r-recommended - 4.0.4-1 main

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