R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S and Sussman's Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency, and many of R's core functions do so. The R distribution contains functionality for a large number of statistical procedures and underlying applied math computations. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.

Additionally, several thousand extension "packages" are available from CRAN, the Comprehensive R Archive Network, many also as Debian packages, named 'r-cran-'.

This Debian package is now a metapackage that depends on a set of packages that are recommended by the upstream R core team as part of a complete R distribution, and distributed along with the source of R itself, as well as directly via the CRAN network of mirrors. This set comprises the following packages (listed in their upstream names): - KernSmooth: Functions for kernel smoothing for Wand & Jones (1995) - Matrix: Classes and methods for dense and sparse matrices and operations on them using Lapack and SuiteSparse - MASS, class, nnet and spatial: packages from Venables and Ripley, `Modern Applied Statistics with S' (4th edition). - boot: Bootstrap R (S-Plus) Functions from the book "Bootstrap Methods and Their Applications" by A.C. Davison and D.V. Hinkley (1997). - cluster: Functions for clustering (by Rousseeuw et al.) - codetools: Code analysis tools for R - foreign: Read data stored by Minitab, S, SAS, SPSS, Stata, ... - lattice: Implementation of Trellis (R) graphics - mgcv: Multiple smoothing parameter estimation and GAMs by GCV - nlme: Linear and nonlinear mixed effects models - rpart: Recursive partitioning and regression trees - survival: Survival analysis, including penalised likelihood.