This package provides functionality for random generation of spatial data in the spatstat family of packages. It generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes (simple sequential inhibition, Matern inhibition models, Matern cluster process, Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes) and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler).

This package contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996), A graphical display of large correlation matrices, The American Statistician 50, 178-180. There are also routines implementing the profile plots described in Bates and Watts (1988), Nonlinear Regression Analysis and its Applications.

Phylogenetic clustering (phyloclustering) is an evolutionary continuous time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance and interfaced with R for visualization.

This package implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation (AIPE), power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions.

This package is an extension to the testthat package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.

This package provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package.

Webshot makes it easy to take screenshots of web pages from within R. It can also run Shiny applications locally and take screenshots of the application; and it can render and screenshot static as well as interactive R Markdown documents.

This package provides various methods for MRI tissue classification.

This package provides tools to compute polychoric and polyserial correlations by quick "two-step" methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.

This is a package for the analysis of music and speech. Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read MP3, read MIDI, perform steps of a transcription, ...

This package provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations).

This package provides an interface to Amazon Web Services machine learning services, including SageMaker managed machine learning service, natural language processing, speech recognition, translation, and more.

This package fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.

This package lets you build complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as LeftOf, RightOf, TopOf, and Beneath and more. Other features include clustering, dendrograms and integration with ggplot2 generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.

This package provides a cross-platform interface to file system operations, built on top of the libuv C library.

This package implements fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. It handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data. It provides a fast interface using subsampling and confidence regions for variable importance.

This package lets you assign, extract, or remove variable labels from R vectors.

This package provides various methods to conduct Spatio-Temporal Analysis and Modelling, including Exploratory Spatio-Temporal Analysis and Inferred Spatio-Temporal Modelling.

This package provides simple mechanisms for defining and interpreting package options. It provides helpers for interpreting environment variables, global options, defining default values and more.

This package provides a collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.

This package is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted.

This package provides tools to execute arbitrary R or C functions some time after the current time, after the R execution stack has emptied.

This package provides tools for the computation of the matrix exponential, logarithm, square root, and related quantities.

This method identifies topological domains in genomes from Hi-C sequence data. The authors published an implementation of their method as an R script. This package originates from those original TopDom R scripts and provides help pages adopted from the original TopDom PDF documentation. It also provides a small number of bug fixes to the original code.