This package provides visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

This package provides an R module for display of maps. Projection code and larger maps are in separate packages (mapproj and mapdata).

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

This package implements the Figueiredo machine learning algorithm for adaptive sparsity and the Wong algorithm for adaptively sparse Gaussian geometric models.

Circular layout is an efficient way to visualise huge amounts of information. This package provides an implementation of circular layout generation in R as well as an enhancement of available software. Its flexibility is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multi-dimensional data.

This package provides fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen glue.

This package uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

This package performs complex string operations compactly and efficiently. It supports string interpolation jointly with over 50 string operations. It also enhances regular string functions (like grep() and co).

This package provides mosaic plots for the ggplot2 framework. Mosaic plot functionality is provided in a single ggplot2 layer by calling the geom mosaic.

This package provides a syntax highlighter for R code based on the results of the R parser. It supports rendering in HTML and LaTeX markup. It includes a custom Sweave driver performing syntax highlighting of R code chunks.

The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This package aims to collect all color palettes across the R ecosystem under the same package with a streamlined API.

This package extends the out of memory vectors of ff with statistical functions and other utilities to ease their usage.

This package provides lots of plotting, various labeling, axis and color scaling functions for R.

This package holds the database for the extrafont package.

This package provides a collection of libraries for numerical computing (numerical integration, optimization, etc.) and their integration with Rcpp.

This package helps you to automate R package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub integration, licenses, Rcpp, RStudio projects, and more.

This tool provides methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation and 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.

This package provides functions to produce rudimentary ASCII graphics directly in the terminal window. This package provides a basic plotting function (and equivalents of curve, density, acf and barplot) as well as a boxplot function.

This package provides portable tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes and their children; restart processes. It can read the standard output and error of the processes, using non-blocking connections. processx can poll a process for standard output or error, with a timeout. It can also poll several processes at once.

This package provides functionality to benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.

This package provides tidy tools for quantifying how well a model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package is a port of the new http://matplotlib.org/ color maps (viridis--the default--, magma, plasma, and inferno) to R. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness.

This package performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.