R package

R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).

[1][2] The large number of packages available for R, and the ease of installing and using them, has been cited as a major factor driving the widespread adoption of the language in data science.

[10] The "Task Views" page (subject list) on the CRAN website[16] lists a wide range of tasks (in fields such as finance, genetics, high performance computing, machine learning, medical imaging, meta-analysis, social sciences and spatial statistics) for which R packages are available.

[6] Since each submission is manually reviewed by a small team of CRAN maintainers, many of whom, according to R core developer Peter Dalgaard, are "approaching pensionable age", there is a concern that this system is not sustainable in the long term.

This includes object-oriented data-handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.

[30] In addition, there are fifteen "recommended packages" from CRAN which are included with binary distributions of R: KernSmooth, MASS, Matrix, boot, class, cluster, codetools, foreign, lattice, mgcv, nlme, nnet, rpart, spatial, and survival.

[30] A group of packages called the tidyverse, which can be considered a "dialect of the R language", is increasingly popular in the R ecosystem.