'ipop' solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the possibility to obtain multiple solutions for linear 
             problems with binary variables. The main function is copied 
             (with small modifications) from the relations package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with a connection to the 'neos' server. 'ROI' optimization
             problems can be directly be sent to the 'neos' server
             and solution obtained in the typical 'ROI' style.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'OSQP'. More information about
         'OSQP' can be found at <https://osqp.org>.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'qpOASES'. More information about
         'qpOASES' can be found at <https://github.com/coin-or/qpOASES>.
Enhances the R Optimization Infrastructure ('ROI') package
    by registering the 'quadprog' solver. It allows for solving quadratic programming (QP)
    problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure ('ROI') package by registering
	     the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Perform optimal transport on somatic point mutations and kernel regression hypothesis testing by integrating pathway level similarities at the gene level (Little et al. (2023) <doi:10.1111/biom.13769>). The software implements balanced and unbalanced optimal transport and omnibus tests with 'C++' across a set of tumor samples and allows for multi-threading to decrease computational runtime.
Data in multidimensional systems is obtained from operational
    systems and is transformed to adapt it to the new structure.
    Frequently, the operations to be performed aim to transform a flat
    table into a ROLAP (Relational On-Line Analytical Processing) star
    database. The main objective of the package is to allow the definition
    of these transformations easily. The implementation of the
    multidimensional database obtained can be exported to work with
    multidimensional analysis tools on spreadsheets or relational
    databases.
Fast and efficient computation of rolling and expanding statistics for time-series data.
Wraps the 'Ollama' <https://ollama.com> API, which can be used to 
    communicate with generative large language models locally.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellsamora/scrollama>) for storytelling.
A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.
Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians.
A Tidy implementation of 'grouping sets', 'rollup' and 'cube' - extensions of the 'group_by' clause that allow for computing multiple 'group_by' clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" <https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.
Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the 'igraph' package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).
Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.
A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.
This R package connects to SWI-Prolog, <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to prolog (consult, query/submit, once, findall).
Implements the rank-ordered logit (RO-logit) model for stratified 
    analysis of continuous outcomes introduced by 
    Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on 
    the heuristic residuals and estimates in linear scales are available from 
    the package, and outcomes with ties are supported.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. 'RolWinWavCor' also plots at the same graphic the time series under study. The 'RolWinWavCor' was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in 'RolWinWavCor' are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in 'RolWinWavCor'. Methods derived from Polanco-Martínez et al (2018) <doi:10.1016/j.physa.2017.08.065>).
Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: <http://www.omdbapi.com/> .
Implements robust median-based Bayesian growth curve models
    that handle Missing Completely at Random (MCAR), Missing At Random (MAR),
    and Missing Not At Random (MNAR) missing-data mechanisms, and allow
    auxiliary variables. Models are fitted via 'rjags' (interface to 'JAGS')
    and summarized with 'coda'.
Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.
R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely.
A simple approach to configuring R projects with different
  parameter values. Configurations are specified using a reduced subset of base
  R and parsed accordingly.
An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.
Statistical distribution in OOP (Object Oriented Programming) way.
             This package proposes a R6 class interface to classic statistical
             distribution, and new distributions can be easily added with the
             class AbstractDist. A useful point is the generic fit() method for
             each class, which uses a maximum likelihood estimation to find the
             parameters of a dataset, see, e.g. Hastie, T. and al (2009)
             <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class
             gives a non-parametric fit, with the same accessors that for the
             classic distribution. Finally, three random generators useful to
             build synthetic data are given: a multivariate normal generator, an
             orthogonal matrix generator, and a symmetric positive definite
             matrix generator, see Mezzadri, F. (2007)
             <arXiv:math-ph/0609050>.
Implements a general framework for globally optimizing 
  user-specified objective functionals over interpretable binary weight functions 
  represented as sparse decision trees, called ROOT (Rashomon Set of Optimal Trees). 
  It searches over candidate trees to construct a 
  Rashomon set of near-optimal solutions and derives a summary tree highlighting stable 
  patterns in the optimized weights. ROOT includes a built-in generalizability mode for 
  identifying subgroups in trial settings for transportability analyses 
  (Parikh et al. (2025) <doi:10.1080/01621459.2025.2495319>).
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Minirhizotrons are widely used to observe and explore roots and
    their growth. This package provides the means to stitch images and 
    divide them into depth layers.
    Please note that this R package was developed alongside the following 
    manuscript:
    Stitching root scans and extracting depth layer information -- a workflow 
    and practical examples, S. Kersting, L. Knüver, and M. Fischer.
    The manuscript is currently in preparation and should be citet as soon as 
    it is available.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Research, Technology and Space (No. 03WIR4805).
Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
The 'ropenblas' package (<https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (<https://netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.
Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> 
    is library of programming functions mainly aimed at real-time computer 
    vision. This 'Lite' version installs the stable base version of 'OpenCV' and 
    some of its experimental externally contributed modules. It does not provide 
    R bindings directly.
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Provides string arithmetic, reassignment operators, logical operators
  that handle missing values, and extra logical operators such as floating point
  equality and all or nothing. The intent is to allow R users to write code that
  is easier to read, write, and maintain while providing a friendlier experience
  to new R users from other language backgrounds (such as 'Python') who are used
  to concepts such as x += 1 and 'foo' + 'bar'.
  Includes operators for not in, easy floating point comparisons, === equivalent, and SQL-like 
  like operations (), etc. 
  We also added in some extra helper functions, such as OS checks, pasting
  in Oxford comma format, and functions to get the first, last, nth, or most common 
  element of a vector or word in a string.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
Read the data from Origin(R) project files ('*.opj')
	<https://www.originlab.com/doc/User-Guide/Origin-File-Types>.
	No write support is planned.
R infrastructure for optimally robust estimation in general smoothly
            parameterized models using S4 classes and methods as described Kohl, M.,
            Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in
            Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.
Perform general purpose optimization in R using C++. A unified 
    wrapper interface is provided to call C functions of the five optimization 
    algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying 
    optim().
A general-purpose optimisation engine that supports 
	i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>]
    and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Černý (1985) <doi:10.1007/BF00940812>] 
	on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte 
	Carlo Optimisation. In each case, the system pseudo-temperature is 
	dynamically adjusted such that the observed acceptance ratio is kept 
	near to the desired (fixed or changing) acceptance ratio.
Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" <doi:10.1086/260062>.
Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' API (<https://orcid.org/>).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Predicts morphological parameters of rorquals (e.g. body mass, 
    flipper length, maximum engulfment capacity) from body length using 
    allometric equations from Kahane-Rapport and Goldbogen (2018) 
    <doi:10.1002/jmor.20846>.
Implements a null model analysis to quantify concurrent temporal niche overlap (i.e., activity or phenology) among biological identities (e.g., individuals, populations, species) using the Rosario randomization algorithm (Castro-Arellano et al. 2010) <doi:10.1111/j.2041-210X.2010.00031.x>.
Functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested
    and their performance accurately assessed. This requires densities
    that offer challenging properties to the novel sampling algorithms.
    One such popular problem is the Rosenbrock function. However, while its
    shape lends itself well to a benchmark problem, no codified multivariate expansion
    of the density exists. We have developed an extension to this class of distributions
    and supplied densities and direct sampler functions to assess the performance
    of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock 
    Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.
ROSE (RObust Semiparametric Efficient) random forests for robust 
  semiparametric efficient estimation in partially parametric models (containing 
  generalised partially linear models).
  Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.
Download and plot Open Street Map <https://www.openstreetmap.org/>,
    Bing Maps <https://www.bing.com/maps> and other tiled map sources. Use to create 
    basemaps quickly and add hillshade to vector-based maps.
Allows one to use 'Osmium Tool'
    (<https://osmcode.org/osmium-tool/>) from R. 'Osmium' is a
    multipurpose command line tool that enables one to manipulate and
    analyze OpenStreetMap files through several different commands.
    Currently, this package does not aim to offer functions that cover the
    entire 'Osmium' API, instead making available functions that wrap only
    a very limited set of its features.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Connect, query, and operate on information available from the 
    'Open Source Vulnerability' database <https://osv.dev/>. Although 'CRAN' 
    has vulnerabilities listed, these are few compared to projects such as 
    'PyPI'. With tighter integration between 'R' and 'Python', having an 
    'R' specific package to access details about vulnerabilities from 
    various sources is a worthwhile enterprise.
Implementation of the tests for rotational symmetry on the
    hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020)
    <doi:10.1080/01621459.2019.1665527>. The package also implements the
    proposed distributions on the hypersphere, based on the tangent-normal
    decomposition, and allows for the replication of the data application
    considered in the paper.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.  References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009),
    Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009),
    Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010),
    Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006),
    Hartley, R and Aftab, K and Trumpf, J. (2011),
    Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013),
    Maonton, Jonathan (2004), 
    Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), 
    Rancourt, D. and Rivest, L.P. and Asselin, J. (2000),
    Chang, Ted and Rivest, Louis-Paul (2001), 
    Fisher, Nicholas I. (1996, ISBN:0521568900).
A rotatogram is a method of displaying an association which is axis non-dominant. This is achieved in two ways: First, the method of estimating the slope and intercept uses the least-products method rather than more typical least squared error for the "dependent" variable. The least products method has no "dependent" variable and is scale independent. Second, the plot is rotated such that the resulting regression line is vertical, reducing the suggestion that the vertical axis is the dominant one. The slope can be read relative to either axis equally.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the
    synthetic tree, and utilities to match taxonomic names to 'Open Tree
    identifiers'. The 'Open Tree of Life' aims at assembling a
    comprehensive phylogenetic tree for all named species.
Conditionally rotate or back-up files based on
    their size or the date of the last backup; inspired by the 'Linux'
    utility 'logrotate'.
Tools to help developers and producers manipulate R objects
    and outputs. It includes tools for displaying results and objects, and
    for formatting them in the correct format.
Visualize networks using the 'javascript' library 'roughjs'. This allows to draw sketchy, hand-drawn-like networks.
Implementations of algorithms for data analysis based on the
    rough set theory (RST) and the fuzzy rough set theory (FRST). We not only
    provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and
    classification based on nearest neighbors. RST was introduced by Zdzisław
    Pawlak in 1982 as a sophisticated mathematical tool to model and process
    imprecise or incomplete information. By using the indiscernibility relation for
    objects/instances, RST does not require additional parameters to analyze the
    data. FRST is an extension of RST. The FRST combines concepts of vagueness and
    indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in
    1965) and RST.
Draw maps using the 'javascript' library 'roughjs'. This allows to draw sketchy, hand-drawn-like maps.
Decimal rounding is non-trivial in binary arithmetic.  ISO
  standard round to even is more rare than typically assumed as most decimal fractions
  are not exactly representable in binary.  Our 'roundX()' versions explore differences
  between current and potential future versions of round() in R.
  Further, provides (some partly related) C99 math lib functions not in base R.
Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.
R functions for generating and/or displaying random Chuck Norris 
  facts. Based on data from the 'Internet Chuck Norris database' ('ICNDb').
Flexible rounding functions for use in error detection. They were
    outsourced from the 'scrutiny' package.
A simple rounding function. The default round() function in R
    uses the IEC 60559 standard and therefore it rounds 0.5 to 0 and rounds
    -1.5 to -2.  The roundx() function accounts for this and helps
    to round 0.5 up to 1.
Detecting outliers using robust methods, 
 	i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013>
        and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>.
        There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
In order to make sure that web request ends up in the correct
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
Provides 'RDF' storage and 'SPARQL' 1.1 query capabilities by wrapping
    the 'Oxigraph' graph database library <https://github.com/oxigraph/oxigraph>. 
    Supports in-memory and persistent ('RocksDB') storage, multiple 'RDF' 
    serialization formats ('Turtle', 'N-Triples', 'RDF-XML', 'N-Quads', 'TriG'), 
    and full 'SPARQL' 1.1 Query and Update support. Built using the 'extendr' 
    framework for 'Rust'-R bindings.
Much as 'roxygen2' allows one to document functions in the same file as the function itself, 'roxut'  allows one to write the unit tests in the same file as the function.  Once processed, the unit tests are moved to the appropriate directory.  Currently supports 'testthat' and 'tinytest' frameworks. The 'roxygen2' package provides much of the infrastructure.
An extension for 'roxygen2' to embed 'Shinylive' applications
    in the package documentation.
Generate your Rd documentation, 'NAMESPACE' file, and
    collation field using specially formatted comments. Writing
    documentation in-line with code makes it easier to keep your
    documentation up-to-date as your requirements change. 'roxygen2' is
    inspired by the 'Doxygen' system for C++.
Converts elements of 'roxygen' documentation to 'markdown'.
Generate utils::globalVariables() from 'roxygen2' @global and @autoglobal tags.
Provides formatting linting to 'roxygen2' tags. Linters report
    'roxygen2' tags that do not conform to a standard style. These linters
    can be a helpful check for building more consistent documentation and 
    to provide reminders about best practices or checks for typos. Default 
    linting suites are provided for common style guides such as the one 
    followed by the 'tidyverse', though custom linters can be registered by 
    other packages or be custom-tailored to a specific package.
Various tests as 'roxygen2' roclets: e.g. 'testthat' and 'tinytest' tests. 
  Also other static analysis tools as checking parameter documentation consistency and others.
Provides typed parameter documentation tags for integration
    with 'roxygen2'. Typed parameter tags provide a consistent interface for
    annotating expected types for parameters and returned values. Tools for
    converting from existing styles are also provided to easily adapt projects
    which implement typed documentation by convention rather than tag. Use the
    default format or provide your own.
Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.
Packed bar charts are a variation of treemaps for visualizing skewed data.  The concept was introduced by Xan Gregg at 'JMP'.
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2025) <doi:10.1007/978-3-031-89669-9>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
'PADRINO' houses textual representations of
    Integral Projection Models which can be converted from their 
    table format into full kernels to reproduce or extend an 
    already published analysis. 'Rpadrino' is an R interface to this database. For
    more information on Integral Projection Models, see Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021)
  for more information on 'ipmr', the engine that powers model reconstruction
  <doi:10.1111/2041-210X.13683>.
A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.
'PaleoClim' <http://www.paleoclim.org> (Brown et al. 2019, 
  <doi:10.1038/sdata.2018.254>) is a set of free, high resolution paleoclimate 
  surfaces covering the whole globe. It includes data on surface temperature,
  precipitation and the standard bioclimatic variables commonly used in 
  ecological modelling, derived from the 'HadCM3' general circulation model and 
  downscaled to a spatial resolution of up to 2.5 minutes. Simulations are
  available for key time periods from the Late Holocene to mid-Pliocene. Data on
  current and Last Glacial Maximum climate is derived from 'CHELSA'  (Karger et 
  al. 2017, <doi:10.1038/sdata.2017.122>) and reprocessed by 'PaleoClim' to 
  match their format; it is available at up to 30 seconds resolution. This 
  package provides a simple interface for downloading 'PaleoClim' data in R, 
  with support for caching and filtering retrieved data by period, resolution,
  and geographic extent.
A set of functions to build simple 
        GUI controls for R functions.  These are built on the 'tcltk' package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.
Provides a simple and efficient way to read data from Paradox 
    database files (.db) directly into R as modern 'tibble' data frames. 
    It uses the underlying 'pxlib' C library, to handle the low-level file format 
    details and provides a clean, user-friendly R interface.
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.
Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by 
             Breiman, Friedman, Olshen and Stone in addition to the 'rpart' package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418).
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
Recursive partitioning methods to build
        classification trees for ordinal responses within the CART
        framework. Trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
This project is a tool for words edit similarity joins (a.k.a. all-pairs similarity search) under small (< 3) edit distance constraints. 
  It works for Levenshtein/Hamming distances and words from any alphabet. 
  The software was originally developed for joining amino-acid/nucleotide sequences from Adaptive Immune Repertoires, 
  where the number of words is relatively large (10^5-10^6) and the average length of words is relatively small (10-100).
Fit and simulate any kind of
    physiologically-based kinetic ('PBK') models whatever the number of compartments.
    Moreover, it allows to account for any link between pairs of compartments, as
    well as any link of each of the compartments with the external medium. Such
    generic PBK models have today applications in pharmacology (PBPK models) to
    describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe
    chemical substance effects. In case of exposure to a parent compound (drug or
    chemical) the 'rPBK' package allows to consider metabolites, whatever their number
    and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for
    dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also
    Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.
Computes the ridge partial correlation
    coefficients in a high or ultra-high dimensional linear regression
    problem. An extended Bayesian information criterion is also
    implemented for variable selection. Users provide the matrix
    of covariates as a usual dense matrix or a sparse matrix
    stored in a compressed sparse column format. Detail of the method
    is given in the manual.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Provides tools to read, write, visualize Protein Data Bank (PDB) files and
    perform some structural manipulations.
Provides an R interface to the 'RCSB' Protein Data Bank ('PDB') Search
 and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute,
 sequence, motif, structure, and chemical searches; retrieval of entry-,
 assembly-, polymer-entity-, and chemical-component-level metadata; and
 conversion of API responses into analysis-ready tables and typed R objects for
 reproducible structural bioinformatics workflows.
Performs multinomial goodness-of-fit test on multinomially distributed data using the Randomized phi-divergence test statistics. Details of this kind of statistics can be found at Nikita Puchkin, Vladimir Ulyanov (2023) <doi:10.1214/22-AIHP1299>.
The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.
Implements the fast iterative shrinkage-thresholding algorithm
             (FISTA) algorithm to fit a Gamma distribution with an elastic net 
             penalty as described in Chen, Arakvin and Martin (2018) 
             <doi:10.48550/arXiv.1804.07780>. An implementation for the case of the 
             exponential distribution is also available, with details available 
             in Chen and Martin (2018) <doi:10.2139/ssrn.3085672>.
Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Chen and Martin (2018) 
             <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019)
             <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
Estimates of standard errors of popular risk and performance 
             measures for asset or portfolio returns using methods as described in 
             Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
Implements diversification analyses using the phylogenetic birth-death-shift model. It leverages belief propagation techniques to calculate branch-specific diversification rates, see Kopperud & Hoehna (2025) <doi:10.1093/sysbio/syaf041>.
This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.
Factor out logic
    and math common to Item Factor Analysis fitting, diagnostics, and
    analysis. It is envisioned as core support code suitable for more
    specialized IRT packages to build upon. Complete access to optimized C
    functions are made available with R_RegisterCCallable().
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Provides a user-friendly interface for managing PostgreSQL
    database connection settings.  The package supplies helper functions to
    create, edit and load connection and option configuration files stored in a
    user-specific directory using the 'odbc' and 'RPostgres' back ends.  These
    helpers make it easy to construct a reproducible connection string from a
    configuration file, either by reading user-defined YAML files or by parsing
    an environment variable.
An enhanced version of the semi-empirical, spatially distributed
    emission and transport model PhosFate implemented in 'R' and 'C++'. It is 
    based on the D-infinity, but also supports the D8 flow method. The currently
    available substances are suspended solids (SS) and particulate phosphorus 
    (PP). A major feature is the allocation of substance loads entering surface 
    waters to their sources of origin, which is a basic requirement for the 
    identification of critical source areas and in consequence a cost-effective
    implementation of mitigation measures. References: Hepp et al. (2022)
    <doi:10.1016/j.jenvman.2022.114514>; Hepp and Zessner (2019)
    <doi:10.3390/w11102161>; Kovacs (2013)
    <http://hdl.handle.net/20.500.12708/9468>.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Work with the PhyloPic Web Service (<http://api-docs.phylopic.org/v2/>) 
    to fetch silhouette images of organisms. Includes functions for adding 
    silhouettes to both base R plots and ggplot2 plots.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Two tests for the well-specification of the linear instrumental
    variable model. The first test is based on trying to predict the residuals of a
    two-stage least-squares regression using a random forest. The second test is robust
    to weak-identification and based on trying to predict the residuals for a particular
    candidate parameter and can also be used to construct confidence sets with an
    Anderson-Rubin-type inversion. Details can be found in Scheidegger, Londschien
    and Bühlmann (2025) "Machine-learning-powered specification testing in linear
    instrumental variable models" <doi:10.48550/arXiv.2506.12771>.
Provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.
Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (<https://www.synapse.org/Synapse:syn59520082/wiki/628063>). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at <https://herdiantrisufriyana.github.io/rplec/doc/placental_aging_analysis.html>.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
      The user can specify the titles, choose the type of the graph, graph output formats
      (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
      whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
      in the previous file. This data file has a tabulated format, with a single character
      (e.g. tab) between each column. Optionally, the file could include data columns for
      showing confidence intervals.
Makes it easy to produce everyday 'ggplot2' 
  charts in a functional way without an extensive "tree"
  implementation. The package includes over 15 functions
  for the production and arrangement of basic graphing.
A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying 'rplum' are derived from the 'rbacon' package by the same authors, and there remains a degree of overlap between the two packages.
Statistical estimation of revealed preference models from data collected on bipartite matchings. The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered.  See Goyal, Handcock, Jackson, Rendall and Yeung (2022) <doi:10.1093/jrsssa/qnad031>.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Implements the P-model 
  (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>),
  predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis,
  assimilation, and dark respiration rates as a function of the environment
  (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
Functions to allow users to build and analyze design consistent 
    tree and random forest models using survey data from a complex sample 
    design.  The tree model algorithm can fit a linear model to survey data 
    in each node obtained by recursively partitioning the data.  The splitting 
    variables and selected splits are obtained using a randomized permutation 
    test procedure which adjusted for complex sample design features used to 
    obtain the data. Likewise the model fitting algorithm produces 
    design-consistent coefficients to any specified least squares linear model 
    between the dependent and independent variables used in the end nodes.
    The main functions return the resulting binary tree or random forest as 
    an object of "rpms" or "rpms_forest" type. The package also provides methods
    modeling a "boosted" tree or forest model and a tree model for zero-inflated
    data as well as a number of functions and methods available for use with 
    these object types.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
Wrapper for the 'PoetryDB' API <http://poetrydb.org> that allows for interaction and data extraction from the 
  database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to 
  enable developers and poets to easily access one of the most comprehensive poetry databases currently available.
Visualizations to explain the results of a topological
  data analysis. The goal of topological data analysis is to identify
  persistent topological structures, such as loops (topological circles)
  and voids (topological spheres), in data sets. The output of an
  analysis using the 'TDA' package is a Rips diagram (named after the
  mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in
  these holes in the data by providing tools to visualize the features
  that help explain the structures found in the Rips diagram. See McGee
  and colleagues (2024) <doi:10.1101/2024.05.16.593927>.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see,  Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].
Provides an interface between R and 'PostGIS'-enabled
    'PostgreSQL' databases to transparently transfer spatial
    data. Both vector (points, lines, polygons) and raster data are
    supported in read and write modes. Also provides convenience
    functions to execute common procedures in 'PostgreSQL/PostGIS'.
Fully DBI-compliant C++-backed interface to PostgreSQL
    <https://www.postgresql.org/>, an open-source relational database.
Database interface and 'PostgreSQL' driver for 'R'.
 This package provides a Database Interface 'DBI' compliant 
 driver for 'R' to access 'PostgreSQL' database systems.  
 In order to build and install this package from source, 'PostgreSQL' 
 itself must be present your system to provide 'PostgreSQL' functionality 
 via its libraries and header files. These files are provided as
 'postgresql-devel' package under some Linux distributions.
 On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used.
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as 'tissue lysate arrays' or simply 'lysate arrays'. The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage  information, and a list of related papers describing methods used in the package please review the vignette 'Guide_to_RPPASPACE'. 'RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. <doi: 10.1093/bioinformatics/btac665>.
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. 
    (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations 
    known as database preferences (see Kießling (2002) 
    <doi:10.1016/B978-155860869-6/50035-4>).
Perform a regression analysis, generate a regression table, create a scatter plot, and download the results. It uses 'stargazer' for generating regression tables and 'ggplot2' for creating plots. With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from 'ChatGPT' was taken. References were Wickham (2016) <doi:10.1007/978-3-319-24277-4>.
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
'Eprime' is a set of programs for administering
    psychological experiments by computer. This package provides functions
    for loading, parsing, filtering and exporting data in the text files
    produced by 'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
Bayes estimation of probit choice models in cross-sectional and panel 
    settings. The package can analyze binary, multivariate, ordered, and ranked 
    choices, as well as heterogeneity of choice behavior among deciders. The 
    main functionality includes model fitting via Gibbs sampling, tools for 
    convergence  diagnostic, choice data simulation, in-sample and out-of-sample 
    choice prediction, and model selection using information criteria and Bayes 
    factors. The latent class model extension facilitates preference-based 
    decider classification, where the number of latent classes can be inferred 
    via the Dirichlet process or a weight-based updating heuristic. This allows 
    for flexible modeling of choice behavior without the need to impose 
    structural constraints. For a reference on the method, see Oelschlaeger and 
    Bauer (2021) <https://trid.trb.org/view/1759753>.
The A() function calculates the A statistic, a nonparametric
    measure of effect size for two independent groups that’s also known as
    the probability of superiority (Ruscio, 2008), along with its standard
    error and a confidence interval constructed using bootstrap methods 
    (Ruscio & Mullen, 2012). Optional arguments can be specified to 
    calculate variants of the A statistic developed for other research 
    designs (e.g., related samples, more than two independent groups or 
    related samples; Ruscio & Gera, 2013). 
    <DOI: 10.1037/1082-989X.13.1.19>.
    <DOI: 10.1080/00273171.2012.658329>.
    <DOI: 10.1080/00273171.2012.738184>.
Performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).
Use rprofile::load() inside a project '.Rprofile' file to ensure that the user-global '.Rprofile' is
  loaded correctly regardless of its location, and other common resources (in particular 'renv') are also set up
  correctly.
Robust, reliable and flexible paths to files below
    a project root. The 'root' of a project is defined as a directory that
    matches a certain criterion, e.g., it contains a certain regular file.
Use JSON templates to create folders and files structure for data science projects. Includes customized templates and accepts your own as JSON files.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our 'JSS'
 paper (2016, <doi:10.18637/jss.v071.i02>. A sufficiently recent version of
 'Protocol Buffers' library is required; currently version 3.3.0 from 2017
 is the tested minimum.
Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
Estimating repeatability (intra-class
    correlation) from Gaussian, binary, proportion and Poisson data.
The 'Public Trading API' <https://public.com/api/docs> allows clients to access their brokerage accounts,  request market data, and place stock/etf/option orders.
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the 
 site <https://www.pushbullet.com> site to obtain a (free) API key.
Provides portable access from 'R' to biomedical image processing toolbox 
    'ANTs' by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless
    integration with the 'Python' implementation 'ANTsPy'. Allows biomedical 
    images to be processed in 'Python' and analyzed in 'R', and vice versa 
    via shared memory. See 'citation("rpyANTs")' for more reference information.
Provides access to ArcGIS geoprocessing tools by building an 
             interface between R and the ArcPy Python side-package via the 
             reticulate package.
Aims to create a single isolated 'Miniconda' 
    and 'Python' environment for reproducible pipeline scripts. 
    The package provides utilities to run system command within the 'conda'
    environment, making it easy to install, launch, manage, and stop 
    'Jupyter-lab'.
A quantile regression method for multivariate data to find linear combinations of explanatory and response variables generalizing canonical correlation. The package consists of functions, rqcan() for fitting the coefficients, and summary.rqcan(), which calls a bootstrap function. For details, see the help files for rqcan() and summary.rqcan(), and the reference: Portnoy (2022) <doi:10.1016/j.jmva.2022.105071>.
Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).
Implements the 'rquery' piped Codd-style query algebra using 'data.table'.  This allows
   for a high-speed in memory implementation of Codd-style data manipulation tools.
The commonly used methods for relative quantification of gene expression levels obtained in real-time PCR (Polymerase Chain Reaction) experiments are the delta Ct methods, encompassing 2^-dCt and 2^-ddCt methods, originally proposed by Kenneth J. Livak and Thomas D. Schmittgen (2001) <doi:10.1006/meth.2001.1262>. The main idea is to normalise gene expression values using endogenous control gene, present gene expression levels in linear form by using the 2^-(value)^ transformation, and calculate differences in gene expression levels between groups of samples (or technical replicates of a single sample). The 'RQdeltaCT' package offers functions that cover both methods for comparison of either independent groups of samples or groups with paired samples, together with importing expression datasets, performing multi-step quality control of data, enabling numerous data visualisations, enrichment of the standard workflow with additional useful analyses (correlation analysis, Receiver Operating Characteristic analysis, logistic regression), and conveniently export obtained results in table and image formats. The package has been designed to be friendly to non-experts in R programming.
It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous,
    and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> 
    for more details.
Modified Poisson, logistic and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090>, Noma (2026)<doi:10.1016/j.spl.2026.110698>, and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals.
Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to article in the R Journal.
Quantitative Structure-Activity Relationship (QSAR) modeling is a valuable tool in computational chemistry and drug design, where it aims to predict the activity or property of chemical compounds based on their molecular structure. In this vignette, we present the 'rQSAR' package, which provides functions for variable selection and QSAR modeling using Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Random Forest algorithms.
Create tests and tasks compliant with the Question & Test Interoperability (QTI) information model version 2.1. Input sources are Rmd/md description files or S4-class objects. Output formats include standalone zip or xml files. Supports the generation of basic task types (single and multiple choice, order, pair association, matching tables, filling gaps and essay) and provides a comprehensive set of attributes for customizing tests.
Non-linear inversion for hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from 'RSEIS' and 'GEOmap' to help researchers pick, locate, and store hypocenters for detailed seismic investigation. Error ellipsoids and station influence are estimated via jackknife analysis. References include  Iversen, E. S., and J. M. Lees (1996)<doi:10.1785/BSSA0860061853>.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, crop parameters, fertilizer application, and crop attainable yield. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation and improvements. There are also functions to compute optimal fertilizer application rates.
A piped query generator based on Edgar F. Codd's relational
    algebra, and on production experience using 'SQL' and 'dplyr' at big data
    scale.  The design represents an attempt to make 'SQL' more teachable by
    denoting composition by a sequential pipeline notation instead of nested
    queries or functions.   The implementation delivers reliable high 
    performance data processing on large data systems such as 'Spark',
    databases, and 'data.table'. Package features include: data processing trees
    or pipelines as observable objects (able to report both columns
    produced and columns used), optimized 'SQL' generation as an explicit
    user visible table modeling step, plus explicit query reasoning and checking.
Functions to conduct hypothesis tests and derive confidence intervals 
    for quantiles, linear combinations of quantiles, 
    ratios of dependent linear combinations and differences 
    and ratios of all of the above for comparisons between independent samples. 
    Additionally, quantile-based measures of inequality are also considered.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <https://graemeblair.com/papers/randresp.pdf>.
Three methods to calculate R2 for models with correlated errors, 
    including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed 
    Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in 
    Ives 2018 <doi:10.1093/sysbio/syy060>.
Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
A single method implementing multiple approaches to generate pseudo-random vectors whose components sum up to one (see, e.g., Maziero (2015) <doi:10.1007/s13538-015-0337-8>). The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach.
The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.
Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.
A tool to exchange data between R and 'Raven' sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package 'warbleR'.
Software for genomic prediction with the RR-BLUP mixed model (Endelman 2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065>. It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point:
        principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
        linear and quadratic discriminant analysis (Todorov and Pires (2007)),
        multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>),
        outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
        See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>,
        Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and
        Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>).
    Several optimization methods alternative to ALS are available 
    (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).
Robust multivariate methods for high dimensional data including
        outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), 
        robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), 
        robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), 
        and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
Efficient framework for ridge redundancy analysis (rrda),
    tailored for high-dimensional omics datasets where the number of
    predictors exceeds the number of samples. The method leverages
    Singular Value Decomposition (SVD) to avoid direct inversion of the
    covariance matrix, enhancing scalability and performance. It also
    introduces a memory-efficient storage strategy for coefficient
    matrices, enabling practical use in large-scale applications. The
    package supports cross-validation for selecting regularization
    parameters and reduced-rank dimensions, making it a robust and
    flexible tool for multivariate analysis in omics research. Please
    refer to our article (Yoshioka et al., 2025) for more details.
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
'IUCN' Red List (<https://api.iucnredlist.org/>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
Provides estimation and data generation tools for several new regression models,
             including the gamma, beta, inverse gamma and beta prime distributions. These models can be
             parameterized based on the mean, median, mode, geometric mean and harmonic mean, as 
             specified by the user.  
             For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and
             Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code supporting multicore execution. This package 
    integrates 'Repast Simphony' models within R environment, making easier 
    the tasks of running and analyzing model output data for 
    automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
An easy way to analyze international large-scale assessments and surveys in education or any other dataset that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) while also allowing for analysis with multiply imputed variables (plausible values). It supports the estimation of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, correlation, linear regression and any other model already implemented in R that takes a data frame and weights as parameters. It also includes  options to prepare the results for publication, following the table formatting standards of the Organization for Economic Cooperation and Development (OECD).
Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.
Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest. Fortran original by Leo Breiman 
    and Adele Cutler, R port by Andy Liaw and Matthew Wiener, 
    Regularized random forest for classification by Houtao Deng, 
    Regularized random forest for regression by Xin Guan.
    Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.
Performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 <doi:10.1111/2041-210X.14066>) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 <doi:10.1111/2041-210X.14478>).
Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).
Fit the reduced-rank multinomial logistic regression model for Markov
    chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio
    (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial
    logistic regression in Markov chains and reduced-rank. It is very useful in 
    a study where multi-states model is assumed and each transition among the 
    states is controlled by a series of covariates. The key advantage is to 
    reduce the number of parameters to be estimated. The final coefficients for 
    all the covariates and the p-values for the interested covariates will be 
    reported. The p-values for the whole coefficient matrix can be calculated by 
    two bootstrap methods.
Combined with 'RRphylo', this package provides a powerful tool to analyse and visualise 3d models (surfaces and meshes) in a phylogenetically explicit context (Melchionna et al., 2024 <doi:10.1038/s42003-024-06710-8>).
Functions for creating and manipulating RNA secondary structure plots.
Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.
Computation of the International Roughness Index (IRI) given a
    longitudinal road profile. The IRI can be calculated for a single road segment
    or for a sequence of segments with a fixed length (e. g. 100m). For the latter,
    an overlap of the segments can be selected. The IRI and likewise the algorithms
    for its determination are defined in Sayers, Michael W; Gillespie, Thomas D;
    Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) :
    establishing correlation and a calibration standard for measurements. World
    Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN
    0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.
Provides robust outlier detection techniques for identifying
    anomalies in multivariate data, with a focus on methods that remain 
    effective under non-Gaussian distributions. For more details see 
    Saluja, Parlak, and Mejia (2026+) <doi:10.48550/arXiv.2505.11806>.
Multivariate regression methodologies including
    classical reduced-rank regression (RRR)
    studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and
    Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>,
    reduced-rank regression via adaptive nuclear norm penalization
    proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and
    Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>,
    robust reduced-rank regression (R4) proposed by
    She and Chen (2017) <doi:10.1093/biomet/asx032>,
    generalized/mixed-response reduced-rank regression (mRRR) proposed by
    Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
    row-sparse reduced-rank regression (SRRR) proposed by
    Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
    reduced-rank regression with a sparse singular value decomposition (RSSVD)
    proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
    and sparse and orthogonal factor regression (SOFAR) proposed by
    Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.
Functions for phylogenetic analysis (Castiglione et al., 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.
Linear model calculations are made for many random versions of data.  
    Using residual randomization in a permutation procedure, sums of squares are 
    calculated over many permutations to generate empirical probability distributions 
    for evaluating model effects.  Additionally, coefficients, statistics, fitted values, and residuals generated over many 
    permutations can be used for various procedures including pairwise tests, prediction, classification, and
    model comparison.  This package should provide most tools one could need for the analysis of
    high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze randomized response 
    (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A 
    survey technique for eliminating evasive answer bias. Journal of the 
    American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). 
    Besides univariate estimates of true proportions, RR variables can be used 
    for correlations, as dependent variable in a logistic regression (with or 
    without random effects), or as predictors in a linear regression
    (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and 
    regression analyses of randomized response data. Journal of Statistical 
    Software, 85(2), 1–29, <doi:10.18637/jss.v085.i02>). For simulations and 
    the estimation of statistical power, RR data can be generated according to 
    several models. The implemented methods also allow to test the link between 
    continuous covariates and dishonesty in cheating paradigms such as the 
    coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). 
    The statistical analysis of cheating paradigms. Behavior Research Methods, 
    49, 724–732, <doi:10.3758/s13428-016-0729-x>).
Methods for estimating online robust reduced-rank regression. 
    The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>.
    The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>.
    The description of the generic stochastic successive upper-bound minimisation method
    and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.
Makes documents containing plots and tables from a table of R codes. 
    Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code.
    In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Implementation of Gibbs sampling algorithm for Bayesian Estimation
    of the Reduced Reparameterized Unified Model ('rrum'), described by 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full second- or 
    third-order polynomial, shifted and rotated squared difference model, 
    rising ridge surfaces, basic squared difference model, asymmetric or 
    level-dependent congruence effect models). The package provides plotting 
    functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. 
    Calculates many surface parameters (a1 to a5, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Empirical best linear unbiased prediction (EBLUP) and
    robust prediction of the area-level means under the basic unit-level
    model. The model can be fitted by maximum likelihood or a (robust)
    M-estimator. Mean square prediction error is computed by a parametric
    bootstrap.
Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.
Provides access to geocomputing and terrain analysis
    functions of the geographical information system (GIS) 'SAGA' (System
    for Automated Geoscientific Analyses) from within R by running the
    command line version of SAGA. This package furthermore provides
    several R functions for handling ASCII grids, including a flexible
    framework for applying local functions (including predict methods of
    fitted models) and focal functions to multiple grids. SAGA GIS is
    available under GPL-2 / LGPL-2 licences from
    <https://sourceforge.net/projects/saga-gis/>.
Provides an R scripting interface to the open-source 'SAGA-GIS' 
    (System for Automated Geoscientific Analyses Geographical Information
    System) software. 'Rsagacmd' dynamically generates R functions for every
    'SAGA-GIS' geoprocessing tool based on the user's currently installed
    'SAGA-GIS' version. These functions are contained within an S3 object
    and are accessed as a named list of libraries and tools. This structure
    facilitates an easier scripting experience by organizing the large number
    of 'SAGA-GIS' geoprocessing tools (>700) by their respective library.
    Interactive scripting can fully take advantage of code autocompletion tools
    (e.g. in 'RStudio'), allowing for each tools syntax to be quickly
    recognized. Furthermore, the most common types of spatial data (via the
    'terra', 'sp', and 'sf' packages) along with non-spatial data are
    automatically passed from R to the 'SAGA-GIS' command line tool for
    geoprocessing operations, and the results are loaded as the appropriate R
    object. Outputs from individual 'SAGA-GIS' tools can also be chained using
    pipes from the 'magrittr' and 'dplyr' packages to combine complex
    geoprocessing operations together in a single statement. 'SAGA-GIS' is
    available under a GPLv2 / LGPLv2 licence from
    <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64
    and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software
    repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions
    from 2.3.1 to 9.5.1 on Windows, Linux and macOS.
A computational resource designed to accurately detect microbial
    nucleic acids while filtering out contaminants and false-positive taxonomic
    assignments from standard transcriptomic sequencing of mammalian tissues.
    For more details, see Ghaddar (2023) <doi:10.1038/s43588-023-00507-1>. This
    implementation leverages the 'polars' package for fast and systematic
    microbial signal recovery and denoising from host tissue genomic sequencing.
Classes and functions to create and summarize different types
    of resampling objects (e.g. bootstrap, cross-validation).
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Fast tools for unequal probability sampling in multi-dimensional spaces, implemented in Rust for high performance.
    The package offers a wide range of methods, including Sampford (Sampford, 1967, <doi:10.1093/biomet/54.3-4.499>) and correlated Poisson sampling (Bondesson and Thorburn, 2008, <doi:10.1111/j.1467-9469.2008.00596.x>), pivotal sampling (Deville and Tillé, 1998, <doi:10.1093/biomet/91.4.893>), and balanced sampling such as the cube method (Deville and Tillé, 2004, <doi:10.1093/biomet/91.4.893>) to ensure auxiliary totals are respected.
    Spatially balanced approaches, including the local pivotal method (Grafström et al., 2012, <doi:10.1111/j.1541-0420.2011.01699.x>), spatially correlated Poisson sampling (Grafström, 2012, <doi:10.1016/j.jspi.2011.07.003>), and locally correlated Poisson sampling (Prentius, 2024, <doi:10.1002/env.2832>), provide efficient designs when the target variable is linked to auxiliary information.
Downloading, customizing, and processing time series of satellite images for a region of interest. 'rsat' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'rsat' also offers capabilities for customizing satellite images, such as tile mosaicking, image cropping and new variables computation. Finally, 'rsat' covers the processing, including cloud masking, compositing and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).
Provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. Many of these tools are based upon and extend the 'RSA' package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results.
The 'SaTScan'(TM) <https://www.satscan.org> software uses spatial and 
	space-time scan statistics to detect and evaluate spatial and space-time clusters. 
	With the 'rsatscan' package, you can run the external 'SaTScan' software from 
	within R using R data formats. To successfully select appropriate parameter 
	settings within 'rsatscan', you must first learn 'SaTScan'.
Package to Handle R Requests from R Service Bus Applications with JSON Payloads in a generic way.
    The incoming request is encoded as a string (character vector of length one) containing 
    the JSON file passed through by the client.
Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.
A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. The modeling output of rSCA is constructed as a cluster tree to represent the complicated relationships between multiple dependent and independent variables. A free tool (named rSCA Tree Generator) for visualizing the cluster tree from rSCA is also released and it can be downloaded at <https://rscatree.weebly.com/>.
As an advanced approach to computerized adaptive testing (CAT), 
  shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically 
  assembles entire shadow tests as a part of 
  selecting items throughout the testing process.
  Selecting items from shadow tests guarantees the compliance of all content 
  constraints defined by the blueprint. 'RSCAT' is an R package for the 
  shadow-test approach to CAT. The objective of 
  'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
  2) Contributing to the academic and scientific community for CAT research.
  RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.
Evaluates R source codes by variable and/or functions names. Similar source codes should deliver similarity coefficients near one. Since neither the frequency nor the order of the used names is considered, a manual inspection of the R source code is required to check for similarity. Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Scelestial infers a lineage tree from single-cell DNA mutation matrix. 
	It generates a tree with approximately maximum parsimony through 
	a Steiner tree approximation algorithm.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Provides a tool for mass deployment of shiny apps to 'RStudio Connect' or 'Shiny Server'. Multiple user accounts and servers can be configured for deployment.
Programmatic deployment interface for 'RPubs',
    'shinyapps.io', and 'Posit Connect'. Supported content types include R
    Markdown documents, Shiny applications, Plumber APIs, plots, and
    static web content.
Provides a generic implementation of the 'RStudio' connection contract to 
    make it easier for database connections, and other type of connections, opened 
    via R packages integrate with the connections pane inside the 'RStudio' interactive
    development environment (IDE).
Uses Elsevier 'Scopus' API
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.
A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/data-documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.
The Stochastic Dominance (SD) is the classical way of comparing two
    random prospects, using their distribution functions. Almost Stochastic
    Dominance (ASD) has also been developed to cover the SD failures due to 
    the extreme utility functions. This package focuses on classical and heuristic methods
    for testing the first and second SD and ASD methods given the probability mass
    function (PMF) of the random prospects. The goal is to apply these methods
    easily, efficiently, and effectively on real-world datasets. For more
    details see Hanoch and Levy (1969) <doi:10.2307/2296431>, Leshno and Levy
    (2002) <doi:10.1287/mnsc.48.8.1074.169>, and Tzeng et al. (2012)
    <doi:10.1287/mnsc.1120.1616>.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Estimation, forecasting, simulation, and portfolio construction for 
    regime-switching models with exogenous variables as in 
    Pelletier (2006) <doi:10.1016/j.jeconom.2005.01.013>.
Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.
Spatial Dispersion Index (SDI) is a generalized measurement index, or rather a family of indices to evaluate spatial dispersion of movements/flows in a network in a problem neutral way as described in: Gencer (2023) <doi:10.1007/s12061-023-09545-8>. This package computes and optionally visualizes this index with minimal hassle.
This is a sudoku game package with a shiny application for playing .
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
Response surface designs with neighbour effects are suitable for experimental situations 
            where it is expected that the treatment combination administered to one experimental unit 
            may affect the response on neighboring units as well as the response on the unit to which it is applied (Dalal et al.,2025 <doi: 10.57805/revstat.v23i2.513>).
            Integrating these effects in the response surface model improves the experiment's precision
            Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)<doi: 10.1080/03610918.2021.1890123>). 
            This package includes sym(), asym1(), asym2(), asym3() and asym4() functions that generates response surface designs which are rotatable under a 
            polynomial model of a given order without interaction term incorporating neighbour effects.
A novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) <doi:10.1080/10485252.2024.2313137> for the details.
A Bayesian-weighted estimator and two unweighted estimators are
 developed to estimate the number of newly found rare species in additional
 ecological samples. Among these methods, the Bayesian-weighted estimator
 and an unweighted (Chao-derived) estimator are of high accuracy and
 recommended for practical applications.
 Technical details of the proposed estimators have been well described
 in the following paper: Shen TJ, Chen YH (2018) A Bayesian
 weighted approach to predicting the number of newly discovered
 rare species. Conservation Biology, In press.
SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.
Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core 'VSEARCH' (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
Provides a set of R bindings for the 'Selenium 2.0 WebDriver'
    (see <https://www.selenium.dev/documentation/>
    for more information) using the 'JsonWireProtocol' (see
    <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more
    information). 'Selenium 2.0 WebDriver' allows driving a web browser
    natively as a user would either locally or on a remote machine using
    the Selenium server it marks a leap forward in terms of web browser
    automation. Selenium automates web browsers (commonly referred to as
    browsers). Using RSelenium you can automate browsers locally or
    remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve is a versatile, scalable server enabling the
	     efficient use of R from other applications through
	     variety of protocols including QAP, WebSockets, HTTP
	     and HTTPS. It acts as a server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for many popular languages allowing applications
	     to use facilities of R without the need of linking to
	     the R binary. Rserve supports remote connections,
	     user authentication and file transfer. A simple R client
	     is included in this package as well. It also supports
	     OCAP mode for secure remote procedure calls,
	     including support for full event loop, asynchronous
	     results/graphics and console I/O.
A report of statistical findings (RSF) project template is
    generated using a 'bookdown' format. 'YAML' fields can be further
    customized. Additional helper functions provide extra features to the
    RSF.
Slow Feature Analysis (SFA), ported to R based on
    'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit'
    2.8 by Wolfgang Konen.
This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
Contains a variety of functions, based around
    regime shift analysis of paleoecological data.
    Citations:
    Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448>
    Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L>
    Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680)
    rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276>
    Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>
    Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.
Downloads spatial data from spatiotemporal asset catalogs 
    ('STAC'), computes standard spectral indices from the Awesome Spectral 
    Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) 
    against raster data, and glues the outputs together into predictor bricks. 
    Methods focus on interoperability with the broader spatial ecosystem; 
    function arguments and outputs use classes from 'sf' and 'terra', and data 
    downloading functions support complex 'CQL2' queries using 'rstac'.
R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) <doi:10.1002/sim.7064>).
The main purpose of this package is to perform simulation-based
   estimation of stochastic actor-oriented models for longitudinal network
   data collected as panel data. Dependent variables can be single or
   multivariate networks, which can be directed, non-directed, or two-mode;
   and associated actor variables.
   There are also functions for testing parameters and checking goodness of fit.
   An overview of these models is given in Snijders (2017),
   <doi:10.1146/annurev-statistics-060116-054035>.
Summarise results from simulation studies and compute Monte Carlo
  standard errors of commonly used summary statistics. This package is modelled 
  on the 'simsum' user-written command in 'Stata' (White I.R., 2010 
  <https://www.stata-journal.com/article.html?article=st0200>), further extending
  it with additional performance measures and functionality.
Easy-to-use functions for downloading air quality data from
    the Mexican National Air Quality Information System (SINAICA).  Allows
    you to query pollution and meteorological parameters from more than a
    hundred monitoring stations located throughout Mexico. See
    <https://sinaica.inecc.gob.mx> for more information.
Provides a native R implementation for encoding and decoding 'sixel'
    graphics (<https://vt100.net/docs/vt3xx-gp/chapter14.html>), and a dedicated
    'sixel' graphics device that allows plots to be rendered directly within
    compatible terminal emulators.
Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor.
             F3 can for example yield 'str(selection)' and F7 open the source
             code of CRAN and base package functions on 'github'.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify submitting R scripts to a 'Slurm' 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0. An early version of the package is
    documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.
We provide functions to perform an empirical small telescopes
    analysis. This package contains 2 functions, SmallTelescopes() and 
    EstimatePower(). Users only need to call SmallTelescopes() to conduct 
    the analysis. For more information on small telescopes analysis see
    Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.
Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.
Implements popular methods for matching in time-varying
    observational studies. Matching is difficult in this scenario because
    participants can be treated at different times which may have an
    influence on the outcomes. The core methods include: "Balanced Risk
    Set Matching" from Li, Propert, and Rosenbaum (2011)
    <doi:10.1198/016214501753208573> and "Propensity Score Matching with
    Time-Dependent Covariates" from Lu (2005)
    <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the
    'Gurobi' optimization back-end to improve the optimization problem
    speed; the 'gurobi' R package and associated software can be
    downloaded from <https://www.gurobi.com> after obtaining a license.
Calculate the matrices in
    Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the
    foundation for many repeat-sales price indexes.
Provides methods for model building and model evaluation of mixed effects models using 
    'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects 
    modeling that must have been installed in order to use 'Rsmlx'. 
    Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs 
    statistical tests for model assessment, bootstrap simulation and likelihood profiling for 
    computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for 
    mixed effects models.
The Snell scoring procedure, implemented in R. This procedure was first described by E.J Snell (1964) <doi:10.2307/2528498> and was later used by Tong et al (1977) <doi:10.4141/cjas77-001> in dairy.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
Facilitates querying data from the ‘Facebook Marketing API', particularly for social science research <https://developers.facebook.com/docs/marketing-apis/>. Data from the 'Facebook Marketing API' has been used for a variety of social science applications, such as for poverty estimation (Marty and Duhaut (2024) <doi:10.1038/s41598-023-49564-6>), disease surveillance (Araujo et al. (2017) <doi:10.48550/arXiv.1705.04045>), and measuring migration (Alexander, Polimis, and Zagheni (2020) <doi:10.1007/s11113-020-09599-3>). The package facilitates querying the number of Facebook daily/monthly active users for multiple location types (e.g., from around a specific coordinate to an administrative region) and for a number of attribute types (e.g., interests, behaviors, education level, etc). The package supports making complex queries within one API call and making multiple API calls across different locations and/or parameters.
Implements the Simulating Optimal FUNctioning framework for 
              site-scale simulations of ecosystem processes, including model 
              calibration. It contains 'Fortran 90' modules for the P-model (Stocker
              et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH
              (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>)
              and BiomeE (Weng et al. (2015)
              <doi:10.5194/bg-12-2655-2015>).
Downloads Southern Oscillation Index, Oceanic Nino
    Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation
    and Arctic Oscillation. Data sources are described in the help files for each function.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
A comprehensive R API for querying Apache Solr databases.
             A Solr core is represented as a data frame or list that
             supports Solr-side filtering, sorting,
             transformation and aggregation, all through the familiar
             base R API. Queries are processed
             lazily, i.e., a query is only sent to the database when
             the data are required.
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
Toolbox with 'shiny' applications for widely used psychometric
    methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis,
    confirmatory factor analysis - structural equation modeling, generating simulated data.
    References:
    Chalmers (2012, <doi:10.18637/jss.v048.i06>);
    Revelle (2022, <https://CRAN.R-project.org/package=psych Version = 2.2.9.>);
    Rosseel (2012, <doi:10.18637/jss.v048.i02>);
    Magis & Raiche (2012, <doi:10.18637/jss.v048.i08>);
    Magis & Barrada (2017, <doi:10.18637/jss.v076.c01>).
Minimally adjust the values of numerical records in a data.frame, such
    that each record satisfies a predefined set of equality and/or inequality
    constraints. The constraints can be defined using the 'validate' package. 
    The core algorithms have recently been moved to the 'lintools' package,
    refer to 'lintools' for a more basic interface and access to a version
    of the algorithm that works with sparse matrices.
Wrapper for the 'RSpace' Electronic Lab Notebook (<https://www.researchspace.com/>)
    API. This packages provides convenience functions to browse, search, create,
    and edit your 'RSpace' documents. In addition, it enables filling 'RSpace' templates
    from R Markdown/Quarto templates or tabular data (e.g., 'Excel' files).
    This R package is not developed or endorsed by 'Research Space'.
Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) <doi:10.1016/j.envpol.2018.08.030>.
An extension package for 'sparklyr' that provides an R interface to
    H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
Implements many algorithms for statistical learning on 
  sparse matrices - matrix factorizations, matrix completion, 
  elastic net regressions, factorization machines. 
  Also 'rsparse' enhances 'Matrix' package by providing methods for 
  multithreaded <sparse, dense> matrix products and native slicing of 
  the sparse matrices in Compressed Sparse Row (CSR) format.
  List of the algorithms for regression problems:
  1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) 
  Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
  2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
  List of algorithms for matrix factorization and matrix completion:
  1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least 
  Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
  2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro 
  (2005, <doi:10.1145/1102351.1102441>)
  3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, 
  Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder 
  et al. (2014, <doi:10.48550/arXiv.1410.2596>)
  4) Linear-Flow matrix factorization, from 'Practical linear models for 
  large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al 
  (2016, ISBN:978-1-57735-770-4)
  5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, 
  Socher, Manning (2014, <https://aclanthology.org/D14-1162/>)
  Package is reasonably fast and memory efficient - it allows to work with large
  datasets - millions of rows and millions of columns. This is particularly useful 
  for practitioners working on recommender systems.
Implementation of Nelson rules for control charts in 'R'. The 'Rspc' implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).
Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.
R interface to the 'Spectra' library
    <https://spectralib.org/> for large-scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function that does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. The matrix to be computed on can be
    dense, sparse, or in the form of an operator defined by the user.
Implements the network clustering algorithm described in 
            Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete 
            iterative algorithm comprises of two steps. In the first step, the 
            network is expressed in terms of its leading eigenvalue and 
            eigenvector and recursively partition into two communities. 
            Partitioning occurs if the maximum positive 
            eigenvalue is greater than the tolerance (10e-5) for the current 
            partition, and if it results in a positive contribution to the 
            Modularity.
            Given an initial separation using the leading eigen step, 'rSpectral' 
            then continues to maximise for the change in Modularity using a 
            fine-tuning step - or variate thereof. The first stage here is to 
            find the node which, when moved from one community to another, 
            gives the maximum change in Modularity. This node’s community is 
            then fixed and we repeat the process until all nodes have been moved. 
            The whole process is repeated from this new state until the change 
            in the Modularity, between the new and old state, is less than the 
            predefined tolerance.
            A slight variant of the fine-tuning step, which can improve speed 
            of the calculation, is also provided. Instead of moving each node 
            into each community in turn, we only consider moves of neighbouring 
            nodes, found in different communities, to the community of the 
            current node of interest. The two steps process is repeatedly 
            applied to each new community found, subdivided each community 
            into two new communities, until we are unable to find any division 
            that results in a positive change in Modularity.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Implementation of various spirometry equations
    in R, currently the GLI-2012 (Global Lung Initiative;
    Quanjer et al. 2012 <doi:10.1183/09031936.00080312>),
    the race-neutral GLI global 2022 (Global Lung Initiative;
    Bowerman et al. 2023 <doi:10.1164/rccm.202205-0963OC>), the
    NHANES3 (National Health and Nutrition Examination Survey;
    Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>)
    and the JRS 2014 (Japanese Respiratory Society; Kubota
    et al. 2014 <doi:10.1016/j.resinv.2014.03.003>) equations.
    Also the GLI-2017 diffusing capacity equations 
    <doi:10.1183/13993003.00010-2017> are implemented.
    Contains user-friendly functions to calculate predicted 
    and LLN (Lower Limit of Normal) values for different 
    spirometric parameters such as FEV1 (Forced Expiratory 
    Volume in 1 second), FVC (Forced Vital Capacity), etc, 
    and to convert absolute spirometry measurements 
    to percent (%) predicted and z-scores.
Enables binary package installations on Linux distributions.
    Provides access to 'RStudio' public repositories at
    <https://packagemanager.posit.co>, and transparent management of
    system requirements without administrative privileges. Currently supported
    distributions are 'CentOS' / 'RHEL', and several 'RHEL' derivatives
    ('Rocky Linux', 'AlmaLinux', 'Oracle Linux', and 'Amazon Linux'),
    'openSUSE' / 'SLES', 'Debian', and 'Ubuntu' LTS.
Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.
An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.
The SPRITE algorithm creates possible distributions of discrete responses
    based on reported sample parameters, such as mean, standard deviation and range 
    (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, 
    drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <https://shiny.ieis.tue.nl/sprite/>. 
    In addition, it supports the modeling of distributions based on multi-item (Likert-type) 
    scales and the use of restrictions on the frequency of particular responses.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).
Allows the user to generate and execute select, insert, update and delete 'SQL' queries the underlying database without having to explicitly write 'SQL' code.
Embeds the SQLite database engine in R and provides an
    interface compliant with the DBI package. The source for the SQLite
    engine (version 3.51.2) and for various extensions is included.
    System libraries will never be consulted because this package relies
    on static linking for the plugins it includes; this also ensures a
    consistent experience across all installations.
A lightweight wrapper around the 'RSQLite' package for streamlined loading of
    data from tabular files (i,e. text delimited files like Comma Separated Values and
    Tab Separated Values, Microsoft Excel, and Arrow Inter-process Communication files)
    in 'SQLite' databases. Includes helper functions for inspecting the structure of
    the input files, and some functions to simplify activities on the 'SQLite' tables.
Parser for 'SQL' statements. Currently, it supports parsing of only 'SELECT' statements.
An implementation of calculating the R-squared measure as a total mediation effect size measure and its confidence interval for moderate- or high-dimensional mediator models. It gives an option to filter out non-mediators using variable selection methods. The original R package is directly related to the paper Yang et al (2021) "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" <doi:10.1101/774877>. The new version contains a choice of using cross-fitting, which is computationally faster. The details of the cross-fitting method are available in the paper Xu et al (2023) "Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting" <doi:10.1101/2023.02.06.527391>.
Provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by 'mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.
We provide an implementation for Sum of Ranking Differences (SRD), 
    a novel statistical test introduced by Héberger (2010) 
    <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of 
    different solutions through a reference by first performing a rank 
    transformation on the input, then calculating and comparing the distances 
    between the solutions and the reference - the latter is measured in the 
    L1 norm. The reference can be an external benchmark (e.g. an established 
    gold standard) or can be aggregated from the data. The calculated distances, 
    called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek 
    (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation 
    test) compares the SRD scores of the solutions to the SRD scores of randomly 
    generated rankings. The second validation option is cross-validation that 
    checks whether the rankings generated from the solutions come from the same 
    distribution or not. For a detailed analysis about the cross-validation 
    process see Sziklai, Baranyi and Héberger (2021) 
    <doi:10.48550/arXiv.2105.11939>. The package offers a wide array of 
    features related to SRD including the computation of the SRD scores, 
    validation options, input preprocessing and plotting tools.
Methods and tools for Singular Spectrum Analysis including decomposition,
             forecasting and gap-filling for univariate and multivariate time series.
             General description of the methods with many examples can be found in the book
             Golyandina (2018, <doi:10.1007/978-3-662-57380-8>).
             See 'citation("Rssa")' for details.
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
Provides useful tools which supplement the use of 'Simulx' software and 'R' connectors ('Monolix Suite'). 'Simulx' is an easy, efficient and flexible application for clinical trial simulations. You need 'Simulx' software to be installed in order to use 'RsSimulx' package. Among others tasks, 'RsSimulx' provides the same functions as package 'mlxR' does with a compatibility with 'Simulx' software.
A collection of implementations of semi-supervised classifiers
    and methods to evaluate their performance. The package includes implementations
    of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
    the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
    Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
Testing the equality of two means using Ranked Set Sampling
             and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. 
             Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling.
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0>
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.
Defines colour palettes and themes for Royal Statistical 
  Society (RSS) publications, including Significance magazine. Palettes and 
  themes are supported in both base R and 'ggplot2' graphics, and are intended 
  to be used by authors submitting to RSS publications.
Provides functions to access, search and download spacetime earth
    observation data via SpatioTemporal Asset Catalog (STAC). This package 
    supports the version 1.0.0 (and older) of the STAC specification
    (<https://github.com/radiantearth/stac-spec>). 
    For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test,
    estimate, and analyze Stan models by accessing the header-only Stan library
    provided by the 'StanHeaders' package. The Stan project develops a probabilistic
    programming language that implements full Bayesian statistical inference
    via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via
    optimization. In all three cases, automatic differentiation is used to quickly
    and accurately evaluate gradients without burdening the user with the need to
    derive the partial derivatives.
Estimates previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Regression methods to quantify the relation 
    between two measurement methods are provided by this package. The
    focus is on a Bayesian Deming regressions family. With a Bayesian
    method the Deming regression can be run in a traditional fashion or
    can be run in a robust way just decreasing the degree of freedom
    d.f. of the sampling distribution. With d.f. = 1 an extremely robust
    Cauchy distribution can be sampled. Moreover, models for dealing
    with heteroscedastic data are also provided. For reference see
    G. Pioda (2024) <https://piodag.github.io/bd1/>.
Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Provides various tools for developers of R packages interfacing
    with 'Stan' <https://mc-stan.org>, including functions to set up the required
    package structure, S3 generics and default methods to unify function naming
    across 'Stan'-based R packages, and vignettes with recommendations for
    developers.
'Stan' implementation of the Theory of Visual Attention (TVA; Bundesen, 1990; <doi:10.1037/0033-295X.97.4.523>) and numerous convenience functions for generating, compiling, fitting, and analyzing TVA models.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, 
    for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. 
    The output of each test is automatically transformed into a tidy data frame to facilitate visualization. 
    Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.  
    Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs 
    (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. 
    It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 
    'Cramer V' for the association between categorical variables. 
    The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
R interface for china national data <http://data.stats.gov.cn/>, 
    some convenient functions for accessing the national data are provided.
'STG' is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.
Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indexes, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.
Functions to access data from the 'Strava v3 API' <https://developers.strava.com/>.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
As of 'RStudio' v1.3, the preferences in the Global Options
    dialog (and a number of other preferences that aren’t) are now saved
    in simple, plain-text JSON files. This package provides an interface
    for working with these 'RStudio' JSON preference files to easily make
    modifications without using the point-and-click option menus. This is
    particularly helpful when working on teams to ensure a unified
    experience across machines and utilizing settings for best practices.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
Create and install 'RStudio' themes derived from Visual
    Studio Code, Positron and 'TextMate' themes. Provides functions to
    convert between 'TextMate' and Visual Studio Code or Positron themes,
    as well as ports of several Visual Studio Code themes.
Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several 
    distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of 
    distributions, also known as the generalized error distribution.
    Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where 
    the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' 
    code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric
    Laplace distributions.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system <http://www.vikamine.org> implementing
	subgroup discovery, pattern mining and analytics in Java.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) <doi:10.48550/arXiv.2406.01750>.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
    This package is based on Cameron A., Conraths F., Frohlich A., Schauer B.,
    Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of 
    functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision 
    making tools for implementing risk-based surveillance, Grant Number 
    no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). 
    Many of the 'RSurveillance' functions are incorporated into the 'epitools'
    website: Sergeant, ESG, 2019. Epitools epidemiological calculators. 
    Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.
'SurveyCTO' is a platform for mobile data collection in offline settings.
  The 'rsurveycto' R package uses the 'SurveyCTO' REST API
  <https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html>
  to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's
  and to download file attachments. The package also has limited support to
  write datasets to a server.
Provides an interface to the 'SurvStat' web service from the
  Robert Koch Institute (<https://tools.rki.de/SurvStat/SurvStatWebService.svc>) 
  allowing downloads of disease time series stratified
  by pathogen type and subtype, age, and geography from notifiable disease
  reports in Germany.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.
Computing singular value decomposition with robustness is a challenging task. 
    This package provides an implementation of computing robust SVD using density power 
    divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation
    based on a tuning parameter. It also provides utility functions to simulate various
    scenarios to compare performances of different algorithms.
Renders vector-based svg images into high-quality custom-size
    bitmap arrays using 'librsvg2'. The resulting bitmap can be written to
    e.g. png, jpeg or webp format. In addition, the package can convert
    images directly to various formats such as pdf or postscript.
Interface to 'SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
'SyncroSim' is a generalized framework for managing scenario-based 
    datasets (<https://syncrosim.com/>). 'rsyncrosim' provides an interface to 
    'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to 
    transform these datasets, taking advantage of general features such as 
    defining scenarios of model inputs, running Monte Carlo simulations, and 
    summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher 
    (API documentation: <https://docs.syncrosim.com/>).
Various functions for querying and reshaping dependency trees, 
    as for instance created with the 'spacyr' or 'udpipe' packages.
    This enables the automatic extraction of useful semantic relations from texts,
    such as quotes (who said what) and clauses (who did what). Method proposed in 
    Van Atteveldt et al. (2017) <doi:10.1017/pan.2016.12>.
Access Synthesize Bio models from their API <https://app.synthesize.bio/> using
  this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users
  to generate realistic gene expression data based on specified biological conditions. This package
  enables researchers to easily access AI-generated transcriptomic data for various
  modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more.
Functions to write messages to the 'syslog' system logger API,
  available on all 'POSIX'-compatible operating systems. Features include
  tagging messages with a priority level and application type, as well as
  masking (hiding) messages below a given priority level.
Provides a programmatic interface to the 'Request Tracker' (RT)
  HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. 'RT' is a popular
  ticket tracking system.
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Play the classic game of tic-tac-toe (naughts and crosses).
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.
Reporting tables often have structure that goes beyond simple
    rectangular data. The 'rtables' package provides a framework for
    declaring complex multi-level tabulations and then applying them to
    data. This framework models both tabulation and the resulting tables
    as hierarchical, tree-like objects which support sibling sub-tables,
    arbitrary splitting or grouping of data in row and column dimensions,
    cells containing multiple values, and the concept of contextual
    summary computations. A convenient pipe-able interface is provided for
    declaring table layouts and the corresponding computations, and then
    applying them to data.
Designed to create and display complex tables with R, the
    'rtables' R package allows cells in an 'rtables' object to contain any
    high-dimensional data structure, which can then be displayed with
    cell-specific formatting instructions. Additionally, the
    'rtables.officer' package supports export formats related to the
    Microsoft Office software suite, including Microsoft Word ('docx') and
    Microsoft PowerPoint ('pptx').
Provides R bindings for 'Tabulator JS' <https://tabulator.info/>.
  Makes it a breeze to create highly customizable interactive tables in 'rmarkdown' documents
  and 'shiny' applications. It includes filtering, grouping, editing, input validation,
  history recording, column formatters, packaged themes and more.
Applies a given global-fit method to random partial tanglegrams of a fixed size
             to identify the associations, terminals, and nodes that maximize phylogenetic
             (in)congruence. It also includes functions to compute more easily the confidence
             intervals of classification metrics and plot results, reducing computational time.
             See Llaberia-Robledillo et al., (2023) <doi:10.1093/sysbio/syad016>.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019,
    <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether
    if an observed trait clustering pattern is related to an increasing environmental constrain.
    The method 1) determines whether exists or not a trait clustering on the sampled communities
    and 2) assess if the observed clustering signal is related or not to an increasing environmental
    constrain along an environmental gradient. Also, when the effect of the environmental gradient
    is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.
The 'TRUST4' or 'MiXCR' is used to identify the clonotypes.
    The goal of 'rTCRBCRr' is to process the results from these clonotyping tools,
    and analyze the clonotype repertoire metrics based on chain names and IGH isotypes.
    The manuscript is still under preparation for publication for now.
    The references describing the methods in this package will be added later.
Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. 
  You can execute database or table handling for resources on Arm Treasure Data.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
Machine learning and visualization package with an 'S7' backend 
    featuring comprehensive type checking and validation, paired with an efficient functional
    user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and 
    unsupervised learning. All configuration steps are performed using setup functions and 
    validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing 
    with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel 
    execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017)
    <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.
A collection of R Markdown templates for nicely structured, reproducible 
    data analyses in R. The templates have embedded examples on how to write 
    citations, footnotes, equations and use colored message/info boxes, how to 
    cross-reference different parts/sections in the report, provide a nice 
    table of contents (toc) with a References section and proper R session 
    information as well as examples using DT tables and ggplot2 graphs. 
    The bookdown Lite template theme supports code folding.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hadamard product for a list of matrices.
Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.
A small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.
Use trend filtering, a type of regularized nonparametric
    regression, to estimate the instantaneous reproduction number, also
    called Rt. This value roughly says how many new infections will result
    from each new infection today.  Values larger than 1 indicate that an
    epidemic is growing while those less than 1 indicate decline. For more 
    details about this methodology, see Liu, Cai, Gustafson, and McDonald (2024)
    <doi:10.1371/journal.pcbi.1012324>.
A machine learning package for automatic text classification 
	that makes it simple for novice users to get started with machine 
	learning, while allowing experienced users to easily experiment 
	with different settings and algorithm combinations. The package 
	includes eight algorithms for ensemble classification (svm, slda, 
	boosting, bagging, random forests, glmnet, decision trees, neural 
	networks), comprehensive analytics, and thorough documentation.
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.
Methods for comparing different regression algorithms for 
    describing the temporal dynamics of secondary tree growth (xylem and 
    phloem). Users can compare the accuracy of the most common fitting methods 
    usually used to analyse xylem and phloem data, i.e., Gompertz function, 
    Double Gompertz function, General Additive Models (GAMs); and an algorithm 
    newly introduced to the field, i.e., Bayesian Regularised Neural Networks 
    (brnn). The core function of the package is XPSgrowth(), while the results 
    can be interpreted using implemented generic S3 methods, such as plot() and 
    summary().
Allow function for using 'TGStat Stat API' and
    'TGStat Search API', for more details see <https://api.tgstat.ru/docs/ru/start/intro.html>.
    'TGStat' provide telegram channel analytics data.
The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
A suite of custom R Markdown formats and templates for
    authoring journal articles and conference submissions.
A tool for processing Articulate
    Assistant Advanced™ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.
'TiddlyWiki' is a unique non-linear notebook for capturing, organising and sharing complex information. 'rtiddlywiki' is a R interface of 'TiddlyWiki' <https://tiddlywiki.com> to create new tiddler from 'R Markdown' file, and then put into a local 'TiddlyWiki' server if it is available.
Calculates tide heights based on tide station harmonics.  It
    includes the harmonics data for 637 US stations.  The harmonics data
    was converted from
    <https://github.com/poissonconsulting/rtide/blob/main/data-raw/harmonics-dwf-20151227-free.tar.bz2>,
    NOAA web site data processed by David Flater for 'XTide'.  The code to
    calculate tide heights from the harmonics is based on 'XTide'.
Extract text or metadata from over a thousand file types, using Apache Tika <https://tika.apache.org/>. Get either plain text or structured XHTML content.
Loading data from 'tiktok Marketing API' <https://business-api.tiktok.com/portal> 
    by business centers, advertisers, budgets and reports.
A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Color palettes from famous artists and paintings.
Rarefy data, calculate diversity and plot the results.
A toolkit for Commodities 'analytics', risk management and
    trading professionals. Includes functions for API calls to
    <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>,
    and <https://www.bankofcanada.ca/valet/docs>.
Provide seamless support for right-to-left (RTL) languages, such as Persian and Arabic,
  in R Markdown documents and 'LaTeX' output. It includes functions and hooks that enable easy
  integration of RTL language content, allowing users to create documents that adhere to RTL writing
  conventions. For in-depth insights into dynamic documents and the 'knitr' package, consider referring
  to Xie, Y (2014) <ISBN: 978-1-482-20353-0>.
Convenience functions to make some common tasks with right-to-left string
    printing easier, more convenient and with no need to remember long Unicode
    characters. Specifically helpful for right-to-left languages such as Arabic,
    Persian and Hebrew.
A set of tools to process and calculate 
             metrics on point clouds derived from terrestrial LiDAR 
             (Light Detection and Ranging; TLS). Its creation is based on key 
             aspects of the TLS application in forestry and ecology. 
             Currently, the main routines are based on filtering, neighboring 
             features of points, voxelization, canopy structure, 
             and the creation of artificial stands. It is written using 
             data.table and C++ language and in most of the functions it is 
             possible to use parallel processing to speed-up the routines.
Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.
Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.
Extends the functionality of the 'RTMB' <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While 'RTMB' enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using 'RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.
The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv)
    is a two-stage estimation method that reformulates structured table-based
    systems - such as allocation problems, transaction matrices, and
    input-output tables - as structured least-squares problems. Based on the
    Convex Least Squares Programming (CLSP) framework, TMPinv solves systems
    with row and column constraints, block structure, and optionally reduced
    dimensionality by (1) constructing a canonical constraint form and applying
    a pseudoinverse-based projection, followed by (2) a convex-programming
    refinement stage to improve fit, coherence, and regularization (e.g., via
    Lasso, Ridge, or Elastic Net).
Provides an interactive wrapper for the 'tmpinv()' function from
    the 'rtmpinv' package with options extending its functionality to pre-
    and post-estimation processing and streamlined incorporation of prior cell
    information. The Tabular Matrix Problems via Pseudoinverse Estimation
    (TMPinv) is a two-stage estimation method that reformulates structured
    table-based systems - such as allocation problems, transaction matrices,
    and input-output tables - as structured least-squares problems. Based
    on the Convex Least Squares Programming (CLSP) framework, TMPinv solves
    systems with row and column constraints, block structure, and optionally
    reduced dimensionality by (1) constructing a canonical constraint form
    and applying a pseudoinverse-based projection, followed by (2) a
    convex-programming refinement stage to improve fit, coherence, and
    regularization (e.g., via Lasso, Ridge, or Elastic Net).
Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models 
      by Klauer and	Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). 
      The RT-MPT class not only incorporate	frequencies like traditional multinomial processing tree (MPT) models, 
      but also latencies. This enables it	to estimate process completion times and encoding plus motor execution times 
      next to the process probabilities	of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior 
      samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs 
      sampler (for diffusion RT-MPTs).
Quickly imports, processes, analyzes, and visualizes mass-spectrometric 
	data.  Includes functions for easily extracting specific data and measurements
	from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object
	classes for manipulating and measuring mass spectrometric peaks and plotting
	peaks and spectra using the 'ggplot2' package.
Read raw and processed data from acoustic ejection mass
	spectrometry (AEMS) files produced by the Sciex EchoMS instrument.  Includes
	functions to create interactive reader objects, extract raw intensity 
	measurements, mass spectra, and fully-processed mass-transition intensity
	areas.  Methods for data processing and analysis are described in Rimmer et
	al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple
	reaction monitoring (MRM) and full-scan (neutral loss and precursor ion)
	data formats.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Allows you to interact with the API of the "Todoist"
    platform.  'Todoist' <https://www.todoist.com/> provides an online task
    manager service for teams.
An implementation of calls designed to collect and organize
    Mastodon data via its Application Program Interfaces (API), which can
    be found at the following URL: <https://docs.joinmastodon.org/>.
Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) <doi:10.1016/j.cageo.2014.02.009>.
Helps to fit thermal performance curves (TPCs). 'rTPC' contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.
Tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-test, Wilcoxon-test, ANOVA models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.
A toolkit for the analysis of paths from spatial tracking experiments and calculation of goal-finding strategies. 
    This package is centered on an approach using machine learning for path classification.
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.
Provides datasets related to the Star Trek fictional universe and functions for working with the data.
    The package also provides access to real world datasets based on the televised series and other related licensed media productions.
    It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), 
    Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) 
    to retrieve data, metadata and other information relating to Star Trek.
    It also contains several local datasets covering a variety of topics. 
    The package also provides functions for working with data from other Star Trek-related 
    R data packages containing larger datasets not stored in 'rtrek'.
The traditional linear regression trend, Modified Mann-Kendall (MK)
    non-parameter trend and bootstrap trend are included in this package. Linear 
    regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'.
    Finally, those functions are about 10 times faster than previous version 
    in R.
    Reference:
    Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for 
    autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 
    <doi:10.1016/S0022-1694(97)00125-X>.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d>
    for more information about TRIM.
A carbon-water coupled model (TRIPLEX-CW-Flux) is based on two well-established models, TRIPLEX-Flux model and Penman–Monteith model, integrates soil water and water vapor pressure deficits into the stomata conductance submodule to estimate net ecosystem production and evapotranspiration in forest ecosystems.<https://github.com/ShulanSun/rTRIPLEX_CW_Flux>.
Embeds sources and headers from Tina's Random
    Number Generator ('TRNG') C++ library. Exposes some functionality for
    easier access, testing and benchmarking into R. Provides examples of
    how to use parallel RNG with 'RcppParallel'. The methods and
    techniques behind 'TRNG' are illustrated in the package vignettes and
    examples. Full documentation is available in Bauke (2021)
    <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Designed to support the application of plant trait data providing easy applicable functions
    for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns
    and rows, excluding trait data according to different attributes, geocoding, long- to wide-table
    transformation, and data export. 'rtry' was initially developed as part of the TRY R project to
    preprocess trait data received via the TRY database.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Supports modelling real-time case data to facilitate the real-time
    surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over
    an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, 
    and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by 
    Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes 
    described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the
    nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.
Frequentist sequential meta-analysis based on 
    'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen 
    Trial Unit (CTU). The primary function is the calculation of group 
    sequential designs for meta-analysis to be used for planning and analysis of
    both prospective and retrospective sequential meta-analyses to preserve 
    type-I-error control under sequential testing. 'RTSA' includes tools for 
    sample size and trial size calculation for meta-analysis and core 
    meta-analyses methods such as fixed-effect and random-effects models and
    forest plots. TSA is described in Wetterslev et. al (2008) 
    <doi:10.1016/j.jclinepi.2007.03.013>. The methods for deriving the
    group sequential designs are based on Jennison and Turnbull (1999,
    ISBN:9780849303166).
Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi
    available here:
    <https://github.com/menta78/tsEva>. It contains an implementation of the
    Transformed-Stationary (TS) methodology for non-stationary extreme 
    value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>.  
    In synthesis this approach consists in:
    (i) transforming a non-stationary time series into a
    stationary one to which the stationary extreme value theory can be applied; and
    (ii) reverse-transforming the result into a non-stationary extreme
    value distribution.
    'RtsEva' offers several options for trend estimation (mean, extremes, seasonal)
    and contains multiple plotting functions displaying different aspects
    of the non-stationarity of extremes.
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
An implementation of calls designed to collect 'Tumblr' data via its Application Program Interfaces (API), which can be found at the following URL: <https://www.tumblr.com/docs/en/api/v2>.
Implements the t-walk algorithm, a general-purpose, self-adjusting
    Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as
    described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires
    no tuning and is robust for a wide range of target distributions, including
    high-dimensional and multimodal problems. This implementation includes an
    option for running multiple chains in parallel to accelerate sampling and
    facilitate convergence diagnostics.
Real Twig is a method to correct branch overestimation in quantitative structure models. Overestimated cylinders are correctly tapered using measured twig diameters of corresponding tree species. Supported quantitative structure modeling software includes 'TreeQSM', 'SimpleForest', 'Treegraph', and 'aRchi'. Also included is a novel database of twig diameters and tools for fractal analysis of point clouds.
A trimmed down copy of the "kent-core source tree"
	turned into a 'C' library for manipulation of '.2bit' files.
	See <https://genome.ucsc.edu/FAQ/FAQformat.html#format7>
	for a quick overview of the '2bit' format. The "kent-core source tree"
	can be found here: <https://github.com/ucscGenomeBrowser/kent-core/>.
	Only the '.c' and '.h' files from the source tree that are related
	to manipulation of '.2bit' files were kept. Note that the package
	is primarily useful to developers of other R packages who wish
	to use the '2bit' 'C' library in their own 'C'/'C++' code.
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform' <https://www.typeform.com/>
    application program interface.  Also provides functions for
    downloading your results.
R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Automatically flags common spatial errors in biological collection data using metadata and specialists' information. RuHere implements a workflow to manage occurrence data through six steps: dataset merging, metadata flagging, validation against expert-derived distribution maps, visualization of flagged records, and sampling bias exploration. It specifically integrates specialist-curated range information to identify geographic errors and introductions that often escape standard automated validation procedures. For details on the methodology, see: Trindade & Caron (2026) <doi:10.64898/2026.02.02.703373>.
Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) <https://eprints.qut.edu.au/110634/> and Burivalova (2017) <doi:10.1111/cobi.12968>.
The real-time quantitative polymerase chain reaction
    (qPCR) technical data sets by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>: (i) the four-point 10-fold dilution
    series; (ii) 380 replicates; and (iii) the competimer data set. These
    three data sets can be used to benchmark qPCR methods. Original data set
    is available at
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This package fixes incorrect annotations in the original data sets.
Tools for creating data validation pipelines and
    tidy reports. This package offers a framework for exploring and
    validating data frame like objects using 'dplyr' grammar of data
    manipulation.
Bindings for additional models for use with the 'parsnip'
    package.  Models include prediction rule ensembles (Friedman and
    Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
    ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
    <doi:10.1007/978-1-4614-6849-3>.
Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the 'arules' package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. 'RulesTools' also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.
This holds r markdown and quarto templates for academic papers and 
    slide decks. It also has templates to create research projects which 
    contain academic papers as vignettes.
Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.
Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of 'ruminate' is to create a 'shiny' interface to make these tools more broadly available while creating reproducible results.
Plots multiple run charts, finds successive signals of 
    improvement, and revises medians when each signal occurs. Finds runs
    above, below, or on both sides of the median, and returns a plot and
    a data.table summarising original medians and any revisions, for all
    groups within the supplied data.
Doubly ranked tests are nonparametric tests for grouped functional and multivariate data.
    The testing procedure first ranks a matrix (or three dimensional array) of data by column (if a matrix) or by cell (across the third dimension if an array).
    By default, it calculates a sufficient statistic for the subject's order within the sample using the observed ranks, taken over the columns or cells.
    Depending on the number of groups, G, the summarized ranks are then analyzed using either a Wilcoxon Rank Sum test (G = 2) or a Kruskal-Wallis (G greater than 2).
Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.
Captures errors encountered when running 'run_examples()', and processes and archives them. The function 'run_examples()' within the 'devtools' package allows batch execution of all of the examples within a given package. This is much more convenient than testing each example manually. However, a major inconvenience is that if an error is encountered, the program stops and does not complete testing the remaining examples. Also, there is not a systematic record of the results, namely which package functions had no examples, which had examples that failed, and which had examples that succeeded. The current package provides the missing functionality.
Implements two methods of estimating runs scored in a softball 
    scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
    distribution using multinomial random simulation.  Scores are based on player-specific input 
    probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
    of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
    a player is "fast" (e.g. the player could stretch home).  These probabilities may be 
    calculated from common player statistics that are publicly available on team's webpages. 
    Scores are evaluated based on a nine-player lineup and may be used to compare lineups, 
    evaluate base scenarios, and compare the offensive potential of individual players.  
    Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for 
    implementation of discrete Markov chains.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
The function runMCMC_btadjust() returns a mcmc.list object which is the output of a 
    Markov Chain Monte Carlo obtained - from either 'JAGS', 'nimble' or 'greta' -
	after adjusting burn-in and thinning parameters to meet pre-specified criteria
	in terms of convergence & effective sample size. Used with 'nimble', runMCMC_btadjust()
	allows extra calculations (e.g. information criteria for model comparison and goodness-of-fit
	p-values for model diagnosis).
Lightweight library for rolling windows operations. Package enables
  full control over the window length, window lag and a time indices. With a runner 
  one can apply any R function on a rolling windows. The package eases work with 
  equally and unequally spaced time series.
Package 'runonce' helps automating the saving of long-running code
    to help running the same code multiple times. If you run some long-running 
    code once, it saves the result in a file on disk. Then, if the result 
    already exists, i.e. if the code has already been run and its output has 
    already been saved, it just reads the result from the stored file instead 
    of running the code again.
Provides methods for fast computation of running sample 
    statistics for time series. These include: (1) mean, (2) 
    standard deviation, and (3) variance over a fixed-length window 
    of time-series, (4) correlation, (5) covariance, and (6) 
    Euclidean distance (L2 norm) between short-time pattern and 
    time-series. Implemented methods utilize Convolution Theorem to 
    compute convolutions via Fast Fourier Transform (FFT).
Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use.
Package to tackle large-scale problems asynchronously across
    a distributed network. Employing a database centric model, rush
    enables workers to communicate tasks and their results over a shared
    'Redis' database. Key features include low task overhead, efficient
    caching, and robust error handling. The package powers the
    asynchronous optimization algorithms in the 'bbotk' and 'mlr3tuning'
    packages.
By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.
Uses the generalized ratio-of-uniforms (RU) method to simulate
    from univariate and (low-dimensional) multivariate continuous distributions.
    The user specifies the log-density, up to an additive constant. The RU
    algorithm is applied after relocation of mode of the density to zero, and
    the user can choose a tuning parameter r. For details see Wakefield, Gelfand
    and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random
    variates via the ratio-of-uniforms method, Statistics and Computing (1991)
    1, 129-133.  A Box-Cox variable transformation can be used to make the input
    density suitable for the RU method and to improve efficiency.  In the
    multivariate case rotation of axes can also be used to improve efficiency.
    From version 1.2.0 the 'Rcpp' package 
    <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
A RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.
Real-time quantitative polymerase chain reaction (qPCR) data
    by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The
    data comprises a six-point, ten-fold dilution series, repeated in five
    independent runs, for two different amplicons. In each run, each standard
    concentration is replicated four times. For the original raw data file see
    the Supplementary Data section:
    <https://academic.oup.com/nar/article/32/22/e178/2375678#supplementary-data>.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>.  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI <https://vcg.isti.cnr.it/>,
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk
    <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN', 'Bioconductor' or 'Github').
A framework with tools to compare two random variables via stochastic dominance. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) <doi:10.1080/10618600.2022.2084405>. Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.
Random vectors, called rvecs. An rvec holds
    multiple draws, but tries to behave like a standard
    R vector, including working well in data frames.
    Rvecs are useful for analysing
    output from a simulation or a Bayesian analysis.
Simple and fast tool for transforming phytosociological 
    vegetation data into digital form for the following analysis.
    Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786).
    Hennekens, and Schaminée (2001) <doi:10.2307/3237010>.
    Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>.
    Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.
Build regular expressions using grammar and functionality inspired 
    by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to
    build expressions in a chain-like fashion.
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all previous
    'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' 
    archives (<https://vertnet.org/>).  Functions allow searching by many 
    parameters, including 'taxonomic' names, places, and dates. In addition, 
    there is an interface for conducting spatially delimited searches, and 
    another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it
    easy to download, then manipulate, HTML and XML.
Vector Graphics devices for 'Microsoft PowerPoint' and
    'Microsoft Excel'. Functions extending package 'officer' are provided
    to embed 'DrawingML' graphics into 'Microsoft PowerPoint'
    presentations and 'Microsoft Excel' workbooks.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
'ViennaCL' is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports 'CUDA', 
  'OpenCL', and 'OpenMP' (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the 'ViennaCL' library and yet you do not need to 
  distribute a copy of this code with your package.
Provides 'Java' graphical user interfaces 
    for viewing, manipulating and plotting graphs. 
    Graphs may be directed or undirected.
The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodríguez, A. and García C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) <doi:10.32614/RJ-2023-010>, Salmerón, R., García, C.B. and García, J. (2025) <doi:10.1007/s10614-024-10575-8> and Salmerón, R., García, C.B, García J. (2023, working paper) <doi:10.48550/arXiv.2005.02245>. You can also view the package vignette using 'browseVignettes("rvif")', the package website (<https://www.ugr.es/local/romansg/rvif/index.html>) using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub (<https://github.com/rnoremlas/rvif_package>).
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling. The 'rvinecopulib' package implements the core features of the
 popular 'VineCopula' package, in particular inference algorithms for both vine 
 copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker 
 and more modern API, improved performances, especially in high dimensions, 
 nonparametric and multi-parameter families, and the ability to model discrete 
 variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only 
 C++ library (currently version 0.7.2). Thus users do not need to install 
 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial 
 releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' 
 is licensed under the GNU GPL version 3.
The Regional Vulnerability Index (RVI), a statistical measure of brain structural abnormality, quantifies an individual's similarity to the expected pattern (effect size) of deficits in schizophrenia (Kochunov P, Fan F, Ryan MC, et al. (2020) <doi:10.1002/hbm.25045>).
Open any data frame with 'visidata', a terminal-based spreadsheet application <https://www.visidata.org>.
Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc.	For more information 
    see API Documentation <https://vk.com/dev/first_guide>.
Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) <doi:10.1007/s11222-024-10419-3>.
Gather boxscore, play-by-play, and auxiliary data from Major League Volleyball (MLV) <https://provolleyball.com>, League One Volleyball Pro (LOVB) <https://www.lovb.com/pro-league>, and Athletes Unlimited Pro Volleyball (AU) <https://auprosports.com/volleyball/> to create a repository of basic and advanced statistics for teams and players.
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.
Provides API to Melbourne pedestrian and weather data
    <https://data.melbourne.vic.gov.au> in tidy data form.
A port of Ruby Warrior.
    Teaches R programming in a fun and interactive way.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.
Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of 'MATLAB' Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).
Provides a wrapper for running the bundled 'Open-WBO' Maximum Satisfiability
    (MaxSAT) solver (<https://github.com/sat-group/open-wbo>).
    Users can pass command-line arguments to the solver and capture its output
    as a character string or file.
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.
A tool to read and manipulate data generated from 'RiverWare'(TM) 
    <https://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' 
    generate data in "rdf", "csv", and "nc" format. This package provides an 
    interface to read, aggregate, and summarize data from one or more 
    simulations in a 'dplyr' pipeline.
Weave and tangle drivers for Sweave extending the
  standard drivers. RweaveExtraLatex and RtangleExtra provide options
  to completely ignore code chunks on weaving, tangling, or both.
  Chunks ignored on weaving are not parsed, yet are written out
  verbatim on tangling. Chunks ignored on tangling may be evaluated as
  usual on weaving, but are completely left out of the tangled
  scripts. The driver RtangleExtra also provides options to control
  the separation between code chunks in the tangled script, and to
  specify the extension of the file name (or remove it entirely) when
  splitting is selected.
An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
Random walk functions to extract new variables based on clients transactional behaviour. For more details, see Eddin et al. (2021) <arXiv:2112.07508v3> and Oliveira et al. (2021) <arXiv:2102.05373v2>.
A straightforward, easy-to-use and robust parsing package which aims to
    digest history files from the popular messenger service 'WhatsApp' in all locales
    and from all devices.
Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.
Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.
A plug in for using 'WinEdt' as an editor for R.
An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.
Implementation of the Integrated Simple Weighted Sum Product Method (WISP), a multiple criteria sorting method create by Dragisa Stanujkic (2021) <doi:10.1109/TEM.2021.3075783>.
Estimates life tables, specifically (crude) death rates and (raw and graduated) death probabilities, using rolling windows in one (e.g., age), two (e.g., age and time) or three (e.g., age, time and income) dimensions. The package can also be utilised for summarising statistics and smoothing continuous variables through rolling windows in other domains, such as estimating averages of self-positioning ideology in political science.
   Acknowledgements:
   The authors wish to thank Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Generalitat Valenciana (grants HIECPU/2023/2, Conselleria de Hacienda, Economía y Administración Pública, and CIGE/2023/7, Conselleria de Educación, Cultura, Universidades y Empleo) for supporting this research.
Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.
Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.
An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Reproducibility is essential to the progress of research, 
  yet achieving it remains elusive even in computational fields. 
  Continuous Integration (CI) platforms offer a powerful way to launch automated workflows 
  to check and document code, but often require considerable time, effort, 
  and technical expertise to setup. We therefore developed the rworkflows suite 
  to make robust CI workflows easy and freely accessible to all R package developers. 
  rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 
  2) an R package to quickly implement a standardised workflow, and 
  3) a centrally maintained GitHub Action.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.
It contains Chinese character frequency data based on 
    news data from 2017 to 2019. Source of these news include Sina, China daily and Tencent.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment, as well as a host of other functions.  Warren, Geneva, and Lanfear (2017), <doi:10.1093/molbev/msw279>.
Tools for getting historical weather information and forecasts 
    from wunderground.com. Historical weather and forecast data includes, but 
    is not limited to, temperature, humidity, windchill, wind speed, dew point, 
    heat index. Additionally, the weather underground weather API also includes 
    information on sunrise/sunset, tidal conditions, satellite/webcam imagery, 
    weather alerts, hurricane alerts and historical high/low temperatures.
Visualize your favorite XKCD comic strip directly from
        R. XKCD <https://xkcd.com> web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) <doi:10.1093/bioinformatics/btv416>.
Facilities for running simulations from ordinary
    differential equation ('ODE') models, such as pharmacometrics and other
    compartmental models.  A compilation manager translates the ODE model
    into C, compiles it, and dynamically loads the object code into R for
    improved computational efficiency.  An event table object facilitates
    the specification of complex dosing regimens (optional) and sampling
    schedules.  NB: The use of this package requires both C and
    Fortran compilers, for details on their use with R please see
    Section 6.3, Appendix A, and Appendix D in the "R Administration and
    Installation" manual. Also the code is mostly released under GPL.  The
    'VODE' and 'LSODA' are in the public domain.  The information is available
    in the inst/COPYRIGHTS.
Provides the log-likelihoods with gradients from 'stan'
    (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed
    for generalized log-likelihood estimation in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is
    split of to reduce computational burden of recompiling 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs
    the 'nlmixr2' models during estimation.
Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.
Provides access to the 'xylib' C library for to import xy 
  data from powder diffraction, spectroscopy and other experimental methods.
Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Load data from 'Yandex Direct' API V5 
    <https://yandex.ru/dev/direct/doc/dg/concepts/about-docpage> into R.
	Provide function for load lists of campaings, ads, keywords and other 
	objects from 'Yandex Direct' account. Also you can load statistic from
	API 'Reports Service' <https://yandex.ru/dev/direct/doc/reports/reports-docpage>.
	And allows keyword bids management.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
Allows work with 'Management API' for load counters, segments, filters,
	user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get 
	information about the statistics of site visits and other data without
	using the web interface, 'Logs API' allows to receive non-aggregated data and 
	'Compatible with Google Analytics Core Reporting API v3' allows 
	receive information about site traffic and other data using field names 
	from Google Analytics Core API.	For more information see official 
	documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
Provide function for get data from 'YouTube Data API' 
    <https://developers.google.com/youtube/v3/docs/>, 'YouTube Analytics API' 
    <https://developers.google.com/youtube/analytics/reference/> and 
    'YouTube Reporting API' <https://developers.google.com/youtube/reporting/v1/reports>.
This tool proposes a new ranking algorithm that utilizes a "Y*WAASB" biplot generated by the 'metan'. The aim of the current package is to effectively distinguish the top-ranked genotypes in MET (Multi-Environmental Trials). For a detailed explanation of the process of obtaining "WAASB", "WAASBY" indices, and a "Y*WAASB" biplot, refer to the manual included in this package as well as the study by Olivoto & Lúcio (2020) <doi:10.1111/2041-210X.13384>. In this context, "WAASB" refers to the "Weighted Average of Absolute Scores" provided by Olivoto et al. (2019) <doi:10.2134/agronj2019.03.0220>, which quantifies the stability of genotypes across different environments using linear mixed-effect models. To run the package, you need to extract the "WAASB" and "WAASBY" coefficients using the 'metan' and apply them. This tool utilizes PCA (Principal Component Analysis) and differentiates the entries which may be genotypes, hybrids, varieties, etc using "WAASB", "WAASBY", and a combination of the specified trait and WAASB index.
Provides functionality to read settings, statuses and readings of
  weather stations from the 'ZENTRA Cloud' API 
  <https://zentracloud.com/api/v1/guide#APIGuidelines>.
Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.
Interface to the 'ZeroMQ' lightweight messaging kernel (see <https://zeromq.org/> for more information).
Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to
    estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes
    with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct
    for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the
    segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the
    parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity
    probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017)
    <doi:10.1111/mec.14324> and Druet and Gautier (2022) <doi:10.1016/j.tpb.2022.03.001>.
Provides R bindings for Google's s2 library for geometric calculations on
    the sphere. High-performance constructors and exporters provide high compatibility
    with existing spatial packages, transformers construct new geometries from existing
    geometries, predicates provide a means to select geometries based on spatial
    relationships, and accessors extract information about geometries.
A set of functions used in teaching STATS 201/208 Data Analysis at
    the University of Auckland. The functions are designed to make parts of R more
    accessible to a large undergraduate population who are mostly not statistics
    majors.
An advanced version of package 's2dverification'. Intended for 
    seasonal to decadal (s2d) climate forecast verification, but also applicable
    to other types of forecasts or general climate analysis. This package is 
    specifically designed for comparing experimental and observational datasets. 
    It provides functionality for data retrieval, post-processing, skill score 
    computation against observations, and visualization. Compared to 
    's2dverification', 's2dv' is more compatible with the package 'startR', able 
    to use multiple cores for computation and handle multi-dimensional arrays 
    with a higher flexibility. The Climate Data Operators (CDO) version used in 
    development is 1.9.8. Implements methods described in Wilks (2011) 
    <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett 
    (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) 
    <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) 
    <doi:10.1007/s00382-003-0350-4>.
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
Download files hosted on 'AWS S3' (Amazon Web Services Simple Storage Service; <https://aws.amazon.com/s3/>) to a local directory based on their URI. Avoid downloading files that are already present locally.  Allow for customization of where to store downloaded files.
A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). 
    The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) 
    Parquet file to be read by Apache Spark.
Access 'Amazon Web Service Simple Storage Service' ('S3') <https://aws.amazon.com/s3/>
    as if it were a file system. Interface based on the R package 'fs'.
Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model.
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
A new object oriented programming system designed to be a
    successor to S3 and S4. It includes formal class, generic, and method
    specification, and a limited form of multiple dispatch. It has been
    designed and implemented collaboratively by the R Consortium
    Object-Oriented Programming Working Group, which includes
    representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and
    the wider R community.
Provides a generic framework for working with YAML (YAML
    Ain't Markup Language) configuration files. Uses 'ajv' (Another JSON
    Schema Validator) via 'V8' to validate configurations against JSON
    Schema definitions. Configuration objects inherit from 'S7' classes
    and base lists, supporting downstream extension through custom classes
    and methods.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
Calculates a degree of spatial association between regionalizations 
    or categorical maps using the information-theoretical V-measure 
    (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
    offers an R implementation of the MapCurve method 
    (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).
A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods.
  Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon.
  Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity,
  acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>,
  Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>)
  to label individual samples and then applies a majority vote approach to identify saccades. The package includes three
  methods but can be extended via custom functions. It also uses a modular approach to compute velocity and
  acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.
Computes the Exposure-At-Default based on  the standardized approach
    of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology.
	Multiple trade types of all the five major asset classes are being supported including the Other Exposure and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees per Counterparty and CSA after
    automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the
    netting sets and the risk factors. The basis and volatility transactions are
    also identified and treated in specific hedging sets whereby the corresponding 
    penalty factors are applied. All the examples appearing on the
    regulatory papers (both for the margined and the unmargined workflow) have been
    implemented including the latest CRR2 developments.
Implementation of the BLEU-Score in 'C++' to evaluate the
    quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002)
    <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of
    generated text. It is based on the n-gram overlap between the generated
    text and reference texts. Additionally, the package provides some smoothing
    methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.
Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.
Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.
Provides the density, distribution, quantile and generation functions of some obscure probability 
    distributions, including the doubly non-central t, F, Beta, and Eta distributions; 
    the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of 
    non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
    the product of non-central chi-squares to powers; the product of doubly non-central
    F variables; the product of independent normals.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.
Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.
Time series area-level models for small area estimation. 
      The package supplements the functionality of the sae package. Specifically, it includes
      EBLUP fitting of the Rao-Yu model in the original form without a spatial component. 
      The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing
      the assumption of stationarity. Both univariate and multivariate applications are
      supported. Of particular note is the allowance for covariance of the area-level sample 
      estimates over time, as encountered in rotating panel designs such as the U.S. National 
      Crime Victimization Survey or present in a time-series of 5-year estimates from the 
      American Community Survey. Key references to the methods include
      J.N.K. Rao and I. Molina (2015, ISBN:9781118735787),
      J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and
      R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.
Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit <https://sae4health.stat.uw.edu/>. Methodological details can be found at Wu et al. (2025) <doi:10.48550/arXiv.2505.01467>.
Select best combination of auxiliary variables with certain criterion.
Estimates the parameter of small area in binary data without
             auxiliary variable using Empirical Bayes technique, mainly from Rao 
             and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". 
             This package provides another option of direct estimation using weight.
             This package also features alpha and beta parameter estimation on calculating process of small area.
             Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> 
             and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.
Provides functionality to fit a zero-inflated estimator for small area estimation.
    This estimator is a combines a linear mixed effects regression model and a logistic
    mixed effects regression model via a two-stage modeling approach. The estimator's mean
    squared error is estimated via a parametric bootstrap method. Chandra and others
    (2012, <doi:10.1080/03610918.2011.598991>) introduce and describe this estimator and mean
    squared error estimator. White and others (2024+, <doi:10.48550/arXiv.2402.03263>) describe the 
    applicability of this estimator to estimation of forest attributes and further assess the
    estimator's properties.
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.
Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).
Several functions are provided for small area estimation at
    the area level using the hierarchical bayesian (HB) method with panel
    data under beta distribution for variable interest. This package also
    provides a dataset produced by data generation. The 'rjags' package is
    employed to obtain parameter estimates. Model-based estimators involve
    the HB estimators, which include the mean and the variation of the
    mean. For the reference, see Rao and Molina (2015, ISBN:
    978-1-118-73578-7).
Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The  model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, Erciulescu et al.(2019) <doi:10.1111/rssa.12390>, and Yudasena (2024).
We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.