This package provides a unified interface to various machine learning algorithms. Confusion matrices are provided too.

This package provides functions for computing the density and the distribution function of multivariate normal and "t" random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods.

This package lets you easily use Bootstrap icons inside Shiny apps and R Markdown documents. More generally, icons can be inserted in any htmltools document through inline SVG.

This package provides helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.

This package provides enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). It also provides multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.).

This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package.

This package support non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) doi:10.1002/9780470316917. The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) doi:10.1016/j.jspi.2017.02.008.

This package provides a graph implementation that can be thought of as two tidy data frames describing node and edge data respectively. It provides an approach to manipulate these two virtual data frames using the API defined in the dplyr package, and it also provides tidy interfaces to a lot of common graph algorithms.

This package provides density, probability and quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan.

This package provides an implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell and modified Tukey non-additivity tests.

This package provides a minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates.

This package provides R bindings to the uchardet encoding detector library from Mozilla. It takes a sequence of bytes in an unknown character encoding without any additional information, and attempts to get the encoding of the text. All return names of the encodings are iconv-compatible.

This package provides methods for species distribution modeling, i.e., predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

This package provides functions for working with magnetic resonance images. It supports reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualization; flexible image manipulation; metadata and sparse image handling.

This package provides procedures to create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ggplot2.

This package provides a collection of helper functions designed to help you to better understand object oriented programming in R, particularly using S3.

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 provides tools to process and print UTF-8 encoded international text (Unicode). Input, validate, normalize, encode, format, and display.

This is a subset of the original spatstat package, containing the user-level code from spatstat which performs geometrical operations, except for the geometry of linear networks.

Download and install R packages stored in GitHub, BitBucket, or plain subversion or git repositories. This package is a lightweight replacement of the install_* functions in the devtools package. Indeed most of the code was copied over from devtools.

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions is made available with R_RegisterCCallable().

This package provides tools for the estimation of indicators on social exclusion and poverty, as well as an implementation of Pareto tail modeling for empirical income distributions.

This package provides a port of the web-based software DAGitty for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

This package provides miscellaneous helper functions for the development of R packages.