R FAQ

Frequently Asked Questions on R

Version 2.8.2009-03-18

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--2009 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, U.S.A.

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 (2009), ``The R FAQ'', and give the above, official URL and the ISBN 3-900051-08-9:

     @Misc{,
       author        = {Kurt Hornik},
       title         = {The {R} {FAQ}},
       year          = {2008},
       note          = {{ISBN} 3-900051-08-9},
       url           = {http://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
     }

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 (http://www.gnu.org/philosophy/free-sw.html) distributed under a GNU-style copyleft (http://www.gnu.org/copyleft/copyleft.html), and an official part of the GNU (http://www.gnu.org/) 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, i386-freebsd, 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 2.8.1.  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 2000 and later (including 64-bit versions of Windows) on ix86 and x86_64 chips. 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://CRAN.R-project.org/bin/windows/base/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 inside a disk image named R.dmg.  Once downloaded 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 a R.app Mac OS X GUI.  Inside bin/macosx/powerpc/contrib/X.Y there are prebuilt binary packages (for powerpc version of Mac OS X) 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 R.app 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/amd64   etch-cran/sarge   Johannes Ranke
     Red Hat    i386/x86_64   fedora7/fedora8/fedora9  Martyn Plummer
       i386/x86_64   el4/el5   Bob Kinney
     SuSE       i586/x86_64   10.2/10.3/11.0   Detlef Steuer
     Ubuntu     i386     dapper/gutsy/hardy/intrepid   Vincent Goulet
       amd64    dapper/gutsy/hardy/intrepid   Michael Rutter

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 at least the stable distribution of Debian are provided by Johannes Ranke, and available from CRAN.  See http://CRAN.R-project.org/bin/linux/debian/README for details on R Debian packages and installing the backports, which should also be suitable for other Debian derivatives.  Native backports for Ubuntu are provided by Vincent Goulet and Michael Rutter.

On SUSE, you can set up an installation source for R within Yast by setting (e.g.)

     Protocol: HTTP
     Server name: software.openSUSE.org
     Directory: /download/home:/dsteuer/openSUSE_10.2/

With this setting, online updates will check for new versions of R.

The bin/solaris directory of a CRAN site contains binary packages for Solaris on the SPARC and x64 platforms, provided by Mithun Sridharan.

No other binary distributions are currently publically available via CRAN.

A ``live'' Linux distribution with a particular focus on R is Quantian, which provides a directly bootable and self-configuring ``Live DVD'' containing numerous applications of interests to scientists and researchers, including several hundred CRAN and Bioconductor packages, the ``ESS'' extensions for Emacs, the ``JGR'' Java GUI for R, the Ggobi visualization tool as well as several other R interfaces. The Quantian website at http://dirk.eddelbuettel.com/quantian/ contains more details as well download information.

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).

  * ``R Internals'' (R-ints) is a guide to R's internal structures. (Added in R 2.4.0.)

An annotated bibliography (BibTeX format) of R-related publications can be found at

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

Books on R by R Core Team members include

     John M. Chambers (2008), ``Software for Data Analysis: Programming with R''.  Springer, New York, ISBN 978-0-387-75935-7, http://stat.stanford.edu/~jmc4/Rbook/.

     Peter Dalgaard (2008), ``Introductory Statistics with R'', 2nd edition. Springer, ISBN 978-0-387-79053-4, http://www.biostat.ku.dk/~pd/ISwR.html.

     Robert Gentleman (2008), ``R Programming for Bioinformatics''.  Chapman & Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7, http://www.bioconductor.org/pub/RBioinf/.

     Stefano M. Iacus (2008), ``Simulation and Inference for Stochastic Differential Equations: With R Examples''. Springer, New York, ISBN 978-0-387-75838-1.

     Deepayan Sarkar (2007), ``Lattice: Multivariate Data Visualization with R''. Springer, New York, ISBN 978-0-387-75968-5.

     W. John Braun and Duncan J. Murdoch (2007), ``A First Course in Statistical Programming with R''.  Cambridge University Press, Cambridge, ISBN 978-0521872652.

     P. Murrell (2005), ``R Graphics'', Chapman & Hall/CRC, ISBN: 1-584-88486-X, http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.

     William N. Venables and Brian D. Ripley (2002), ``Modern Applied Statistics with S'' (4th edition).  Springer, ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4/.

     Jose C. Pinheiro and Douglas M. Bates (2000), ``Mixed-Effects Models in S and S-Plus''. Springer, ISBN 0-387-98957-0.

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.

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         = 2008,
       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 with a web browser, using an NNTP news reader, 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 Wirtschaftsuniversität 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/
     (WU Wien, Austria)
     http://cran.au.R-project.org/
     (PlanetMirror, Australia)
     http://cran.br.R-project.org/
     (Universidade Federal do 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.pt.R-project.org/
     (Universidade do Porto, Portugal)
     http://cran.uk.R-project.org/
     (U of Bristol, United Kingdom)
     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-2, GPL-3, 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.  Unless --no-environ was given on the command line, R searches for site and user files to process for setting environment variables.  Then, R searches for a site-wide startup profile unless the command line option --no-site-file was given.  This code is loaded in package base.  Then, unless --no-init-file was given, R searches for a user profile file, 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-data or --no-restore were specified).  Next, a function .First() is run if found on the search path.  Finally, function .First.sys in the base package is run.  When terminating an R session, by default a function .Last is run if found on the search path, followed by .Last.sys.  If needed, the functions .First() and .Last() should be defined in the appropriate startup profiles.  See the help pages for .First and .Last for more details.

  * 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.

  * S has a two-argument version of atan and no atan2.  A call in S such as atan(x1, x2) is equivalent to R's atan2(x1, x2).  However, beware of named arguments since S's atan(x = a, y = b) is equivalent to R's atan2(y = a, x = b) with the meanings of x and y interchanged.  (R used to have undocumented support for a two-argument atan with positional arguments, but this has been withdrawn to avoid further confusion.)

  * Numeric constants with no fractional and exponent (i.e., only integer) part are taken as integer in S-PLUS 6.x or later, but as double in R.

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?
===============

For a very long time, there was no such thing.

XLSolutions Corporation (http://www.xlsolutions-corp.com/) is currently beta testing a commercially supported version of R named R+ (read R plus).

REvolution Computing (http://www.revolution-computing.com/) has released REvolution R (http://www.revolution-computing.com/products/revolution-r.php), an enterprise-class statistical analysis system based on R, suitable for deployment in professional, commercial and regulated environments.

Random Technologies (http://www.random-technologies-llc.com/) offers RStat (http://random-technologies-llc.com/products/RStat/rstat), an enterprise-strength statistical computing environment which combines R with enterprise-level validation, documentation, software support, and consulting services, as well as related R-based products.

See also http://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R for pointers to commercialized versions of R.

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.jstatsoft.org/v04/i01/).

Ulf Bartel has developed 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.

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/.

CGI-based web access to R is also provided at http://hermes.sdu.dk/cgi-bin/go/.  There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.

David Firth (http://www.warwick.ac.uk/go/dfirth) 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, and allows submission of data using both GET and POST methods.  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.  David's paper ``CGIwithR: Facilities for Processing Web Forms Using R'' was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/).  The package is now maintained by Duncan Temple Lang and has a web page at http://www.omegahat.org/CGIwithR/.

Rpad (http://www.rpad.org/Rpad), developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of Javascript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc.

Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond).  A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.

Rserve (http://stats.math.uni-augsburg.de/Rserve/) is a project actively developed by Simon Urbanek.  It implements a TCP/IP server which allows other programs to use facilities of R.  Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).

OpenStatServer (http://openstatserver.org/index.html) is being developed by a team lead by Greg Warnes; it aims ``to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface'' and to turn computational services into web services.

Two projects use PHP to provide a web interface to R. R_PHP_Online (http://steve-chen.net/R_PHP/) by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb.  R-php (http://dssm.unipa.it/R-php/?cmd=home) is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.

webbioc (http://www.bioconductor.org/) is ``an integrated web interface for doing microarray analysis using several of the Bioconductor packages'' and is designed to be installed at local sites as a shared computing resource.

Finally, Rwui (http://rwui.cryst.bbk.ac.uk) is a web application to to create user-friendly web interfaces for R scripts.  All code for the web interface is created automatically.  There is no need for the user to do any extra scripting or learn any new scripting techniques.

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.)

ADaCGH
     Analysis of data from aCGH experiments.

AER
     Functions, data sets, examples and vignettes for the book ``Applied Econometrics with R'' by Christian Kleiber and Achim Zeileis, 2008, Springer-Verlag, New York.

AIGIS
     Areal Interpolation for GIS data.

AIS
     Tools to look at the data (``Ad Inidicia Spectata'').

ALS
     Multivariate curve resolution alternating least squares (MCR-ALS).

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

ARES
     Allelic richness estimation, with extrapolation beyond the sample size.

AcceptanceSampling
     Creation and evaluation of acceptance sampling plans,

AdMit
     Adaptive mixture of Student t distributions.

AdaptFit
     Adaptive semiparametic regression.

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

Amelia
     Amelia II: a program for missing data.

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

ArDec
     Time series autoregressive decomposition.

BACCO
     Bayesian Analysis of Computer Code Output.  Contains approximator, calibrator, and emulator, for Bayesian prediction of complex computer codes, calibration of computer models, and emulation of computer programs, respectively.

BARD
     Better Automated ReDistricting.

BAS
     Bayesian model averaging using Bayesian Adaptive Sampling.

BAYSTAR
     Bayesian analysis of threshold autoregressive models.

BB
     Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints.

BCE
     Bayesian Composition Estimator for sample (taxonomic) composition from biomarker data.

BGSIMD
     Block Gibbs Sampler with Incomplete Multinomial Distribution.

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.

BLCOP
     Black-Litterman and copula-opinion pooling frameworks.

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

BPHO
     Bayesian Prediction with High-order Interactions.

BaM
     Functions and datasets for ``Bayesian Methods: A Social and Behavioral Sciences Approach'' (2nd edition) by Jeff Gill, 2007, CRC Press.

BayHaz
     Functions for Bayesian Hazard rate estimation.

BayesDA
     Functions and data sets for the book ``Bayesian Data Analysis'' by A. Gelman, J. B. Carlin, H. S. Stern and D. B. Rubin, 2003, Chapman & Hall/CRC.

BayesTree
     Bayesian methods for tree based models.

BayesValidate
     Bayesian software validation using posterior quantiles.

BayesX
     Utilities accompanying the BayesX software for Bayesian Inference in structured additive regression models.

Bchron
     Create chronologies based on radiocarbon and non-radiocarbon dated depths.

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

BiasedUrn
     Biased urn model distributions.

BioIDMapper
     Mapping between BioIDs.

Biodem
     A number of functions for biodemographycal analysis.

BiodiversityR
     GUI for biodiversity and community ecology analysis.

BiplotGUI
     Interactive biplots in R.

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

BootCL
     Bootstrapping test for chromosomal localization.

BootPR
     Bootstrap prediction intervals and bias-corrected forecasting.

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

Brobdingnag
     Very large numbers in R.

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

BSagri
     Statistical methods for safety assessment in agricultural field trials.

BsMD
     Bayes screening and model discrimination follow-up designs.

CADFtest
     Hansen's Covariate-Augmented Dickey-Fuller (CADF) test.

CADStat
     A GUI to several statistical methods for biological inferences.

CCA
     Canonical correlation analysis.

CDNmoney
     Components of Canadian monetary aggregates.

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

CHsharp
     Choi and Hall clustering in 3d.

CORREP
     Multivariate correlation estimation.

COZIGAM
     Constrained Zero-Inflated Generalized Additive Model.

CPE
     Concordance probability estimates in survival analysis.

CTFS
     The CTFS large plot forest dynamics analyses.

CTT
     Classical Test Theory functions.

CVThresh
     Level-dependent Cross-Validation Thresholding.

Cairo
     Graphics device using cairographics library for creating high-quality PNG, PDF, SVG, PostScript output and interactive display devices such as X11.

CarbonEL
     Carbon Event Loop.

CellularAutomaton
     One-dimensional cellular automata.

ChainLadder
     Mack- and Munich-chain-ladder methods for insurance claims reserving.

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.

ComPairWise
     Compare phylogenetic or population genetic data alignments.

CombMSC
     Combined Model Selection Criteria.

CompetingRiskFrailty
     Competing risk model with frailties for right censored survival data.

Containers
     Object-oriented data structures including stack, queue, and binary search tree.

ConvCalendar
     Converts dates between calendars.

ConvergenceConcepts
     Seeing convergence concepts in action.

CoxBoost
     Cox survival models by likelihood based boosting.

CreditMetrics
     Functions for calculating the CreditMetrics risk model.

CvM2SL1Test
     Cramer-von Mises two sample tests, L1 version.

CvM2SL2Test
     Cramer-von Mises two sample tests.

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.

DAAGbio
     Data sets and functions, for demonstrations with expression arrays.

DAAGxtras
     Data sets and functions additional to DAAG.

DAKS
     Data Analysis and Knowledge Spaces.

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.

DDHFm
     Variance stabilization by Data-Driven Haar-Fisz (for microarrays).

DEA
     Data Envelopment Analysis.

DEoptim
     Differential Evolution Optimization.

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

DPpackage
     Semiparametric Bayesian analysis using Dirichlet process priors.

DTK
     Dunnett-Tukey-Kramer: pairwise multiple comparison test adjusted for unequal variances and unequal sample sizes.

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

Defaults
     Create global function defaults.

Depela
     Semiparametric estimation of copula models.

DescribeDisplay
     R interface to the DescribeDisplay GGobi plugin.

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.

Devore7
     Data sets and sample analyses from ``Probability and Statistics for Engineering and the Sciences (7th ed)'' by Jay L. Devore, 2008, Thomson.

DiagnosisMed
     Diagnostic test accuracy evaluation for medical professionals.

DierckxSpline
     R companion to ``Curve and Surface Fitting with Splines'' by Paul Dierckx, 1993, Oxford University Press.

EDR
     Estimation of the effective dimension reduction (EDR) space.

EMC
     Evolutionary Monte Carlo (EMC) algorithm.

EMCC
     Evolutionary Monte Carlo (EMC) methods for clustering.

EMD
     Empirical mode decomposition and Hilbert spectral analysis.

ETC
     Tests and simultaneous confidence intervals for equivalence to control.

EVER
     Estimation of Variance by Efficient Replication.

EbayesThresh
     Empirical Bayes thresholding and related methods.

Ecdat
     Data sets from econometrics textbooks.

EffectiveDose
     Estimate the effective dose level for quantal bioassay data by nonparametric techniques.

ElemStatLearn
     Data sets, functions and examples from the book ``The Elements of Statistical Learning: Data Mining, Inference, and Prediction'' by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001), Springer.

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

FAiR
     Factor Analysis in R, using genetic algorithms.

FGN
     Fractional Gaussian Noise model fitting.

FITSio
     FITS (Flexible Image Transport System) utilities.

FKBL
     Fuzzy Knowledge Base Learning.

FKF
     Fast Kalman Filter.

FTICRMS
     Analysis of Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry data.

FactoClass
     Combination of factorial methods and cluster analysis.

FactoMineR
     Factor analysis and data mining with R.

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

FieldSim
     Random fields simulations.

FinTS
     Companion to the book ``Analysis of Financial Time Series'' (2nd edition) by Ruey Tsay (2005), Wiley.

FitAR
     Subset AR model fitting.

Flury
     Data sets from from ``A First Course in Multivariate Statistics'' by Bernard Flury (1997), Springer.

Formula
     Infrastructure for extended formulas.

FrF2
     Analysis of fractional factorial designs with 2-level factors.

FracSim
     Simulation of one- and two-dimensional fractional and multifractional Levy motions.

FunCluster
     Functional profiling of cDNA microarray expression data.

FunNet
     Functional analysis of gene co-expression networks.

G1DBN
     Dynamic Bayesian Network inference using 1st order conditional dependencies.

GAMBoost
     Generalized additive models by likelihood based boosting.

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

GEOmap
     Topographic and geologic mapping.

GExMap
     Functions for the analysis of genomic distribution of genes lists produced by transcriptomic studies.

GFMaps
     Visualization of high-throughput genetic or proteomic experiments.

GLDEX
     Fit RS and FMKL generalised lambda distributions using discretized and maximum likelihood methods.

GOSim
     Computation of functional similarities between GO terms and gene products.

GPArotation
     Gradient Projection Algorithm rotation for factor analysis.

GRRGI
     Gauge R and R Confidence Intervals.

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.

GSA
     Gene set analysis.

GSM
     Gamma Shape Mixture.

GenABEL
     Genome-wide SNP association analysis.

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

GeneCycle
     Identification of periodically expressed genes.

GeneF
     Generalized F-statistics.

GeneNT
     Relevance or Dependency network and signaling pathway discovery.

GeneNet
     Modeling and inferring gene networks.

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

GeoXp
     Interactive exploratory spatial data analysis.

GillespieSSA
     Gillespie's Stochastic Simulation Algorithm (SSA).

GridR
     Executes functions on remote hosts, clusters or grids.

GroupSeq
     Computations related to group-seqential boundaries.

HAPim
     Methods for QTL detection and fine mapping.

HH
     Support software for ``Statistical Analysis and Data Display'' by Richard M. Heiberger and Burt Holland, Springer, 2005.

HI
     Simulation from distributions supported by nested hyperplanes.

HSAUR
     Functions, data sets, analyses and examples from the book ``A Handbook of Statistical Analyses Using R'' by Brian S. Everitt and Torsten Hothorn (2006), Chapman & Hall/CRC.

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

HFWutils
     Utilities by H. Felix Wittmann: Excel connections, string matching, and passing by reference.

HardyWeinberg
     Graphical tests for Hardy-Weinberg equilibrium.

HiddenMarkov
     Hidden Markov Models.

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.

HydroMe
     Estimation of soil hydraulic parameters from experimental data.

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.

IBrokers
     R API to Interactive Brokers Trader Workstation.

ICE
     Iterated Conditional Expectation: kernel estimators for interval-censored data.

ICEinfer
     Incremental Cost-Effectiveness (ICE) statistical inference (from two unbiased samples).

ICS
     ICS/ICA computation based on two scatter matrices.

ICSNP
     Tools for multivariate nonparametrics.

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

ISA
     Functions to support ``Introduzione alla Statistica Applicata con esempi in R'' by Federico M. Stefanini, Pearson Education Milano, 2007.

ISOcodes
     ISO language, territory, currency, script and character codes.

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

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

Iso
     Functions to perform isotonic regression.

JADE
     JADE and ICA performance criteria.

JGR
     Java Gui for R.

JM
     Joint Modeling of longitudinal and survival data.

JavaGD
     Java Graphics Device.

JointGLM
     Joint modeling of mean and dispersion through two interlinked GLM's. Defunct in favor of JointModeling.

JointModeling
     Joint modeling of mean and dispersion.

JudgeIt
     Calculates bias, responsiveness, and other characteristics of two-party electoral systems, with district-level electoral and other data.

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

Kendall
     Kendall rank correlation and Mann-Kendall trend test.

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.

LDtests
     Exact tests for linkage disequilibrium and Hardy-Weinberg equilibrium.

LIM
     Linear Inverse Model examples and solution methods.

LIStest
     Longest increasing subsequence independence test.

LLAhclust
     Hierarchical clustering of variables or objects based on the likelihood linkage analysis method.

LLN
     Learning with latent networks.

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

LearnBayes
     Functions for Learning Bayesian Inference.

LearnEDA
     Functions for Learning Exploratory Data Analysis.

Lmoments
     Estimation of L-moments and the parameters of normal and Cauchy polynomial quantile mixtures.

LogConcDEAD
     Maximum likelihood estimation of a log-concave density.

LogicReg
     Routines for Logic Regression.

LoopAnalyst
     A collection of tools to conduct Levins' Loop Analysis.

LowRankQP
     Low Rank Quadratic Programming: QP problems where the hessian is represented as the product of two matrices.

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

MAclinical
     Class prediction based on microarray data and clinical parameters.

MAMSE
     Calculation of Minimum Averaged Mean Squared Error (MAMSE) weights.

MBA
     Multilevel B-spline Approximation.

MBESS
     Methods for the Behavioral, Educational, and Social Sciences.

MCAPS
     Weather and air pollution data, risk estimates, and other information from the Medicare Air Pollution Study (MCAPS) of 204 U.S. counties, 1999--2002.

MCE
     Tools for evaluating Monte Carlo Error.

MCMCglmm
     MCMC Generalized Linear Mixed Models.

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

MCPAN
     Multiple comparisons using normal approximation.

MCPMod
     Design and analysis of dose-finding studies.

MChtest
     Monte Carlo hypothesis tests.

MDD
     Minimum Detectable Difference calculations for several continuous and binary endpoints.

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

MFDA
     Model Based Functional Data Analysis.

MIfuns
     Pharmacometric tools for data preparation, analysis, simulation, and reporting.

MKLE
     Maximum kernel likelihood estimation.

MKmisc
     Miscellaneous Functions from M. Kohl.

MLDA
     Methylation Linear Discriminant Analysis (MLDA).

MLDS
     Maximum Likelihood Difference Scaling.

MLEcens
     Computation of the MLE for bivariate (interval) censored data.

MMG
     Mixture Model on Graphs.

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.

MSVAR
     Markov Switching VAR.

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

MasterBayes
     Maximum likelihood and Markov chain Monte Carlo methods for pedigree reconstruction, analysis and simulation.

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.

Metabonomic
     GUI for metabonomic analysis.

MiscPsycho
     Miscellaneous Psychometrics.

MultEq
     Equivalence tests and simultaneous confidence intervals for multiple endpoints.

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.

NMMAPSlite
     U.S. National Morbidity, Mortality, and Air Pollution Study data lite.

NMRS
     NMR spectroscopy.

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

NRAIA
     Data sets with sample code from ``Nonlinear Regression Analysis and Its Applications'' by Doug Bates and Donald Watts, 1988, Wiley.

NestedCohort
     Survival analysis for cohorts with missing covariate information.

NetIndices
     Estimates network indices, including trophic structure of foodwebs.

OAIHarvester
     Harvest metadata using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) version 2.0.

OPE
     Fit an outer-product emlator to the multivariate evaluations of a computer model.

ORMDR
     Odds ratio based multivactor-dimensionality reduction method for detecting gene-gene interactions.

Oarray
     Arrays with arbitrary offsets.

Oncotree
     Estimation of oncogenetic trees.

PASWR
     Data and functions for the book ``Probability and Statistics with R'' by M. D. Ugarte, A. F. Militino and A. T. Arnholt, 2008, Chapman & Hall/CRC.

PBSddesolve
     Solver for delay differential equations.

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.

PBSmodelling
     Software to facilitate the design, testing, and operation of computer models.

PET
     Simulation and reconstruction of PET images.

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.

PKfit
     A nonlinear regression (including a genetic algorithm) program designed to deal with curve fitting for pharmacokinetics.

PKtools
     Unified computational interfaces for pop PK.

PMA
     Penalized Multivariate Analysis.

POT
     Generalized Pareto distribution and Peaks Over Threshold.

PSAgraphics
     Propensity Score Analysis Graphics.

PSM
     Non-linear mixed-effects modeling using stochastic differential equations.

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.

PairViz
     Visualization using Eulerian tours and Hamiltonian decompositions.

Peaks
     Spectrum manipulation: background estimation, Markov smoothing, deconvolution and peaks search functions.

PearsonICA
     Independent component analysis using score functions from the Pearson system.

PerformanceAnalytics
     Econometric tools for performance and risk analysis.

PhViD
     Pharmacovigilance signal detection methods extended to the multiple comparison setting.

PhySim
     Phylogenetic tree simulation.

PolynomF
     Univariate polynomials.

Pomic
     Pattern oriented modeling information criterion.

PredictiveRegression
     Prediction intervals for three basic statistical models.

PresenceAbsence
     Presence-absence model evaluation.

ProfessR
     Programs to determine student grades and create examinations from question banks.

PtProcess
     Time dependent point process modeling.

PwrGSD
     Power in a Group Sequential Design.

QCA
     Qualitative Comparative Analysis for crisp sets.

QCAGUI
     QCA Graphical User Interface.

QRMlib
     Code to examine Quantitative Risk Management concepts.

QuantPsyc
     Quantitative Psychology tools.

R.cache
     Fast and light-weight caching of objects.

R.huge
     Methods for accessing huge amounts of data.

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

R.methodsS3
     Utility functions for defining S3 methods.

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

R.rsp
     R server pages.

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.

R2jags
     Call JAGS from R.

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

RBGL
     Interface to the boost C++ graph library.

RBloomberg
     Fetch data from a Bloomberg API using COM.

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

RDieHarder
     R interface to the dieharder random number generator test suite.

REQS
     R/EQS interface.

RExcelInstaller
     Integration of R and Excel under MS Windows.

RFA
     Regional Frequency Analysis.

RFOC
     Graphics for spherical distributions and earthquake focal mechanisms.

RFreak
     An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK.g

RGtk2
     Facilities for programming graphical interfaces using Gtk (the Gimp Tool Kit) version 2.

RGrace
     Mouse/menu driven interactive plotting application.

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

RHRV
     Heart rate variability analysis of ECG data.

RHmm
     Hidden Markov Model simulations and estimations.

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

RItools
     Randomization inference tools.

RJDBC
     Access to databases through the JDBC interface.

RJaCGH
     Reversible Jump MCMC for the analysis of CGH arrays.

RKEA
     R/KEA interface for extracting keyphrases from text documents.

RLMM
     A genotype calling algorithm for Affymetrix SNP arrays.

RLRsim
     Exact (Restricted) Likelihood Ratio tests for mixed and additive models.

RLadyBug
     Analysis of infectious diseases using stochastic epidemic models.

RM2
     Revenue management and pricing.

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.

ROptEst
     Optimally robust estimation.

ROptEstOld
     Optimally robust estimation, old version.

ROptEstTS
     Optimally robust estimation for regression-type models.

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

RPMG
     Poor Man's Gui: create interactive R analysis sessions.

RPostgreSQL
     R interface to the PostgreSQL database system.

RPyGeo
     ArcGIS Geoprocessing in R via Python.

RQDA
     Qualitative Data Analysis.

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.

RSAGA
     SAGA geoprocessing and terrain analysis in R.

RSEIS
     Seismic time series analysis tools.

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

RScaLAPACK
     An interface to ScaLAPACK functions from R.

RSVGTipsDevice
     An R SVG graphics device with dynamic tips and hyperlinks.

RSeqMeth
     analysis of Sequenom EpiTYPER data.

RSurvey
     Analysis of spatially distributed data.

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

RTOMO
     Visualization for seismic tomography.

RTisean
     R interface to Tisean algorithms.

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

RWeka
     An R interface to Weka, a rich collection of machine learning algorithms for data mining tasks.

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

RXshrink
     Maximum Likelihood Shrinkage via Ridge or Least Angle Regression.

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).

RandVar
     Implementation of random variables by means of S4 classes and methods.

RandomFields
     Creating random fields using various methods.

RankAggreg
     Weighted rank aggregation.

RaschSampler
     Sampling binary matrices with fixed margins.

Ratings
     Model-based ratings figures.

Rcapture
     Loglinear models in capture-recapture experiments.

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

RcmdrPlugin.Export
     Graphically export objects to LaTeX or HTML.

RcmdrPlugin.FactoMineR
     Rcmdr plug-in for the FactoMineR package.

RcmdrPlugin.HH
     Rcmdr support for the introductory course at Temple University.

RcmdrPlugin.IPSUR
     Rcmdr plugin for ``Introduction to Probability and Statistics Using R''.

RcmdrPlugin.SurvivalT
     Rcmdr survival plug-in.

RcmdrPlugin.TeachingDemos
     Rcmdr Teaching Demos plug-in.

RcmdrPlugin.epack
     Rcmdr epack demos plug-in.

RcmdrPlugin.orloca
     Rcmdr orloca plug-in.

RcmdrPlugin.qcc
     Rcmdr qcc plug-in.

RcmdrPlugin.survival
     Rcmdr plugin for the survival package.

Rcplex
     R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs.

Rcpp
     R/C++ interface library and package template.

Rcsdp
     R interface to the CSDP semidefinite programming library.

Read.isi
     Access old data saved in fixed-width format based on ISI-formatted codebooks.

Reliability
     Functions for estimating parameters in software reliability models.

ResearchMethods
     Using GUIs to help teach statistics to non-statistics students.

ResistorArray
     Electrical properties of resistor networks.

Rfwdmv
     Forward Search for Multivariate Data.

Rglpk
     R/GNU Linear Programming Kit interface.

RiboSort
     Classification and analysis of microbial community profiles.

Rigroup
     Provides small integer group functions.

Rlab
     Functions and data sets for the NCSU ST370 class.

Rlabkey
     Data retrieval from a Labkey database.

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.

RobAStBase
     Base classes and functions for robust asymptotic statistics.

RobLox
     Optimally robust influence curves for location and scale.

RobRex
     Optimally robust influence curves for regression and scale.

Rpad
     Utility functions for the Rpad workbook-style interface.

Rsac
     Seismic tools for R.

Rserve
     A socket server (TCP/IP or local sockets) which allows binary requests to be sent to R.

Rsge
     Interface to the SGE cluster/grid queuing system.

Rsundials
     SUite of Nonlinear DIfferential ALgebraic equations Solvers in R.

Rsymphony
     An R interface to the SYMPHONY mixed integer linear program (MILP) solver.

Runuran
     Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators.

Rvelslant
     Downhole seismic analysis.

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.

Ryacas
     An R interfaces to the yacas computer algebra system.

SASPECT
     Significant AnalysiS of PEptide CounTs.

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.

SASxport
     Read and write SAS XPORT files.

SDDA
     Stepwise Diagonal Discriminant Analysis.

SDaA
     Functions and data sets from ``Sampling: Design and Analysis'' by S. Lohr, 1999, Duxbury.

SGCS
     Spatial Graph based Clustering Summaries for spatial point patterns.

SGP
     Student growth percentile and percentile growth projection/trajectory functions.

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

SLmisc
     Miscellaneous Functions for analysis of gene expression data at SIRS-Lab GmbH.

SMC
     Sequential Monte Carlo (SMC) Algorithm.

SMPracticals
     Data sets and a few functions for use with the practicals outlined in Appendix A of the book ``Statistical Models'' by Anthony Davison, 2003, Cambridge University Press.

SMVar
     Structural Model for Variances to detect differentially expressed genes.

SNPMaP
     SNP Microarrays and Pooling in R.

SNPMaP.cdm
     Annotation for SNP microarrays and pooling in R.

SNPassoc
     SNP-based whole genome association studies.

SNPmaxsel
     Maximally selected statistics for SNP data.

SQLiteMap
     Manage vector graphical maps using SQLite.

SQLiteDF
     Stores data frames and matrices in SQLite tables.

SRPM
     Shared Reproducibility Package Management.

STAR
     Spike Train Analysis with R.

ScottKnott
     Multiple comparison test of means using the clustering method of Scott & Knott.

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.

SensoMineR
     Sensory data analysis.

SeqKnn
     Sequential KNN imputation.

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

SiZer
     Significant Zero crossings.

SimComp
     Simultaneous Comparisons for multiple endpoints.

SimHap
     A comprehensive modeling framework for epidemiological outcomes and a multiple-imputation approach to haplotypic analysis of population-based data.

SimpleTable
     Bayesian inference and sensitivity analysis for causal effects from 2 x 2 and 2 x 2 x K tables in the presence of unmeasured confounding.

Snowball
     Snowball stemmers.

SoDA
     Utilities and examples from the book ``Software for Data Analysis: Programming with R'' by John Chambers, Springer, 2008.

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

SparseM
     Basic linear algebra for sparse matrices.

SpatialExtremes
     Modeling spatial extremes.

SpatialNP
     Multivariate nonparametric methods based on spatial signs and ranks.

SpectralGEM
     Discovering genetic ancestry using spectral graph theory.

SpherWave
     Spherical Wavelets and SW-based spatially adaptive methods.

StatDA
     Statistical analysis for environmental data, a companion to the book ``Statistical Data Analysis Explained: Applied Environmental Statistics with R'' by C. Reimann, P. Filzmoser, R. G. Garrett, and R. Dutter, 2008, John Wiley and Sons.

StatDataML
     Read and write StatDataML.

StatFingerprints
     Processing and statistical analysis of molecular fingerprint profiles.

StatMatch
     Functions to perform statistical matching between two data sources.

Stem
     Spatio-temporal models in R.

StreamMetabolism
     Calculation of single station metabolism from diurnal oxygen curves.

SubpathwayMiner
     Annotation and identification of metabolic sub-pathways and pathways.

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).

SweaveListingUtils
     Utilities for Sweave together with TeX listings package.

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

SyNet
     Inference and analysis of sympatry networks.

Synth
     Causal inference using the synthetic control group method.

TIMP
     A problem solving environment for fitting superposition models.

TRAMPR
     Terminal Restriction Fragment Length Polymorphism (TRFLP) Analysis and Matching Package for R.

TRIANG
     Discrete triangular distributions.

TSA
     Functions and datasets detailed in the book ``Time Series Analysis With Applications in R'' (3rd edition) by Jonathan Cryer and Kung-Sik Chan, 2008, Springer.

TSHRC
     Two Stage Hazard Rate Comparison.

TSMySQL
     Time Series Database Interface extensions for MySQL.

TSP
     Traveling Salesperson Problem (TSP).

TSPostgreSQL
     Time Series Database Interface extensions for PostgreSQL.

TSSQLite
     Time Series Database Interface extensions for SQLite.

TSdbi
     Time Series Database Interface.

TSfame
     Time Series Database Interface extensions for fame.

TShistQuote
     Time Series Database Interface interface for get.hist.quote.

TSodbc
     Time Series Database Interface extensions for ODBC.

TSpadi
     Connect to a time series database (e.g., Fame) via PADI (Protocol for Application Database Interface), using the TSdbi infrastructure.

TTR
     Technical Trading Rules.

TWIX
     Trees WIth eXtra splits.

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.

TeachingSampling
     Sampling designs and parameter estimation in finite populations.

TinnR
     Resources for the Tinn-R GUI/Editor for R.

TraMineR
     Sequences and trajectories mining for social scientists.

TwoWaySurvival
     Additive two-way hazards modeling of right censored survival data.

TwslmSpikeWeight
     Normalization of cDNA microarray data with the two-way semilinear model (TW-SLM).

UNF
     Tools for creating universal numeric fingerprints for data.

USPS
     Unsupervised and Supervised methods of Propensity Score adjustment for bias.

Umacs
     Universal MArkov Chain Sampler.

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

VDCutil
     Utilities supporting VDC, an open source digital library system for quantitative data.

VGAM
     Vector Generalized Linear and Additive Models.

VIM
     Visualization and Imputation of Missing Values.

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).

VhayuR
     R interface to the Vhayu Velocity high volume fast financial market data archival and analysis products.

WINRPACK
     Reads in WIN pickfile and waveform files.

WWGbook
     Functions and datasets for the book ``Linear Mixed Models: A Practical Guide Using Statistical Software'' by B. West, K. Welch, and A. Galecki, 2006, Chapman & Hall/CRC.

WaveCGH
     Wavelet changepoint detection for array CGH.

WeedMap
     Map of weed intensity.

WhatIf
     Software for evaluating counterfactuals.

XML
     Tools for reading XML documents and DTDs.

XReg
     Extreme regression.

YaleToolkit
     Data exploration tools from Yale University.

YourCast
     YourCast: time series cross-sectional forecasts.

ZIGP
     Zero Inflated Generalized Poisson (ZIGP) regression models.

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.

actuar
     Functions related to actuarial science applications.

ada
     Performs boosting algorithms for a binary response.

adabag
     Adaboost.M1 and Bagging.

adapt
     Adaptive quadrature in up to 20 dimensions.

ade4
     Multivariate data analysis and graphical display.

ade4TkGUI
     Tcl/Tk Graphical User Interface for ade4.

adegenet
     Genetic data handling for multivariate analysis using ade4.

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

adimpro
     Adaptive smoothing of digital images.

adk
     Anderson-Darling K-sample test and combinations of such tests.

adlift
     Adaptive Wavelet transforms for signal denoising.

ads
     Spatial point patterns analysis.

afc
     Calculate the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC).

agce
     Analysis of growth curve experiments.

agreement
     Analyze the agreement between two measurement methods.

agricolae
     Statistical procedures for agricultural research.

agsemisc
     Miscellaneous plotting and utility functions.

akima
     Linear or cubic spline interpolation for irregularly gridded data.

allelic
     A fast, unbiased and exact allelic exact test.

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

amap
     Another Multidimensional Analysis Package.

amei
     Adaptive Management of Epidemiological Interventions.

anacor
     Simple and Canonical Correspondence Analysis.

analogue
     Analogue methods for palaeoecology.

anapuce
     Tools for microarray data analysis.

animation
     Demonstrate animations in statistics.

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.

aplpack
     Another PLot PACKage: stem.leaf, bagplot, faces, spin3R, ....

apsrtable
     American Political Science Review style table formatting.

archetypes
     Archetypal analysis.

argosfilter
     Argos locations filter.

arm
     Data Analysis using Regression and Multilevel/hierarchical models.

arrayImpute
     Missing imputation for microarray data.

arrayMissPattern
     Exploratory analysis of missing patterns for microarray data.

ars
     Adaptive Rejection Sampling.

arules
     Mining association rules and frequent itemsets with R.

arulesSequences
     Mining frequent sequences.

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

aspace
     Estimating centrographic statistics and computational geometries from spatial point patterns.

aspect
     Aspects of multivariables.

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).

asympTest
     Asymptotic testing.

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

audio
     Audio interface for R.

asuR
     Functions and data sets for a lecture in ``Advanced Statistics using R''.

aws
     Functions to perform adaptive weights smoothing.

aylmer
     A generalization of Fisher's exact test.

backfitRichards
     Backfitted independent values of Richards curves.

backtest
     Exploring portfolio-based hypotheses about financial instruments.

bark
     Bayesian Additive Regression Kernels.

bayesCGH
     Bayesian analysis of array CGH data.

bayesGARCH
     Bayesian estimation of the GARCH(1,1) model with Student's t innovations.

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

bayesclust
     Tests/searches for significant clusters in genetic data.

bayescount
     Bayesian analysis of count distributions with JAGS.

bayesm
     Bayes Inference for Marketing/Micro-econometrics.

bayesmix
     Bayesian mixture models of univariate Gaussian distributions using JAGS.

bbmle
     Modifications and extensions of stats4 MLE code.

bcp
     Bayesian Change Point based on the Barry and Hartigan product partition model.

beanplot
     Visualization via beanplots.

bear
     Bioavability and bioequivalence data analysis with crossover design.

benchden
     28 benchmark densities from Berlinet/Devroye (1994).

bentcableAR
     Bent-cable regression for independent data or autoregressive time series.

betaper
     Distance decay of similarity among biological inventories in the face of taxonomic uncertainty.

betareg
     Beta regression for modeling rates and proportions.

bs
     Utilities for the Birnbaum-Saunders distribution.

biOps
     Basic image operations and image processing.

biOpsGUI
     GUI for Basic image operations.

biclust
     BiCluster algorithms.

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

bifactorial
     Inferences for bi- and trifactorial trial designs.

biglm
     Linear regression for data too large to fit in memory.

bigmemory
     Manage massive matrices in R using C++, with UNIX support for shared memory.

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

binGroup
     Evaluation and experimental design for binomial group testing.

binMto
     Asymptotic simultaneous confidence intervals for many-to-one comparisons of proportions.

bindata
     Generation of correlated artificial binary data.

binom
     Binomial confidence intervals for several parameterizations.

bio.infer
     Compute biological inferences.

biopara
     Self-contained parallel system for R.

bipartite
     Visualises bipartite networks and calculates some ecological indices.

birch
     Dealing with very large datasets using BIRCH.

bise
     Auxiliary functions for phenological data analysis.

bit
     A class for vectors of 1-bit booleans.

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.

blockTools
     Block, randomly assign, and diagnose potential problems between units in randomized experiments.

blockmodeling
     Generalized and classical blockmodeling of valued networks.

blockrand
     Randomization for block random clinical trials.

bmd
     Benchmark dose analysis for dose-response data.

bnlearn
     Bayesian network structure learning.

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.

bootStepAIC
     Model selection by bootstrapping the stepAIC() procedure.

bootspecdens
     Bootstrap for testing equality of spectral densities.

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.

bpca
     Biplot of multivariate data based on Principal Components Analysis.

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

brainwaver
     Basic wavelet analysis of multivariate time series with a vizualisation and parametrization using graph theory.

brew
     Templating framework for report generation.

brglm
     Bias-reduction in binomial-response GLMs.

bspec
     Bayesian inference on the (discrete) power spectrum of time series.

bvls
     The Stark-Parker algorithm for bounded-variable least squares.

ca
     Simple, multiple and joint Correspondence Analysis.

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.

cacheSweave
     Tools for caching Sweave computations.

cacher
     Tools for caching and distributing statistical analyses.

cairoDevice
     Loadable CAIRO/GTK device driver.

calib
     Statistical tool for calibration of plate based bioassays.

calibrate
     Calibration of biplot axes.

candisc
     Generalized canonical discriminant analysis.

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.

caret
     Classification and REgression Training.

caretLSF
     Classification and REgression Training, LSF style.

caretNWS
     Classification and REgression Training in parallel using NetworkSpaces.

cat
     Analysis of categorical-variable datasets with missing values.

catmap
     Case-control and TDT meta-analysis package.

catspec
     Special models for categorical variables.

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

ccgarch
     Conditional Correlation GARCH models.

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.

ccems
     Combinatorially Complex Equilibrium Model Selection.

cellVolumeDist
     Functions to fit cell volume distributions and thereby estimate cell growth rates and division times.

celsius
     Retrieve Affymetrix microarray measurements and metadata from Celsius.

cem
     The coarsened exact matching algorithm (and many extensions).

cfa
     Analysis of configuration frequencies.

cggd
     Continuous Generalized Gradient Descent.

cgh
     Analysis of microarray comparative genome hybridisation data using the Smith-Waterman algorithm.

cghFLasso
     Hot spot detecting for CGH array data with fused lasso regression.

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

changeLOS
     Change in length of hospital stay (LOS).

cheb
     Discrete linear Chebyshev approximation.

chemCal
     Calibration functions for analytical chemistry.

chemometrics
     Companion to the book ``Introduction to Multivariate Statistical Analysis in Chemometrics'' by K. Varmuza and P. Filzmoser, CRC Press, to appear.

choplump
     Choplump tests (permutation tests for comparing two groups with some positive but many zero responses).

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

cir
     Nonparametric estimation of monotone functions via isotonic regression and centered isotonic regression.

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

clValid
     Statistical and biological validation of clustering results.

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.

classGraph
     Construct graph of S4 class hierarchies.

classInt
     Choose univariate class intervals for mapping or other graphics purposes.

classifly
     Explore classification models in high dimensions.

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.

clinfun
     Utilities for clinical study design and data analyses.

clinsig
     Functions for calculating clinical significance.

clue
     CLUster Ensembles.

clues
     Clustering method based on local shrinking.

clustTool
     GUI for clustering data with spatial information.

cluster
     Functions for cluster analysis.  Recommended.

clusterGeneration
     Random cluster generation (with specified degree of separation).

clusterRepro
     Reproducibility of gene expression clusters.

clusterSim
     Searching for optimal clustering procedure for a data set.

clusterfly
     Explore clustering interactively using R and GGobi.

clustvarsel
     Variable selection for model-based clustering.

clv
     Cluster validation techniques.

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

cobs99
     Constrained B-splines: outdated 1999 version.

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

cocorresp
     Co-correspondence analysis ordination methods for community ecology.

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

codetools
     Code analysis tools.  Recommended for R 2.5.0 or later.

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

colorRamp
     Builds single and double gradient color maps.

colorspace
     Mapping between assorted color spaces.

combinat
     Combinatorics utilities.

compHclust
     Complementary hierarchical clustering.

compOverlapCorr
     Comparing overlapping correlation coefficients.

compare
     Comparing objects for differences.

compoisson
     Conway-Maxwell-Poisson distribution.

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.

connectedness
     Find disconnected sets for two-way classification.

contfrac
     Continued fractions.

contrast
     A collection of contrast methods.

convexHaz
     Nonparametric MLE/LSE of convex hazard.

copas
     Statistical methods to model and adjust for bias in meta-analysis.

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

corcounts
     Generate correlated count random variables.

corpcor
     Efficient estimation of covariance and (partial) correlation.

corpora
     Utility functions for the statistical analysis of corpus frequency data.

corrgram
     Plot a correlogram.

corrperm
     Permutation tests of correlation with repeated measurements.

covRobust
     Robust covariance estimation via nearest neighbor cleaning.

coxphf
     Cox regression with Firth's penalized likelihood.

coxphw
     Weighted estimation for Cox regression.

coxrobust
     Robust Estimation in the Cox proportional hazards regression model.

cramer
     Routine for the multivariate nonparametric Cramer test.

crank
     Functions for completing and recalculating rankings.

crawl
     (C)orrelated (RA)ndom (W)alk (L)ibrary for fitting continuous-time correlated random walk models for animal movement data.

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.

crosshybDetector
     Detection of cross-hybridization events in microarray experiments.

crq
     Quantile regression for randomly censored data.

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

cts
     Continuous time autoregressive models and the Kalman filter.

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

curvetest
     Test the equality of two curves, or one curve with 0.

cwhmisc
     Miscellaneous functions by Christian W. Hoffmann.

cyclones
     Cyclone identification.

data.table
     Extension of data frames to allow subscripting by expressions evaluated within the frame.

dataframes2xls
     Write data frames to .xls files.

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.

ddesolve
     Solver for Delay Differential Equations.

ddst
     Data driven smooth Neyman test.

deSolve
     General solvers for ordinary differential equations (ODE) and for differential algebraic equations (DAE).

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.

degreenet
     Models for skewed count distributions relevant to networks.

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

delt
     Estimation of multivariate densities with adaptive histograms.

demogR
     Analysis of age-structured demographic models.

denpro
     Visualization of multivariate density functions and estimates with level set trees and shape trees, and visualization of multivariate data with tail trees.

denstrip
     Density strips and other methods for compactly illustrating distributions.

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

depmixS4
     Dependent Mixture Models: fit latent (hidden) Markov models on mixed categorical and continuous (time series) data.

depth
     Depth functions tools for multivariate analysis.

desirability
     Desirabiliy function optimization and ranking.

dfcrm
     Dose-finding by the continual reassessment method.

dglm
     Double generalized linear models.

diagram
     Functions for visualising simple graphs (networks) and plotting flow diagrams.

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.

dice
     Calculate probabilities of various dice-rolling events.

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

diffractometry
     Baseline identification and peak decomposition for x-ray diffractograms.

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.

dirichlet
     Dirichlet model of consumer buying behavior for marketing research.

dispmod
     Functions for modelling dispersion in GLMs.

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

distrDoc
     Documentation for packages distr, distrEx, distrSim, and distrTEst.

distrEx
     Extensions of package distr.

distrMod
     Object orientated implementation of probability models based on distr and distrEx.

distrSim
     Simulation classes based on package distr.

distrTEst
     Estimation and Testing classes based on package distr.

distrTeach
     Extensions of distr for teaching stochastics/statistics in secondary school.

distributions
     Probability distributions based on TI-83 Plus.

divagis
     Tools for quality checks of georeferenced plant species accessions.

diveMove
     Dive analysis and calibration.

dlm
     Maximum likelihood and Bayesian analysis of Dynamic Linear Models.

dlmap
     Detection Localization Mapping for QTL.

doBy
     Facilities for groupwise computations.

dplR
     Dendrochronology Program Library in R.

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.

drc
     Non-linear regression analysis for multiple curves with focus on concentration-response, dose-response and time-response curves.

drm
     Regression and association models for clustered categorical responses.

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) and dse2 (extensions for evaluating estimation techniques, forecasting, and for evaluating forecasting model).

dti
     DTI (Diffusion Tensor Image) analysis.

dtt
     Discrete Trigonometric Transforms.

dtw
     Dynamic Time Warping algorithms.

dyn
     Time series regression.

dynCorr
     Dynamic correlation.

dynamicGraph
     Interactive graphical tool for manipulating graphs.

dynamicTreeCut
     Methods for detection of clusters in hierarchical clustering dendrograms.

dynamo
     Estimation, simulation, regularization and prediction of univariate dynamic models including ARMA, ARMA-GARCH, ACD, and MEM.

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.

eRm
     Estimating extended Rasch models.

earth
     Earth: multivariate adaptive regression spline models.

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

ecespa
     Functions and data for spatial point pattern analysis.

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

ecodist
     Dissimilarity-based functions for ecological analysis.

ecolMod
     Figures, data sets and examples from the book ``A Practical Guide to Ecological Modelling --- Using R as a Simulation Platform'' by Karline Soetaert and Peter M. J. Herman, 2008, Springer.

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.

eiPack
     Ecological inference and higher-dimension data management.

eigenmodel
     Semiparametric factor and regression models for symmetric relational data.

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.

elrm
     Exact Logistic Regression via MCMC.

emdbook
     Data sets and auxiliary functions for ``Ecological Models and Data'' by Ben Bolker (work in progress).

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

empiricalBayes
     A bundle for dealing with extreme multiple testing problems by estimating local false discovery rates.  Contains packages localFDR and HighProbability.

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

emu
     Interface to the Emu speech database system.

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.

entropy
     Entropy estimation.

epiR
     Functions for analyzing epidemiological data.

epibasix
     Elementary functions for epidemiological analysis.

epicalc
     Epidemiological calculator.

epitools
     Basic tools for applied epidemiology.

eqtl
     Tools for analyzing eQTL experiments.

equivalence
     Tests and graphics for assessing tests of equivalence.

ergm
     An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM).

etm
     Empirical Transition Matrix.

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.

exactmaxsel
     Exact methods for maximally selected statistics for binary response variables.

exams
     Automatic generation of simple (statistical) exams.

experiment
     Designing and analyzing randomized experiments.

expert
     Modeling of data using expert opinion.

extRemes
     Extreme value toolkit.

fArma
     The Rmetrics module for ``ARMA Time Series Modelling''.

fAsianOptions
     The Rmetrics module for ``Option Valuation''.

fAssets
     The Rmetrics module for ``Assets Selection and Modelling''.

fBasics
     The Rmetrics module for ``Markets and Basic Statistics''.

fBonds
     The Rmetrics module for ``Bonds and Interest Rate Models''.

fCalendar
     The Rmetrics module for ``Chronological and Calendarical Objects''.

fCopulae
     The Rmetrics module for ``Dependence Structures with Copulas''.

fEcofin
     The Rmetrics module for ``Economic and Financial Data Sets''.

fExoticOptions
     The Rmetrics module for ``Option Valuation''.

fExtremes
     The Rmetrics module for ``Extreme Financial Market Data''.

fGarch
     The Rmetrics module for ``Autoregressive Conditional Heteroskedastic Modelling''.

fImport
     The Rmetrics module for ``Chronological and Calendarical Objects''.

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

fNonlinear
     The Rmetrics module for ``Nonlinear and Chaotic Time Series Modelling''.

fOptions
     The Rmetrics module for ``Basics of Option Valuation''.

fPortfolio
     The Rmetrics module for ``Portfolio Selection and Optimization''.

fRegression
     The Rmetrics module for ``Regression Based Decision and Prediction''.

fSeries
     The Rmetrics module for ``Financial Time Series Objects''.

fTrading
     The Rmetrics module for ``Technical Trading Analysis''.

fUnitRoots
     The Rmetrics module for ``The Dynamical Process Behind Markets''.

fUtilities
     The Rmetrics module for ``Rmetrics Function Utilities''.

fame
     Interface for FAME time series database.

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.

fbati
     Gene by environment interaction tests.

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

fdim
     Functions for calculating fractal dimension.

fdrtool
     Estimation and control of (local) False Discovery Rates.

feature
     Feature significance for multivariate kernel density estimation.

fechner
     Fechnerian scaling of discrete object sets.

ff
     Flat file database designed for large vectors and multi-dimensional arrays.

ffmanova
     Fifty-fifty MANOVA.

fgac
     Families of Generalized Archimedean Copulas.

fgui
     Function GUI.

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.

filehashSQLite
     Simple key-value database using SQLite as the backend.

financial
     Solving financial problems in R.

fingerprint
     Functions to operate on binary fingerprint data.

fishmethods
     Fisheries methods and models.

fit4NM
     Platform for NONMEM.

fitdistrplus
     Fit parametric distributions to non-censored or censored data.

flashClust
     Implementation of optimal hierarchical clustering.

flexclust
     Flexible cluster algorithms.

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

fmri
     Functions for the analysis of fMRI experiments.

foba
     Forward, backward, and foba sparse learning algorithms for ridge regression.

forecasting
     A bundle with functions and datasets for forecasting.  Contains forecast (time series forecasting), fma (data sets from the book ``Forecasting: Methods and Applications'' by Makridakis, Wheelwright & Hyndman, 1998), and Mcomp (data from the M-competitions).

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

forensic
     Statistical methods in forensic genetics.

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.

fossil
     Palaeoecological and palaeogeographical analysis tools.

fpc
     Fixed point clusters, clusterwise regression and discriminant plots.

fpca
     Restricted MLE for Functional Principal Components Analysis.

fpow
     Compute the non-centrality parameter of the non-central F distribution.

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

fractal
     Insightful fractal time series modeling and analysis.

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

frontier
     Maximum likelihood estimation of stochastic frontier production and cost functions.

fso
     Fuzzy set ordination.

ftnonpar
     Features and strings for nonparametric regression.

fts
     Fast operations for time series objects via an interface to tslib (a C++ time series library).

fuzzyFDR
     Exact calculation of fuzzy decision rules for multiple testing.

fuzzyOP
     Fuzzy numbers and the main mathematical operations on these.

fuzzyRankTests
     Fuzzy rank tests and confidence intervals.

fxregime
     Frankel-Wei regression and structural change tools for estimating, testing, dating and monitoring (de facto) exchange rate regimes.

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

gRain
     Probability propagation in graphical independence networks.

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.

gRc
     Inference in graphical Gaussian models with edge and vertex symmetries.

gWidgets
     gWidgets API for building toolkit-independent, interactive GUIs.

gWidgetsRGtk2
     Toolkit implementation of gWidgets for RGtk2.

gWidgetsWWW
     Toolkit implementation of gWidgets for www.

gWidgetsrJava
     Toolkit implementation of gWidgets for rJava.

gWidgetstcltk
     Toolkit implementation of gWidgets for tcltk.

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).

gamair
     Data sets used in the book ``Generalized Additive Models: An Introduction with R'' by S. Wood (2006).

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

gamlss.cens
     A GAMLSS add on package for censored data.

gamlss.dist
     Extra distributions for GAMLSS modeling.

gamlss.mx
     A GAMLSS add on package for fitting mixture distributions.

gamlss.nl
     A GAMLSS add on package for fitting non linear parametric models.

gamlss.tr
     A GAMLSS add on for generating and fitting truncated (gamlss.family) distributions.

gap
     Genetic analysis package for both population and family data.

gbev
     Gradient Boosted regression trees with Errors-in-Variables.

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.

gbs
     Generalized Birnbaum-Saunders distributions.

gcExplorer
     Graphical cluster explorer.

gcl
     Compute a fuzzy rules or tree classifier from data.

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.

geiger
     Analysis of evolutionary diversification.

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

gene2pathway
     Prediction of KEGG pathway membership for individual genes based on InterPro domain signatures.

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.

geomapdata
     Data for topographic and geologic mapping.

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

geonames
     Interface to www.geonames.org web service.

geozoo
     Definition of geometric objects and display via rggobi.

getopt
     C-like getopt behavior for R scripts.

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

ggplot
     Grammar of graphics based plots for R.

ggplot2
     An implementation of the Grammar of Graphics in R.

ghyp
     Univariate and multivariate generalized hyperbolic distributions.

giRaph
     Data structures and algorithms for computations on graphs.

gibbs.met
     Naive Gibbs sampling with Metropolis steps.

glasso
     Graphical lasso.

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

glmc
     Fitting Generalized Linear Models subject to Constraints.

glmmAK
     Generalized Linear Mixed Models.

glmmBUGS
     Generalised Linear Mixed Models with WinBUGS.

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

glmnet
     Lasso and elastic-net regularized generalized linear models.

glmpath
     L1 regularization path for Generalized Linear Models.

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

gmaps
     Wrapper and auxiliary functions for the maps package to work with the grid graphics system.

gmm
     Generalized Method of Moments.

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.

goalprog
     Weighted and lexicographical goal programming and optimization.

gof
     Model-diagnostics based on cumulative residuals.

gogarch
     Generalized Orthogonal GARCH (GO-GARCH) models.

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.

grImport
     Importing vector graphics.

grade
     Binary grading functions.

granova
     Graphical Analysis of Variance.

graph
     Handling of graph data structures.

graphicsQC
     Quality Control for graphics in R.

grasp
     Generalized Regression Analysis and Spatial Predictions for R.

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

gridBase
     Integration of base and grid graphics.

grnnR
     A Generalized Regression Neural Network.

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

grplasso
     Fit user specified models with group lasso penalty.

grpreg
     Regularization paths for regression models with grouped covariates.

gsarima
     functions for Generalized SARIMA time series simulation.

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.

gsubfn
     Miscellaneous string utilities.

gtm
     Generative topographic mapping.

gtools
     Various functions to help manipulate data.

gumbel
     Functions for the Gumbel-Hougaard copula.

gvlma
     Global Validation of Linear Models Assumptions.

hacks
     Some convenience functions.

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

haplo.ccs
     Estimate haplotype relative risks in case-control data.

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

hapsim
     Haplotype data simulation.

hbim
     Hill/Bliss Independence Model for combination vaccines.

hddplot
     Use known groups in high-dimensional data to derive scores for plots.

hdeco
     Hierarchical DECOmposition of entropy for categorical map comparisons.

hdf5
     Interface to the NCSA HDF5 library.

hdrcde
     Highest Density Regions and Conditional Density Estimation.

heatmap.plus
     Heatmap with sensible behavior.

helloJavaWorld
     A demonstration how to interface to a jar file that resides inside an R package.

heplots
     Visualizing tests in multivariate linear models.

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

hexView
     Viewing binary files.

hexbin
     Hexagonal binning routines.

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.

hints
     Provide hints on what to do next.

hlr
     Hidden logistic regression.

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.

homtest
     Homogeneity tests for regional frequency analysis.

hopach
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

hot
     Computation on micro-arrays.

howmany
     A lower bound for the number of correct rejections.

hsmm
     Hidden Semi Markov Models.

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.

hwriter
     Easy-to-use and versatile functions to output R objects in HTML format.

hybridHclust
     Hybrid hierarchical clustering via mutual clusters.

hydrogeo
     Groundwater data presentation and interpretation.

hydrosanity
     Graphical user interface for exploring hydrological time series.

hyperdirichlet
     Routines for the hyperdirichlet distribution.

hypergeo
     The hypergeometric function over the whole complex plane.

ibdreg
     Regression methods for IBD linkage with covariates.

ic.infer
     Inequality constrained inference in linear normal situations.

ic50
     Evaluation of compound screens.

icomp
     Calculates the ICOMP criterion and its variations.

identity
     Jacquard condensed coefficients of identity.

ifa
     Independent Factor Analysis.

ifs
     Iterated Function Systems distribution function estimator.

ifultools
     Insightful research tools.

ig
     Robust and classical versions of the inverse Gaussian distribution.

igraph
     Routines for simple graphs.

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

imprProbEst
     Minimum distance estimation in an imprecise probability model.

impute
     Imputation for microarray data (currently KNN only).

imputeMDR
     Multifactor Dimensionality Reduction (MDR) analysis for imcomplete data.

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

inetwork
     Network analysis and plotting.

inline
     Inline C/C++ function calls from R.

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

intervals
     Tools for working with points and intervals.

introgress
     Analysis of introgression of genotypes between divergent, hybridizing lineages.

iplots
     Interactive graphics for R.

ipptoolbox
     Uncertainty quantification and propagation in the framework of Dempster-Shafer theory and imprecise probabilities.

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.

irtProb
     Utilities and probability distributions related to multidimensional person Item Response Models (IRT).

irtoys
     Simple interface to the estimation and plotting of IRT models.

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.

ivivc
     In vitro in vivo correlation (IVIVC) modeling.

jit
     Just-in-time compiler.

jointDiag
     Joint approximate diagonalization of a set of square matrices.

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

kerfdr
     Semi-parametric kernel-based approach to local fdr estimations.

kernelPop
     Spatially explicit population genetic simulations.

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

kin.cohort
     Analysis of kin-cohort studies.

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.

klin
     Linear equations with Kronecker structure.

kml
     K-Means for Longitudinal data.

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.

knnflex
     A more flexible k-NN.

knorm
     Microarray data from multiple biologically interrelated experiments.

kohonen
     Supervised and unsupervised self-organising maps.

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

kst
     Knowledge Space Theory: a set-theoretical framework which proposes mathematical formalisms to operationalize knowledge structures in a particular domain.

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

kzft
     Kolmogorov-Zurbenko Fourier Transform and application.

kzs
     Kolmogorov-Zurbenko Spline.

labdsv
     Laboratory for Dynamic Synthetic Vegephenomenology.

labeltodendro
     Convert labels or tables to a dendrogram.

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.

laercio
     Tests to compare means.

lago
     LAGO for rare target detection.

lancet.iraqmortality
     Surveys of Iraq mortality published in The Lancet.

languageR
     Data sets and functions for the book ``Analyzing Linguistic Data: A practical introduction to statistics'' by R. H. Baayen, 2007, Cambridge: Cambridge University Press.

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.

laser
     Likelihood Analysis of Speciation/Extinction Rates from phylogenies.

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.

latentnetHRT
     Latent position and cluster models for statistical networks, implementing the original specification by Handcock, Raftery and Tantrum.

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

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

latticist
     A Lattice-based tool for exploratory visualization.

lawstat
     Statistical tests widely utilized in biostatistics, public policy and law.

lazy
     Lazy learning for local regression.

ldDesign
     Design of experiments for detection of linkage disequilibrium,

lda.cv
     Cross-validation for linear discriminant analysis.

ldbounds
     Lan-DeMets method for group sequential boundaries.

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.

lga
     Tools for Linear Grouping Analysis (LGA).

lgtdl
     A set of methods for longitudinal data objects.

lhs
     Latin Hypercube Samples.

limSolve
     Solving linear inverse models.

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

ljr
     Logistic Joinpoint Regression.

lme4
     Fit linear and generalized linear mixed-effects models.

lmec
     Linear mixed-effects models with censored responses.

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

lmm
     Linear mixed models.

lmodel2
     Model II simple linear regression.

lmom
     L-moments.

lmomRFA
     Regional Frequency Analysis using L-moments.

lmomco
     L-moments and L-comoments.

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.

lnMLE
     Marginally specified logistic normal models for longitudinal binary data.

locfdr
     Computation of local false discovery rates.

locfit
     Local Regression, likelihood and density estimation.

locpol
     Kernel local polynomial regression.

lodplot
     Assorted plots of location score versus genetic map position.

logcondens
     Estimate a log-concave probability density from i.i.d. observations.

logilasso
     Analysis of sparse contingency tables with penalization approaches.

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

loglognorm
     Double log normal distribution functions.

logregperm
     Inference in Logistic Regression using permutation tests.

logspline
     Logspline density estimation.

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

longRPart
     Recursive partitioning of longitudinal data using mixed-effects models.

longitudinal
     Analysis of multiple time course data.

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.

lpSolveAPI
     An R interface to the lp_solve library API.

lpc
     Lassoed principal components for testing significance of features.

lpridge
     Local polynomial (ridge) regression.

lsa
     Latent Semantic Analysis.

lspls
     LS-PLS (least squares --- partial least squares) models.

lss
     Accelerated failure time model to right censored data based on least-squares principle.

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

ltsa
     Linear Time Series Analysis.

luca
     Likelihood Under Covariate Assumptions (LUCA).

lvplot
     Letter-value box plots.

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

mFilter
     Miscellenous time series filters.

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.

mapLD
     Linkage Disequilibrium mapping.

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.

marelac
     Datasets, constants, conversion factors, utilities for the marine and lacustrine sciences.

marginTree
     Margin trees for high-dimensional classification.

marginalmodelplots
     Marginal model plots for linear and generalized linear models.

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

matlab
     Emulate MATLAB code using R.

matrixcalc
     Collection of functions for matrix differential calculus.

maxLik
     Tools for Maximum Likelihood Estimation.

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.

mboost
     Gradient boosting for fitting generalized linear, additive and interaction models.

mcgibbsit
     Warnes and Raftery's MCGibbsit MCMC diagnostic.

mclust
     Model-based clustering and normal mixture modeling including Bayesian regularization.

mclust02
     Model-based cluster analysis: the 2002 version of MCLUST.

mcmc
     Functions for Markov Chain Monte Carlo (MCMC).

mco
     Multi criteria optimization algorithms.

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.

meboot
     Maximum entropy bootstrap for time series.

mefa
     Faunistic count data handling and reporting.

meifly
     Interactive model exploration using GGobi.

memisc
     Miscellaneous Tools for data management, simulation, and presentation of estimates.

merror
     Accuracy and precision of measurements.

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

metacor
     Meta-analysis of correlation coefficients.

metaMA
     Meta-analysis for MicroArrays.

mfp
     Multiple Fractional Polynomials.

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

mhsmm
     Parameter estimation and prediction for multiple hidden Markov and semi-Markov models.

mi
     Missing-data imputation and model checking.

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.

minet
     Mutual Information NETwork.

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.

minxent
     Entropy optimization distribution under constraints.

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

mirf
     Multiple Imputation and Random Forests for unobservable phase, high-dimensional data.

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

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

mixPHM
     Mixtures of proportional hazard models.

mixdist
     Finite mixture distribution models.

mixer
     Random graph clustering via estimation of Erdős-Rényi mixtures.

mixlow
     Assessing drug synergism/antagonism.

mixreg
     Functions to fit mixtures of regressions.

mixstock
     Mixed stock analysis functions.

mixtools
     Tools for mixture models.

mlCopulaSelection
     Copula selection and fitting using maximum likelihood.

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

mlegp
     Maximum Likelihood Estimates of Gaussian Processes.

mlica
     Independent Component Analysis using Maximum Likelihood.

mlmRev
     Examples from Multilevel Modelling Software Review.

mlogit
     Estimation of the multinomial logit model with choice specific variables.

mmcm
     Modified Maximum Contrast Method.

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.

mnormt
     The multivariate normal and t distributions.

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

modeest
     Mode estimation and Chernoff distribution.

modehunt
     Multiscale analysis for density functions.

modeltools
     A collection of tools to deal with statistical models.

moduleColor
     Methods for color labeling, calculation of eigengenes, and merging of closely related modules.

mokken
     Mokken Scale Analysis for test and questionnaire data.

mombf
     Moment and inverse moment Bayes factors.

moments
     Moments, skewness, kurtosis and related tests.

monomvn
     Estimation for multivariate normal data with monotone missingness.

monreg
     Estimation of monotone regression and variance functions in nonparametric models.

monoProc
     Strictly monotone smoothing procedure.

moonsun
     Basic astronomical calculations.

mota
     Mean Optimal Transformation Approach.

mpm
     Spectral map analysis.

mprobit
     Multivariate probit model for binary/ordinal response.

mra
     Analysis of capture-recapture data.

mratios
     Inferences for ratios of coefficients in the general linear model.

mrdrc
     Model-robust concentration-response analysis.

msBreast
     Protein mass spectra dataset from a breast cancer study.

msDilution
     Protein mass spectra dataset from a dilution experiment.

msProcess
     Tools for protein mass spectra processing including data preparation, denoising, noise estimation, baseline correction, intensity normalization, peak detection, peak alignment, peak quantification, and various functionalities for data ingestion/conversion, mass calibration, data quality assessment, and protein mass spectra simulation.

msProstate
     Protein mass spectra dataset from a prostate cancer study.

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.

muS2RC
     S-plus to R Compatibility for package muStat.

muStat
     Prentice rank sum test and McNemar test.

muUtil
     Utility functions for package muStat.

muhaz
     Hazard function estimation in survival analysis.

multcomp
     Multiple comparison procedures for the one-way layout.

multcompView
     Visualizations of paired comparisons.

multic
     Quantitative linkage analysis tools using the variance components approach.

multicore
     Parallel processing of R code on machines with multiple cores or CPUs.

multilevel
     Analysis of multilevel data by organizational and social psychologists.

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

multipol
     Utilities to manipulate multivariate polynomials.

multtest
     Resampling-based multiple hypothesis testing.

muscor
     Multi-stage Convex Relaxation.

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

mvna
     Nelson-Aalen estimator of the cumulative hazard in multistate models.

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.

mvtBinaryEP
     Generate correlated binary data based on the method of Emrich and Piedmonte.

mvtnorm
     Multivariate normal and t distributions.

mvtnormpcs
     Multivariate Normal and T Distribution functions of Dunnett (1989).

nFDR
     Nonparametric Estimate of FDR Based on Bernstein polynomials.

nFactors
     Non-graphical solution to the Cattell Scree Test.

ncdf
     Interface to Unidata netCDF data files.

ncf
     Spatial nonparametric covariance functions.

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.

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.

netmodels
     Tools for the study of scale free and small world networks.

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

networksis
     Simulate bipartite graphs with fixed marginals through sequential importance sampling.

neural
     RBF and MLP neural networks with graphical user interface.

neuralnet
     Training of neural networks.

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

nleqslv
     Solve systems of non-linear equations.

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.

nlrwr
     Non-linear regression with R.

nls2
     Non-linear regression with brute force.

nlstools
     Tools for nonlinear regression diagnostics.

nlt
     A nondecimated lifting transform for signal denoising.

nlts
     (Non)linear time series analysis.

nltm
     NonLinear Transformation Models for survival analysis.

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

nnls
     The Lawson-Hanson NNLS algorithm for non-negative least squares.

noia
     Implementation of the Natural and Orthogonal InterAction (NOIA) model.

nonbinROC
     ROC-type analysis for non-binary gold standards.

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.

normwm.test
     Normality and white noise testing.

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.

np
     Nonparametric kernel smoothing methods for mixed datatypes.

nparcomp
     Nonparametric relative contrast effects.

npde
     Normalized prediction distribution errors for nonlinear mixed-effect models.

nplplot
     Plotting non-parametric LOD scores from multiple input files.

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

nsRFA
     Non-supervised Regional Frequency Analysis.

numDeriv
     Accurate numerical derivatives.

nws
     Functions for NetWorkSpaces and Sleigh.

obsSens
     Sensitivity analysis for observational studies.

oc
     Optimal Classification roll call analysis.

oce
     Analysis of oceanographic data.

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

odfWeave
     Sweave processing of Open Document Format (ODF) files.

ofw
     Optimal Feature Weighting algorithm.

onemap
     Analysis of molecular marker data from non-model systems to simultaneously estimate linkage and linkage phases (genetic map construction).

onion
     A collection of routines to manipulate and visualize quaternions and octonions.

openNLP
     An interface to openNLP (http://opennlp.sourceforge.net/), a collection of natural language processing tools including a sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, and named-entity detector, using the Maxent (http://maxent.sourceforge.net/) Java package for training and using maximum entropy models.

openNLPmodels
     English and spanish models for openNLP.

opentick
     Interface to opentick real time and historical market data.

operators
     Additional binary operators for R.

optmatch
     Functions to perform optimal matching, particularly full matching.

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

orloca
     Operations Research LOCational Analysis models.

orloca.es
     Spanish version of orloca package.

orth
     Multivariate logistic regressions using orthogonalized residuals.

orthopolynom
     Functions for orthogonal and orthonormal polynomials.

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.

pARccs
     Estimation of partial attributable risks from case-control data.

pack
     Create and manipulate raw vectors.

packClassic
     Illustrate the tutorial ``S4: From Idea To Package''.

pairwiseCI
     Calculate and plot unadjusted confidence intervals for two sample comparisons.

paleoTS
     Modeling evolution in paleontological time-series.

paltran
     Functions for paleolimnology.

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.

paran
     Horn's test of principal components/factors.

partitions
     Additive partitions of integers.

partsm
     Periodic AutoRegressive Time Series Models.

party
     Unbiased recursive partitioning in a conditional inference framework.

pastecs
     Package for Analysis of Space-Time Ecological Series.

pbatR
     Frontend to PBAT to run within R.

pcaPP
     Robust PCA by Projection Pursuit.

pcalg
     Standard and robust estimation of the skeleton (ugraph) of a Directed Acyclic Graph (DAG) via the PC algorithm.

pcse
     Panel-Corrected Standard Error estimation.

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.

pec
     Prediction Error Curves for survival models.

pedigree
     Pedigree functions.

penalized
     Penalized estimation in generalized linear models.

penalizedSVM
     Feature selection SVM using penalty functions.

peperr
     Parallelised Estimation of Prediction ERRor.

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.

pga
     An ensemble method for variable selection by carrying out Darwinian evolution in parallel universes.

pgam
     Poisson-Gamma Additive Models.

pgirmess
     Functions for analysis and display of ecological and spatial data.

phangorn
     Phylogenetic analysis in R.

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

phmm
     Proportional Hazards with Mixed Model.

phpSerialize
     Serialize R to PHP associative array.

picante
     Tools for integrating phylogenies and ecology.

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

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

plRasch
     Log linear by linear asscociation models.

playwith
     A GUI for interactive plots using GTK+.

plink
     Separate calibration linking methods.

plm
     Linear models for panel data.

plotSEMM
     Graphing nonlinear latent variable interactions in SEMM.

plotpc
     Plot principal component histograms around a scatter plot.

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).

plsgenomics
     PLS analyses for genomics.

plyr
     Tools for splitting, applying and combining data.

pmg
     Poor Man's GUI.

pmml
     Generate Predictive Modelling Markup Language (PMML) for various models.

poLCA
     POlytomous variable Latent Class Analysis.

poilog
     Poisson lognormal and bivariate Poisson lognormal distribution.

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.

polyapost
     Simulating from the Polya posterior.

polycor
     Polychoric and polyserial correlations.

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

pomp
     Partially-observed Markov processes.

popbio
     Construction and analysis of matrix population models.

popgen
     Statistical and POPulation GENetics.

poplab
     Population Lab, a tool for constructing a virtual electronic population evolving over time.

portfolio
     Classes for analyzing and implementing portfolios.

portfolioSim
     Framework for simulating equity portfolio strategies.

powell
     Optimizes a function using Powell's UObyQA algorithm.

powerGWASinteraction
     Power calculations for interactions for GWAS.

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

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

ppls
     Penalized Partial Least Squares.

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.

predbayescor
     Classification rule based on Bayesian naive Bayes models with feature selection bias corrected.

predmixcor
     Classification rule based on Bayesian mixture models with feature selection bias corrected.

prefmod
     Utilities to fit paired comparison models for preferences.

prettyR
     Pretty descriptive stats.

prim
     Patient Rule Induction Method (PRIM).

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

prob
     Elementary probability on finite sample spaces.

prodlim
     Product limit estimation.

profileModel
     Tools for profiling inference functions for various model classes.

profr
     Alternative display for profiling information.

proftools
     Profile output processing tools for R.

proj4
     A simple interface to the PROJ.4 cartographic projections library.

proptest
     Tests of the proportional hazards assumption in the Cox model.

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

proxy
     Distance and similarity measures.

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

pseudo
     Pseudo-observations.

pspearman
     Spearman's rank correlation test.

pspline
     Smoothing splines with penalties on order m derivatives.

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

psych
     Procedures for personality and psychological research.

psychometric
     Applied psychometric theory: functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility.

psyphy
     Functions for analyzing psychophysical data in R.

pwr
     Basic functions for power analysis.

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.

qAnalyst
     Variables and attributes control charts.

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.

qdg
     Infer QTL-directed Dependency Graphs for phenotype networks.

qgen
     Quantitative Genetics using R.

qlspack
     Quasi least squares package.

qp
     q-order partial correlation graph search algorithm.

qpcR
     Modelling and analysis of real-time PCR data.

qtl
     Analysis of experimental crosses to identify QTLs.

qtlDesign
     Tools for the design of QTL experiments.

qtlbim
     QTL Bayesian Interval Mapping.

quadprog
     For solving quadratic programming problems.

qualV
     Qualitative methods for the validation of models.

quantchem
     Quantitative chemical analysis: calibration and evaluation of results.

quantmod
     Quantitative financial modelling framework.

quantreg
     Quantile regression and related methods.

quantregForest
     Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles.

qvalue
     Q-value estimation for false discovery rate control.

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

r2lUniv
     R to LaTeX Univariate: perform basic analysis and generate corresponding LaTeX code.

rJava
     Low-level R to Java interface.  Allows creation of objects, calling methods and accessing fields.

rPorta
     An R interface to PORTA, a collection of routines for analyzing polytopes and polyhedra.

rSymPy
     R interface to SymPy computer algebra system.

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

rake
     Raking survey datasets by re-weighting.

ramps
     Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm.

randaes
     Random number generator based on AES cipher.

random
     True random numbers using random.org.

randomLCA
     Random effects Latent Class Analysis.

randomSurvivalForest
     Ishwaran and Kogalur's random survival forest.

randomForest
     Breiman's random forest classifier.

randtoolbox
     Toolbox for pseudo and quasi random number generation.

rankreg
     Rank regression estimator for the AFT model with right censored data.

rateratio.test
     Exact rate ratio test.

rattle
     A graphical user interface for data mining in R using GTK.

rbounds
     Perform Rosenbaum bounds sensitivity tests for matched data.

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.

rcdk
     Interface to the CDK libraries, a Java framework for cheminformatics.

rcdklibs
     CDK libraries packaged for R.

rcom
     R COM Client Interface and internal COM Server.

rcompgen
     Completion generator for R.  Recommended for R 2.5.0 or 2.6.0.

rconifers
     Interface to the CONIFERS forest growth model.

rda
     Shrunken Centroids Regularized Discriminant Analysis.

rdetools
     Relevant Dimension Estimation (RDE) in feature spaces.

realized
     Realized variance toolkit.

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.

regsubseq
     Detect and test regular sequences and subsequences.

regtest
     Regression testing.

rela
     Item analysis with standard errors.

relaimpo
     RELAtive IMPOrtance of regressors in linear models.

relations
     Data structures for k-ary relations with arbitrary domains, predicate functions, and fitters for consensus relations.

relax
     Functions for report writing, presentation, and programming.

relaxo
     Relaxed Lasso.

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.

remMap
     Regularized multivariate regression for identifying master predictors.

repolr
     Repeated measures proportional odds logistic regression.

reporttools
     Generate LaTeX tables of descriptive statistics

reshape
     Flexibly reshape data.

resper
     Sampling from restricted permutations.

reweight
     Adjustment of survey respondent weights.

rgcvpack
     R interface for GCVPACK Fortran package.

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

rgenoud
     R version of GENetic Optimization Using Derivatives.

rggobi
     Interface between R and GGobi.

rgl
     3D visualization device system (OpenGL).

rgr
     The GSC (Geological Survey of Canada) applied geochemistry EDA package.

rgrs
     Functions to make R usage in social sciences easier (documentation in french).

rhosp
     Side effect risks in hospital: simulation and estimation.

richards
     Richards curves.

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

rindex
     Indexing for R.

risksetROC
     Riskset ROC curve estimation from censored survival data.

rjacobi
     Jacobi polynomials and Gauss-Jacobi quadrature related operations.

rjags
     Bayesian graphical models via an interface to the JAGS MCMC library.

rjson
     JSON (JavaScript Object Notation) for R.

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.

rngwell19937
     WELL19937a random number generator.

robCompositions
     Robust estimation for compositional data.

robfilter
     Robust time series filters.

robust
     Insightful robust package.

robustbase
     Basic Robust Statistics.

rootSolve
     Nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations.

roxygen
     A Doxygen-like in-source documentation system for Rd, collation, namespace and callgraphs.

rpanel
     Simple interactive controls for R using the tcltk package.

rpart
     Recursive PARTitioning and regression trees.  Recommended.

rpubchem
     R interface to the PubChem collection.

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.

rrp
     Random Recursive Partitioning.

rscproxy
     A portable C-style interface to R (StatConnector).

rsm
     Response-Surface Models.

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.

rtiff
     Read TIFF format images and return them as pixmap objects.

rtv
     Random Time Variable objects.

runjags
     Run Bayesian MCMC models in the BUGS syntax using JAGS.

rv
     Simulation-based random variable object class.

rwm
     R Workspace Manager.

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

s20x
     Stats 20x functions.

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

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.

sampleSelection
     Estimation of sample selection models.

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

samr
     Significance Analysis of Microarrays.

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

sapa
     Insightful Spectral Analysis for Physical Applications.

sbgcop
     Semiparametric Bayesian Gaussian copula estimation.

sca
     Simple Component Analysis.

scaleboot
     Approximately unbiased p-values via multiscale bootstrap.

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

scapeMCMC
     Markov-chain Monte Carlo diagnostic plots, accompanying the scape package.

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

schoolmath
     Functions and datasets for math used in school.

sciplot
     Scientific graphing functions for factorial designs.

scope
     Data manipulation using arbitrary row and column criteria.

scout
     Scout method for covariance-regularized regression.

scrime
     Tools for the analysis of high-dimensional data developed/implemented at the group ``Statistical Complexity Reduction In Molecular Epidemiology'' (SCRIME), with main focus on SNP data.

scuba
     Scuba diving calculations and decompression models.

sda
     Shrinkage Discriminant Analysis.

sdcMicro
     Statistical Disclosure Control methods for the generation of public and scientific use files.

sdcTable
     Statistical Disclosure Control for tabular data.

sde
     Simulation and inference for Stochastic Differential Equations.

sdtalt
     Signal Detection Theory measures and ALTernatives.

sdtoolkit
     Scenario discovery tools to support robust decision making.

seacarb
     Calculates parameters of the seawater carbonate system.

seas
     Detailed seasonal plots of temperature and precipitation data.

seewave
     Time wave analysis and graphical representation.

segclust
     Segmentation and segmentation/clustering.

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

selectiongain
     Calculate the gain from a model selection.

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

sendplot
     Tool for sending interactive plots.

sensR
     Thurstonian models for sensory discrimination.

sensitivity
     Sensitivity analysis.

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

seqmon
     Sequential monitoring of clinical trials.

seriation
     Infrastructure for seriation.

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

setRNG
     Set (normal) random number generator and seed.

sets
     Data structures and basic operations for ordinary sets, and generalizations such as fuzzy sets, multisets, and fuzzy multisets.

sfsmisc
     Utilities from Seminar fuer Statistik ETH Zurich.

sgeostat
     An object-oriented framework for geostatistical modeling.

shape
     Functions for plotting graphical shapes.

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.

siar
     Stable Isotope Analysis in R.

sigma2tools
     Test of hypothesis about sigma2.

signal
     A set of generally Matlab/Octave-compatible signal processing functions.

signalextraction
     Real-time signal extraction (Direct Filter Approach).

simba
     Functions for similarity calculation of binary data.

simco
     Import Structure files and deduce similarity coefficients from them.

simecol
     SIMulation of ECOLogical (and other) dynamic systems.

simex
     SIMEX and MCSIMEX algorithms for measurement error models.

similarityRichards
     Similarity of Richards curves.

simone
     Statistical Inference for MOdular NEtworks (SIMoNe).

simpleboot
     Simple bootstrap routines.

singlecase
     Tests for single case studies in neuropsychology.

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.

smacof
     Multidimensional scaling based on stress minimization by means of majorization (smacof).

smatr
     (Standardized) Major Axis estimation and Testing Routines.

smoothSurv
     Survival regression with smoothed error distribution.

smoothtail
     Smooth estimation of generalized Pareto distribution shape parameter.

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.

snowfall
     Wrapper around snow for easier development of parallel R programs.

snp.plotter
     Plots of p-values using single SNP and/or haplotype data.

snpXpert
     Tools to analyze SNP data.

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.

spBayes
     Fit Gaussian models with potentially complex hierarchical error structures by Markov chain Monte Carlo (MCMC).

space
     Sparse PArtial Correlation Estimation.

spam
     SPArse Matrix algebra.

sparseLDA
     Sparse Linear Discriminant Analysis for gaussians and mixture of gaussians models.

spatclus
     Arbitrarily shaped multiple spatial cluster detection for case event data.

spatgraphs
     Graphs for 2-d point patterns.

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.

spatialkernel
     Nonparameteric estimation of spatial segregation in a multivariate point process.

spatialsegregation
     Segregation measures for multitype spatial point patterns.

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.

spcosa
     SPatial COverage SAmpling.

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.

spgrass6
     Interface between the GRASS 6.0 geographical information system and R.

spgwr
     Geographically weighted regression.

splancs
     Spatial and space-time point pattern analysis functions.

spls
     Sparse Partial Least Squares (SPLS) regression.

splus2R
     Insightful package providing missing S-PLUS functionality in R.

spssDDI
     Read SPSS system files and produce valid DDI version 3.0 documents.

spsurvey
     Spatial survey design and analysis.

spuRs
     Functions and datasets from the book ``An Introduction to Scientific Programming and Simulation Using R'' by O. Jones, R. Maillardet and A. Robinson, 2009, CRC Press.

sqldf
     Perform SQL selects on R data frames.

ssanv
     Sample Size Adjusted for Nonadherence or Variability of input parameters.

ssize.fdr
     Sample size calculations for microarray experiments.

sspir
     State SPace models In R.

sspline
     Smoothing splines on the sphere.

st
     Shrinkage t statistic.

staRt
     Inferenza classica con TI-83 Plus.

stab
     Data analysis of drug stability.

startupmsg
     Utilities for start-up messages.

stashR
     A Set of Tools for Administering SHared Repositories.

statmod
     Miscellaneous biostatistical modelling functions.

statnet
     Software tools for the statistical modeling of network data.

stepPlr
     L2 penalized logistic regression with a stepwise variable selection.

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

stinepack
     Stineman interpolation package.

stochasticGEM
     Fitting Stochastic General Epidemic Models.

stochmod
     Learning and inference algorithms for a variety of probabilistic models.

stream.net
     Building and analyzing binary stream networks.

strucchange
     Various tests on structural change in linear regression models.

subplex
     The subplex algorithm for unconstrained optimization.

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.

sudoku
     Sudoku puzzle solver.

supclust
     Methodology for supervised grouping of predictor variables.

superpc
     Supervised principal components.

surv2sample
     Two-sample tests for survival analysis.

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

survcomp
     Performance assessment and comparison for survival analysis.

surveillance
     Outbreak detection algorithms for surveillance data.

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

surveyNG
     Complex survey samples: database interface, sparse matrices.

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

survivalROC
     Time-dependent ROC curve estimation from censored survival data.

survrec
     Survival analysis for recurrent event data.

svGUI
     SciViews GUI API: functions to manage GUI clients.

svIDE
     SciViews GUI API: functions to interact with external IDE/code editors.

svMisc
     SciViews GUI API: miscellaneous functions.

svSocket
     SciViews GUI API socket server.

svcR
     A support vector machine technique for clustering.

svcm
     2d and 3d Space-Varying Coefficient Models.

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).

taskPR
     Task-Parallel R package.

tcltk2
     A series of widgets and functions to supplement tcltk.

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

tdm
     A tool for Therapeutic Drug Monitoring.

tdthap
     Transmission/disequilibrium tests for extended marker haplotypes.

tensor
     Tensor product of arrays.

tensorA
     Advanced tensors arithmetic with named indices.

termstrc
     Term structure and credit spread estimation.

tframe
     Time Frame coding kernel: functions for writing code that is independent of the way time is represented.

tframePlus
     Time Frame coding kernel extensions.

tgp
     Bayesian regression and adaptive sampling with Treed Gaussian Process models.

tileHMM
     Hidden Markov Models for ChIP-on-Chip analysis.

time
     Time tracking for developers.

timeDate
     The Rmetrics module for ``Chronological and Calendarical Objects''.

timeSeries
     The Rmetrics module for ``Financial Time Series Objects''.

timereg
     Code and data sets for the book ``Dynamic Regression Models for Survival Data'' by T.  Martinussen and T. Scheike, 2006, Springer Verlag, plus more recent developments.

timsac
     TIMe Series Analysis and Control package.

tis
     Time indexes and time indexed series.

titan
     Titration analysis for mass spectrometry data.

titecrm
     TIme-To-Event Continual Reassessment Method and calibration tools.

tkrgl
     TK widget tools for rgl package.

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

tlemix
     Trimmed maximum likelihood estimation for robust fitting of finite mixture models.

tlnise
     Two-level normal independent sampling estimation.

tm
     A framework for text mining applications within R.

topmodel
     An R implementation of TOPMODEL.

tossm
     Testing Of Spatial Structure Methods.

tpr
     Temporal Process Regression.

trackObjs
     Track objects.

tradeCosts
     Post-trade analysis of transaction costs.

tree
     Classification and regression trees.

treelet
     Treelet: a novel construction of multi-scale bases that extends wavelets to non-smooth signals.

triangle
     Standard distribution functions for the triangle distribution.

trimcluster
     Cluster analysis with trimming.

trip
     Spatial analysis of animal track data.

tripEstimation
     Metropolis sampler and supporting functions for estimating animal movement from archival tags and satellite fixes.

tripack
     A constrained two-dimensional Delaunay triangulation package.

truncgof
     Goodness-of-fit tests allowing for left truncated data.

truncnorm
     Truncated normal distribution.

truncreg
     Truncated regression models.

trust
     Local optimization using two derivatives and trust regions.

tsDyn
     Time series analysis based on dynamical systems theory.

tsModel
     Time series modeling for air pollution and health.

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

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

tsfa
     Time Series Factor Analysis.

tslars
     Least angle regression for time series analysis.

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

tutoR
     Student-friendly package to mask common functions.

twang
     Toolkit for Weighting and Analysis of Nonequivalent Groups.

tweedie
     Maximum likelihood computations for Tweedie exponential family models.

twslm
     A two-way semilinear model for normalization and analysis of cDNA microarray data.

ucminf
     Unconstrained nonlinear optimization via UCMINF.

udunits
     Interface to Unidata's routines to convert units.

ump
     Uniformly Most Powerful tests.

unbalhaar
     Function estimation via Unbalanced Haar wavelets.

uncompress
     For uncompressing .Z files.

untb
     Ecological drift under the UNTB (Unified Neutral Theory of Biodiversity).

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.

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

vars
     VAR modeling.

vbmp
     Variational Bayesian Multinomial Probit Regression.

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.

vowels
     Vowel manipulation, normalization, and plotting.

vrtest
     Variance ratio tests for weak-form market efficiency.

wasim
     Tools for data processing and visualization of results of the WASIM-ETH hydrological model.

waveclock
     Time-frequency analysis of cycling cell luminescence data.

waved
     WaveD transform in R.

wavelets
     Functions for computing wavelet filters, wavelet transforms and multiresolution analyses.

waveslim
     Basic wavelet routines for time series analysis.

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

wccsom
     SOM networks for comparing patterns with peak shifts.

wgaim
     Whole Genome Average Interval Mapping for QTL detection using mixed models.

wikibooks
     Functions and datasets for the German WikiBook ``GNU R''.

wle
     Robust statistical inference via a weighted likelihood approach.

wmtsa
     Insightful Wavelet Methods for Time Series Analysis.

wnominate
     WNOMINATE roll call analysis software.

wombsoft
     Wombling computation.

wordnet
     WordNet interface.

write.snns
     Function for exporting data to SNNS (Stuttgart Neural Network Simulator) pattern files.

x12
     A wrapper function and GUI for the X12 binaries under windows.

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

xtable
     Export data to LaTeX and HTML tables.

xts
     Extensible time series.

yaImpute
     Performs popular nearest neighbor routines.

yacca
     Yet Another Canonical Correlation Analysis package.

yaml
     Methods to convert R to YAML and back.

yest
     Gaussian independence models.

zipfR
     Statistical models for word frequency distributions.

zoeppritz
     Zoeppritz equations: calculate and plot scattering coefficients of seismic waves when they interact at an interface between two layers.

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

zyp
     Zhang & Yue-Pilon approach to determining trends in climate data.

See CRAN src/contrib/PACKAGES for more information.

Some CRAN packages that do not build out of the box on Windows, require additional software, or are shipping third party libraries for Windows cannot be made available on CRAN in form of a Windows binary packages.  Nevertheless, some of these packages are available at the ``CRAN extras'' repository at http://www.stats.ox.ac.uk/pub/RWin/ kindly provided by Brian D. Ripley.  Note that this repository is a default repository for recent versions of R for Windows.

There used to be a CRAN src/contrib/Devel directory with packages still ``under development'' or depending on features only present in the current development versions of R.  This area is no longer provided, with packages formerly in this area either in the regular package area or the archive src/contrib/Archive.

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.

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

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.

IDynDocs
     Interactive and dynamic Documents with XML/XSL.

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.

RGoogleDocs
     Initial, elementary interface to Google's Document API.

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.

RGtkIPrimitives
     A collection of low-level primitives for interactive use with R graphics and the gtkDevice using RGtk.

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.

RKML
     Simple tools for creating KML displays from R.

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.

Rcompression
     In-memory decompression for GNU zip and bzip2 formats.

Rexif
     Extract meta-information from JPEG files.

Rflickr
     R interface to Flickr API.

Rlibstree
     Suffix Trees in R via the libstree C library.

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

RwxDevice
     R graphics device using wxWidgets.

RwxWidgets
     Facilities to program GUIs using wxWidgets in R.

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.

SVGAnnotation
     Tools for post-processing SVG plots created in R.

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

SWinTypeLibs
     Provides ways to extract type information from type libraries and/or DCOM objects that describes the methods, properties, etc., of an interface.

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.

Zillow
     Simple interface to Zillow.com's house price estimate API.

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.

ABarray
     Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Micorarray (AB1700) gene expression data.

ACME
     Algorithms for Calculating Microarray Enrichment (ACME).

AffyCompatible
     Affymetrix GeneChip software compatibility

AffyExpress
     Affymetrix quality assessment and analysis tool.

AnnBuilder
     Processing annotation date from public data repositories and building annoation data packages or XML data documents using the source data.

AnnotationDbi
     Annotation DataBase Interface.

BAC
     Bayesian Analysis of Chip-chip experiment.

BCRANK
     Predicting binding site consensus from ranked DNA sequences.

BGmix
     Bayesian models for differential gene expression.

BSgenome
     Infrastructure for Biostrings-based genome data packages.

BioMVCClass
     Model-View-Controller (MVC) classes that use Biobase.

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

BiocCaseStudies
     Support for the Bioconductor Case Studies monograph.

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

BufferedMatrix
     Microarray analysis methods that use BufferedMatrix objects.

BufferedMatrixMethods
     A matrix data storage object held in temporary files.

CALIB
     Calibration model for estimating absolute expression levels from microarray data.

CGHcall
     Calling aberrations for array CGH tumor profiles.

CORREP
     Multivariate correlation estimation and statistical inference.

Category
     A collection of tools for performing category analysis.

CoCiteStats
     A collection of software tools for dealing with co-citation data.

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.

EBImage
     Image processing and image analysis toolkit.

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

GEOmetadb
     A compilation of metadata from NCBI GEO.

GEOquery
     Get data from NCBI Gene Expression Omnibus (GEO).

GGBase
     Infrastructure for genetics of gene expression.

GGtools
     Software and data for genetical genomics.

GLAD
     Gain and Loss Analysis of DNA.

GOstats
     Tools for manipulating GO and microarrays.

GSEABase
     Gene set enrichment data structures and methods.

GSEAlm
     Linear model toolset for Gene Set Enrichment Analysis.

GeneMeta
     A collection of meta-analysis tools for analyzing high throughput experimental data.

GeneR
     Package manipulating nucleotidic sequences (Embl, Fasta, GenBank).

GeneRfold
     R for genes and sequences, using viennaRNA package (fold).

GeneSelector
     GeneSelector.

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

GeneTraffic
     GeneTraffic R integration functions.

GeneticsBase
     Classes and functions for handling genetic data.

GeneticsDesign
     Functions for designing genetics studies.

GeneticsPed
     Pedigree and genetic relationship functions.

GenomeGraphs
     Plotting genomic information from Ensembl.

GlobalAncova
     Calculates a global test for differential gene expression between groups.

GraphAT
     Graph theoretic Association Tests.

GraphAlignment
     GraphAlignment.

HEM
     Heterogeneous Error Model for analysis of microarray data.

Harshlight
     A ``corrective make-up'' program for microarray chips.

Heatplus
     A heat map displaying covariates and coloring clusters.

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

KEGGSOAP
     Client-side SOAP access KEGG.

LBE
     Estimation of the false discovery rate.

LMGene
     Analysis of microarray data using a linear model and glog data transformation.

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

MANOR
     Micro-Array NORmalization.

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

MVCClass
     Model-View-Controller (MVC) classes.

MantelCorr
     Compute Mantel Cluster Correlations.

MassSpecWavelet
     Mass spectrum processing by wavelet-based algorithms.

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.

Mfuzz
     Soft clustering of time series gene expression data.

MiPP
     Misclassification Penalized Posterior Classification.

OCplus
     Operating characteristics plus sample size and local fdr for microarray experiments.

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

OLINgui
     Graphical user interface for OLIN.

OrderedList
     Similarities of ordered gene lists.

OutlierD
     Outlier detection using quantile regression on the M-A scatterplots of high-throughput data.

PCpheno
     Phenotypes and cellular organizational units.

PGSEA
     Parametric Gene Set Enrichment Analysis.

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.

RBioinf
     Support for R for Bioinformatics monograph.

RLMM
     A genotype calling algorithm for Affymetrix SNP arrays.

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.

RWebServices
     Expose R functions as web services through Java/Axis/Apache.

RankProd
     Rank Product method for identifying differentially expressed genes.

RbcBook1
     Support for Springer monograph on Bioconductor.

Rdbi
     Generic framework for database access in R.

RdbiPgSQL
     Methods for accessing data stored in PostgreSQL tables.

Rdisop
     Decomposition of isotopic patterns.

RefPlus
     Functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods.

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.

Ringo
     R Investigation of NimbleGen Oligoarrays..

Rintact
     Interface to EBI Intact protein interaction data base.

Rredland
     Interface to redland RDF utilities.

Rtreemix
     Mutagenetic trees mixture models.

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

SAGx
     Statistical Analysis of the GeneChip.

SBMLR
     Systems Biology Markup Language (SBML) interface and biochemical system analysis tools.

SLGI
     Synthetic Lethal Genetic Interaction.

SLqPCR
     Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH.

SMAP
     A Segmental Maximum A Posteriori approach to array-CGH copy number profiling.

SNPchip
     Classes and methods for high throughput SNP chip data.

ScISI
     In Silico Interactome.

SemSim
     Gene ontology-based semantic similarity measures.

TypeInfo
     Optional type specification prototype.

VanillaICE
     Methods for fitting Hidden Markov Models to SNP chip data.

XDE
     A Bayesian hierarchical model for cross-study analysis of differential gene expression.

aCGH
     Classes and functions for Array Comparative Genomic Hybridization data.

adSplit
     Annotation-driven clustering.

affxparser
     Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR).

affy
     Methods for Affymetrix Oligonucleotide Arrays.

affyPLM
     For fitting Probe Level Models.

affyPara
     Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays.

affyQCReport
     QC Report Generation for affyBatch objects.

affycomp
     Graphics toolbox for assessment of Affymetrix expression measures.

affycoretools
     Functions useful for those doing repetitive analyses.

affyio
     Tools for parsing Affymetrix data files.

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.

annotationTools
     Annotate microarrays and perform cross-species gene expression analyses using flat file databases.

apComplex
     Estimate protein complex membership using AP-MS protein data.

aroma.light
     Light-weight methods for normalization and visualization of microarray data using only basic R data types.

arrayQuality
     Performing print-run and array level quality assessment.

arrayQualityMetrics
     Quality metrics on ExpressionSets.

beadarray
     Quality control and low-level analysis of BeadArrays.

beadarraySNP
     Normalization and reporting of Illumina SNP bead arrays.

bgafun
     A method to identify specifity determining residues in protein families.

bgx
     Bayesian Gene eXpression.

bioDist
     A collection of software tools for calculating distance measures.

biocGraph
     Graph examples and use cases in Bioinformatics.

biocViews
     Categorized views of R package repositories.

biomaRt
     Interface to BioMart databases (e.g., Ensembl)

bridge
     Bayesian Robust Inference for Differential Gene Expression.

cellHTS
     Analysis of cell-based screens.

cellHTS2
     Analysis of cell-based screens --- revised version of cellHTS.

cghMCR
     Find chromosome regions showing common gains/losses.

clusterStab
     Compute cluster stability scores for microarray data.

codelink
     Manipulation of Codelink Bioarrays data.

convert
     Convert Microarray Data Objects.

copa
     Functions to perform cancer outlier profile analysis.

cosmo
     Supervised detection of conserved motifs in DNA sequences.

cosmoGUI
     GUI for constructing constraint sets used by the cosmo package.

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.

diffGeneAnalysis
     Performs differential Gene expression Analysis.

ecolitk
     Metadata and tools to work with E. coli.

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

exonmap
     High level analysis of Affymetrix exon array data.

explorase
     GUI for exploratory data analysis of systems biology data.

externalVector
     Vector objects for R with external storage.

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.

fbat
     Family Based Association Tests for genetic data.

fdrame
     FDR Adjustments of Microarray Experiments (FDR-AME).

flowClust
     Clustering for flow cytometry.

flowCore
     Basic structures for flow cytometry data.

flowQ
     Qualitiy control for flow cytometry.

flowUtils
     Utilities for flow cytometry.

flowViz
     Visualization for flow cytometry.

gaga
     GaGa hierarchical model for microarray data analysis.

gaggle
     Broadcast data between R and Java bioinformatics programs.

gcrma
     Background adjustment using sequence information.

genArise
     A tool for dual color microarray data.

geneRecommender
     A gene recommender algorithm to identify genes coexpressed with a query set of genes.

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.

goProfiles
     Statistical analysis of functional profiles.

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.

hexbin
     Binning functions, in particular hexagonal bins for graphing.

hopach
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

hypergraph
     Capabilities for representing and manipulating hypergraphs.

idiogram
     Plotting genomic data by chromosomal location.

impute
     Imputation for microarray data (currently KNN only).

keggorth
     Graph support for KO, KEGG Orthology.

lapmix
     Laplace mixture model in microarray experiments.

limma
     Linear models for microarray data.

limmaGUI
     Graphical User Interface for package limma.

logicFS
     Identification of SNP interactions.

lumi
     BeadArray specific methods for Illumina microarrays.

maCorrPlot
     Visualize artificial correlation in microarray data.

maDB
     Microarray database and utility functions for microarray analysis.

maSigPro
     Significant gene expression profile differeneces in time course microarray data.

maanova
     Tools for analyzing micro array experiments.

macat
     MicroArray Chromosome Analysis Tool.

made4
     Multivariate analysis of microarray data using ADE4.

maigesPack
     Functions to handle cDNA microarray data, including several methods of data analysis.

makePlatformDesign
     Creates the Platform Design environments (PDenvs) required by oligo.

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.

mdqc
     Mahalanobis Distance Quality Control for microarrays.

metaArray
     Integration of microarray data for meta-analysis.

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.

nem
     Nested Effects Models to reconstruct phenotypic hierarchies.

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

nudge
     Normal Uniform Differential Gene Expression detection.

occugene
     Functions for multinomial occupancy distribution.

oligo
     Oligonucleotide arrays.

oligoClasses
     Classes for high-throughput SNP arrays.

oneChannelGUI
     Extend the capabilities of affylmGUI.

ontoTools
     Graphs and sparse matrices for working with ontologies; formal objects for nomenclatures with provenance management.

pamr
     Pam: Prediction Analysis for Microarrays.

panp
     Presence-Absence calls from Negative strand matching Probesets.

pathRender
     Render molecular pathways.

pcaMethods
     A collection of PCA methods.

pcot2
     Principal coordinates and Hotelling's T-square method.

pdInfoBuilder
     Platform design information package builder.

pdmclass
     CLASSification of microarray samples using Penalized Discriminant Methods.

pgUtils
     Utility functions for PostgreSQL databases.

pickgene
     Adaptive gene picking for microarray expression data analysis.

pkgDepTools
     Package dependency tools.

plgem
     Power Law Global Error Model.

plier
     Implements the Affymetrix PLIER (Probe Logarithmic Error Intensity Estimate) algorithm.

plw
     Probe level Locally moderated Weighted t-tests.

ppiStats
     Protein-Protein Interaction Statistical package.

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

preprocessCore
     A collection of pre-processing functions.

puma
     Propagating Uncertainty in Microarray Analysis.

quantsmooth
     Quantile smoothing and genomic visualization of array data.

qvalue
     Q-value estimation for false discovery rate control.

rHVDM
     Hidden Variable Dynamic Modeling.

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

rbsurv
     Robust likelihood-based survival modeling with microarray data.

reb
     Regional Expression Biases.

rflowcyt
     Statistical tools and data structures for analytic flow cytometry.

rsbml
     R support for SBML, using libsbml.

rtracklayer
     R interface to genome browsers and their annotation tracks.

safe
     Significance Analysis of Function and Expression.

sagenhaft
     Functions for reading and comparing SAGE (Serial Analysis of Gene Expression) libraries.

seqLogo
     Sequence logos for DNA sequence alignments.

sigPathway
     Pathway analysis.

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.

simulatorAPMS
     Computationally simulates the AP-MS technology.

sizepower
     Sample size and power calculation in microrarray studies.

snapCGH
     Segmentation, normalization and processing of aCGH data.

snpMatrix
     The snp.matrix and X.snp.matrix classes.

spikeLI
     Affymetrix Spike-in Langmuir Isotherm data analysis tool.

splicegear
     A set of tools to work with alternative splicing.

splots
     Visualization routines for high throughput screens.

spotSegmentation
     Microarray spot segmentation and gridding for blocks of microarray spots.

sscore
     S-score algorithm for Affymetrix oligonucleotide microarrays.

ssize
     Estimate microarry sample size.

stam
     STructured Analysis of Microarray data.

stepNorm
     Stepwise normalization functions for cDNA microarrays.

tilingArray
     Analysis of tiling arrays.

timecourse
     Statistical analysis for developmental microarray time course data.

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

topGO
     Enrichment analysis for Gene Ontology.

twilight
     Estimation of local false discovery rate.

vbmp
     Variational Bayesian Multinomial Probit regression.

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

weaver
     Tools and extensions for processing Sweave documents.

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.

xcms
     LC/MS and GC/MS data analysis: framework for processing and visualization of chromatographically separated mass spectral data.

xps
     Methods for processing and analysis of Affymetrix Oligonucleotide Arrays.

yaqcaffy
     Affymetrix expression data quality control and reproducibility analysis.

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://popgen.unimaas.nl/~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 in your library search path (see the help page for .libPaths() for details on how the search path is determined).

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 repositories such as CRAN.  See the help page for available.packages() 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.)
          codetools     Code Analysis Tools for R
          datasets      The R Datasets Package
          foreign       Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat,
                        dBase, ...
          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 a Unix-alike, if your installation supports the type="cairo" option to the png() device there should be no problems, and the default settings should just work.  This option is not available for versions of R prior to 2.7.0, or without support for cairo.  From R 2.7.0 png() by default uses the Quartz device on Mac OS X, and that too works in batch mode.

Earlier versions of 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 or PDF file then converts it to any bitmap format supported by Ghostscript.  On some installations this produces ugly output, on others it is perfectly satisfactory.  Many systems now come with Xvfb from X.Org (http://www.x.org/Downloads.html) (possibly as an optional install), which is an X11 server that does not require a screen; and there is the GDD package from CRAN, which produces PNG, JPEG and GIF bitmaps without X11.

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() (e.g., DF[order(DF$a, DF[["b"]]), ] to sort the data frame DF on columns named a and b).

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 inadvertently 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.

To quote from ``The Elements of Programming Style'' by Kernighan and Plauger:

     10.0 times 0.1 is hardly ever 1.0.

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).

Why are powers of negative numbers wrong?
=========================================

You are probably seeing something like

     R> -2^2
     [1] -4

and misunderstanding the precedence rules for expressions in R. Write

     R> (-2)^2
     [1] 4

to get the square of -2.

The precedence rules are documented in ?Syntax, and to see how R interprets  an expression you can look at the parse tree

     R> as.list(quote(-2^2))
     [[1]]
     `-`

     [[2]]
     2^2

How can I save the result of each iteration in a loop into a separate file?
===========================================================================

One way is to use paste() (or sprintf()) to concatenate a stem filename and the iteration number while file.path() constructs the path.  For example, to save results into files result1.rda, ..., result100.rda in the subdirectory Results of the current working directory, one can use

     for(i in 1:100) {
       ## Calculations constructing "some_object" ...
       fp <- file.path("Results", paste("result", i, ".rda", sep = ""))
       save(list = "some_object", file = fp)
     }

Why are p-values not displayed when using lmer()?
=================================================

Doug Bates has kindly provided an extensive response in a post to the r-help list, which can be reviewed at https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html.

Why are there unwanted borders, lines or grid-like artifacts when viewing a plot saved to a PS or PDF file?
===========================================================================================================

This can occur when using functions such as polygon(), filled.contour(), image() or other functions which may call these internally.  In the case of polygon(), you may observe unwanted borders between the polygons even when setting the border argument to NA or "transparent".

The source of the problem is the PS/PDF viewer when the plot is anti-aliased.  The details for the solution will be different depending upon the viewer used, the operating system and may change over time. For some common viewers, consider the following:

Acrobat Reader (cross platform)
     There are options in Preferences to enable/disable text smoothing, image smoothing and line art smoothing. Disable line art smoothing.

Preview (Mac OS X)
     There is an option in Preferences to enable/disable anti-aliasing of text and line art. Disable this option.

GSview (cross platform)
     There are settings for Text Alpha and Graphics Alpha. Change Graphics Alpha from 4 bits to 1 bit to disable graphic anti-aliasing.

gv (Linux/Unix X)
     There is an option to enable/disable anti-aliasing. Disable this option.

Evince (Linux/GNOME)
     There is not an option to disable anti-aliasing in this viewer.

Okular (Linux/KDE)
     There is not an option in the GUI to enable/disable anti-aliasing. From a console command line, use:

          $ kwriteconfig --file okularpartrc --group 'Dlg Performance' \
                         --key TextAntialias Disabled

     Then restart Okular.  Change the final word to Enabled to restore the original setting.

Why does backslash behave strangely inside strings?
===================================================

This question most often comes up in relation to file names (see How do file names work in Windows?)  but it also happens that people complain that they cannot seem to put a single \ character into a text string unless it happens to be followed by certain other characters.

To understand this, you have to distinguish between character strings and representations of character strings.  Mostly, the representation in R is just the string with a single or double quote at either end, but there are strings that cannot be represented that way, e.g., strings that themselves contains the quote character.  So

     > str <- "This \"text\" is quoted"
     > str
     [1] "This \"text\" is quoted"
     > cat(str, "\n")
     This "text" is quoted

The escape sequences \" and \n represent a double quote and the newline character respectively. Printing text strings, using print() or by typing the name at the prompt will use the escape sequences too, but the cat() function will display the string as-is. Notice that "\n" is a one-character string, not two; the backslash is not actually in the string, it is just generated in the printed representation.

     > nchar("\n")
     [1] 1
     > substring("\n", 1, 1)
     [1] "\n"

So how do you put a backslash in a string? For this, you have to escape the escape character. I.e., you have to double the backslash. as in

     > cat("\\n", "\n")
     \n

Some functions, particularly those involving regular expression matching, themselves use metacharacters, which may need to be escaped by the backslash mechanism.  In those cases you may need a quadruple backslash to represent a single literal one.

In versions of R up to 2.4.1 an unknown escape sequence like \p was quietly interpreted as just p.  Current versions of R emit a warning.

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.  For example, people sometimes think there is a bug in R's mathematics because they don't understand how finite-precision arithmetic works.  Rather than jumping to conclusions, show the problem to someone who knows for certain. Unexpected results of comparison of decimal numbers, for example 0.28 * 100 != 28 or 0.1 + 0.2 != 0.3, are not a bug. See Why doesn't R think these numbers are equal?, for more details.

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 (e.g., http://en.wikipedia.org/wiki/Reproducibility) 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.  Finally, check carefully whether the bug is with R, or a contributed package.  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.

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/.  Please try including results of sessionInfo() in your bug report.

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 come soon ...