This package provides weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. There are also functions to facilitate the character encoding conversion of objects, to quickly convert fixed width files into CSV ones, and to export a data.frame to a text file with the necessary R and SPSS codes to reread the data.

This package provides regression models for grouped and coarse data, under the coarsened at random assumption.

Single cell RNA sequencing datasets can be large, consisting of matrices that contain expression data for several thousand features across several thousand cells. This package is designed to easily install, manage, and learn about various single-cell datasets, provided Seurat objects and distributed as independent packages.

This package provides miscellaneous functions commonly used in other packages maintained by Yihui Xie.

This package provides tools for generating and handling of Universally Unique Identifiers (UUIDs).

This package provides functions to handle basic input output. These functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.

This package provides functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.

This package provides a collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in http://pj.freefaculty.org/guides. The package 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.

This package provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.

The r-zoeppritz package calculates and plots scattering matrix coefficients or scattering amplitudes, for seismological P and S-waves at an interface.

This is a package for mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and so on.

This r-abctools package provides tools for approximate Bayesian computation including summary statistic selection and assessing coverage. This includes recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tune the choice of threshold.

This is a subset of the spatstat package, containing its functionality for spatial data on a linear network.

This package provides gradient projection algorithms for factor rotation. For details see ?GPArotation.

This package provides a DBI interface to MySQL / MariaDB. The RMySQL package contains an old implementation based on legacy code from S-PLUS which is being phased out. A modern MySQL client based on Rcpp is available from the RMariaDB package.

This package supports the analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).

This package provides an implementation of scatter plots for plotting. a three dimensional point cloud.

This package provides functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. It boasts support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. It also supports phase-type distributions commonly used to compute ruin probabilities.

Render R Markdown to Markdown (without using knitr), and Markdown to lightweight HTML or LaTeX documents with the commonmark package (instead of Pandoc). Some missing Markdown features in commonmark are also supported, such as raw HTML or LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all Pandoc Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and knitr. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and LaTeX). Book and website projects of multiple input documents are also supported.

This package provides the URL checking tools available in R 4.1+ as a package for earlier versions of R. It also uses concurrent requests so can be much faster than the serial versions.

This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

OOMPA offers R packages for gene expression and proteomics analysis. OOMPA uses S4 classes to construct object-oriented tools with a consistent user interface. All higher level analysis tools in OOMPA are compatible with the eSet classes defined in BioConductor. The lower level processing tools offer an alternative to parts of BioConductor, but can also be used to enhance existing BioConductor packages.

This package contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.

It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first.