This package 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.

This package provides an R implementation of Kolmogorov-Arnold Fourier Networks using the torch backend. The package supports regression, binary classification, multiclass classification, formula and matrix interfaces, mini-batch training, validation splits, early stopping, standardization, best-model restoration, and KAF-specific diagnostics.

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'.

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") *****.

Efficient implementation of several Optimal Transport algorithms in Fangzhou Xie (2025) <doi:10.48550/arXiv.2504.08722> and the Wasserstein Index Generation (WIG) model in Fangzhou Xie (2020) <doi:10.1016/j.econlet.2019.108874>.

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.

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.

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.

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.

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.

This package provides 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 (BartoÅ¡, 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.

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.

Wrapper and plotting utilities for the spatial population genetics tool EEMS (Estimated Effective Migration Surfaces) for SNP (Single Nucleotide Polymorphism) data, originally provided as a command-line tool written in C++ together with an accompanying R package for plotting the output of the EEMS tool itself (<https://github.com/dipetkov/eems/>). There are four main motivations for offering this to R users as a package. Firstly, to remove the installation and configuration burden for the EEMS command-line tool, which relies on manually installed Boost and Eigen system libraries and configuring their location; secondly, to streamline the workflow by having a singe environment (the R system) for the entire analysis rather than a file-based command-line executable whose output files are then to be imported and analysed by a separate R script; thirdly, to make the input formats compatible with other, R'-based spatial population genetics tools such as the ConStruct package; and lastly, to allow for easily running several chains in parallel and combining them for plotting and further analysis. The package also adds more intuitive, streamlined tooling around creating more complex habitats. The method of estimating effective migration surfaces was first described by Petkova, D., Novembre, J. & Stephens, M. (2016) <doi:10.1038/ng.3464>.

Color palettes from famous artists and paintings.

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.

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>.

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>.

This package provides 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 package 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.

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.

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>.

This package provides 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.

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.

This package provides 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.