This package provides a ggplot2 extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). Text boxes with automatic word wrap are also supported.

Function-oriented Make-like declarative pipelines for statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) <doi:10.21105/joss.00550>.

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The NNG-powered mirai R package by Gao (2023) <doi:10.5281/zenodo.7912722> is a scheduler that efficiently processes these intense workloads. The crew package extends mirai with a unifying interface for third-party worker launchers.

This is yet another command-line argument parser which wraps the powerful Perl module Getopt::Long and with some adaptation for easier use in R. It also provides a simple way for variable interpolation in R.

The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls-like calls using the nlsLM function.

This package provides functions for importing, exporting, plotting and other manipulations of bitmapped images.

This package provides a set of Shiny apps for effective communication and understanding in statistics. The current version includes properties of normal distribution, properties of sampling distribution, one-sample z and t tests, two samples independent (unpaired) t test and analysis of variance.

This package provides tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.

This package provides a simple interface for creating active bindings where the bound function accepts additional arguments.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This package provides functions for converting, importing, and drawing PostScript pictures in R plots.

This package provides a small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode.

Generalized Additive Mixed Modeling (GAMM; Lin & Zhang, 1999) as implemented in the R package mgcv is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

This package provides utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models, this package implements features like standardization or bootstrapping of parameters and models, feature reduction (feature extraction and variable selection) as well as conversion between indices of effect size.

This package provides distance-based parametric bootstrap tests for clustering with spatial neighborhood information. It implements some distance measures, clustering of presence-absence, abundance and multilocus genetical data for species delimitation, nearest neighbor based noise detection.

This R package provides tools for training gapped-kmer SVM classifiers for DNA and protein sequences. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel.

This package facilitates RNA secondary structure plotting.

This package provides tools to create Class Cover Catch Digraphs, neighborhood graphs, and relatives.

This is a package for maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) <doi:10.18637/jss.v095.i11> and the underlying methods in Train (2009) <doi:10.1017/CBO9780511805271>.

This package provides extra themes and scales for ggplot2 that replicate the look of plots by Edward Tufte and Stephen Few in Fivethirtyeight, The Economist, Stata, Excel, and The Wall Street Journal, among others. This package also provides geoms for Tufte's box plot and range frame.

The r-abd package contains data sets and sample code for the Analysis of biological data by Michael Whitlock and Dolph Schluter.

This package provides three functions for dealing with dates: parse_iso_8601 recognizes and parses all valid ISO 8601 date and time formats, parse_date parses dates in unspecified formats, and format_iso_8601 formats a date in ISO 8601 format.

This package provides bindings to ImageMagick, a comprehensive image processing library. It supports many common formats (PNG, JPEG, TIFF, PDF, etc.) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio, images are automatically previewed when printed to the console, resulting in an interactive editing environment.

This package provides a cross between a 2D density plot and a scatter plot, implemented as a ggplot2 geom. Points in the scatter plot are colored by the number of neighboring points. This is useful to visualize the 2D-distribution of points in case of overplotting.