R FAQ

Frequently Asked Questions on R

Version 2.2.2005-10-05

ISBN 3-900051-08-9

Kurt Hornik

R FAQ
*****

Introduction
************

This document contains answers to some of the most frequently asked questions about R.

Legalese
========

This document is copyright © 1998--2005 by Kurt Hornik.

This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version.

This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.

A copy of the GNU General Public License is available via WWW at

     http://www.gnu.org/copyleft/gpl.html.

You can also obtain it by writing to the Free Software Foundation, Inc., 59 Temple Place --- Suite 330, Boston, MA 02111-1307, USA.

Obtaining this document
=======================

The latest version of this document is always available from

     http://CRAN.R-project.org/doc/FAQ/

From there, you can obtain versions converted to plain ASCII text, DVI, GNU info, HTML, PDF, PostScript as well as the Texinfo source used for creating all these formats using the GNU Texinfo system (http://texinfo.org/).

You can also obtain the R FAQ from the doc/FAQ subdirectory of a CRAN site (see What is CRAN?).

Citing this document
====================

In publications, please refer to this FAQ as Hornik (2005), ``The R FAQ'', and give the above, official URL and the ISBN 3-900051-08-9.

Notation
========

Everything should be pretty standard.  R> is used for the R prompt, and a $ for the shell prompt (where applicable).

Feedback
========

Feedback via email to Kurt.Hornik@R-project.org is of course most welcome.

In particular, note that I do not have access to Windows or Macintosh systems.  Features specific to the Windows and Mac OS X ports of R are described in the ``R for Windows FAQ'' (http://cran.r-project.org/bin/windows/base/rw-FAQ.html) and the ``R for Mac OS X FAQ (http://cran.R-project.org/bin/macosx/RMacOSX-FAQ.html).  If you have information on Macintosh or Windows systems that you think should be added to this document, please let me know.

R Basics
********

What is R?
==========

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 (see What is S?) and Sussman's Scheme (http://www.cs.indiana.edu/scheme-repository/home.html). Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. See What are the differences between R and S?, for further details.

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.  The R distribution contains functionality for a large number of statistical procedures.  Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing.  There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.  Additional modules (``add-on packages'') are available for a variety of specific purposes (see R Add-On Packages).

R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand.  In addition, a large group of individuals has contributed to R by sending code and bug reports.

Since mid-1997 there has been a core group (the ``R Core Team'') who can modify the R source code archive.  The group currently consists of Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley, Duncan Temple Lang, Luke Tierney, and Simon Urbanek.

R has a home page at http://www.R-project.org/.  It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (``GNU S'').

What machines does R run on?
============================

R is being developed for the Unix, Windows and Mac families of operating systems.  Support for Mac OS Classic ended with R 1.7.1.

The current version of R will configure and build under a number of common Unix platforms including CPU-linux-gnu for the i386, alpha, arm, hppa, ia64, m68k, mips/mipsel, powerpc, s390, sparc (e.g., http://buildd.debian.org/build.php?&pkg=r-base), and x86_64 CPUs, powerpc-apple-darwin, mips-sgi-irix, rs6000-ibm-aix, and sparc-sun-solaris.

If you know about other platforms, please drop us a note.

What is the current version of R?
=================================

The current released version is 2.2.0.  Based on this `major.minor.patchlevel' numbering scheme, there are two development versions of R, a patched version of the current release (`r-patched') and one working towards the next minor or eventually major (`r-devel') releases of R, respectively.  Version r-patched is for bug fixes mostly. New features are typically introduced in r-devel.

How can R be obtained?
======================

Sources, binaries and documentation for R can be obtained via CRAN, the ``Comprehensive R Archive Network'' (see What is CRAN?).

Sources are also available via https://svn.r-project.org/R/, the R Subversion repository, but currently not via anonymous rsync (nor CVS).

Tarballs with daily snapshots of the r-devel and r-patched development versions of R can be found at ftp://ftp.stat.math.ethz.ch/Software/R.

How can R be installed?
=======================

How can R be installed (Unix)
-----------------------------

If R is already installed, it can be started by typing R at the shell prompt (of course, provided that the executable is in your path).

If binaries are available for your platform (see Are there Unix binaries for R?), you can use these, following the instructions that come with them.

Otherwise, you can compile and install R yourself, which can be done very easily under a number of common Unix platforms (see What machines does R run on?).  The file INSTALL that comes with the R distribution contains a brief introduction, and the ``R Installation and Administration'' guide (see What documentation exists for R?) has full details.

Note that you need a FORTRAN compiler or perhaps f2c in addition to a C compiler to build R.  Also, you need Perl version 5 to build the R object documentations.  (If this is not available on your system, you can obtain a PDF version of the object reference manual via CRAN.)

In the simplest case, untar the R source code, change to the directory thus created, and issue the following commands (at the shell prompt):

     $ ./configure
     $ make

If these commands execute successfully, the R binary and a shell script front-end called R are created and copied to the bin directory.  You can copy the script to a place where users can invoke it, for example to /usr/local/bin.  In addition, plain text help pages as well as HTML and LaTeX versions of the documentation are built.

Use make dvi to create DVI versions of the R manuals, such as refman.dvi (an R object reference index) and R-exts.dvi, the ``R Extension Writers Guide'', in the doc/manual subdirectory.  These files can be previewed and printed using standard programs such as xdvi and dvips.  You can also use make pdf to build PDF (Portable Document Format) version of the manuals, and view these using e.g. Acrobat.  Manuals written in the GNU Texinfo system can also be converted to info files suitable for reading online with Emacs or stand-alone GNU Info; use make info to create these versions (note that this requires Makeinfo version 4.5).

Finally, use make check to find out whether your R system works correctly.

You can also perform a ``system-wide'' installation using make install.  By default, this will install to the following directories:

${prefix}/bin
     the front-end shell script

${prefix}/man/man1
     the man page

${prefix}/lib/R
     all the rest (libraries, on-line help system, ...).  This is the ``R Home Directory'' (R_HOME) of the installed system.

In the above, prefix is determined during configuration (typically /usr/local) and can be set by running configure with the option

     $ ./configure --prefix=/where/you/want/R/to/go

(E.g., the R executable will then be installed into /where/you/want/R/to/go/bin.)

To install DVI, info and PDF versions of the manuals, use make install-dvi, make install-info and make install-pdf, respectively.

How can R be installed (Windows)
--------------------------------

The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, ME, NT4, 2000, and XP (at least) on Intel and clones (but not on other platforms).  The Windows version of R was created by Robert Gentleman and Guido Masarotto, and is now being developed and maintained by Duncan Murdoch and Brian D. Ripley.

For most installations the Windows installer program will be the easiest tool to use.

See the ``R for Windows FAQ'' (http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) for more details.

How can R be installed (Macintosh)
----------------------------------

The bin/macosx directory of a CRAN site contains a standard Apple installer package named RAqua.pkg.sit compressed in Aladdin Stuffit format.  Once downloaded, uncompressed and executed, the installer will install the current non-developer release of R.  RAqua is a native Mac OS X Darwin version of R with an Aqua GUI.  Inside bin/macosx/X.Y there are prebuilt binary packages to be used with RAqua corresponding to the ``X.Y'' release of R. The installation of these packages is available through the ``Package'' menu of the RAqua GUI.  This port of R for Mac OS X is maintained by Stefano Iacus.  The ``R for Mac OS X FAQ (http://cran.R-project.org/bin/macosx/RMacOSX-FAQ.html) has more details.

The bin/macos directory of a CRAN site contains bin-hexed (hqx) and stuffit (sit) archives for a base distribution and a large number of add-on packages of R 1.7.1 to run under Mac OS 8.6 to Mac OS 9.2.2.  This port of R for Macintosh is no longer supported.

Are there Unix binaries for R?
==============================

The bin/linux directory of a CRAN site contains the following packages.

       CPU   Versions   Provider
     Debian     i386   stable   Christian Steigies
     Mandrake   i386   10.0   Michele Alzetta
     Red Hat    i386   Fedora3/Fedora4   Martyn Plummer
       x86_64   Fedora3   Brian Ripley
       i386     Enterprise Linux   Matthew P. Cox
     SuSE       i386     7.3/8.0/8.1/8.2   Detlef Steuer
       i586     9.0/9.1/9.2/9.3   Detlef Steuer
       x86_64   9.2/9.3   Detlef Steuer
     VineLinux   i386   3.1   Susunu Tanimura

Debian packages, maintained by Dirk Eddelbuettel and Doug Bates, have long been part of the Debian distribution, and can be accessed through APT, the Debian package maintenance tool.  Use e.g. apt-get install r-base r-recommended to install the R environment and recommended packages.  If you also want to build R packages from source, also run apt-get install r-base-dev to obtain the additional tools required for this.  So-called ``backports'' of the current R packages for the stable distribution are provided by Christian Steigies, and available from CRAN.  Simply add the line

     deb http://cran.R-project.org/bin/linux/debian stable

(feel free to use a CRAN mirror instead of the master) to the file /etc/apt/sources.list, and install as usual.

No other binary distributions are currently publically available.

What documentation exists for R?
================================

Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(NAME) (or ?NAME) at the R prompt, where NAME is the name of the topic help is sought for.  (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.)

This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?.  An up-to-date HTML version is always available for web browsing at http://stat.ethz.ch/R-manual/.

Printed copies of the R reference manual for some version(s) are available from Network Theory Ltd, at http://www.network-theory.co.uk/R/base/.  For each set of manuals sold, the publisher donates USD 10 to the R Foundation (see What is the R Foundation?).

The R distribution also comes with the following manuals.

  * ``An Introduction to R'' (R-intro) includes information on data types, programming elements, statistical modeling and graphics.  This document is based on the ``Notes on S-PLUS'' by Bill Venables and David Smith.

  * ``Writing R Extensions'' (R-exts) currently describes the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API.

  * ``R Data Import/Export'' (R-data) is a guide to importing and exporting data to and from R.

  * ``The R Language Definition'' (R-lang), a first version of the ``Kernighan & Ritchie of R'', explains evaluation, parsing, object oriented programming, computing on the language, and so forth.

  * ``R Installation and Administration'' (R-admin).

Books on R include

     P. Dalgaard (2002), ``Introductory Statistics with R'', Springer: New York, ISBN 0-387-95475-9.

     http://www.biostat.ku.dk/~pd/ISwR.html.

     J. Fox (2002), ``An R and S-PLUS Companion to Applied Regression'', Sage Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2 (hardcover),

     http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/.

     J. Maindonald and J. Braun (2003), ``Data Analysis and Graphics Using R: An Example-Based Approach'', Cambridge University Press, ISBN 0-521-81336-0,

     http://wwwmaths.anu.edu.au/~johnm/.

     S. M. Iacus and G. Masarotto (2002), ``Laboratorio di statistica con R'', McGraw-Hill, ISBN 88-386-6084-0 (in Italian),

     http://www.ateneonline.it/LibroAteneo.asp?item_id=1436.

     P. Murrell (2005), ``R Graphics'', Chapman & Hall/CRC, ISBN: 1-584-88486-X,

     http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.

The book

     W. N. Venables and B. D. Ripley (2002), ``Modern Applied Statistics with S.  Fourth Edition''.  Springer, ISBN 0-387-95457-0

has a home page at http://www.stats.ox.ac.uk/pub/MASS4/ providing additional material.  Its companion is

     W. N. Venables and B. D. Ripley (2000), ``S Programming''.  Springer, ISBN 0-387-98966-8

and provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-PLUS and the Open Source R data analysis software systems.  See http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ for more information.

In addition to material written specifically or explicitly for R, documentation for S/S-PLUS (see R and S) can be used in combination with this FAQ (see What are the differences between R and S?).  Introductory books include

     P. Spector (1994), ``An introduction to S and S-PLUS'', Duxbury Press.

     A. Krause and M. Olsen (2002), ``The Basics of S-PLUS'' (Third Edition).  Springer, ISBN 0-387-95456-2

The book

     J. C. Pinheiro and D. M. Bates (2000), ``Mixed-Effects Models in S and S-PLUS'', Springer, ISBN 0-387-98957-0

provides a comprehensive guide to the use of the nlme package for linear and nonlinear mixed-effects models.

As an example of how R can be used in teaching an advanced introductory statistics course, see

     D. Nolan and T. Speed (2000), ``Stat Labs: Mathematical Statistics Through Applications'', Springer Texts in Statistics, ISBN 0-387-98974-9

This integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.  More information can be found at http://www.stat.Berkeley.EDU/users/statlabs/.

Last, but not least, Ross' and Robert's experience in designing and implementing R is described in Ihaka & Gentleman (1996), ``R: A Language for Data Analysis and Graphics'', Journal of Computational and Graphical Statistics, 5, 299--314.

An annotated bibliography (BibTeX format) of R-related publications which includes most of the above references can be found at

     http://www.R-project.org/doc/bib/R.bib

Citing R
========

To cite R in publications, use

     @Manual{,
       title        = {R: A Language and Environment for Statistical
                       Computing},
       author       = {{R Development Core Team}},
       organization = {R Foundation for Statistical Computing},
       address      = {Vienna, Austria},
       year         = 2005,
       note         = {{ISBN} 3-900051-07-0},
       url          = {http://www.R-project.org}
     }

Citation strings (or BibTeX entries) for R and R packages can also be obtained by citation().

What mailing lists exist for R?
===============================

Thanks to Martin Maechler, there are four mailing lists devoted to R.

R-announce
     A moderated list for major announcements about the development of R and the availability of new code.

R-packages
     A moderated list for announcements on the availability of new or enhanced contributed packages.

R-help
     The `main' R mailing list, for discussion about problems and solutions using R, announcements (not covered by `R-announce' and `R-packages') about the development of R and the availability of new code.

R-devel
     This list is for questions and discussion about code development in R.

Please read the posting guide (http://www.R-project.org/posting-guide.html) before sending anything to any mailing list.

Note in particular that R-help is intended to be comprehensible to people who want to use R to solve problems but who are not necessarily interested in or knowledgeable about programming.  Questions likely to prompt discussion unintelligible to non-programmers (e.g., questions involving C or C++) should go to R-devel.

Convenient access to information on these lists, subscription, and archives is provided by the web interface at http://stat.ethz.ch/mailman/listinfo/.  One can also subscribe (or unsubscribe) via email, e.g. to R-help by sending subscribe (or unsubscribe) in the body of the message (not in the subject!) to R-help-request@lists.R-project.org.

Send email to R-help@lists.R-project.org to send a message to everyone on the R-help mailing list.  Subscription and posting to the other lists is done analogously, with R-help replaced by R-announce, R-packages, and R-devel, respectively. Note that the R-announce and R-packages lists are gatewayed into R-help. Hence, you should subscribe to either of them only in case you are not subscribed to R-help.

It is recommended that you send mail to R-help rather than only to the R Core developers (who are also subscribed to the list, of course).  This may save them precious time they can use for constantly improving R, and will typically also result in much quicker feedback for yourself.

Of course, in the case of bug reports it would be very helpful to have code which reliably reproduces the problem.  Also, make sure that you include information on the system and version of R being used.  See R Bugs for more details.

See http://www.R-project.org/mail.html for more information on the R mailing lists.

The R Core Team can be reached at R-core@lists.R-project.org for comments and reports.

Many of the R project's mailing lists are also available via Gmane (http://gmane.org), from which they can be read on the web, using an NNTP newsreader, or via RSS feeds.  See http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r. for the available mailing lists, and http://www.gmane.org/rss.php for details on RSS feeds.

What is CRAN?
=============

The ``Comprehensive R Archive Network'' (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries.

The CRAN master site at TU Wien, Austria, can be found at the URL

     http://cran.R-project.org/

Daily mirrors are available at URLs including

     http://cran.at.R-project.org/
     (TU Wien, Austria)
     http://cran.au.R-project.org/
     (PlanetMirror, Australia)
     http://cran.br.R-project.org/
     (Universidade Federal de Paraná, Brazil)
     http://cran.ch.R-project.org/
     (ETH Zürich, Switzerland)
     http://cran.dk.R-project.org/
     (SunSITE, Denmark)
     http://cran.es.R-project.org/
     (Spanish National Research Network, Madrid, Spain)
     http://cran.fr.R-project.org/
     (INRA, Toulouse, France)
     http://cran.hu.R-project.org/
     (Semmelweis U, Hungary)
     http://cran.pt.R-project.org/
     (Universidade do Porto, Portugal)
     http://cran.uk.R-project.org/
     (U of Bristol, United Kingdom)
     http://cran.us.R-project.org/
     (pair Networks, USA)
     http://cran.za.R-project.org/
     (Rhodes U, South Africa)

See http://cran.R-project.org/mirrors.html for a complete list of mirrors.  Please use the CRAN site closest to you to reduce network load.

From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current source trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Mac OS Classic, Mac OS X, and MS Windows).  CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.

To ``submit'' to CRAN, simply upload to ftp://cran.R-project.org/incoming/ and send an email to cran@R-project.org.  Note that CRAN generally does not accept submissions of precompiled binaries due to security reasons.  In particular, binary packages for Windows and Mac OS X are provided by the respective binary package maintainers.

     It is very important that you indicate the copyright (license) information (GPL, BSD, Artistic, ...) in your submission.

Please always use the URL of the master site when referring to CRAN.

Can I use R for commercial purposes?
====================================

R is released under the GNU General Public License (GPL).  If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel.  We are in no position to offer legal advice.

It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting).  The GPL, like all Open Source licenses, permits all and any use of the package.  It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (``No Discrimination Against Fields of Endeavor'') of the Open Source Definition (http://www.opensource.org/docs/definition.html):

     The license must not restrict anyone from making use of the program in a specific field of endeavor.  For example, it may not restrict the program from being used in a business, or from being used for genetic research.

It is also explicitly stated in clause 0 of the GPL, which says in part

     Activities other than copying, distribution and modification are not covered by this License; they are outside its scope.  The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program.

Most add-on packages, including all recommended ones, also explicitly allow commercial use in this way.  A few packages are restricted to ``non-commercial use''; you should contact the author to clarify whether these may be used or seek the advice of your legal counsel.

None of the discussion in this section constitutes legal advice.  The R Core Team does not provide legal advice under any circumstances.

Why is R named R?
=================

The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language `S' (see What is S?).

What is the R Foundation?
=========================

The R Foundation is a not for profit organization working in the public interest.  It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing, provide a reference point for individuals, institutions or commercial enterprises that want to support or interact with the R development community, and to hold and administer the copyright of R software and documentation.  See http://www.R-project.org/foundation/ for more information.

R and S
*******

What is S?
==========

S is a very high level language and an environment for data analysis and graphics.  In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for

     the S system, which has forever altered the way people analyze, visualize, and manipulate data ...

     S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers.

The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S.

  * Richard A. Becker and John M. Chambers (1984), ``S.  An Interactive Environment for Data Analysis and Graphics,'' Monterey: Wadsworth and Brooks/Cole.

     This is also referred to as the ``Brown Book'', and of historical interest only.

  * Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), ``The New S Language,'' London: Chapman & Hall.

     This book is often called the ``Blue Book'', and introduced what is now known as S version 2.

  * John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models in S,''  London: Chapman & Hall.

     This is also called the ``White Book'', and introduced S version 3, which added structures to facilitate statistical modeling in S.

  * John M. Chambers (1998), ``Programming with Data,'' New York: Springer, ISBN 0-387-98503-4 (http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/).

     This ``Green Book'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.

See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html for further information on ``Stages in the Evolution of S''.

There is a huge amount of user-contributed code for S, available at the S Repository (http://lib.stat.cmu.edu/S/) at CMU.

What is S-PLUS?
===============

S-PLUS is a value-added version of S sold by Insightful Corporation. Based on the S language, S-PLUS provides functionality in a wide variety of areas, including robust regression, modern non-parametric regression, time series, survival analysis, multivariate analysis, classical statistical tests, quality control, and graphics drivers. Add-on modules add additional capabilities.

See the Insightful S-PLUS page (http://www.insightful.com/products/splus/) for further information.

What are the differences between R and S?
=========================================

We can regard S as a language with three current implementations or ``engines'', the ``old S engine'' (S version 3; S-PLUS 3.x and 4.x), the ``new S engine'' (S version 4; S-PLUS 5.x and above), and R. Given this understanding, asking for ``the differences between R and S'' really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines.

For the remainder of this section, ``S'' refers to the S engines and not the S language.

Lexical scoping
---------------

Contrary to other implementations of the S language, R has adopted an evaluation model in which nested function definitions are lexically scoped.  This is analogous to the evalutation model in Scheme.

This difference becomes manifest when free variables occur in a function.  Free variables are those which are neither formal parameters (occurring in the argument list of the function) nor local variables (created by assigning to them in the body of the function).  In S, the values of free variables are determined by a set of global variables (similar to C, there is only local and global scope).  In R, they are determined by the environment in which the function was created.

Consider the following function:

     cube <- function(n) {
       sq <- function() n * n
       n * sq()
     }

Under S, sq() does not ``know'' about the variable n unless it is defined globally:

     S> cube(2)
     Error in sq():  Object "n" not found
     Dumped
     S> n <- 3
     S> cube(2)
     [1] 18

In R, the ``environment'' created when cube() was invoked is also looked in:

     R> cube(2)
     [1] 8

As a more ``interesting'' real-world problem, suppose you want to write a function which returns the density function of the r-th order statistic from a sample of size n from a (continuous) distribution.  For simplicity, we shall use both the cdf and pdf of the distribution as explicit arguments.  (Example compiled from various postings by Luke Tierney.)

The S-PLUS documentation for call() basically suggests the following:

     dorder <- function(n, r, pfun, dfun) {
       f <- function(x) NULL
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       PF <- call(substitute(pfun), as.name("x"))
       DF <- call(substitute(dfun), as.name("x"))
       f[[length(f)]] <-
         call("*", con,
              call("*", call("^", PF, r - 1),
                   call("*", call("^", call("-", 1, PF), n - r),
                        DF)))
       f
     }

Rather tricky, isn't it?  The code uses the fact that in S, functions are just lists of special mode with the function body as the last argument, and hence does not work in R (one could make the idea work, though).

A version which makes heavy use of substitute() and seems to work under both S and R is

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
                       list(PF = substitute(pfun), DF = substitute(dfun),
                            a = r - 1, b = n - r, K = con)))
     }

(the eval() is not needed in S).

However, in R there is a much easier solution:

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       function(x) {
         con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
       }
     }

This seems to be the ``natural'' implementation, and it works because the free variables in the returned function can be looked up in the defining environment (this is lexical scope).

Note that what you really need is the function closure, i.e., the body along with all variable bindings needed for evaluating it.  Since in the above version, the free variables in the value function are not modified, you can actually use it in S as well if you abstract out the closure operation into a function MC() (for ``make closure''):

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       MC(function(x) {
            con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
          },
          list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
     }

Given the appropriate definitions of the closure operator, this works in both R and S, and is much ``cleaner'' than a substitute/eval solution (or one which overrules the default scoping rules by using explicit access to evaluation frames, as is of course possible in both R and S).

For R, MC() simply is

     MC <- function(f, env) f

(lexical scope!), a version for S is

     MC <- function(f, env = NULL) {
       env <- as.list(env)
       if (mode(f) != "function")
         stop(paste("not a function:", f))
       if (length(env) > 0 && any(names(env) == ""))
         stop(paste("not all arguments are named:", env))
       fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
       fargs <- c(fargs, env)
       if (any(duplicated(names(fargs))))
         stop(paste("duplicated arguments:", paste(names(fargs)),
              collapse = ", "))
       fbody <- f[length(f)]
       cf <- c(fargs, fbody)
       mode(cf) <- "function"
       return(cf)
     }

Similarly, most optimization (or zero-finding) routines need some arguments to be optimized over and have other parameters that depend on the data but are fixed with respect to optimization.  With R scoping rules, this is a trivial problem; simply make up the function with the required definitions in the same environment and scoping takes care of it.  With S, one solution is to add an extra parameter to the function and to the optimizer to pass in these extras, which however can only work if the optimizer supports this.

Nested lexically scoped functions allow using function closures and maintaining local state.  A simple example (taken from Abelson and Sussman) is obtained by typing demo("scoping") at the R prompt. Further information is provided in the standard R reference ``R: A Language for Data Analysis and Graphics'' (see What documentation exists for R?) and in Robert Gentleman and Ross Ihaka (2000), ``Lexical Scope and Statistical Computing'', Journal of Computational and Graphical Statistics, 9, 491--508.

Nested lexically scoped functions also imply a further major difference. Whereas S stores all objects as separate files in a directory somewhere (usually .Data under the current directory), R does not.  All objects in R are stored internally.  When R is started up it grabs a piece of memory and uses it to store the objects.  R performs its own memory management of this piece of memory, growing and shrinking its size as needed.  Having everything in memory is necessary because it is not really possible to externally maintain all relevant ``environments'' of symbol/value pairs.  This difference also seems to make R faster than S.

The down side is that if R crashes you will lose all the work for the current session.  Saving and restoring the memory ``images'' (the functions and data stored in R's internal memory at any time) can be a bit slow, especially if they are big.  In S this does not happen, because everything is saved in disk files and if you crash nothing is likely to happen to them.  (In fact, one might conjecture that the S developers felt that the price of changing their approach to persistent storage just to accommodate lexical scope was far too expensive.) Hence, when doing important work, you might consider saving often (see How can I save my workspace?) to safeguard against possible crashes.  Other possibilities are logging your sessions, or have your R commands stored in text files which can be read in using source().

     If you run R from within Emacs (see R and Emacs), you can save the contents of the interaction buffer to a file and conveniently manipulate it using ess-transcript-mode, as well as save source copies of all functions and data used.

Models
------

There are some differences in the modeling code, such as

  * Whereas in S, you would use lm(y ~ x^3) to regress y on x^3, in R, you have to insulate powers of numeric vectors (using I()), i.e., you have to use lm(y ~ I(x^3)).

  * The glm family objects are implemented differently in R and S.  The same functionality is available but the components have different names.

  * Option na.action is set to "na.omit" by default in R, but not set in S.

  * Terms objects are stored differently.  In S a terms object is an expression with attributes, in R it is a formula with attributes.  The attributes have the same names but are mostly stored differently.

  * Finally, in R y ~ x + 0 is an alternative to y ~ x - 1 for specifying a model with no intercept.  Models with no parameters at all can be specified by y ~ 0.

Others
------

Apart from lexical scoping and its implications, R follows the S language definition in the Blue and White Books as much as possible, and hence really is an ``implementation'' of S.  There are some intentional differences where the behavior of S is considered ``not clean''.  In general, the rationale is that R should help you detect programming errors, while at the same time being as compatible as possible with S.

Some known differences are the following.

  * In R, if x is a list, then x[i] <- NULL and x[[i]] <- NULL remove the specified elements from x.  The first of these is incompatible with S, where it is a no-op.  (Note that you can set elements to NULL using x[i] <- list(NULL).)

  * In S, the functions named .First and .Last in the .Data directory can be used for customizing, as they are executed at the very beginning and end of a session, respectively.

     In R, the startup mechanism is as follows.  R first sources the system startup file $R_HOME/library/base/R/Rprofile.  Then, it searches for a site-wide startup profile unless the command line option --no-site-file was given.  The name of this file is taken from the value of the R_PROFILE environment variable.  If that variable is unset, the default is $R_HOME/etc/Rprofile.site ($R_HOME/etc/Rprofile in versions prior to 1.4.0).  This code is loaded in package base.  Then, unless --no-init-file was given, R searches for a file called .Rprofile in the current directory or in the user's home directory (in that order) and sources it into the user workspace.  It then loads a saved image of the user workspace from .RData in case there is one (unless --no-restore was specified).  If needed, the functions .First() and .Last() should be defined in the appropriate startup profiles.

  * In R, T and F are just variables being set to TRUE and FALSE, respectively, but are not reserved words as in S and hence can be overwritten by the user.  (This helps e.g. when you have factors with levels "T" or "F".)  Hence, when writing code you should always use TRUE and FALSE.

  * In R, dyn.load() can only load shared objects, as created for example by R CMD SHLIB.

  * In R, attach() currently only works for lists and data frames, but not for directories.  (In fact, attach() also works for R data files created with save(), which is analogous to attaching directories in S.)  Also, you cannot attach at position 1.

  * Categories do not exist in R, and never will as they are deprecated now in S.  Use factors instead.

  * In R, For() loops are not necessary and hence not supported.

  * In R, assign() uses the argument envir= rather than where= as in S.

  * The random number generators are different, and the seeds have different length.

  * R passes integer objects to C as int * rather than long * as in S.

  * R has no single precision storage mode.  However, as of version 0.65.1, there is a single precision interface to C/FORTRAN subroutines.

  * By default, ls() returns the names of the objects in the current (under R) and global (under S) environment, respectively.  For example, given

          x <- 1; fun <- function() {y <- 1; ls()}

     then fun() returns "y" in R and "x" (together with the rest of the global environment) in S.

  * R allows for zero-extent matrices (and arrays, i.e., some elements of the dim attribute vector can be 0).  This has been determined a useful feature as it helps reducing the need for special-case tests for empty subsets.  For example, if x is a matrix, x[, FALSE] is not NULL but a ``matrix'' with 0 columns.  Hence, such objects need to be tested for by checking whether their length() is zero (which works in both R and S), and not using is.null().

  * Named vectors are considered vectors in R but not in S (e.g., is.vector(c(a = 1:3)) returns FALSE in S and TRUE in R).

  * Data frames are not considered as matrices in R (i.e., if DF is a data frame, then is.matrix(DF) returns FALSE in R and TRUE in S).

  * R by default uses treatment contrasts in the unordered case, whereas S uses the Helmert ones.  This is a deliberate difference reflecting the opinion that treatment contrasts are more natural.

  * In R, the argument of a replacement function which corresponds to the right hand side must be named value.  E.g., f(a) <- b is evaluated as a <- "f<-"(a, value = b).  S always takes the last argument, irrespective of its name.

  * In S, substitute() searches for names for substitution in the given expression in three places: the actual and the default arguments of the matching call, and the local frame (in that order).  R looks in the local frame only, with the special rule to use a ``promise'' if a variable is not evaluated.  Since the local frame is initialized with the actual arguments or the default expressions, this is usually equivalent to S, until assignment takes place.

  * In S, the index variable in a for() loop is local to the inside of the loop.  In R it is local to the environment where the for() statement is executed.

  * In S, tapply(simplify=TRUE) returns a vector where R returns a one-dimensional array (which can have named dimnames).

  * In S(-PLUS) the C locale is used, whereas in R the current operating system locale is used for determining which characters are alphanumeric and how they are sorted.  This affects the set of valid names for R objects (for example accented chars may be allowed in R) and ordering in sorts and comparisons (such as whether "aA" < "Bb" is true or false).  From version 1.2.0 the locale can be (re-)set in R by the Sys.setlocale() function.

  * In S, missing(ARG) remains TRUE if ARG is subsequently modified; in R it doesn't.

  * From R version 1.3.0, data.frame strips I() when creating (column) names.

  * In R, the string "NA" is not treated as a missing value in a character variable.  Use as.character(NA) to create a missing character value.

  * R disallows repeated formal arguments in function calls.

  * In S, dump(), dput() and deparse() are essentially different interfaces to the same code.  In R from version 2.0.0, this is only true if the same control argument is used, but by default it is not.  By default dump() tries to write code that will evaluate to reproduce the object, whereas dput() and deparse() default to options for producing deparsed code that is readable.

  * In R, indexing a vector, matrix, array or data frame with [ using a character vector index looks only for exact matches (whereas [[ and $ allow partial matches).  In S, [ allows partial matches.

There are also differences which are not intentional, and result from missing or incorrect code in R.  The developers would appreciate hearing about any deficiencies you may find (in a written report fully documenting the difference as you see it).  Of course, it would be useful if you were to implement the change yourself and make sure it works.

Is there anything R can do that S-PLUS cannot?
==============================================

Since almost anything you can do in R has source code that you could port to S-PLUS with little effort there will never be much you can do in R that you couldn't do in S-PLUS if you wanted to.  (Note that using lexical scoping may simplify matters considerably, though.)

R offers several graphics features that S-PLUS does not, such as finer handling of line types, more convenient color handling (via palettes), gamma correction for color, and, most importantly, mathematical annotation in plot texts, via input expressions reminiscent of TeX constructs.  See the help page for plotmath, which features an impressive on-line example.  More details can be found in Paul Murrell and Ross Ihaka (2000), ``An Approach to Providing Mathematical Annotation in Plots'', Journal of Computational and Graphical Statistics, 9, 582--599.

What is R-plus?
===============

There is no such thing.

R Web Interfaces
****************

Rweb is developed and maintained by Jeff Banfield.  The Rweb Home Page (http://www.math.montana.edu/Rweb/) provides access to all three versions of Rweb---a simple text entry form that returns output and graphs, a more sophisticated Javascript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language.  All of the Rweb versions can analyze Web accessible datasets if a URL is provided.

The paper ``Rweb: Web-based Statistical Analysis'', providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.stat.ucla.edu/journals/jss/v04/i01/).

Ulf Bartel is working on R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible.  There is no need for a local installation since the only requirement for the user is a JavaScript capable browser.  See http://osvisions.com/r-online/ for more information.

David Firth has written CGIwithR, an R add-on package available from CRAN.  It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server.  It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions.

Rcgi is a CGI WWW interface to R by MJ Ray.  It had the ability to use ``embedded code'': you could mix user input and code, allowing the HTML author to do anything from load in data sets to enter most of the commands for users without writing CGI scripts.  Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active. Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/.

See also http://franklin.imgen.bcm.tmc.edu/R.web.servers/ for more information on R web interface projects.

R Add-On Packages
*****************

Which add-on packages exist for R?
==================================

Add-on packages in R
--------------------

The R distribution comes with the following packages:

base
     Base R functions (and datasets before R 2.0.0).

datasets
     Base R datasets (added in R 2.0.0).

grDevices
     Graphics devices for base and grid graphics (added in R 2.0.0).

graphics
     R functions for base graphics.

grid
     A rewrite of the graphics layout capabilities, plus some support for interaction.

methods
     Formally defined methods and classes for R objects, plus other programming tools, as described in the Green Book.

splines
     Regression spline functions and classes.

stats
     R statistical functions.

stats4
     Statistical functions using S4 classes.

tcltk
     Interface and language bindings to Tcl/Tk GUI elements.

tools
     Tools for package development and administration.

utils
     R utility functions.

These ``base packages'' were substantially reorganized in R 1.9.0.  The former base was split into the four packages base, graphics, stats, and utils.  Packages ctest, eda, modreg, mva, nls, stepfun and ts were merged into stats, package lqs returned to the recommended package MASS, and package mle moved to stats4.

Add-on packages from CRAN
-------------------------

The following packages are available from the CRAN src/contrib area.  (Packages denoted as Recommended are to be included in all binary distributions of R.)

AMORE
     A MORE flexible neural network package, providing the TAO robust neural network algorithm.

AlgDesign
     Algorithmic experimental designs.  Calculates exact and approximate theory experimental designs for D, A, and I criteria.

AnalyzeFMRI
     Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE format.

BHH2
     Functions and data sets reproducing some examples in ``Statistics for Experimenters II'' by G. E. P. Box, J. S. Hunter, and W. C. Hunter, 2005, John Wiley and Sons.

BMA
     Bayesian Model Averaging for linear models, generalizable linear models and survival models (Cox regression).

BRugs
     OpenBUGS and its R interface BRugs.

Bhat
     Functions for general likelihood exploration (MLE, MCMC, CIs).

Biodem
     A number of functions for biodemographycal analysis.

Bolstad
     Functions and data sets for the book ``Introduction to Bayesian Statistics'' by W. M. Bolstad, 2004, John Wiley and Sons.

BradleyTerry
     Specify and fit the Bradley-Terry model and structured versions.

BSDA
     Data sets for the book ``Basic Statistics and Data Analysis'' by L. J. Kitchens, 2003, Duxbury.

BsMD
     Bayes screening and model discrimination follow-up designs.

CDNmoney
     Components of Canadian monetary aggregates.

CGIwithR
     Facilities for the use of R to write CGI scripts.

CircStats
     Circular Statistics, from ``Topics in Circular Statistics'' by S. Rao Jammalamadaka and A. SenGupta, 2001, World Scientific.

CoCo
     Graphical modeling for contingency tables using CoCo.

DAAG
     Various data sets used in examples and exercises in ``Data Analysis and Graphics Using R'' by John H. Maindonald and W. John Brown, 2003.

DBI
     A common database interface (DBI) class and method definitions.  All classes in this package are virtual and need to be extended by the various DBMS implementations.

DCluster
     A set of functions for the detection of spatial clusters of diseases using count data.

DEoptim
     Differential Evolution Optimization.

DICOM
     Import and manipulate medical imaging data using the Digital Imaging and Communications in Medicine (DICOM) Standard.

Davies
     Functions for the Davies quantile function and the Generalized Lambda distribution.

Design
     Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit.  Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications.  It also contains new functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models.  Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model.

Devore5
     Data sets and sample analyses from ``Probability and Statistics for Engineering and the Sciences (5th ed)'' by Jay L. Devore, 2000, Duxbury.

Devore6
     Data sets and sample analyses from ``Probability and Statistics for Engineering and the Sciences (6th ed)'' by Jay L. Devore, 2003, Duxbury.

EMV
     Estimation of missing values in a matrix by a k-th nearest neighboors algorithm.

EbayesThresh
     Empirical Bayes thresholding and related methods.

Ecdat
     Data sets from econometrics textbooks.

Epi
     Statistical analysis in epidemiology, with functions for demographic and epidemiological analysis in the Lexis diagram.

Fahrmeir
     Data from the book ``Multivariate Statistical Modelling Based on Generalized Linear Models'' by Ludwig Fahrmeir and Gerhard Tutz (1994),  Springer.

GDD
     Platform and X11 independent device for creating bitmaps (png, gif and jpeg) using the GD graphics library.

GPArotation
     Gradient Projection Algorithm rotation for factor analysis.

GRASS
     An interface between the GRASS geographical information system and R, based on starting R from within the GRASS environment and chosen LOCATION_NAME and MAPSET.  Wrapper and helper functions are provided for a range of R functions to match the interface metadata structures.

GenKern
     Functions for generating and manipulating generalised binned kernel density estimates.

GeneNT
     Relevance or Dependency network and signaling pathway discovery.

GeneTS
     A package for analysing multiple gene expression time series data. Currently, implements methods for cell cycle analysis and for inferring large sparse graphical Gaussian models.

Geneland
     MCMC inference from individual genetic data based on a spatial statistical model.

HI
     Simulation from distributions supported by nested hyperplanes.

HTMLapplets
     Functions inserting dynamic scatterplots and grids in documents generated by R2HTML.

HighProbability
     Estimation of the alternative hypotheses having frequentist or Bayesian probabilities at least as great as a specified threshold, given a list of p-values.

Hmisc
     Functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, recoding variables, and bootstrap repeated measures analysis.

HyperbolicDist
     Basic functions for the hyperbolic distribution: probability density function, distribution function, quantile function, a routine for generating observations from the hyperbolic, and a function for fitting the hyperbolic distribution to data.

IDPmisc
     Utilities from the Institute of Data Analyses and Process Design, IDP/ZHW.

ISwR
     Data sets for ``Introductory Statistics with R'' by Peter Dalgaard, 2002, Springer.

Icens
     Functions for computing the NPMLE for censored and truncated data.

JLLprod
     Nonparametric estimation of homothetic and generalized homothetic production functions.

KMsurv
     Data sets and functions for ``Survival Analysis, Techniques for Censored and Truncated Data'' by Klein and Moeschberger, 1997, Springer.

KernSmooth
     Functions for kernel smoothing (and density estimation) corresponding to the book ``Kernel Smoothing'' by M. P. Wand and M. C. Jones, 1995. Recommended.

LDheatmap
     Heat maps of linkage disequilibrium measures.

LMGene
     Date transformation and identification of differentially expressed genes in gene expression arrays.

LogicReg
     Routines for Logic Regression.

MASS
     Functions and datasets from the main package of Venables and Ripley, ``Modern Applied Statistics with S''.  Contained in the VR bundle.  Recommended.

MCMCpack
     Markov chain Monte Carlo (MCMC) package: functions for posterior simulation for a number of statistical models.

MEMSS
     Data sets and sample analyses from ``Mixed-effects Models in S and S-PLUS'' by J. Pinheiro and D. Bates, 2000, Springer.

MNP
     Fitting Bayesian Multinomial Probit models via Markov chain Monte Carlo. Along with the standard Multinomial Probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives.

MPV
     Data sets from the book ``Introduction to Linear Regression Analysis'' by D. C. Montgomery, E. A. Peck, and C. G. Vining, 2001, John Wiley and Sons.

MSBVAR
     Bayesian vector autoregression models, impulse responses and forecasting.

Malmig
     An implementation of Malecot migration model together with a number of related functions.

MarkedPointProcess
     Non-parametric analysis of the marks of marked point processes.

MatchIt
     Select matched samples of the original treated and control groups with similar covariate distributions.

Matching
     Multivariate and propensity score matching with formal tests of balance.

Matrix
     A Matrix package.

NADA
     Methods described in ``Nondetects And Data Analysis: Statistics for Censored Environmental Data'' by Dennis R. Helsel, 2004, John Wiley and Sons.

NISTnls
     A set of test nonlinear least squares examples from NIST, the U.S. National Institute for Standards and Technology.

NORMT3
     Evaluates complex erf, erfc and density of sum of Gaussian and Student's t.

Oarray
     Arrays with arbitrary offsets.

PBSmapping
     Software evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo, British Columbia, Canada.  Draws maps and implements other GIS procedures.

PHYLOGR
     Manipulation and analysis of phylogenetically simulated data sets (as obtained from PDSIMUL in package PDAP) and phylogenetically-based analyses using GLS.

PK
     Estimation of pharmacokinetic parameters.

POT
     Generalized Pareto distribution and Peaks Over Threshold.

PTAk
     A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations.  Also includes some other multiway methods.

ProbForecastGOP
     Probabilistic weather field forecasts using the Geostatistical Output Perturbation method introduced by Gel, Raftery and Gneiting (2004).

R.matlab
     Read and write of MAT files together with R-to-Matlab connectivity.

R.oo
     R object-oriented programming with or without references.

R.utils
     Utility classes and methods useful when programming in R and developing R packages.

R2HTML
     Functions for exporting R objects & graphics in an HTML document.

R2WinBUGS
     Running WinBUGS from R: call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R.

RArcInfo
     Functions to import Arc/Info V7.x coverages and data.

RColorBrewer
     ColorBrewer palettes for drawing nice maps shaded according to a variable.

RFA
     Regional Frequency Analysis.

RGrace
     Mouse/menu driven interactive plotting application.

RGraphics
     Data and functions from the book ``R Graphics'' by Paul Murrell, 2005, Chapman & Hall/CRC.

RII
     Estimation of the relative index of inequality for interval-censored data using natural cubic splines.

RLMM
     A genotype calling algorithm for Affymetrix SNP arrays.

RMySQL
     An interface between R and the MySQL database system.

RNetCDF
     An interface to Unidata's NetCDF library functions (version 3) and furthermore access to Unidata's udunits calendar conversions.

ROCR
     Visualizing the performance of scoring classifiers.

RODBC
     An ODBC database interface.

ROracle
     Oracle Database Interface driver for R.  Uses the ProC/C++ embedded SQL.

RQuantLib
     Provides access to (some) of the QuantLib functions from within R; currently limited to some Option pricing and analysis functions.  The QuantLib project aims to provide a comprehensive software framework for quantitative finance.

RSQLite
     Database Interface R driver for SQLite.  Embeds the SQLite database engine in R.

RScaLAPACK
     An interface to ScaLAPACK functions from R.

RSvgDevice
     A graphics device for R that uses the new w3.org XML standard for Scalable Vector Graphics.

RUnit
     Functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.

RWinEdt
     A plug in for using WinEdt as an editor for R.

RadioSonde
     A collection of programs for reading and plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument).

RandomFields
     Creating random fields using various methods.

Rcmdr
     A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

ResistorArray
     Electrical properties of resistor networks.

Rfwdmv
     Forward Search for Multivariate Data.

Rlab
     Functions and data sets for the NCSU ST370 class.

Rlsf
     Interface to the LSF queuing system.

Rmpi
     An interface (wrapper) to MPI (Message-Passing Interface) APIs.  It also provides an interactive R slave environment in which distributed statistical computing can be carried out.

Rpad
     Utility functions for the Rpad workbook-style interface.

Rwave
     An environment for the time-frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals), based on the book ``Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S'' by Rene Carmona, Wen L. Hwang and Bruno Torresani, 1998, Academic Press.

SAGx
     Retrieval, preparation and analysis of data from the Affymetrix GeneChip.

SASmixed
     Data sets and sample linear mixed effects analyses corresponding to the examples in ``SAS System for Mixed Models'' by R. C. Littell, G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.

SIN
     A SINful approach to selection of Gaussian Graphical Markov Models.

SciViews
     A bundle of packages to implement a full reusable GUI API for R. Contains svGUI with the main GUI features, svDialogs for the dialog boxes, svIO for data import/export, svMisc with miscellaneous supporting functions, and svViews providing views and report features (views are HTML presentations of the content of R objects, combining text, tables and graphs in the same document).

SemiPar
     Functions for semiparametric regression analysis, to complement the book ``Semiparametric Regression'' by R. Ruppert, M. P. Wand, and R. J. Carroll, 2003, Cambridge University Press.

SenSrivastava
     Collection of datasets from ``Regression Analysis, Theory, Methods and Applications'' by A. Sen and M. Srivastava, 1990, Springer.

SeqKnn
     Sequential KNN imputation.

SharedHT2
     Shared Hotelling T^2 test for small sample microarray experiments.

SoPhy
     Soil Physics Tools: simulation of water flux and solute transport in soil.

SparseLogReg
     Sparse logistic regression.

SparseM
     Basic linear algebra for sparse matrices.

StatDataML
     Read and write StatDataML.

SuppDists
     Ten distributions supplementing those built into R (Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficiant, Johnson distributions, normal scores and generalized hypergeometric distributions).

SwissAir
     Air quality data of Switzerland for one year in 30 min resolution.

TeachingDemos
     A set of demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.

UNF
     Tools for creating universal numeric fingerprints for data.

UsingR
     Data sets to accompany the textbook ``Using R for Introductory Statistics'' by J. Verzani, 2005, Chapman & Hall/CRC.

VLMC
     Functions, classes & methods for estimation, prediction, and simulation (bootstrap) of VLMC (Variable Length Markov Chain) models.

VaR
     Methods for calculation of Value at Risk (VaR).

XML
     Tools for reading XML documents and DTDs.

Zelig
     Everyone's statistical software: an easy-to-use program that can estimate, and help interpret the results of, an enormous range of statistical models.

aaMI
     Mutual information for protein sequence alignments.

abind
     Combine multi-dimensional arrays.

accuracy
     A suite of tools designed to test and improve the accuracy of statistical computation.

acepack
     ACE (Alternating Conditional Expectations) and AVAS (Additivity and VAriance Stabilization for regression) methods for selecting regression transformations.

adapt
     Adaptive quadrature in up to 20 dimensions.

ade4
     Multivariate data analysis and graphical display.

adehabitat
     A collection of tools for the analysis of habitat selection by animals.

adlift
     Adaptive Wavelet transforms for signal denoising.

agce
     Analysis of growth curve experiments.

akima
     Linear or cubic spline interpolation for irregularly gridded data.

alr3
     Methods and data to accompany the textbook ``Applied Linear Regression'' by S. Weisberg, 2005, Wiley.

amap
     Another Multidimensional Analysis Package.

anm
     Analog model for statistical/empirical downscaling.

aod
     Analysis of Overdispersed Data.

apTreeshape
     Analyses of phylogenetic treeshape.

ape
     Analyses of Phylogenetics and Evolution, providing functions for reading and plotting phylogenetic trees in parenthetic format (standard Newick format), analyses of comparative data in a phylogenetic framework, analyses of diversification and macroevolution, computing distances from allelic and nucleotide data, reading nucleotide sequences from GenBank via internet, and several tools such as Mantel's test, computation of minimum spanning tree, or the population parameter theta based on various approaches.

arules
     Mining association rules and frequent itemsets with R.

ash
     David Scott's ASH routines for 1D and 2D density estimation.

assist
     A suite of functions implementing smoothing splines.

aster
     Functions and datasets for Aster modeling (forest graph exponential family conditional or unconditional canonical statistic models for life history trait modeling).

asypow
     A set of routines that calculate power and related quantities utilizing asymptotic likelihood ratio methods.

aws
     Functions to perform adaptive weights smoothing.

bayesSurv
     Bayesian survival regression with flexible error and (later on also random effects) distributions.

bayesm
     Bayes Inference for Marketing/Micro-econometrics.

bayesmix
     Bayesian mixture models of univariate Gaussian distributions using JAGS.

baymvb
     Bayesian analysis of multivariate binary data.

betareg
     Beta regression for modeling rates and proportions.

bicreduc
     Reduction algorithm for the NPMLE for the distribution function of bivariate interval-censored data.

bim
     Bayesian interval mapping diagnostics:  functions to interpret QTLCart and Bmapqtl samples.

bindata
     Generation of correlated artificial binary data.

biopara
     Self-contained parallel system for R.

bitops
     Functions for Bitwise operations on integer vectors.

bivpois
     Bivariate Poisson models using the EM algorithm.

blighty
     Function for drawing the coastline of the United Kingdom.

boa
     Bayesian Output Analysis Program for MCMC.

boolean
     Boolean logit and probit: a procedure for testing Boolean hypotheses.

boost
     Boosting methods for real and simulated data, featuring `BagBoost', `LogitBoost', `AdaBoost', and `L2Boost'.

boot
     Functions and datasets for bootstrapping from the book ``Bootstrap Methods and Their Applications'' by A. C. Davison and D. V. Hinkley, 1997, Cambridge University Press.  Recommended.

bootstrap
     Software (bootstrap, cross-validation, jackknife), data and errata for the book ``An Introduction to the Bootstrap'' by B. Efron and R. Tibshirani, 1993, Chapman and Hall.

bqtl
     QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools.

brlr
     Bias-reduced logistic regression: fits logistic regression models by maximum penalized likelihood.

butler
     Unit testing, profiling and benchmarking for R.

caMassClass
     Processing and Classification of protein mass spectra (SELDI) data.

caTools
     Miscellaneous utility functions, including reading/writing ENVI binary files, a LogitBoost classifier, and a base64 encoder/decoder.

car
     Companion to Applied Regression, containing functions for applied regession, linear models, and generalized linear models, with an emphasis on regression diagnostics, particularly graphical diagnostic methods.

cat
     Analysis of categorical-variable datasets with missing values.

catspec
     Special models for categorical variables.

cba
     Clustering for Business Analytics, including implementations of Proximus and Rock.

cclust
     Convex clustering methods, including k-means algorithm, on-line update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning) and calculation of several indexes for finding the number of clusters in a data set.

cfa
     Analysis of configuration frequencies.

chplot
     Augmented convex hull plots: informative and nice plots for grouped bivariate data.

changeLOS
     Change in length of hospital stay (LOS).

chron
     A package for working with chronological objects (times and dates).

circular
     Circular statistics, from ``Topics in Circular Statistics'' by Rao Jammalamadaka and A. SenGupta, 2001, World Scientific.

clac
     Clust Along Chromosomes, a method to call gains/losses in CGH array data.

class
     Functions for classification (k-nearest neighbor and LVQ). Contained in the VR bundle.  Recommended.

classPP
     Projection Pursuit for supervised classification.

clim.pact
     Climate analysis and downscaling for monthly and daily data.

climatol
     Functions to fill missing data in climatological (monthly) series and to test their homogeneity, plus functions to draw wind-rose and Walter&Lieth diagrams.

clines
     Calculates Contour Lines.

clue
     CLUster Ensembles.

cluster
     Functions for cluster analysis.  Recommended.

cmprsk
     Estimation, testing and regression modeling of subdistribution functions in competing risks.

cobs
     Constrained B-splines: qualitatively constrained (regression) smoothing via linear programming.

coda
     Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC) simulations.

coin
     COnditional INference procedures for the general independence problem including two-sample, K-sample, correlation, censored, ordered and multivariate problems.

colorspace
     Mapping between assorted color spaces.

combinat
     Combinatorics utilities.

compositions
     Functions for the consistent analysis of compositional data (e.g., portions of substances) and positive numbers (e.g., concentrations).

concor
     Concordance, providing ``SVD by blocks''.

concord
     Measures of concordance and reliability.

conf.design
     A series of simple tools for constructing and manipulating confounded and fractional factorial designs.

copula
     Classes of commonly used copulas (including elliptical and Archimedian), and methods for density, distribution, random number generators, and plotting.

corpcor
     Efficient estimation of covariance and (partial) correlation.

covRobust
     Robust covariance estimation via nearest neighbor cleaning.

cramer
     Routine for the multivariate nonparametric Cramer test.

crossdes
     Functions for the construction and randomization of balanced carryover balanced designs, to check given designs for balance, and for simulation studies on the validity of two randomization procedures.

cslogistic
     Likelihood and posterior analysis of conditionally specified logistic regression models.

ctv
     Server-side and client-side tools for CRAN task views.

cyclones
     Cyclone identification.

date
     Functions for dealing with dates.  The most useful of them accepts a vector of input dates in any of the forms 8/30/53, 30Aug53, 30 August 1953, ..., August 30 53, or any mixture of these.

dblcens
     Calculates the NPMLE of the survival distribution for doubly censored data.

deal
     Bayesian networks with continuous and/or discrete variables can be learned and compared from data.

debug
     Debugger for R functions, with code display, graceful error recovery, line-numbered conditional breakpoints, access to exit code, flow control, and full keyboard input.

deldir
     Calculates the  Delaunay triangulation and the Dirichlet or Voronoi tesselation (with respect to the entire plane) of a planar point set.

depmix
     Dependent Mixture Models: fit (multi-group) mixtures of latent Markov models on mixed categorical and continuous (time series) data.

dglm
     Double generalized linear models.

diamonds
     Functions for illustrating aperture-4 diamond partitions in the plane, or on the surface of an octahedron or icosahedron, for use as analysis or sampling grids.

dichromat
     Color schemes for dichromats: collapse red-green distinctions to simulate the effects of colour-blindness.

digest
     Two functions for the creation of ``hash'' digests of arbitrary R objects using the md5 and sha-1 algorithms permitting easy comparison of R language objects.

diptest
     Compute Hartigan's dip test statistic for unimodality.

dispmod
     Functions for modelling dispersion in GLMs.

distr
     An object orientated implementation of distributions and some additional functionality.

dprep
     Data preprocessing and visualization functions for classification.

dr
     Functions, methods, and datasets for fitting dimension reduction regression, including pHd and inverse regression methods SIR and SAVE.

drfit
     Dose-response data evaluation.

dse
     Dynamic System Estimation, a multivariate time series package bundle. Contains dse1 (the base system, including multivariate ARMA and state space models), dse2 (extensions for evaluating estimation techniques, forecasting, and for evaluating forecasting model), tframe (functions for writing code that is independent of the representation of time), and setRNG (a mechanism for generating the same random numbers in S and R).

dyn
     Time series regression.

dynamicGraph
     Interactive graphical tool for manipulating graphs.

dynlm
     Dynamic linear models and time series regression.

e1071
     Miscellaneous functions used at the Department of Statistics at TU Wien (E1071), including moments, short-time Fourier transforms, Independent Component Analysis, Latent Class Analysis, support vector machines, and fuzzy clustering, shortest path computation, bagged clustering, and some more.

eba
     Fitting and testing probabilistic choice models, especially the BTL, elimination-by-aspects (EBA), and preference tree (Pretree) models.

eco
     Fitting Bayesian models of ecological inference in 2 by 2 tables.

edci
     Edge Detection and Clustering in Images.

effects
     Graphical and tabular effect displays, e.g., of interactions, for linear and generalised linear models.

eha
     A package for survival and event history analysis.

elasticnet
     Elastic net regularization and variable selection.

ellipse
     Package for drawing ellipses and ellipse-like confidence regions.

elliptic
     A suite of elliptic and related functions including Weierstrass and Jacobi forms.

emme2
     Functions to read from and write to an EMME/2 databank.

emplik
     Empirical likelihood ratio for means/quantiles/hazards from possibly right censored data.

energy
     E-statistics (energy) tests for comparing distributions: multivariate normality, Poisson test, multivariate k-sample test for equal distributions, hierarchical clustering by e-distances.

ensembleBMA
     Probabilistic forecasting using Bayesian Model Averaging of ensembles using a mixture of normal distributions.

epitools
     Basic tools for applied epidemiology.

epsi
     Edge Preserving Smoothing for Images.

evd
     Functions for extreme value distributions.  Extends simulation, distribution, quantile and density functions to univariate, bivariate and (for simulation) multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate models.

evdbayes
     Functions for the bayesian analysis of extreme value models, using MCMC methods.

evir
     Extreme Values in R: Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.

exactLoglinTest
     Monte Carlo exact tests for log-linear models.

exactRankTests
     Computes exact p-values and quantiles using an implementation of the Streitberg/Roehmel shift algorithm.

fBasics
     The Rmetrics module for ``Markets, basic statistics, and stylized facts''.  Rmetrics is an environment and software collection for teaching financial engineering and computational finance (http://www.Rmetrics.org/).

fCalendar
     The Rmetrics module for ``Date, Time and Calendars''.

fExtremes
     The Rmetrics module for ``Beyond the Sample, Dealing with Extreme Values''.

fMultivar
     The Rmetrics module for ``Multivariate Data Analysis''.

fOptions
     The Rmetrics module for ``The Valuation of Options''.

fPortfolio
     The Rmetrics module for ``Pricing and Hedging of Options''.

fSeries
     The Rmetrics module for ``The Dynamical Process Behind Financial Markets''.

far
     Modelization for Functional AutoRegressive processes.

faraway
     Functions and datasets for books by Julian Faraway.

fastICA
     Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.

fda
     Functional Data Analysis: analysis of data where the basic observation is a function of some sort.

fdim
     Functions for calculating fractal dimension.

fgac
     Families of Generalized Archimedean Copulas.

fields
     A collection of programs for curve and function fitting with an emphasis on spatial data.  The major methods implemented include cubic and thin plate splines, universal Kriging and Kriging for large data sets.  The main feature is that any covariance function implemented in R can be used for spatial prediction.

filehash
     Simple file-based hash table.

flexmix
     Flexible Mixture Modeling: a general framework for finite mixtures of regression models using the EM algorithm.

foreign
     Functions for reading and writing data stored by statistical software like Minitab, S, SAS, SPSS, Stata, Systat, etc.  Recommended.

fork
     Functions for handling multiple processes: simple wrappers around the Unix process management API calls.

fortunes
     R fortunes.

forward
     Forward search approach to robust analysis in linear and generalized linear regression models.

fpc
     Fixed point clusters, clusterwise regression and discriminant plots.

fracdiff
     Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied Statistics, 1989).

frailtypack
     Fit a shared gamma frailty model and Cox proportional hazards model using a Penalized Likelihood on the hazard function.

ftnonpar
     Features and strings for nonparametric regression.

g.data
     Create and maintain delayed-data packages (DDP's).

gRbase
     A package for graphical modelling in R.  Defines S4 classes for graphical meta data and graphical models, and illustrates how hierarchical log-linear models may be implemented and combined with dynamicGraph.

gafit
     Genetic algorithm for curve fitting.

gam
     Functions for fitting and working with Generalized Additive Models, as described in chapter 7 of the White Book, and in ``Generalized Additive Models'' by T. Hastie and R. Tibshirani (1990).

gamlss
     Functions to fit Generalized Additive Models for Location Scale and Shape.

gap
     Genetic analysis package for both population and family data.  Includes pathmix for path analysis of pairs of relatives, and pointer for complex segregation analysis.

gbm
     Generalized Boosted Regression Models: implements extensions to Freund and Schapire's AdaBoost algorithm and J. Friedman's gradient boosting machine.  Includes regression methods for least squares, absolute loss, logistic, Poisson, Cox proportional hazards partial likelihood, and AdaBoost exponential loss.

gclus
     Clustering Graphics.  Orders panels in scatterplot matrices and parallel coordinate displays by some merit index.

gcmrec
     Parameters estimation of the general semiparametric model for recurrent event data proposed by Peña and Hollander.

gdata
     Various functions to manipulate data.

gee
     An implementation of the Liang/Zeger generalized estimating equation approach to GLMs for dependent data.

geepack
     Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link.  Can also handle clustered categorical responses.

genalg
     R based genetic algorithm for binary and floating point chromosomes.

genetics
     Classes and methods for handling genetic data.  Includes classes to represent genotypes and haplotypes at single markers up to multiple markers on multiple chromosomes, and functions for allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, computing disequlibrium, testing Hardy-Weinberg equilibrium, ...

geoR
     Functions to perform geostatistical data analysis including model-based methods.

geoRglm
     Functions for inference in generalised linear spatial models.

geometry
     Mesh generation and surface tesselation, based on the Qhull library.

ggm
     Functions for defining directed acyclic graphs and undirected graphs, finding induced graphs and fitting Gaussian Markov models.

giRaph
     Data structures and algorithms for computations on graphs.

gld
     Basic functions for the generalised (Tukey) lambda distribution.

gllm
     Routines for log-linear models of incomplete contingency tables, including some latent class models via EM and Fisher scoring approaches.

glmmML
     A Maximum Likelihood approach to generalized linear models with random intercept.

glpk
     Interface to the GNU Linear Programming Kit (GLPK).

gmodels
     Various functions to manipulate models.

gmp
     Arithmetic ``without limitations'' using the GNU Multiple Precision library.

gmt
     Interface between the GMT 4.0 map-making software and R.

gnm
     Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science.

gpclib
     General polygon clipping routines for R based on Alan Murta's C library.

gpls
     Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification.

gplots
     Various functions to draw plots.

grasper
     Generalized Regression Analysis and Spatial Predictions for R.

gregmisc
     Miscellaneous functions written/maintained by Gregory R. Warnes.

gridBase
     Integration of base and grid graphics.

grouped
     Regression models for grouped and coarse data, under the Coarsened At Random assumption.

gsl
     Wrapper for special functions of the Gnu Scientific Library (GSL).

gss
     A comprehensive package for structural multivariate function estimation using smoothing splines.

gstat
     multivariable geostatistical modelling, prediction and simulation. Includes code for variogram modelling; simple, ordinary and universal point or block (co)kriging, sequential Gaussian or indicator (co)simulation, and map plotting functions.

gtkDevice
     GTK graphics device driver that may be used independently of the R-GNOME interface and can be used to create R devices as embedded components in a GUI using a Gtk drawing area widget, e.g., using RGtk.

gtools
     Various functions to help manipulate data.

hapassoc
     Likelihood inference of trait associations with SNP haplotypes and other attributes using the EM Algorithm.

haplo.stats
     Statistical analysis of haplotypes with traits and covariates when linkage phase is ambiguous.

hapsim
     Haplotype data simulation.

hdf5
     Interface to the NCSA HDF5 library.

hett
     Functions for the fitting and summarizing of heteroscedastic t-regression.

hier.part
     Hierarchical Partitioning: variance partition of a multivariate data set.

hierfstat
     Estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, and tests for the significance of each F and variance components.

hmm.discnp
     Hidden Markov models with discrete non-parametric observation distributions.

hoa
     A bundle of packages for higher order likelihood-based inference. Contains cond for approximate conditional inference for logistic and loglinear models, csampling for conditional simulation in regression-scale models, marg for approximate marginal inference for regression-scale models, and nlreg for higher order inference for nonlinear heteroscedastic models.

homals
     Homogeneity Analysis (HOMALS) package with optional Tcl/Tk interface.

hopach
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

httpRequest
     Implements HTTP Request protocols (GET, POST, and multipart POST requests).

hwde
     Models and tests for departure from Hardy-Weinberg equilibrium and independence between loci.

ifs
     Iterated Function Systems distribution function estimator.

iid.test
     Testing whether data is independent and identically distributed.

impute
     Imputation for microarray data (currently KNN only).

ineq
     Inequality, concentration and poverty measures, and Lorenz curves (empirical and theoretic).

intcox
     Implementation of the Iterated Convex Minorant Algorithm for the Cox proportional hazard model for interval censored event data.

ipred
     Improved predictive models by direct and indirect bootstrap aggregation in classification and regression as well as resampling based estimators of prediction error.

irr
     Coefficients of Interrater Reliability and Agreement for quantitative, ordinal and nominal data.

ismev
     Functions to support the computations carried out in ``An Introduction to Statistical Modeling of Extreme Values;' by S. Coles, 2001, Springer. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

its
     An S4 class for handling irregular time series.

kappalab
     The ``laboratory for capacities'', an S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation on a finite setting.

kernlab
     Kernel-based machine learning methods including support vector machines.

kinship
     Mixed-effects Cox models, sparse matrices, and modeling data from large pedigrees.

kknn
     Weighted k-nearest neighbors classification and regression.

klaR
     Miscellaneous functions for classification and visualization developed at the Department of Statistics, University of Dortmund.

knnTree
     Construct or predict with k-nearest-neighbor classifiers, using cross-validation to select k, choose variables (by forward or backwards selection), and choose scaling (from among no scaling, scaling each column by its SD, or scaling each column by its MAD).  The finished classifier will consist of a classification tree with one such k-nn classifier in each leaf.

knncat
     Nearest-neighbor classification with categorical variables.

ks
     Kernel smoothing: bandwidth matrices for kernel density estimators and kernel discriminant analysis for bivariate data.

kza
     Kolmogorov-Zurbenko Adpative filter for locating change points in a time series.

labdsv
     Laboratory for Dynamic Synthetic Vegephenomenology.

labstatR
     Functions for the book ``Laboratorio di statistica con R'' by S. M. Iacus and G. Masarotto, 2002, McGraw-Hill.  Function names and documentation in Italian.

lars
     Least Angle Regression, Lasso and Forward Stagewise: efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit.

lasso2
     Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998).

latentnet
     Latent position and cluster models for statistical networks.

lattice
     Lattice graphics, an implementation of Trellis Graphics functions. Recommended.

latticeExtra
     Generic functions and standard methods for Trellis-based displays.

lazy
     Lazy learning for local regression.

ldDesign
     Design of experiments for detection of linkage disequilibrium,

leaps
     A package which performs an exhaustive search for the best subsets of a given set of potential regressors, using a branch-and-bound algorithm, and also performs searches using a number of less time-consuming techniques.

lgtdl
     A set of methods for longitudinal data objects.

limma
     LInear Models for MicroArray data.

linprog
     Solve linear programming/linear optimization problems by using the simplex algorithm.

lme4
     Fit linear and generalized linear mixed-effects models.

lmeSplines
     Fit smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

lmm
     Linear mixed models.

lmtest
     A collection of tests on the assumptions of linear regression models from the book ``The linear regression model under test'' by W. Kraemer and H. Sonnberger, 1986, Physica.

locfdr
     Computation of local false discovery rates.

locfit
     Local Regression, likelihood and density estimation.

lodplot
     Assorted plots of location score versus genetic map position.

logistf
     Firth's bias reduced logistic regression approach with penalized profile likelihood based confidence intervals for parameter estimates.

logspline
     Logspline density estimation.

lokern
     Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.

longmemo
     Datasets and Functionality from the textbook ``Statistics for Long-Memory Processes'' by J. Beran, 1994, Chapman & Hall.

lpSolve
     Functions that solve general linear/integer problems, assignment problems, and transportation problems via interfacing Lp_solve.

lpridge
     Local polynomial (ridge) regression.

ltm
     Analysis of multivariate Bernoulli data using latent trait models (including the Rasch model) under the Item Response Theory approach.

mAr
     Estimation of multivariate AR models through a computationally efficient stepwise least-squares algorithm.

maanova
     Analysis of N-dye Micro Array experiments using mixed model effect. Contains anlysis of variance, permutation and bootstrap, cluster and consensus tree.

magic
     A variety of methods for creating magic squares of any order greater than 2, and various magic hypercubes.

mapdata
     Supplement to package maps, providing the larger and/or higher-resolution databases.

mapproj
     Map Projections: converts latitude/longitude into projected coordinates.

maps
     Draw geographical maps.  Projection code and larger maps are in separate packages.

maptools
     Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles.

maptree
     Functions with example data for graphing and mapping models from hierarchical clustering and classification and regression trees.

mathgraph
     Tools for constructing and manipulating objects from a class of directed and undirected graphs.

matlab
     Emulate MATLAB code using R.

maxstat
     Maximally selected rank and Gauss statistics with several p-value approximations.

mblm
     Median-based Linear models, using Theil-Sen single or Siegel repeated medians.

mcgibbsit
     Warnes and Raftery's MCGibbsit MCMC diagnostic.

mclust
     Model-based cluster analysis: the 2002 version of MCLUST.

mcmc
     Functions for Markov Chain Monte Carlo (MCMC).

mda
     Code for mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), multivariate additive regression splines (MARS), adaptive back-fitting splines (BRUTO), and penalized regression.

meanscore
     Mean Score method for missing covariate data in logistic regression models.

merror
     Accuracy and precision of measurements.

meta
     Fixed and random effects meta-analysis, with functions for tests of bias, forest and funnel plot.

mfp
     Multiple Fractional Polynomials.

mgcv
     Routines for GAMs and other genralized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE. Recommended.

micEcon
     Tools for microeconomic analysis and microeconomic modelling.

mice
     Multivariate Imputation by Chained Equations.

mimR
     An R interface to MIM for graphical modeling in R.

minpack.lm
     R interface for two functions from the MINPACK least squares optimization library, solving the nonlinear least squares problem by a modification of the Levenberg-Marquardt algorithm.

misc3d
     A collection of miscellaneous 3d plots, including rgl-based isosurfaces.

mitools
     Tools to perform analyses and combine results from multiple-imputation datasets.

mix
     Estimation/multiple imputation programs for mixed categorical and continuous data.

mixreg
     Functions to fit mixtures of regressions.

mlbench
     A collection of artificial and real-world machine learning benchmark problems, including the Boston housing data.

mlica
     Independent Component Analysis using Maximum Likelihood.

mlmRev
     Examples from Multilevel Modelling Software Review.

mmlcr
     Mixed-mode latent class regression (also known as mixed-mode mixture model regression or mixed-mode mixture regression models) which can handle both longitudinal and one-time responses.

moc
     Fits a variety of mixtures models for multivariate observations with user-difined distributions and curves.

modeltools
     A collection of tools to deal with statistical models.

moments
     Moments, skewness, kurtosis and related tests.

monoProc
     Strictly monotone smoothing procedure.

msm
     Functions for fitting continuous-time Markov multi-state models to categorical processes observed at arbitrary times, optionally with misclassified responses, and covariates on transition or misclassification rates.

muhaz
     Hazard function estimation in survival analysis.

multcomp
     Multiple comparison procedures for the one-way layout.

multinomRob
     Overdispersed multinomial regression using robust (LQD and tanh) estimation.

multtest
     Resampling-based multiple hypothesis testing.

mvbutils
     Utilities by Mark V. Bravington for project organization, editing and backup, sourcing, documentation (formal and informal), package preparation, macro functions, and more.

mvnmle
     ML estimation for multivariate normal data with missing values.

mvnormtest
     Generalization of the Shapiro-Wilk test for multivariate variables.

mvoutlier
     Multivariate outlier detection based on robust estimates of location and covariance structure.

mvpart
     Multivariate partitioning.

mvtnorm
     Multivariate normal and t distributions.

nFDR
     Nonparametric Estimate of FDR Based on Bernstein polynomials.

ncdf
     Interface to Unidata netCDF data files.

ncomplete
     Functions to perform the regression depth method (RDM) to binary regression to approximate the minimum number of observations that can be removed such that the reduced data set has complete separation.

ncvar
     High-level R interface to netCDF datasets.

negenes
     Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler.

network
     Tools to create and modify network objects, which can represent a range of relational data types.

neural
     RBF and MLP neural networks with graphical user interface.

nice
     Get or set UNIX priority (niceness) of running R process.

nlme
     Fit and compare Gaussian linear and nonlinear mixed-effects models. Recommended.

nlmeODE
     Combine the nlme and odesolve packages for mixed-effects modelling using differential equations.

nlrq
     Nonlinear quantile regression routines.  Defunct.

nnet
     Software for single hidden layer perceptrons (``feed-forward neural networks''), and for multinomial log-linear models.  Contained in the VR bundle.  Recommended.

nor1mix
     One-dimensional normal mixture models classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities.

norm
     Analysis of multivariate normal datasets with missing values.

normalp
     A collection of utilities for normal of order p distributions (General Error Distributions).

nortest
     Five omnibus tests for the composite hypothesis of normality.

noverlap
     Functions to perform the regression depth method (RDM) to binary regression to approximate the amount of overlap, i.e., the minimal number of observations that need to be removed such that the reduced data set has no longer overlap.

npmc
     Nonparametric Multiple Comparisons:  provides simultaneous rank test procedures for the one-way layout without presuming a certain distribution.

nprq
     Nonparametric quantile regression.  Defunct.

odesolve
     An interface for the Ordinary Differential Equation (ODE) solver lsoda. ODEs are expressed as R functions.

orientlib
     Representations, conversions and display of orientation SO(3) data.

ouch
     Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses.

outliers
     A collection of some tests commonly used for identifying outliers.

oz
     Functions for plotting Australia's coastline and state boundaries.

pamr
     Pam: Prediction Analysis for Microarrays.

pan
     Multiple imputation for multivariate panel or clustered data.

panel
     Functions and datasets for fitting models to Panel data.

papply
     Parallel apply function using MPI.

partsm
     Periodic AutoRegressive Time Series Models.

party
     Unbiased recursive partitioning in a conditional inference framework.

pastecs
     Package for Analysis of Space-Time Ecological Series.

pcurve
     Fits a principal curve to a numeric multivariate dataset in arbitrary dimensions.  Produces diagnostic plots.  Also calculates Bray-Curtis and other distance matrices and performs multi-dimensional scaling and principal component analyses.

pear
     Periodic Autoregression Analysis.

permax
     Functions intended to facilitate certain basic analyses of DNA array data, especially with regard to comparing expression levels between two types of tissue.

permtest
     Permutation test to compare variability within and distance between two groups.

perturb
     Perturbation analysis for evaluating collinearity.

pgam
     Poisson-Gamma Additive Models.

pheno
     Some easy-to-use functions for time series analyses of (plant-) phenological data sets.

phpSerialize
     Serialize R to PHP associative array.

phyloarray
     Software to process data from phylogenetic or identification microarrays.

pinktoe
     Converts S trees to HTML/Perl files for interactive tree traversal.

pixmap
     Functions for import, export, plotting and other manipulations of bitmapped images.

plotrix
     Various useful functions for enhancing plots.

plugdensity
     Kernel density estimation with global bandwidth selection via ``plug-in''.

pls
     Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR).

pls.pcr
     Multivariate regression by PLS and PCR.

plsgenomics
     PLS analyses for genomics.

polspline
     Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.

polycor
     Polychoric and polyserial correlations.

polynom
     A collection of functions to implement a class for univariate polynomial manipulations.

popgen
     Statistical and POPulation GENetics.

powerpkg
     Power analyses for the affected sib pair and the TDT design.

ppc
     Sample classification of protein mass spectra by peak probabilty contrasts.

pps
     Functions to select samples using PPS (probability proportional to size) sampling, for stratified simple random sampling, and to compute joint inclusion probabilities for Sampford's method of PPS sampling.

prabclus
     Distance based parametric bootstrap tests for clustering, mainly thought for presence-absence data (clustering of species distribution maps). Jaccard and Kulczynski distance measures, clustering of MDS scores, and nearest neighbor based noise detection.

princurve
     Fits a principal curve to a matrix of points in arbitrary dimension.

proto
     An object oriented system using prototype or object-based (rather than class-based) object oriented ideas.

pscl
     R in the Political Science Computational Laboratory, Stanford University.

pspline
     Smoothing splines with penalties on order m derivatives.

psy
     Various procedures used in psychometry: Kappa, ICC, Cronbach alpha, screeplot, PCA and related methods.

pwt
     The Penn World Table providing purchasing power parity and national income accounts converted to international prices for 168 countries for some or all of the years 1950--2000.

pvclust
     Hierarchical clustering with p-value.

qcc
     Quality Control Charts.  Shewhart quality control charts for continuous, attribute and count data.  Cusum and EWMA charts.  Operating characteristic curves.  Process capability analysis.  Pareto chart and cause-and-effect chart.

qtl
     Analysis of experimental crosses to identify QTLs.

qtlDesign
     Tools for the design of QTL experiments.

quadprog
     For solving quadratic programming problems.

quantchem
     Quantitative chemical analysis: calibration and evaluation of results.

quantreg
     Quantile regression and related methods.

qvalue
     Q-value estimation for false discovery rate control.

qvcalc
     Functions to compute quasi-variances and associated measures of approximation error.

race
     Implementation of some racing methods for the empirical selection of the best.

randaes
     Random number generator based on AES cipher.

randomForest
     Breiman's random forest classifier.

rbugs
     Functions to prepare files needed for running BUGS in batch mode, and running BUGS from R.  Support for Linux systems with Wine is emphasized.

rcdd
     C Double Description for R, an interface to the CDD computational geometry library.

rcom
     R COM Client Interface and internal COM Server.

ref
     Functions for creating references, reading from and writing ro references and a memory efficient refdata type that transparently encapsulates matrices and data frames.

regress
     Fitting Gaussian linear models where the covariance structure is a linear combination of known matrices by maximising the residual log likelihood.  Can be used for multivariate models and random effects models.

relax
     Functions for report writing, presentation, and programming.

reldist
     Functions for the comparison of distributions, including nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in ``Relative Distribution Methods in the Social Sciences'' by Mark S. Handcock and Martina Morris, 1999, Springer.

relimp
     Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model.

relsurv
     Various functions for regression in relative survival.

reshape
     Flexibly reshape data.

resper
     Sampling from restricted permutations.

rgdal
     Provides bindings to Frank Warmerdam's Geospatial Data Abstraction Library (GDAL).

rgenoud
     R version of GENetic Optimization Using Derivatives.

rgl
     3D visualization device system (OpenGL).

rimage
     Functions for image processing, including Sobel filter, rank filters, fft, histogram equalization, and reading JPEG files.

rlecuyer
     R interface to RNG with multiple streams.

rmeta
     Functions for simple fixed and random effects meta-analysis for two-sample comparison of binary outcomes.

rmetasim
     An interface between R and the metasim simulation engine.  Facilitates the use of the metasim engine to build and run individual based population genetics simulations.

rpart
     Recursive PARTitioning and regression trees.  Recommended.

rpart.permutation
     Permutation tests of rpart models.

rpvm
     R interface to PVM (Parallel Virtual Machine).  Provides interface to PVM APIs, and examples and documentation for its use.

rqmcmb2
     Markov chain marginal bootstrap for quantile regression.

rrcov
     Functions for robust location and scatter estimation and robust regression with high breakdown point.

rsprng
     Provides interface to SPRNG (Scalable Parallel Random Number Generators) APIs, and examples and documentation for its use.

rstream
     Unified object oriented interface for multiple independent streams of random numbers from different sources.

rwt
     Rice Wavelet Toolbox wrapper, providing a set of functions for performing digital signal processing.

sac
     Semiparametric empirical likelihood ratio based test of changepoint with one-change or epidemic alternatives with data-based model diagnostic.

sampling
     A set of tools to select and to calibrate samples.

sampfling
     Implements a modified version of the Sampford sampling algorithm.  Given a quantity assigned to each unit in the population, samples are drawn with probability proportional to te product of the quantities of the units included in the sample.

samr
     Significance Analysis of Microarrays.

sandwich
     Model-robust standard error estimators for time series and longitudinal data.

sca
     Simple Component Analysis.

scape
     functions to import and plot results from statistical catch-at-age models, used in fisheries stock assessments.

scatterplot3d
     Plots a three dimensional (3D) point cloud perspectively.

seacarb
     Calculates parameters of the seawater carbonate system.

seao
     Simple Evolutionary Algorithm Optimization.

seao.gui
     Simple Evolutionary Algorithm Optimization: graphical user interface.

segmented
     Functions to estimate break-points of segmented relationships in regression models (GLMs).

sem
     Functions for fitting general linear Structural Equation Models (with observed and unobserved variables) by the method of maximum likelihood using the RAM approach.

seqinr
     Exploratory data analysis and data visualization for biological sequence (DNA and protein) data.

seqmon
     Sequential monitoring of clinical trials.

session
     Functions for interacting with, saving and restoring R sessions.

setRNG
     Set (normal) random number generator and seed.

sfsmisc
     Utilities from Seminar fuer Statistik ETH Zurich.

sgeostat
     An object-oriented framework for geostatistical modeling.

shapefiles
     Functions to read and write ESRI shapefiles.

shapes
     Routines for the statistical analysis of shapes, including procrustes analysis, displaying shapes and principal components, testing for mean shape difference, thin-plate spline transformation grids and edge superimposition methods.

simex
     SIMEX and MCSIMEX algorithms for measurement error models.

simpleboot
     Simple bootstrap routines.

skewt
     Density, distribution function, quantile function and random generation for the skewed t distribution of Fernandez and Steel.

sm
     Software linked to the book ``Applied Smoothing Techniques for Data Analysis:  The Kernel Approach with S-PLUS Illustrations'' by A. W. Bowman and A. Azzalini, 1997, Oxford University Press.

sma
     Functions for exploratory (statistical) microarray analysis.

smoothSurv
     Survival regression with smoothed error distribution.

sn
     Functions for manipulating skew-normal probability distributions and for fitting them to data, in the scalar and the multivariate case.

sna
     A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, p* modeling, and network visualization.

snow
     Simple Network of Workstations: support for simple parallel computing in R.

snowFT
     Fault Tolerant Simple Network of Workstations.

som
     Self-Organizing Maps (with application in gene clustering).

sound
     A sound interface for R: Basic functions for dealing with .wav files and sound samples.

sp
     A package that provides classes and methods for spatial data, including utility functions for plotting data as maps, spatial selection, amd much more.

spatial
     Functions for kriging and point pattern analysis from ``Modern Applied Statistics with S'' by W. Venables and B. Ripley.  Contained in the VR bundle.  Recommended.

spatialCovariance
     Computation of spatial covariance matrices for data on rectangles using one dimensional numerical integration and analytic results.

spatstat
     Data analysis and modelling of two-dimensional point patterns, including multitype points and spatial covariates.

spc
     Statistical Process Control: evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length), setting up control charts for given in-control ARL, and plotting of the related figures.

spdep
     A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tesselations, for summarising these objects, and for permitting their use in spatial data analysis; a collection of tests for spatial autocorrelation, including global Moran's I and Geary's C, local Moran's I, saddlepoint approximations for global and local Moran's I; and functions for estimating spatial simultaneous autoregressive (SAR) models.  (Was formerly the three packages: spweights, sptests, and spsarlm.)

spe
     Stochastic Proximity Embedding.

spectralGP
     Approximate Gaussian processes using the Fourier basis.

spectrino
     Spectra organizer, visualization and data extraction from within R.

splancs
     Spatial and space-time point pattern analysis functions.

sspir
     State SPace models In R.

statmod
     Miscellaneous biostatistical modelling functions.

stepwise
     A stepwise approach to identifying recombination breakpoints in a sequence alignment.

strucchange
     Various tests on structural change in linear regression models.

subselect
     A collection of functions which assess the quality of variable subsets as surrogates for a full data set, and search for subsets which are optimal under various criteria.

supclust
     Methodology for supervised grouping of predictor variables.

superpc
     Supervised principal components.

survBayes
     Fits a proportional hazards model to time to event data by a Bayesian approach.

survey
     Summary statistics, generalized linear models, and general maximum likelihood estimation for stratified, cluster-sampled, unequally weighted survey samples.

survival
     Functions for survival analysis, including penalised likelihood. Recommended.

survrec
     Survival analysis for recurrent event data.

svmpath
     Computes the entire regularization path for the two-class svm classifier with essentialy the same cost as a single SVM fit.

systemfit
     Contains functions for fitting simultaneous systems of equations using Ordinary Least Sqaures (OLS), Two-Stage Least Squares (2SLS), and Three-Stage Least Squares (3SLS).

tapiR
     Tools for accessing (UK) parliamentary information in R.

taskPR
     Task-Parallel R package.

tdist
     Computes the distribution of a linear combination of independent Student's t variables.

tdthap
     Transmission/disequilibrium tests for extended marker haplotypes.

tensor
     Tensor product of arrays.

time
     Time tracking for developers.

tkrplot
     Simple mechanism for placing R graphics in a Tk widget.

tlnise
     Two-level normal independent sampling estimation.

tree
     Classification and regression trees.

treeglia
     Stem analysis functions for volume increment and carbon uptake assessment from tree-rings.

tripack
     A constrained two-dimensional Delaunay triangulation package.

tseries
     Package for time series analysis with emphasis on non-linear modelling.

tseriesChaos
     Routines for the analysis of non-linear time series.

tuneR
     Collection of tools to analyze music, handle wave files, transcription, etc.

tweedie
     Maximum likelihood computations for Tweedie exponential family models.

twostage
     Functions for optimal design of two-stage-studies using the Mean Score method.

udunits
     Interface to Unidata's routines to convert units.

ump
     Uniformly Most Powerful tests.

urca
     Unit root and cointegration tests for time series data.

urn
     Functions for sampling without replacement (simulated urns).

uroot
     Unit root tests and graphics for seasonal time series.

vabayelMix
     Variational Bayesian mixture model.

varSelRF
     Variable selection using random forests.

vardiag
     Interactive variogram diagnostics.

varmixt
     Mixture model on the variance for the analysis of gene expression data.

vcd
     Functions and data sets based on the book ``Visualizing Categorical Data'' by Michael Friendly.

vegan
     Various help functions for vegetation scientists and community ecologists.

verification
     Utilities for verification of discrete and probabilistic forecasts.

verify
     Construction of test suites using verify objects.

vioplot
     Violin plots, which are a combination of a box plot and a kernel density plot.

waveslim
     Basic wavelet routines for time series analysis.

wavethresh
     Software to perform 1-d and 2-d wavelet statistics and transforms.

wle
     Robust statistical inference via a weighted likelihood approach.

xgobi
     Interface to the XGobi and XGvis programs for graphical data analysis.

xtable
     Export data to LaTeX and HTML tables.

zicounts
     Fit classical, zero-inflated and interval censored count data regression models.

zoo
     A class with methods for totally ordered indexed observations such as irregular time series.

See CRAN src/contrib/PACKAGES for more information.

There is also a CRAN src/contrib/Devel directory which contains packages still ``under development'' or depending on features only present in the current development versions of R.  Volunteers are invited to give these a try, of course.  This area of CRAN currently contains

GLMMGibbs
     Generalised Linear Mixed Models by Gibbs sampling.

RPgSQL
     Provides methods for accessing data stored in PostgreSQL tables.

dseplus
     Extensions to dse, the Dynamic Systems Estimation multivariate time series package.  Contains PADI, juice and monitoring extensions.

ensemble
     Ensembles of tree classifiers.

runStat
     Running median and mean.

write.snns
     Function for writing a SNNS pattern file from a data frame or matrix.

Add-on packages from Omegahat
-----------------------------

The Omegahat Project for Statistical Computing (http://www.omegahat.org/) provides a variety of open-source software for statistical applications, with special emphasis on web-based software, Java, the Java virtual machine, and distributed computing.  A CRAN style R package repository is available via http://www.omegahat.org/R/.

Currently, there are the following packages.

Aspell
     An interface to facilities in the aspell library.

CORBA
     Dynamic CORBA client/server facilities for R.  Connects to other CORBA-aware applications developed in arbitrary languages, on different machines and allows R functionality to be exported in the same way to other applications.

Combinations
     Compute the combinations of choosing r items from n elements.

IDocs
     Infrastructure for interactive documents.

OOP
     OOP style classes and methods for R and S-PLUS.  Object references and class-based method definition are supported in the style of languages such as Java and C++.

RCurl
     Allows one to compose HTTP requests to fetch URIs, post forms, etc., and process the results returned by the Web server.

RDCOMClient
     Provides dynamic client-side access to (D)COM applications from within R.

RDCOMEvents
     Provides facilities to use R functions and objects as handlers for DCOM events.

RDCOMServer
     Facilities for exporting S objects and functions as COM objects.

REmbeddedPostgres
     Allows R functions and objects to be used to implement SQL functions --- per-record, aggregate and trigger functions.

REventLoop
     An abstract event loop mechanism that is toolkit independent and can be used to to replace the R event loop.

RGdkPixbuf
     S language functions to access the facilities in the GdkPixbuf library for manipulating images.

RGnumeric
     A plugin for the Gnumeric spreadsheet that allows R functions to be called from cells within the sheet, automatic recalculation, etc.

RGtk
     Facilities in the S language for programming graphical interfaces using Gtk, the Gnome GUI toolkit.

RGtkBindingGenerator
     A meta-package which generates C and R code to provide bindings to a Gtk-based library.

RGtkExtra
     A collection of S functions that provide an interface to the widgets in the gtk+extra library such as the GtkSheet data-grid display, icon list, file list and directory tree.

RGtkGlade
     S language bindings providing an interface to Glade, the interactive Gnome GUI creator.

RGtkHTML
     A collection of S functions that provide an interface to creating and controlling an HTML widget which can be used to display HTML documents from files or content generated dynamically in S.

RGtkViewers
     A collection of tools for viewing different S objects, databases, class and widget hierarchies, S source file contents, etc.

RJavaDevice
     A graphics device for R that uses Java components and graphics. APIs.

RMatlab
     A bi-directional interface between R and Matlab.

RObjectTables
     The C and S code allows one to define R objects to be used as elements of the search path with their own semantics and facilities for reading and writing variables.  The objects implement a simple interface via R functions (either methods or closures) and can access external data, e.g., in other applications, languages, formats, ...

RSMethods
     An implementation of S version 4 methods and classes for R, consistent with the basic material in ``Programming with Data'' by John M. Chambers, 1998, Springer NY.

RSPerl
     An interface from R to an embedded, persistent Perl interpreter, allowing one to call arbitrary Perl subroutines, classes and methods.

RSPython
     Allows Python programs to invoke S functions, methods, etc., and S code to call Python functionality.

RXLisp
     An interface to call XLisp-Stat functions from within R.

Rstem
     Interface to Snowball implementation of Porter's word stemming algorithm.

SASXML
     Example for reading XML files in SAS 8.2 manner.

SJava
     An interface from R to Java to create and call Java objects and methods.

SLanguage
     Functions and C support utilities to support S language programming that can work in both R and S-PLUS.

SNetscape
     Plugin for Netscape and JavaScript.

SSOAP
     A client interface to SOAP (Simple Object Access Protocol) servers from within S.

SWinRegistry
     Provides access from within R to read and write the Windows registry.

SXalan
     Process XML documents using XSL functions implemented in R and dynamically substituting output from R.

Slcc
     Parses C source code, allowing one to analyze and automatically generate interfaces from S to that code, including the table of S-accessible native symbols, parameter count and type information, S constructors from C objects, call graphs, etc.

Sxslt
     An extension module for libxslt, the XML-XSL document translator, that allows XSL functions to be implemented via R functions.

XML
     Tools for reading XML documents and DTDs.

Add-on packages from Bioconductor
---------------------------------

The Bioconductor Project (http://www.bioconductor.org/) produces an open source software framework that will assist biologists and statisticians working in bioinformatics, with primary emphasis on inference using DNA microarrays.  A CRAN style R package repository is available via http://www.bioconductor.org/.

The following R packages are contained in the current release of Bioconductor, with more packages under development.

AnnBuilder
     Assemble and process genomic annotation data, from databases such as GenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSC Human Genome Project.

Biobase
     Object-oriented representation and manipulation of genomic data (S4 class structure).

Biostrings
     Class definitions and generics for biological sequences along with pattern matching algorithms.

ChromoViz
     Draw gene expression profile onto chromosome using SVG.

DEDS
     Differential Expression via Distance Summary for microarray data.

DNAcopy
     Segments DNA copy number data using circular binary segmentation to detect regions with abnormal copy number.

DynDoc
     Functionality to create and interact with dynamic documents, vignettes, and other navigable documents.

EBarrays
     Empirical Bayes tools for the analysis of replicated microarray data across multiple conditions.

GLAD
     Gain and Loss Analysis of DNA.

GOstats
     Tools for manipulating GO and microarrays.

GeneSpring
     Functions and class definitions to be able to read and write GeneSpring specific data objects and convert them to Bioconductor objects.

GeneTS
     A package for analysing multiple gene expression time series data. Currently, implements methods for cell cycle analysis and for inferring large sparse graphical Gaussian models.

GeneTraffic
     GeneTraffic R integration functions.

GraphAT
     Graph theoretic Association Tests.

HEM
     Heterogeneous Error Model for analysis of microarray data.

Icens
     Functions for computing the NPMLE for censored and truncated data.

KEGGSOAP
     Client-side SOAP access KEGG.

LPE
     Significance analysis of microarray data with small number of replicates using the Local Pooled Error (LPE) method.

MLInterfaces
     Uniform interfaces to machine learning code for the exprSet class from Bioconductor.

MeasurementError.cor
     Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation.

MergeMaid
     Cross-study comparison of gene expression array data.

OLIN
     Optimized Local Intensity-dependent Normalisation of two-color microarrays.

OLINgui
     Graphical user interface for OLIN.

PROcess
     Ciphergen SELDI-TOF processing.

RBGL
     An interface between the graph package and the Boost graph libraries, allowing for fast manipulation of graph objects in R.

RMAGEML
     Functionality to handle MAGEML documents.

ROC
     Receiver Operating Characteristic (ROC) approach for identifying genes that are differentially expressed in two types of samples.

RSNPper
     Interface to chip.org::SNPper for SNP-related data.

RdbiPgSQL
     Methods for accessing data stored in PostgreSQL tables.

Rdbi
     Generic framework for database access in R.

Resourcerer
     Read annotation data from TIGR Resourcerer or convert the annotation data into Bioconductor data package.

Rgraphviz
     An interface with Graphviz for plotting graph objects in R.

Ruuid
     Creates Universally Unique ID values (UUIDs) in R.

SAGElyzer
     Locates genes based on SAGE tags.

SNAData
     Data from the book ``Social Network Analysis'' by Wasserman & Faust, 1999.

aCGH
     Classes and functions for Array Comparative Genomic Hybridization data.

affy
     Methods for Affymetrix Oligonucleotide Arrays.

affyPLM
     For fitting Probe Level Models.

affycomp
     Graphics toolbox for assessment of Affymetrix expression measures.

affydata
     Affymetrix data for demonstration purposes.

affylmGUI
     Graphical User Interface for affy analysis using package limma.

affypdnn
     Probe Dependent Nearest Neighbors (PDNN) for the affy package.

altcdfenvs
     Utilities to handle cdfenvs.

annaffy
     Functions for handling data from Bioconductor Affymetrix annotation data packages.

annotate
     Associate experimental data in real time to biological metadata from web databases such as GenBank, LocusLink and PubMed.  Process and store query results.  Generate HTML reports of analyses.

arrayMagic
     Utilities for quality control and processing for two-color cDNA microarray data.

arrayQuality
     Performing print-run and array level quality assessment.

bim
     Bayesian interval mapping diagnostics:  functions to interpret QTLCart and Bmapqtl samples.

convert
     Convert Microarray Data Objects.

ctc
     Tools to export and import Tree and Cluster to other programs.

daMA
     Functions for the efficient design of factorial two-color microarray experiments and for the statistical analysis of factorial microarray data.

ecolitk
     Metadata and tools to work with E. coli.

edd
     Expression density diagnostics: graphical methods and pattern recognition algorithms for distribution shape classification.

exprExternal
     Implementation of exprSet using externalVectors.

externalVector
     Basic class definitions and generics for external pointer based vector objects for R.

factDesign
     A set of tools for analyzing data from factorial designed microarray experiments.  The functions can be used to evaluate appropriate tests of contrast and perform single outlier detection.

gcrma
     Background adjustment using sequence information.

genArise
     A tool for dual color microarray data.

genefilter
     Tools for sequentially filtering genes using a wide variety of filtering functions.  Example of filters include: number of missing value, coefficient of variation of expression measures, ANOVA p-value, Cox model p-values.  Sequential application of filtering functions to genes.

geneplotter
     Graphical tools for genomic data, for example for plotting expression data along a chromosome or producing color images of expression data matrices.

globaltest
     Testing globally whether a group of genes is significantly related to some clinical variable of interest.

goCluster
     Analysis of clustering results in conjunction with annotation data.

goTools
     Functions for description/comparison of oligo ID list using the Gene Ontology database.

gpls
     Classification using generalized partial least squares for two-group and multi-group classification.

graph
     Classes and tools for creating and manipulating graphs within R.

gtkWidgets
     Widgets built using RGtk.

hexbin
     Binning functions, in particular hexagonal bins for graphing.

hopach
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

iSPlot
     Link views that are based on the same data set.

impute
     Imputation for microarray data (currently KNN only).

limma
     Linear models for microarray data.

limmaGUI
     Graphical User Interface for package limma.

makecdfenv
     Two functions.  One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping.  The other creates a package that automatically loads that environment.

marray
     Exploratory analysis for two-color spotted microarray data.

matchprobes
     Tools for sequence matching of probes on arrays.

msbase
     Basic classes and methods for mass spectrometric mass list manipulation.

multtest
     Multiple testing procedures for controlling the family-wise error rate (FWER) and the false discovery rate (FDR).  Tests can be based on t- or F-statistics for one- and two-factor designs, and permutation procedures are available to estimate adjusted p-values.

nnNorm
     Spatial and intensity based normalization of cDNA microarray data based on robust neural nets.

pairseqsim
     Pairwise sequence alignment and scoring algorithms for global, local and overlap alignment with affine gap penalty.

pamr
     Pam: Prediction Analysis for Microarrays.

pickgene
     Adaptive gene picking for microarray expression data analysis.

prada
     Tools for analyzing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection.

qvalue
     Q-value estimation for false discovery rate control.

rama
     Robust Analysis of MicroArrays: robust estimation of cDNA microarray intensities with replicates using a Bayesian hierarchical model.

reposTools
     Tools for dealing with file repositories and allow users to easily install, update, and distribute packages, vignettes, and other files.

siggenes
     Identifying differentially expressed genes and estimating the False Discovery Rate (FDR) with both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

simpleaffy
     Very simple high level analysis of Affymetrix data.

splicegear
     A set of tools to work with alternative splicing.

stam
     STructured Analysis of Microarray data.

stepNorm
     Stepwise normalization functions for cDNA microarrays.

tkWidgets
     Widgets in Tcl/Tk that provide functionality for Bioconductor packages.

twilight
     Estimation of local false discovery rate.

vsn
     Calibration and variance stabilizing transformations for both Affymetrix and cDNA array data.

webbioc
     Integrated web interface for doing microarray analysis using several of the Bioconductor packages.

widgetInvoke
     Evaluation widgets for functions.

widgetTools
     Tools for creating Tcl/Tk widgets, i.e., small-scale graphical user interfaces.

Other add-on packages
---------------------

Jim Lindsey has written a collection of R packages for nonlinear regression and repeated measurements, consisting of event (event history procedures and models), gnlm (generalized nonlinear regression models), growth (multivariate normal and elliptically-contoured repeated measurements models), repeated (non-normal repeated measurements models), rmutil (utilities for nonlinear regression and repeated measurements), and stable (probability functions and generalized regression models for stable distributions).  All analyses in the new edition of his book ``Models for Repeated Measurements'' (1999, Oxford University Press) were carried out using these packages. Jim has also started dna, a package with procedures for the analysis of DNA sequences.  Jim's packages can be obtained from http://www.luc.ac.be/~jlindsey/rcode.html.

More code has been posted to the R-help mailing list, and can be obtained from the mailing list archive.

How can add-on packages be installed?
=====================================

(Unix only.)  The add-on packages on CRAN come as gzipped tar files named PKG_VERSION.tar.gz, which may in fact be ``bundles'' containing more than one package.  Provided that tar and gzip are available on your system, type

     $ R CMD INSTALL /path/to/PKG_VERSION.tar.gz

at the shell prompt to install to the library tree rooted at the first directory given in R_LIBS (see below) if this is set and non-null, and to the default library (the library subdirectory of R_HOME) otherwise.  (Versions of R prior to 1.3.0 installed to the default library by default.)

To install to another tree (e.g., your private one), use

     $ R CMD INSTALL -l LIB /path/to/PKG_VERSION.tar.gz

where LIB gives the path to the library tree to install to.

Even more conveniently, you can install and automatically update packages from within R if you have access to CRAN.  See the help page for CRAN.packages() for more information.

You can use several library trees of add-on packages.  The easiest way to tell R to use these is via the environment variable R_LIBS which should be a colon-separated list of directories at which R library trees are rooted.  You do not have to specify the default tree in R_LIBS.  E.g., to use a private tree in $HOME/lib/R and a public site-wide tree in /usr/local/lib/R-contrib, put

     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS

into your (Bourne) shell profile or even preferably, add the line

     R_LIBS="~/lib/R:/usr/local/lib/R-contrib"

your ~/.Renviron file.  (Note that no export statement is needed or allowed in this file; see the on-line help for Startup for more information.)

How can add-on packages be used?
================================

To find out which additional packages are available on your system, type

     library()

at the R prompt.

This produces something like

          Packages in `/home/me/lib/R':

          mystuff       My own R functions, nicely packaged but not documented

          Packages in `/usr/local/lib/R/library':

          KernSmooth    Functions for kernel smoothing for Wand & Jones (1995)
          MASS          Main Package of Venables and Ripley's MASS
          base          The R Base package
          boot          Bootstrap R (S-Plus) Functions (Canty)
          class         Functions for Classification
          cluster       Functions for clustering (by Rousseeuw et al.)
          datasets      The R datasets Package
          foreign       Read data stored by Minitab, S, SAS, SPSS, Stata, ...
          grDevices     The R Graphics Devices and Support for Colours and Fonts
          graphics      The R Graphics Package
          grid          The Grid Graphics Package
          lattice       Lattice Graphics
          methods       Formal Methods and Classes
          mgcv          GAMs with GCV smoothness estimation and GAMMs by REML/PQ
          nlme          Linear and nonlinear mixed effects models
          nnet          Feed-forward Neural Networks and Multinomial Log-Linear
                        Models
          rpart         Recursive partitioning
          spatial       Functions for Kriging and Point Pattern Analysis
          splines       Regression Spline Functions and Classes
          stats         The R Stats Package
          stats4        Statistical functions using S4 classes
          survival      Survival analysis, including penalised likelihood
          tcltk         Tcl/Tk Interface
          tools         Tools for Package Development
          utils         The R Utils Package

You can ``load'' the installed package PKG by

     library(PKG)

You can then find out which functions it provides by typing one of

     library(help = PKG)
     help(package = PKG)

You can unload the loaded package PKG by

     detach("package:PKG")

How can add-on packages be removed?
===================================

Use

     $ R CMD REMOVE PKG_1 ... PKG_N

to remove the packages PKG_1, ..., PKG_N from the library tree rooted at the first directory given in R_LIBS if this is set and non-null, and from the default library otherwise.  (Versions of R prior to 1.3.0 removed from the default library by default.)

To remove from library LIB, do

     $ R CMD REMOVE -l LIB PKG_1 ... PKG_N

How can I create an R package?
==============================

A package consists of a subdirectory containing the files DESCRIPTION and INDEX, and the subdirectories R, data, demo, exec, inst, man, src, and tests (some of which can be missing).  Optionally the package can also contain script files configure and cleanup which are executed before and after installation.

See section ``Creating R packages'' in Writing R Extensions, for details.  This manual is included in the R distribution, see What documentation exists for R?, and gives information on package structure, the configure and cleanup mechanisms, and on automated package checking and building.

R version 1.3.0 has added the function package.skeleton() which will set up directories, save data and code, and create skeleton help files for a set of R functions and datasets.

See What is CRAN?, for information on uploading a package to CRAN.

How can I contribute to R?
==========================

R is in active development and there is always a risk of bugs creeping in.  Also, the developers do not have access to all possible machines capable of running R.  So, simply using it and communicating problems is certainly of great value.

One place where functionality is still missing is the modeling software as described in ``Statistical Models in S'' (see What is S?); some of the nonlinear modeling code is not there yet.

The R Developer Page (http://developer.R-project.org/) acts as an intermediate repository for more or less finalized ideas and plans for the R statistical system.  It contains (pointers to) TODO lists, RFCs, various other writeups, ideas lists, and CVS miscellanea.

Many (more) of the packages available at the Statlib S Repository might be worth porting to R.

If you are interested in working on any of these projects, please notify Kurt Hornik.

R and Emacs
***********

Is there Emacs support for R?
=============================

There is an Emacs package called ESS (``Emacs Speaks Statistics'') which provides a standard interface between statistical programs and statistical processes.  It is intended to provide assistance for interactive statistical programming and data analysis. Languages supported include: S dialects (R, S 3/4, and S-PLUS 3.x/4.x/5.x/6.x/7.x), LispStat dialects (XLispStat, ViSta), SAS, Stata, and BUGS.

ESS grew out of the need for bug fixes and extensions to S-mode 4.8 (which was a GNU Emacs interface to S/S-PLUS version 3 only).  The current set of developers desired support for XEmacs, R, S4, and MS Windows.  In addition, with new modes being developed for R, Stata, and SAS, it was felt that a unifying interface and framework for the user interface would benefit both the user and the developer, by helping both groups conform to standard Emacs usage.  The end result is an increase in efficiency for statistical programming and data analysis, over the usual tools.

R support contains code for editing R source code (syntactic indentation and highlighting of source code, partial evaluations of code, loading and error-checking of code, and source code revision maintenance) and documentation (syntactic indentation and highlighting of source code, sending examples to running ESS process, and previewing), interacting with an inferior R process from within Emacs (command-line editing, searchable command history, command-line completion of R object and file names, quick access to object and search lists, transcript recording, and an interface to the help system), and transcript manipulation (recording and saving transcript files, manipulating and editing saved transcripts, and re-evaluating commands from transcript files).

The latest stable version of ESS are available via CRAN or the ESS web page (http://ESS.R-project.org/).  The HTML version of the documentation can be found at http://stat.ethz.ch/ESS/.

ESS comes with detailed installation instructions.

For help with ESS, send email to ESS-help@stat.math.ethz.ch.

Please send bug reports and suggestions on ESS to ESS-bugs@stat.math.ethz.ch.  The easiest way to do this from is within Emacs by typing M-x ess-submit-bug-report or using the [ESS] or [iESS] pulldown menus.

Should I run R from within Emacs?
=================================

Yes, definitely.  Inferior R mode provides a readline/history mechanism, object name completion, and syntax-based highlighting of the interaction buffer using Font Lock mode, as well as a very convenient interface to the R help system.

Of course, it also integrates nicely with the mechanisms for editing R source using Emacs.  One can write code in one Emacs buffer and send whole or parts of it for execution to R; this is helpful for both data analysis and programming.  One can also seamlessly integrate with a revision control system, in order to maintain a log of changes in your programs and data, as well as to allow for the retrieval of past versions of the code.

In addition, it allows you to keep a record of your session, which can also be used for error recovery through the use of the transcript mode.

To specify command line arguments for the inferior R process, use C-u M-x R for starting R.

Debugging R from within Emacs
=============================

To debug R ``from within Emacs'', there are several possibilities.  To use the Emacs GUD (Grand Unified Debugger) library with the recommended debugger GDB, type M-x gdb and give the path to the R binary as argument.  At the gdb prompt, set R_HOME and other environment variables as needed (using e.g. set env R_HOME /path/to/R/, but see also below), and start the binary with the desired arguments (e.g., run --quiet).

If you have ESS, you can do C-u M-x R RET - d SPC g d b RET to start an inferior R process with arguments -d gdb.

A third option is to start an inferior R process via ESS (M-x R) and then start GUD (M-x gdb) giving the R binary (using its full path name) as the program to debug.  Use the program ps to find the process number of the currently running R process then use the attach command in gdb to attach it to that process.  One advantage of this method is that you have separate *R* and *gud-gdb* windows.  Within the *R* window you have all the ESS facilities, such as object-name completion, that we know and love.

When using GUD mode for debugging from within Emacs, you may find it most convenient to use the directory with your code in it as the current working directory and then make a symbolic link from that directory to the R binary.  That way .gdbinit can stay in the directory with the code and be used to set up the environment and the search paths for the source, e.g. as follows:

     set env R_HOME /opt/R
     set env R_PAPERSIZE letter
     set env R_PRINTCMD lpr
     dir /opt/R/src/appl
     dir /opt/R/src/main
     dir /opt/R/src/nmath
     dir /opt/R/src/unix

R Miscellanea
*************

How can I set components of a list to NULL?
===========================================

You can use

     x[i] <- list(NULL)

to set component i of the list x to NULL, similarly for named components.  Do not set x[i] or x[[i]] to NULL, because this will remove the corresponding component from the list.

For dropping the row names of a matrix x, it may be easier to use rownames(x) <- NULL, similarly for column names.

How can I save my workspace?
============================

save.image() saves the objects in the user's .GlobalEnv to the file .RData in the R startup directory.  (This is also what happens after q("yes").)  Using save.image(FILE) one can save the image under a different name.

How can I clean up my workspace?
================================

To remove all objects in the currently active environment (typically .GlobalEnv), you can do

     rm(list = ls(all = TRUE))

(Without all = TRUE, only the objects with names not starting with a . are removed.)

How can I get eval() and D() to work?
=====================================

Strange things will happen if you use eval(print(x), envir = e) or D(x^2, "x").  The first one will either tell you that "x" is not found, or print the value of the wrong x. The other one will likely return zero if x exists, and an error otherwise.

This is because in both cases, the first argument is evaluated in the calling environment first.  The result (which should be an object of mode "expression" or "call") is then evaluated or differentiated.  What you (most likely) really want is obtained by ``quoting'' the first argument upon surrounding it with expression().  For example,

     R> D(expression(x^2), "x")
     2 * x

Although this behavior may initially seem to be rather strange, is perfectly logical.  The ``intuitive'' behavior could easily be implemented, but problems would arise whenever the expression is contained in a variable, passed as a parameter, or is the result of a function call.  Consider for instance the semantics in cases like

     D2 <- function(e, n) D(D(e, n), n)

or

     g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
     g(a * b)

See the help page for deriv() for more examples.

Why do my matrices lose dimensions?
===================================

When a matrix with a single row or column is created by a subscripting operation, e.g., row <- mat[2, ], it is by default turned into a vector.  In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is created by subscripting it will be coerced into a 2 x 3 x 4 array, losing the unnecessary dimension.  After much discussion this has been determined to be a feature.

To prevent this happening, add the option drop = FALSE to the subscripting.  For example,

     rowmatrix <- mat[2, , drop = FALSE]  # creates a row matrix
     colmatrix <- mat[, 2, drop = FALSE]  # creates a column matrix
     a <- b[1, 1, 1, drop = FALSE]        # creates a 1 x 1 x 1 array

The drop = FALSE option should be used defensively when programming.  For example, the statement

     somerows <- mat[index, ]

will return a vector rather than a matrix if index happens to have length 1, causing errors later in the code.  It should probably be rewritten as

     somerows <- mat[index, , drop = FALSE]

How does autoloading work?
==========================

R has a special environment called .AutoloadEnv.  Using autoload(NAME, PKG), where NAME and PKG are strings giving the names of an object and the package containing it, stores some information in this environment.  When R tries to evaluate NAME, it loads the corresponding package PKG and reevaluates NAME in the new package's environment.

Using this mechanism makes R behave as if the package was loaded, but does not occupy memory (yet).

See the help page for autoload() for a very nice example.

How should I set options?
=========================

The function options() allows setting and examining a variety of global ``options'' which affect the way in which R computes and displays its results.  The variable .Options holds the current values of these options, but should never directly be assigned to unless you want to drive yourself crazy---simply pretend that it is a ``read-only'' variable.

For example, given

     test1 <- function(x = pi, dig = 3) {
       oo <- options(digits = dig); on.exit(options(oo));
       cat(.Options$digits, x, "\n")
     }
     test2 <- function(x = pi, dig = 3) {
       .Options$digits <- dig
       cat(.Options$digits, x, "\n")
     }

we obtain:

     R> test1()
     3 3.14
     R> test2()
     3 3.141593

What is really used is the global value of .Options, and using options(OPT = VAL) correctly updates it.  Local copies of .Options, either in .GlobalEnv or in a function environment (frame), are just silently disregarded.

How do file names work in Windows?
==================================

As R uses C-style string handling, \ is treated as an escape character, so that for example one can enter a newline as \n. When you really need a \, you have to escape it with another \.

Thus, in filenames use something like "c:\\data\\money.dat".  You can also replace \ by / ("c:/data/money.dat").

Why does plotting give a color allocation error?
================================================

On an X11 device, plotting sometimes, e.g., when running demo("image"), results in ``Error: color allocation error''. This is an X problem, and only indirectly related to R.  It occurs when applications started prior to R have used all the available colors. (How many colors are available depends on the X configuration; sometimes only 256 colors can be used.)

One application which is notorious for ``eating'' colors is Netscape. If the problem occurs when Netscape is running, try (re)starting it with either the -no-install (to use the default colormap) or the -install (to install a private colormap) option.

You could also set the colortype of X11() to "pseudo.cube" rather than the default "pseudo".  See the help page for X11() for more information.

How do I convert factors to numeric?
====================================

It may happen that when reading numeric data into R (usually, when reading in a file), they come in as factors.  If f is such a factor object, you can use

     as.numeric(as.character(f))

to get the numbers back.  More efficient, but harder to remember, is

     as.numeric(levels(f))[as.integer(f)]

In any case, do not call as.numeric() or their likes directly for the task at hand (as as.numeric() or unclass() give the internal codes).

Are Trellis displays implemented in R?
======================================

The recommended package lattice (which is based on another recommended package, grid) provides graphical functionality that is compatible with most Trellis commands.

You could also look at coplot() and dotchart() which might do at least some of what you want.  Note also that the R version of pairs() is fairly general and provides most of the functionality of splom(), and that R's default plot method has an argument asp allowing to specify (and fix against device resizing) the aspect ratio of the plot.

(Because the word ``Trellis'' has been claimed as a trademark we do not use it in R.  The name ``lattice'' has been chosen for the R equivalent.)

What are the enclosing and parent environments?
===============================================

Inside a function you may want to access variables in two additional environments: the one that the function was defined in (``enclosing''), and the one it was invoked in (``parent'').

If you create a function at the command line or load it in a package its enclosing environment is the global workspace.  If you define a function f() inside another function g() its enclosing environment is the environment inside g().  The enclosing environment for a function is fixed when the function is created.  You can find out the enclosing environment for a function f() using environment(f).

The ``parent'' environment, on the other hand, is defined when you invoke a function.  If you invoke lm() at the command line its parent environment is the global workspace, if you invoke it inside a function f() then its parent environment is the environment inside f().  You can find out the parent environment for an invocation of a function by using parent.frame() or sys.frame(sys.parent()).

So for most user-visible functions the enclosing environment will be the global workspace, since that is where most functions are defined.  The parent environment will be wherever the function happens to be called from.  If a function f() is defined inside another function g() it will probably be used inside g() as well, so its parent environment and enclosing environment will probably be the same.

Parent environments are important because things like model formulas need to be evaluated in the environment the function was called from, since that's where all the variables will be available.  This relies on the parent environment being potentially different with each invocation.

Enclosing environments are important because a function can use variables in the enclosing environment to share information with other functions or with other invocations of itself (see the section on lexical scoping).  This relies on the enclosing environment being the same each time the function is invoked.  (In C this would be done with static variables.)

Scoping is hard.  Looking at examples helps.  It is particularly instructive to look at examples that work differently in R and S and try to see why they differ.  One way to describe the scoping differences between R and S is to say that in S the enclosing environment is always the global workspace, but in R the enclosing environment is wherever the function was created.

How can I substitute into a plot label?
=======================================

Often, it is desired to use the value of an R object in a plot label, e.g., a title.  This is easily accomplished using paste() if the label is a simple character string, but not always obvious in case the label is an expression (for refined mathematical annotation).  In such a case, either use parse() on your pasted character string or use substitute() on an expression.  For example, if ahat is an estimator of your parameter a of interest, use

     title(substitute(hat(a) == ahat, list(ahat = ahat)))

(note that it is == and not =).  Sometimes bquote() gives a more compact form, e.g.,

     title(bquote(hat(a) = .(ahat)))

where subexpressions enclosed in .() are replaced by their values.

There are more worked examples in the mailing list achives.

What are valid names?
=====================

When creating data frames using data.frame() or read.table(), R by default ensures that the variable names are syntactically valid.  (The argument check.names to these functions controls whether variable names are checked and adjusted by make.names() if needed.)

To understand what names are ``valid'', one needs to take into account that the term ``name'' is used in several different (but related) ways in the language:

 1. A syntactic name is a string the parser interprets as this type of expression.  It consists of letters, numbers, and the dot and (for version of R at least 1.9.0) underscore characters, and starts with either a letter or a dot not followed by a number.  Reserved words are not syntactic names.

 2. An object name is a string associated with an object that is assigned in an expression either by having the object name on the left of an assignment operation or as an argument to the assign() function.  It is usually a syntactic name as well, but can be any non-empty string if it is quoted (and it is always quoted in the call to assign()).

 3. An argument name is what appears to the left of the equals sign when supplying an argument in a function call (for example, f(trim=.5)).  Argument names are also usually syntactic names, but again can be anything if they are quoted.

 4. An element name is a string that identifies a piece of an object (a component of a list, for example.)  When it is used on the right of the $ operator, it must be a syntactic name, or quoted. Otherwise, element names can be any strings.  (When an object is used as a database, as in a call to eval() or attach(), the element names become object names.)

 5. Finally, a file name is a string identifying a file in the operating system for reading, writing, etc.  It really has nothing much to do with names in the language, but it is traditional to call these strings file ``names''.

Are GAMs implemented in R?
==========================

Package gam from CRAN implements all the Generalized Additive Models (GAM) functionality as described in the GAM chapter of the White Book.  In particular, it implements backfitting with both local regression and smoothing splines, and is extendable.  There is a gam() function for GAMs in package mgcv, but it is not an exact clone of what is described in the White Book (no lo() for example).  Package gss can fit spline-based GAMs too.  And if you can accept regression splines you can use glm().  For gaussian GAMs you can use bruto() from package mda.

Why is the output not printed when I source() a file?
=====================================================

Most R commands do not generate any output. The command

     1+1

computes the value 2 and returns it; the command

     summary(glm(y~x+z, family=binomial))

fits a logistic regression model, computes some summary information and returns an object of class "summary.glm" (see How should I write summary methods?).

If you type 1+1 or summary(glm(y~x+z, family=binomial)) at the command line the returned value is automatically printed (unless it is invisible()), but in other circumstances, such as in a source()d file or inside a function it isn't printed unless you specifically print it.

To print the value use

     print(1+1)

or

     print(summary(glm(y~x+z, family=binomial)))

instead, or use source(FILE, echo=TRUE).

Why does outer() behave strangely with my function?
===================================================

As the help for outer() indicates, it does not work on arbitrary functions the way the apply() family does.  It requires functions that are vectorized to work elementwise on arrays.  As you can see by looking at the code, outer(x, y, FUN) creates two large vectors containing every possible combination of elements of x and y and then passes this to FUN all at once.  Your function probably cannot handle two large vectors as parameters.

If you have a function that cannot handle two vectors but can handle two scalars, then you can still use outer() but you will need to wrap your function up first, to simulate vectorized behavior.  Suppose your function is

     foo <- function(x, y, happy) {
       stopifnot(length(x) == 1, length(y) == 1) # scalars only!
       (x + y) * happy
     }

If you define the general function

     wrapper <- function(x, y, my.fun, ...) {
       sapply(seq(along = x), FUN = function(i) my.fun(x[i], y[i], ...))
     }

then you can use outer() by writing, e.g.,

     outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)

Why does the output from anova() depend on the order of factors in the model?
=============================================================================

In a model such as ~A+B+A:B, R will report the difference in sums of squares between the models ~1, ~A, ~A+B and ~A+B+A:B.  If the model were ~B+A+A:B, R would report differences between ~1, ~B, ~A+B, and ~A+B+A:B . In the first case the sum of squares for A is comparing ~1 and ~A, in the second case it is comparing ~B and ~B+A.  In a non-orthogonal design (i.e., most unbalanced designs) these comparisons are (conceptually and numerically) different.

Some packages report instead the sums of squares based on comparing the full model to the models with each factor removed one at a time (the famous `Type III sums of squares' from SAS, for example).  These do not depend on the order of factors in the model.  The question of which set of sums of squares is the Right Thing provokes low-level holy wars on R-help from time to time.

There is no need to be agitated about the particular sums of squares that R reports.  You can compute your favorite sums of squares quite easily.  Any two models can be compared with anova(MODEL1, MODEL2), and drop1(MODEL1) will show the sums of squares resulting from dropping single terms.

How do I produce PNG graphics in batch mode?
============================================

Under Unix, the png() device uses the X11 driver, which is a problem in batch mode or for remote operation.  If you have Ghostscript you can use bitmap(), which produces a PostScript file then converts it to any bitmap format supported by ghostscript.  On some installations this produces ugly output, on others it is perfectly satisfactory.  In theory one could also use Xvfb from X.Org (http://www.x.org/Downloads.html), which provides an X server with no display.

How can I get command line editing to work?
===========================================

The Unix command-line interface to R can only provide the inbuilt command line editor which allows recall, editing and re-submission of prior commands provided that the GNU readline library is available at the time R is configured for compilation.  Note that the `development' version of readline including the appropriate headers is needed: users of Linux binary distributions will need to install packages such as libreadline-dev (Debian) or readline-devel (Red Hat).

How can I turn a string into a variable?
========================================

If you have

     varname <- c("a", "b", "d")

you can do

     get(varname[1]) + 2

for

     a + 2

or

     assign(varname[1], 2 + 2)

for

     a <- 2 + 2

or

     eval(substitute(lm(y ~ x + variable),
                     list(variable = as.name(varname[1]))

for

     lm(y ~ x + a)

At least in the first two cases it is often easier to just use a list, and then you can easily index it by name

     vars <- list(a = 1:10, b = rnorm(100), d = LETTERS)
     vars[["a"]]

without any of this messing about.

Why do lattice/trellis graphics not work?
=========================================

The most likely reason is that you forgot to tell R to display the graph.  Lattice functions such as xyplot() create a graph object, but do not display it (the same is true of Trellis graphics in S-PLUS).  The print() method for the graph object produces the actual display.  When you use these functions interactively at the command line, the result is automatically printed, but in source() or inside your own functions you will need an explicit print() statement.

How can I sort the rows of a data frame?
========================================

To sort the rows within a data frame, with respect to the values in one or more of the columns, simply use order().

Why does the help.start() search engine not work?
=================================================

The browser-based search engine in help.start() utilizes a Java applet.  In order for this to function properly, a compatible version of Java must installed on your system and linked to your browser, and both Java and JavaScript need to be enabled in your browser.

There have been a number of compatibility issues with versions of Java and of browsers. For further details please consult section ``Enabling search in HTML help'' in R Installation and Administration.  This manual is included in the R distribution, see What documentation exists for R?, and its HTML version is linked from the HTML search page.

Why did my .Rprofile stop working when I updated R?
===================================================

Did you read the NEWS file?  For functions that are not in the base package you need to specify the correct package namespace, since the code will be run before the packages are loaded.  E.g.,

     ps.options(horizontal = FALSE)
     help.start()

needs to be

     grDevices::ps.options(horizontal = FALSE)
     utils::help.start()

(graphics::ps.options(horizontal = FALSE) in R 1.9.x).

Where have all the methods gone?
================================

Many functions, particularly S3 methods, are now hidden in namespaces. This has the advantage that they cannot be called inadvertantly with arguments of the wrong class, but it makes them harder to view.

To see the code for an S3 method (e.g., [.terms) use

     getS3method("[", "terms")

To see the code for an unexported function foo() in the namespace of package "bar" use bar:::foo.  Don't use these constructions to call unexported functions in your own code---they are probably unexported for a reason and may change without warning.

How can I create rotated axis labels?
=====================================

To rotate axis labels (using base graphics), you need to use text(), rather than mtext(), as the latter does not support par("srt").

     ## Increase bottom margin to make room for rotated labels
     par(mar = c(7, 4, 4, 2) + 0.1)
     ## Create plot with no x axis and no x axis label
     plot(1 : 8, xaxt = "n",  xlab = "")
     ## Set up x axis with tick marks alone
     axis(1, labels = FALSE)
     ## Create some text labels
     labels <- paste("Label", 1:8, sep = " ")
     ## Plot x axis labels at default tick marks
     text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1,
          labels = labels, xpd = TRUE)
     ## Plot x axis label at line 6 (of 7)
     mtext(1, text = "X Axis Label", line = 6)

When plotting the x axis labels, we use srt = 45 for text rotation angle, adj = 1 to place the right end of text at the tick marks, and xpd = TRUE to allow for text outside the plot region.  You can adjust the value of the 0.25 offset as required to move the axis labels up or down relative to the x axis.  See ?par for more information.

Also see Figure 1 and associated code in Paul Murrell (2003), ``Integrating grid Graphics Output with Base Graphics Output'', R News, 3/2, 7--12.

Why is read.table() so inefficient?
===================================

By default, read.table() needs to read in everything as character data, and then try to figure out which variables to convert to numerics or factors.  For a large data set, this takes condiderable amounts of time and memory.  Performance can substantially be improved by using the colClasses argument to specify the classes to be assumed for the columns of the table.

What is the difference between package and library?
===================================================

A package is a standardized collection of material extending R, e.g. providing code, data, or documentation.  A library is a place (directory) where R knows to find packages it can use (i.e., which were installed).  R is told to use a package (to ``load'' it and add it to the search path) via calls to the function library. I.e., library() is employed to load a package from libraries containing packages.

See R Add-On Packages, for more details.  See also Uwe Ligges (2003), ``R Help Desk: Package Management'', R News, 3/3, 37--39.

I installed a package but the functions are not there
=====================================================

To actually use the package, it needs to be loaded using library().

See R Add-On Packages and What is the difference between package and library? for more information.

Why doesn't R think these numbers are equal?
============================================

The only numbers that can be represented exactly in R's numeric type are integers and fractions whose denominator is a power of 2.  Other numbers have to be rounded to (typically) 53 binary digits accuracy.  As a result, two floating point numbers will not reliably be equal unless they have been computed by the same algorithm, and not always even then.  For example

     R> a <- sqrt(2)
     R> a * a == 2
     [1] FALSE
     R> a * a - 2
     [1] 4.440892e-16

The function all.equal() compares two objects using a numeric tolerance of .Machine$double.eps ^ 0.5.  If you want much greater accuracy than this you will need to consider error propagation carefully.

For more information, see e.g. David Goldberg (1991), ``What Every Computer Scientist Should Know About Floating-Point Arithmetic'', ACM Computing Surveys, 23/1, 5--48, also available via http://docs.sun.com/source/806-3568/ncg_goldberg.html.

How can I capture or ignore errors in a long simulation?
========================================================

Use try(), which returns an object of class "try-error" instead of an error, or preferably tryCatch(), where the return value can be configured more flexibly.  For example

     beta[i,] <- tryCatch(coef(lm(formula, data)),
                          error = function(e) rep(NaN, 4))

would return the coefficients if the lm() call succeeded and would return c(NaN, NaN, NaN, NaN) if it failed (presumably there are supposed to be 4 coefficients in this example).

R Programming
*************

How should I write summary methods?
===================================

Suppose you want to provide a summary method for class "foo". Then summary.foo() should not print anything, but return an object of class "summary.foo", and you should write a method print.summary.foo() which nicely prints the summary information and invisibly returns its object.  This approach is preferred over having summary.foo() print summary information and return something useful, as sometimes you need to grab something computed by summary() inside a function or similar.  In such cases you don't want anything printed.

How can I debug dynamically loaded code?
========================================

Roughly speaking, you need to start R inside the debugger, load the code, send an interrupt, and then set the required breakpoints.

See section ``Finding entry points in dynamically loaded code'' in Writing R Extensions.  This manual is included in the R distribution, see What documentation exists for R?.

How can I inspect R objects when debugging?
===========================================

The most convenient way is to call R_PV from the symbolic debugger.

See section ``Inspecting R objects when debugging'' in Writing R Extensions.

How can I change compilation flags?
===================================

Suppose you have C code file for dynloading into R, but you want to use R CMD SHLIB with compilation flags other than the default ones (which were determined when R was built).

Starting with R 2.1.0, users can provide personal Makevars configuration files in $HOME/.R to override the default flags. See section ``Add-on packages'' in R Installation and Administration.

For earlier versions of R, you could change the file $R_HOME/etc/Makeconf to reflect your preferences, or (at least for systems using GNU Make) override them by the environment variable MAKEFLAGS. See section ``Creating shared objects'' in Writing R Extensions.

How can I debug S4 methods?
===========================

Use the trace() function with argument signature= to add calls to the browser or any other code to the method that will be dispatched for the corresponding signature.  See ?trace for details.

R Bugs
******

What is a bug?
==============

If R executes an illegal instruction, or dies with an operating system error message that indicates a problem in the program (as opposed to something like ``disk full''), then it is certainly a bug.  If you call .C(), .Fortran(), .External() or .Call() (or .Internal()) yourself (or in a function you wrote), you can always crash R by using wrong argument types (modes).  This is not a bug.

Taking forever to complete a command can be a bug, but you must make certain that it was really R's fault.  Some commands simply take a long time.  If the input was such that you know it should have been processed quickly, report a bug.  If you don't know whether the command should take a long time, find out by looking in the manual or by asking for assistance.

If a command you are familiar with causes an R error message in a case where its usual definition ought to be reasonable, it is probably a bug. If a command does the wrong thing, that is a bug.  But be sure you know for certain what it ought to have done.  If you aren't familiar with the command, or don't know for certain how the command is supposed to work, then it might actually be working right.  Rather than jumping to conclusions, show the problem to someone who knows for certain.

Finally, a command's intended definition may not be best for statistical analysis.  This is a very important sort of problem, but it is also a matter of judgment.  Also, it is easy to come to such a conclusion out of ignorance of some of the existing features.  It is probably best not to complain about such a problem until you have checked the documentation in the usual ways, feel confident that you understand it, and know for certain that what you want is not available.  If you are not sure what the command is supposed to do after a careful reading of the manual this indicates a bug in the manual.  The manual's job is to make everything clear.  It is just as important to report documentation bugs as program bugs.  However, we know that the introductory documentation is seriously inadequate, so you don't need to report this.

If the online argument list of a function disagrees with the manual, one of them must be wrong, so report the bug.

How to report a bug
===================

When you decide that there is a bug, it is important to report it and to report it in a way which is useful.  What is most useful is an exact description of what commands you type, starting with the shell command to run R, until the problem happens.  Always include the version of R, machine, and operating system that you are using; type version in R to print this.

The most important principle in reporting a bug is to report facts, not hypotheses or categorizations.  It is always easier to report the facts, but people seem to prefer to strain to posit explanations and report them instead.  If the explanations are based on guesses about how R is implemented, they will be useless; others will have to try to figure out what the facts must have been to lead to such speculations.  Sometimes this is impossible.  But in any case, it is unnecessary work for the ones trying to fix the problem.

For example, suppose that on a data set which you know to be quite large the command

     R> data.frame(x, y, z, monday, tuesday)

never returns.  Do not report that data.frame() fails for large data sets.  Perhaps it fails when a variable name is a day of the week. If this is so then when others got your report they would try out the data.frame() command on a large data set, probably with no day of the week variable name, and not see any problem.  There is no way in the world that others could guess that they should try a day of the week variable name.

Or perhaps the command fails because the last command you used was a method for "["() that had a bug causing R's internal data structures to be corrupted and making the data.frame() command fail from then on.  This is why others need to know what other commands you have typed (or read from your startup file).

It is very useful to try and find simple examples that produce apparently the same bug, and somewhat useful to find simple examples that might be expected to produce the bug but actually do not.  If you want to debug the problem and find exactly what caused it, that is wonderful.  You should still report the facts as well as any explanations or solutions.  Please include an example that reproduces the problem, preferably the simplest one you have found.

Invoking R with the --vanilla option may help in isolating a bug.  This ensures that the site profile and saved data files are not read.

Before you actually submit a bug report, you should check whether the bug has already been reported and/or fixed.  First, try the ``Search Existing Reports'' facility in the Bug Tracking page at http://bugs.R-project.org/.  Second, consult https://svn.R-project.org/R/trunk/NEWS, which records changes that will appear in the next release of R, including some bug fixes that do not appear in Bug Tracking.  (Windows users should additionally consult https://svn.R-project.org/R/trunk/src/gnuwin32/CHANGES.) Third, if possible try the current r-patched or r-devel version of R. If a bug has already been reported or fixed, please do not submit further bug reports on it.

On Unix systems a bug report can be generated using the function bug.report().  This automatically includes the version information and sends the bug to the correct address.  Alternatively the bug report can be emailed to R-bugs@R-project.org or submitted to the Web page at http://bugs.R-project.org/.

Bug reports on contributed packages should be sent first to the package maintainer, and only submitted to the R-bugs repository by package maintainers, mentioning the package in the subject line.

There is a section of the bug repository for suggestions for enhancements for R labelled wishlist.  Suggestions can be submitted in the same ways as bugs, but please ensure that the subject line makes clear that this is for the wishlist and not a bug report, for example by starting with Wishlist:.

Comments on and suggestions for the Windows port of R should be sent to R-windows@R-project.org.

Corrections to and comments on message translation should be sent to the last translator (listed at the top of the appropriate .po file) or to the translation team as listed at http://developer.r-project.org/TranslationTeams.html.

Acknowledgments
***************

Of course, many many thanks to Robert and Ross for the R system, and to the package writers and porters for adding to it.

Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D. Ripley, Anthony Rossini, and Andreas Weingessel for their comments which helped me improve this FAQ.

More to some soon ...