List of R software and tools
This is a list of software and programming tools for the R programming language, including IDEs, package managers, libraries, debugging tools, numerical and scientific computing tools, and related projects.
Integrated development environments (IDEs) and editors
- Emacs Speaks Statistics — Emacs interface for R and other statistical software
- Jupyter — supports R through IRkernel
- RKWard — KDE/Qt-based IDE and GUI for R
- RStudio — popular cross-platform IDE for R
- StatET — Eclipse-based IDE
- Visual Studio Code — supports R via extensions
Graphical user interfaces
- Deducer — GUI front-end and data analysis package
- jamovi — GUI statistical environment built on R for data analysis and performing statistical tests
- Java GUI for R — cross-platform R console, script editor, and spreadsheet/data view.
- Rattle GUI — data mining GUI for R
- R Commander — basic GUI for statistics in R, often used for teaching and introductory work.
Implementations of R
- CXXR — experimental R engine with modernized C++ codebase
- FastR — R language implementation on the GraalVM
- GNU R — main implementation of R, maintained by the R Core Team, and distributed as part of the GNU Project.
- pqR — “pretty quick R”
- Renjin — JVM-based interpreter for R
R packages
- Bioconductor — repository for bioinformatics and computational biology R packages
- CRAN — Comprehensive R Archive Network, primary repository for R packages.
- devtools — simplifies R package development
- Knitr — integration of R code into LaTeX, LyX, HTML, Markdown, AsciiDoc, and reStructuredText documents.
- Packrat — dependency management system
- renv — project-specific package management system
- R CMD build / R CMD check — R package build and validation tools
- Tidyverse — Ggplot2, dplyr, and other packages.
Mathematical and numerical libraries
- lme4 — linear mixed-effects models
- Matrix — sparse and dense matrix computations
- mgcv — generalized additive models
- nlme — nonlinear mixed-effects models
- numDeriv — numerical derivatives
- optim — built-in optimization functions
- optimx – provides a replacement and extension of the optim
- Rmpfr — multiple-precision floating-point arithmetic
Scientific and statistical libraries
- dplyr — data manipulation toolkit
- edgeR — differential expression analysis of RNA-seq data
- forecast — time series forecasting
- ggplot2 — data visualization based on the grammar of graphics
- phyloseq — analysis of microbiome census data
- shiny — interactive web applications
- survival — survival analysis
- tidyr — tidy data reshaping
Debugging and performance tools
- bench – accurately benchmark and analyze execution times
- lineprof — line-by-line profiling tool
- microbenchmark — benchmarking
- profvis — interactive R profiler
- Rcpp — integration of R and C++ for performance
- Rprof — built-in R profiler
Parallel and high-performance computing
- BiocParallel — parallel evaluation framework for R, used across Bioconductor packages.
- doParallel – provides a parallel backend for the foreach package, enabling easy parallel execution of R code.
- foreach — looping construct for parallel execution
- future — unified parallel and distributed computing
- parallel — built-in R package for parallel processing
- Rmpi — R interface to the Message Passing Interface
- snow — simple network of workstations
Machine learning and AI libraries
- caret — training and tuning for machine learning models
- keras — R interface to Keras deep learning
- mlbench — collection of artificial and real-world benchmark datasets for evaluating machine learning algorithms
- mlr — machine learning
- mlr3 — modern successor to mlr
- randomForest — ensemble learning using random forests
- tidymodels — collection of R packages for machine learning and modeling, designed with tidyverse principles.
- torch — R interface to PyTorch
- xgboost — gradient boosting framework with R bindings
Documentation and code analysis tools
- covr — test coverage
- lintr — static code analysis
- roxygen2 — documentation generation for R packages
- styler — code formatter for R scripts and packages
Testing frameworks
- checkmate — fast argument checks and assertions for R functions
- RUnit — implementing a standard Unit Testing framework
- testthat — unit testing framework
- tinytest — lightweight unit testing framework