mlr3learners extends mlr3 and mlr3proba with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

This package provides a set of predicates and assertions for checking the properties of variables, such as length, names and attributes. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package enables conversions between R objects and JavaScript Object Notation (JSON) using the rapidjsonr library.

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 package provides functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.

This package lets you fit generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.

This package provides classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

This package provides a developer-facing interface to Arrow Database Connectivity (ADBC) for the purposes of driver development, driver testing, and building high-level database interfaces for users. ADBC is an API standard for database access libraries that uses Arrow for result sets and query parameters.

This package provides an R interface to the JAGS MCMC library. JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation.

This package provides functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

Genomic analysis of model organisms often requires the use of databases based on human data or making comparisons to patient-derived resources. This requires converting genes between human and non-human analogues. The babelgene R package provides predicted gene orthologs/homologs for frequently studied model organisms in an R-friendly tidy/long format. The package integrates orthology assertion predictions sourced from multiple databases as compiled by the HGNC Comparison of Orthology Predictions (HCOP).

The vegan package provides tools for descriptive community ecology. It has most basic functions of diversity analysis, community ordination and dissimilarity analysis. Most of its multivariate tools can be used for other data types as well.

This is a package for estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The brglmFit fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>.

This package performs 2D Delaunay triangulation, constrained or unconstrained, with the help of the C++ library CDT. A function to plot the triangulation is provided. The constrained Delaunay triangulation has applications in geographic information systems.

This package provides tools to identify and read BMP, JPEG, PNG, and TIFF format bitmap images. Identification defaults to the use of the magic number embedded in the file rather than the file extension.

This package converts latitude/longitude into projected coordinates.

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

This package provides a ggplot2 extension for drawing gene arrow maps.

This package provides a cross-platform Perl-based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.

This package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) pre-whitening approaches to determining trends in climate data.

This R package provides a suite of tools to evaluate clustering algorithms, clusterings, and individual clusters.

This package provides tools to compute marginal effects from statistical models and return the result as tidy data frames. These data frames are ready to use with the ggplot2 package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggeffect(). There is a generic plot() method to plot the results using ggplot2.

This package provides a pipeline toolkit for statistics and data science in R; the targets package brings function-oriented programming to Make-like declarative pipelines. It orchestrates a pipeline as a graph of dependencies, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results are reproducible given the underlying code and data. The methodology in this package borrows from GNU Make (2015, ISBN:978-9881443519) and drake (2018, <doi:10.21105/joss.00550>).