R FAQ
Frequently Asked Questions on R
Version 2.2.2005-12-19
ISBN 3-900051-08-9
Kurt Hornik


Table of Contents
*****************

R FAQ
1 Introduction
  1.1 Legalese
  1.2 Obtaining this document
  1.3 Citing this document
  1.4 Notation
  1.5 Feedback
2 R Basics
  2.1 What is R?
  2.2 What machines does R run on?
  2.3 What is the current version of R?
  2.4 How can R be obtained?
  2.5 How can R be installed?
    2.5.1 How can R be installed (Unix)
    2.5.2 How can R be installed (Windows)
    2.5.3 How can R be installed (Macintosh)
  2.6 Are there Unix binaries for R?
  2.7 What documentation exists for R?
  2.8 Citing R
  2.9 What mailing lists exist for R?
  2.10 What is CRAN?
  2.11 Can I use R for commercial purposes?
  2.12 Why is R named R?
  2.13 What is the R Foundation?
3 R and S
  3.1 What is S?
  3.2 What is S-PLUS?
  3.3 What are the differences between R and S?
    3.3.1 Lexical scoping
    3.3.2 Models
    3.3.3 Others
  3.4 Is there anything R can do that S-PLUS cannot?
  3.5 What is R-plus?
4 R Web Interfaces
5 R Add-On Packages
  5.1 Which add-on packages exist for R?
    5.1.1 Add-on packages in R
    5.1.2 Add-on packages from CRAN
    5.1.3 Add-on packages from Omegahat
    5.1.4 Add-on packages from Bioconductor
    5.1.5 Other add-on packages
  5.2 How can add-on packages be installed?
  5.3 How can add-on packages be used?
  5.4 How can add-on packages be removed?
  5.5 How can I create an R package?
  5.6 How can I contribute to R?
6 R and Emacs
  6.1 Is there Emacs support for R?
  6.2 Should I run R from within Emacs?
  6.3 Debugging R from within Emacs
7 R Miscellanea
  7.1 How can I set components of a list to NULL?
  7.2 How can I save my workspace?
  7.3 How can I clean up my workspace?
  7.4 How can I get eval() and D() to work?
  7.5 Why do my matrices lose dimensions?
  7.6 How does autoloading work?
  7.7 How should I set options?
  7.8 How do file names work in Windows?
  7.9 Why does plotting give a color allocation error?
  7.10 How do I convert factors to numeric?
  7.11 Are Trellis displays implemented in R?
  7.12 What are the enclosing and parent environments?
  7.13 How can I substitute into a plot label?
  7.14 What are valid names?
  7.15 Are GAMs implemented in R?
  7.16 Why is the output not printed when I source() a file?
  7.17 Why does outer() behave strangely with my function?
  7.18 Why does the output from anova() depend on the order of factors in the model?
  7.19 How do I produce PNG graphics in batch mode?
  7.20 How can I get command line editing to work?
  7.21 How can I turn a string into a variable?
  7.22 Why do lattice/trellis graphics not work?
  7.23 How can I sort the rows of a data frame?
  7.24 Why does the help.start() search engine not work?
  7.25 Why did my .Rprofile stop working when I updated R?
  7.26 Where have all the methods gone?
  7.27 How can I create rotated axis labels?
  7.28 Why is read.table() so inefficient?
  7.29 What is the difference between package and library?
  7.30 I installed a package but the functions are not there
  7.31 Why doesn't R think these numbers are equal?
  7.32 How can I capture or ignore errors in a long simulation?
8 R Programming
  8.1 How should I write summary methods?
  8.2 How can I debug dynamically loaded code?
  8.3 How can I inspect R objects when debugging?
  8.4 How can I change compilation flags?
  8.5 How can I debug S4 methods?
9 R Bugs
  9.1 What is a bug?
  9.2 How to report a bug
10 Acknowledgments


R FAQ
*****

1 Introduction
**************

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

1.1 Legalese
============

This document is copyright (C) 1998-2005 by Kurt Hornik.

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

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

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

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

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

1.2 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 (*note What is CRAN?::).

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

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

1.4 Notation
============

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

1.5 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.

2 R Basics
**********

2.1 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 (*note 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.  *Note 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 (*note R Add-On Packages::).

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

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

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

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

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

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

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

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

The current released version is 2.2.1.  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.

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

Sources, binaries and documentation for R can be obtained via CRAN, the
"Comprehensive R Archive Network" (see *Note 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'.

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

2.5.1 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 *Note 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 *Note 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 (*note 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.

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

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

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

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

2.5.3 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 <Stefano.Iacus@R-project.org>.
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.

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

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

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

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

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

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

   No other binary distributions are currently publically available.

2.7 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
*Note 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 (*note What is the R
Foundation?::).

   The R distribution also comes with the following manuals.

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

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

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

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

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

   Books on R include

     P. Dalgaard (2002), "Introductory Statistics with R", Springer: New
     York, ISBN 0-387-95475-9.
     `http://www.biostat.ku.dk/~pd/ISwR.html'.

     J. Fox (2002), "An R and S-PLUS Companion to Applied Regression", Sage
     Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2
     (hardcover),
     `http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/'.

     J. Maindonald and J. Braun (2003), "Data Analysis and Graphics Using R:
     An Example-Based Approach", Cambridge University Press, ISBN
     0-521-81336-0,
     `http://wwwmaths.anu.edu.au/~johnm/'.

     S. M. Iacus and G. Masarotto (2002), "Laboratorio di statistica con
     R", McGraw-Hill, ISBN 88-386-6084-0 (in Italian),
     `http://www.ateneonline.it/LibroAteneo.asp?item_id=1436'.

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


The book

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

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

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

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

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

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

     A. Krause and M. Olsen (2005), "The Basics of S-PLUS" (Fourth
     Edition).  Springer, ISBN 0-387-26109-5.

   The book

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

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

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

     D. Nolan and T. Speed (2000), "Stat Labs: Mathematical Statistics
     Through Applications", Springer Texts in Statistics, ISBN 0-387-98974-9

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

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

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

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

2.8 Citing R
============

To cite R in publications, use

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

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

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

Thanks to Martin Maechler <Martin.Maechler@R-project.org>, 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 *Note R
Bugs:: for more details.

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

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

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

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

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

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

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

Daily mirrors are available at URLs including

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

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

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

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

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

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

2.11 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.

2.12 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' (*note What is S?::).

2.13 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.

3 R and S
*********

3.1 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.

3.2 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.

3.3 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.

3.3.1 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" (*note 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 *Note 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()'.

     Note: If you run R from within Emacs (see *Note 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.

3.3.2 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'.

3.3.3 Others
------------

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

   Some known differences are the following.

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

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

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

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

   * In R, `dyn.load()' can only load _shared objects_, as created for
     example by `R CMD SHLIB'.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   * R disallows repeated formal arguments in function calls.

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

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

   * 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.)


   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.

3.4 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.

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

There is no such thing.

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

*Rweb* is developed and maintained by Jeff Banfield
<jeff@math.montana.edu>.  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 <ulfi@cs.tu-berlin.de> 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 <mjr@dsl.pipex.com>.  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 <mai@ms.uky.edu> (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
<duncan@wald.ucdavis.edu> 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 (wiht sortable tables, clickable graphics,
editable output), etc.

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

   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.

   Finally, 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.

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

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

5.1.1 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*.

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

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

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

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

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

*ArDec*
     Time series autoregressive decomposition.

*BACCO*
     Bayesian Analysis of Computer Code Output.  Contains *emulator* and
     *emulator* for Bayesian calibration of computer models and emulation
     of computer programs.

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

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

*BRugs*
     OpenBUGS and its R interface BRugs.

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

*Biodem*
     A number of functions for biodemographycal analysis.

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

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

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

*BsMD*
     Bayes screening and model discrimination follow-up designs.

*CDNmoney*
     Components of Canadian monetary aggregates.

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

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

*CoCo*
     Graphical modeling for contingency tables using CoCo.

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

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

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

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

*DEoptim*
     Differential Evolution Optimization.

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

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

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

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

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

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

*EbayesThresh*
     Empirical Bayes thresholding and related methods.

*Ecdat*
     Data sets from econometrics textbooks.

*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.

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

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

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

*GPArotation*
     Gradient Projection Algorithm rotation for factor analysis.

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

*GammaTest*
     Gamma Test data analysis.

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

*GeneNT*
     Relevance or Dependency network and signaling pathway discovery.

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

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

*GroupSeq*
     Computations related to group-seqential boundaries.

*HI*
     Simulation from distributions supported by nested hyperplanes.

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

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

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

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

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

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

*ISwR*
     Data sets for "Introductory Statistics with R" by Peter Dalgaard,
     2002, Springer.

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

*JLLprod*
     Nonparametric estimation of homothetic and generalized homothetic
     production functions.

*JointGLM*
     Joint modeling of mean and dispersion through two interlinked GLM's.

*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.

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

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

*LogicReg*
     Routines for Logic Regression.

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

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

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

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

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

*MSBVAR*
     Bayesian vector autoregression models, impulse responses and
     forecasting.

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

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

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

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

*Matrix*
     A Matrix package.

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

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

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

*Oarray*
     Arrays with arbitrary offsets.

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

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

*PK*
     Estimation of pharmacokinetic parameters.

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

*POT*
     Generalized Pareto distribution and Peaks Over Threshold.

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

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

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

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

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

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

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

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

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

*RFA*
     Regional Frequency Analysis.

*RGrace*
     Mouse/menu driven interactive plotting application.

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

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

*RLMM*
     A genotype calling algorithm for Affymetrix SNP arrays.

*RMySQL*
     An interface between R and the MySQL database system.

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

*ROCR*
     Visualizing the performance of scoring classifiers.

*RODBC*
     An ODBC database interface.

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

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

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

*RScaLAPACK*
     An interface to ScaLAPACK functions from R.

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

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

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

*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.

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

*ResistorArray*
     Electrical properties of resistor networks.

*Rfwdmv*
     Forward Search for Multivariate Data.

*Rlab*
     Functions and data sets for the NCSU ST370 class.

*Rlsf*
     Interface to the LSF queuing system.

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

*Rpad*
     Utility functions for the Rpad workbook-style interface.

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

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

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

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

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

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

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

*SensoMineR*
     Sensory data analysis.

*SeqKnn*
     Sequential KNN imputation.

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

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

*SparseLogReg*
     Sparse logistic regression.

*SparseM*
     Basic linear algebra for sparse matrices.

*StatDataML*
     Read and write StatDataML.

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

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

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

*UNF*
     Tools for creating universal numeric fingerprints for data.

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

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

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

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

*WhatIf*
     Software for evaluating counterfactuals.

*XML*
     Tools for reading XML documents and DTDs.

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

*aaMI*
     Mutual information for protein sequence alignments.

*abind*
     Combine multi-dimensional arrays.

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

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

*adapt*
     Adaptive quadrature in up to 20 dimensions.

*ade4*
     Multivariate data analysis and graphical display.

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

*adlift*
     Adaptive Wavelet transforms for signal denoising.

*agce*
     Analysis of growth curve experiments.

*akima*
     Linear or cubic spline interpolation for irregularly gridded data.

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

*amap*
     Another Multidimensional Analysis Package.

*aod*
     Analysis of Overdispersed Data.

*apTreeshape*
     Analyses of phylogenetic treeshape.

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

*arules*
     Mining association rules and frequent itemsets with R.

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

*assist*
     A suite of functions implementing smoothing splines.

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

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

*aws*
     Functions to perform adaptive weights smoothing.

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

*bayesm*
     Bayes Inference for Marketing/Micro-econometrics.

*bayesmix*
     Bayesian mixture models of univariate Gaussian distributions using
     JAGS.

*baymvb*
     Bayesian analysis of multivariate binary data.

*betareg*
     Beta regression for modeling rates and proportions.

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

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

*bindata*
     Generation of correlated artificial binary data.

*biopara*
     Self-contained parallel system for R.

*bitops*
     Functions for Bitwise operations on integer vectors.

*bivpois*
     Bivariate Poisson models using the EM algorithm.

*blighty*
     Function for drawing the coastline of the United Kingdom.

*boa*
     Bayesian Output Analysis Program for MCMC.

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

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

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

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

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

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

*butler*
     Unit testing, profiling and benchmarking for R.

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

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

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

*cat*
     Analysis of categorical-variable datasets with missing values.

*catspec*
     Special models for categorical variables.

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

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

*cfa*
     Analysis of configuration frequencies.

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

*changeLOS*
     Change in length of hospital stay (LOS).

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

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

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

*class*
     Functions for classification (k-nearest neighbor and LVQ).  Contained
     in the `VR' bundle.  _Recommended_.

*classPP*
     Projection Pursuit for supervised classification.

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

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

*clines*
     Calculates Contour Lines.

*clue*
     CLUster Ensembles.

*cluster*
     Functions for cluster analysis.  _Recommended_.

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

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

*cocorresp*
     Co-correspondence analysis ordination methods for community ecology.

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

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

*colorspace*
     Mapping between assorted color spaces.

*combinat*
     Combinatorics utilities.

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

*concor*
     Concordance, providing "SVD by blocks".

*concord*
     Measures of concordance and reliability.

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

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

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

*corpcor*
     Efficient estimation of covariance and (partial) correlation.

*covRobust*
     Robust covariance estimation via nearest neighbor cleaning.

*cramer*
     Routine for the multivariate nonparametric Cramer test.

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

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

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

*cyclones*
     Cyclone identification.

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

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

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

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

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

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

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

*dglm*
     Double generalized linear models.

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

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

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

*diptest*
     Compute Hartigan's dip test statistic for unimodality.

*dispmod*
     Functions for modelling dispersion in GLMs.

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

*distrEx*
     Extensions of package *distr*.

*distrSim*
     Simulation classes based on package *distr*.

*distrTEst*
     Estimation and Testing classes based on package *distr*.

*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.

*drfit*
     Dose-response data evaluation.

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

*dyn*
     Time series regression.

*dynamicGraph*
     Interactive graphical tool for manipulating graphs.

*dynlm*
     Dynamic linear models and time series regression.

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

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

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

*edci*
     Edge Detection and Clustering in Images.

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

*eha*
     A package for survival and event history analysis.

*elasticnet*
     Elastic net regularization and variable selection.

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

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

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

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

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

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

*epitools*
     Basic tools for applied epidemiology.

*epsi*
     Edge Preserving Smoothing for Images.

*equivalence*
     Tests and graphics for assessing tests of equivalence.

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

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

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

*exactLoglinTest*
     Monte Carlo exact tests for log-linear models.

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

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

*fCalendar*
     The Rmetrics module for "Date, Time and Calendars".

*fExtremes*
     The Rmetrics module for "Beyond the Sample, Dealing with Extreme
     Values".

*fMultivar*
     The Rmetrics module for "Multivariate Data Analysis".

*fOptions*
     The Rmetrics module for "The Valuation of Options".

*fPortfolio*
     The Rmetrics module for "Pricing and Hedging of Options".

*fSeries*
     The Rmetrics module for "The Dynamical Process Behind Financial
     Markets".

*far*
     Modelization for Functional AutoRegressive processes.

*faraway*
     Functions and datasets for books by Julian Faraway.

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

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

*fdim*
     Functions for calculating fractal dimension.

*fgac*
     Families of Generalized Archimedean Copulas.

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

*filehash*
     Simple file-based hash table.

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

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

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

*fortunes*
     R fortunes.

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

*fpc*
     Fixed point clusters, clusterwise regression and discriminant plots.

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

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

*ftnonpar*
     Features and strings for nonparametric regression.

*fuzzyRankTests*
     Fuzzy rank tests and confidence intervals.

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

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

*gafit*
     Genetic algorithm for curve fitting.

*gam*
     Functions for fitting and working with Generalized Additive Models, as
     described in chapter 7 of the White Book, and in "Generalized Additive
     Models" by T. Hastie and R. Tibshirani (1990).

*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.

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

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

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

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

*gdata*
     Various functions to manipulate data.

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

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

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

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

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

*geoRglm*
     Functions for inference in generalised linear spatial models.

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

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

*giRaph*
     Data structures and algorithms for computations on graphs.

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

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

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

*glmpath*
     L1 regularization path for Generalized Linear Models.

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

*gmodels*
     Various functions to manipulate models.

*gmp*
     Arithmetic "without limitations" using the GNU Multiple Precision
     library.

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

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

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

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

*gplots*
     Various functions to draw plots.

*grasper*
     Generalized Regression Analysis and Spatial Predictions for R.

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

*gridBase*
     Integration of base and grid graphics.

*grnnR*
     A Generalized Regression Neural Network.

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

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

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

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

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

*gtools*
     Various functions to help manipulate data.

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

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

*hapsim*
     Haplotype data simulation.

*hdf5*
     Interface to the NCSA HDF5 library.

*hdrcde*
     Highest Density Regions and Conditional Density Estimation.

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

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

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

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

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

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

*hopach*
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

*howmany*
     A lower bound for the number of correct rejections.

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

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

*ifs*
     Iterated Function Systems distribution function estimator.

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

*impute*
     Imputation for microarray data (currently KNN only).

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

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

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

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

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

*its*
     An S4 class for handling irregular time series.

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

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

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

*kknn*
     Weighted k-nearest neighbors classification and regression.

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

*km.ci*
     Confidence intervals for the Kaplan-Meier estimator.

*knnFinder*
     Fast nearest neighbor search.

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

*knncat*
     Nearest-neighbor classification with categorical variables.

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

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

*labdsv*
     Laboratory for Dynamic Synthetic Vegephenomenology.

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

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

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

*latentnet*
     Latent position and cluster models for statistical networks.

*lattice*
     Lattice graphics, an implementation of Trellis Graphics functions.
     _Recommended_.

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

*lazy*
     Lazy learning for local regression.

*ldDesign*
     Design of experiments for detection of linkage disequilibrium,

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

*lgtdl*
     A set of methods for longitudinal data objects.

*limma*
     LInear Models for MicroArray data.

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

*lme4*
     Fit linear and generalized linear mixed-effects models.

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

*lmm*
     Linear mixed models.

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

*locfdr*
     Computation of local false discovery rates.

*locfit*
     Local Regression, likelihood and density estimation.

*lodplot*
     Assorted plots of location score versus genetic map position.

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

*logspline*
     Logspline density estimation.

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

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

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

*lpridge*
     Local polynomial (ridge) regression.

*lsa*
     Latent Semantic Analysis.

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

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

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

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

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

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

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

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

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

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

*matlab*
     Emulate MATLAB code using R.

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

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

*mcgibbsit*
     Warnes and Raftery's MCGibbsit MCMC diagnostic.

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

*mcmc*
     Functions for Markov Chain Monte Carlo (MCMC).

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

*merror*
     Accuracy and precision of measurements.

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

*mfp*
     Multiple Fractional Polynomials.

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

*micEcdat*
     Data sets for micro-econometrics.

*micEcon*
     Tools for microeconomic analysis and microeconomic modelling.

*mice*
     Multivariate Imputation by Chained Equations.

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

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

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

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

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

*mixreg*
     Functions to fit mixtures of regressions.

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

*mlica*
     Independent Component Analysis using Maximum Likelihood.

*mlmRev*
     Examples from Multilevel Modelling Software Review.

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

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

*modeltools*
     A collection of tools to deal with statistical models.

*moments*
     Moments, skewness, kurtosis and related tests.

*monoProc*
     Strictly monotone smoothing procedure.

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

*muhaz*
     Hazard function estimation in survival analysis.

*multcomp*
     Multiple comparison procedures for the one-way layout.

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

*multtest*
     Resampling-based multiple hypothesis testing.

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

*mvnmle*
     ML estimation for multivariate normal data with missing values.

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

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

*mvpart*
     Multivariate partitioning.

*mvtnorm*
     Multivariate normal and t distributions.

*nFDR*
     Nonparametric Estimate of FDR Based on Bernstein polynomials.

*ncdf*
     Interface to Unidata netCDF data files.

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

*ncvar*
     High-level R interface to netCDF datasets.

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

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

*neural*
     RBF and MLP neural networks with graphical user interface.

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

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

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

*nlrq*
     Nonlinear quantile regression routines.  _Defunct_.

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

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

*norm*
     Analysis of multivariate normal datasets with missing values.

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

*nortest*
     Five omnibus tests for the composite hypothesis of normality.

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

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

*nprq*
     Nonparametric quantile regression.  _Defunct_.

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

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

*optmatch*
     Functions to perform optimal matching, particularly full matching.

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

*ouch*
     Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses.

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

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

*pamr*
     Pam: Prediction Analysis for Microarrays.

*pan*
     Multiple imputation for multivariate panel or clustered data.

*panel*
     Functions and datasets for fitting models to Panel data.

*papply*
     Parallel apply function using MPI.

*partsm*
     Periodic AutoRegressive Time Series Models.

*party*
     Unbiased recursive partitioning in a conditional inference framework.

*pastecs*
     Package for Analysis of Space-Time Ecological Series.

*pbatR*
     Frontend to PBAT to run within R.

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

*pear*
     Periodic Autoregression Analysis.

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

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

*perturb*
     Perturbation analysis for evaluating collinearity.

*pgam*
     Poisson-Gamma Additive Models.

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

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

*phpSerialize*
     Serialize R to PHP associative array.

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

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

*plotrix*
     Various useful functions for enhancing plots.

*plugdensity*
     Kernel density estimation with global bandwidth selection via
     "plug-in".

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

*pls.pcr*
     Multivariate regression by PLS and PCR.

*plsgenomics*
     PLS analyses for genomics.

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

*polycor*
     Polychoric and polyserial correlations.

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

*popgen*
     Statistical and POPulation GENetics.

*portfolio*
     Classes for analyzing and implementing portfolios.

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

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

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

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

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

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

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

*pspline*
     Smoothing splines with penalties on order m derivatives.

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

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

*pvclust*
     Hierarchical clustering with p-value.

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

*qtl*
     Analysis of experimental crosses to identify QTLs.

*qtlDesign*
     Tools for the design of QTL experiments.

*quadprog*
     For solving quadratic programming problems.

*quantchem*
     Quantitative chemical analysis: calibration and evaluation of results.

*quantreg*
     Quantile regression and related methods.

*qvalue*
     Q-value estimation for false discovery rate control.

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

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

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

*randaes*
     Random number generator based on AES cipher.

*randomForest*
     Breiman's random forest classifier.

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

*rcom*
     R COM Client Interface and internal COM Server.

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

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

*relaimpo*
     RELAtive IMPOrtance of regressors in linear models.

*relax*
     Functions for report writing, presentation, and programming.

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

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

*relsurv*
     Various functions for regression in relative survival.

*reshape*
     Flexibly reshape data.

*resper*
     Sampling from restricted permutations.

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

*rgenoud*
     R version of GENetic Optimization Using Derivatives.

*rgl*
     3D visualization device system (OpenGL).

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

*riv*
     Robust Instrumental Variables estimators using high breakpoint point
     S-estimators of multivariate location and scatter matrices.

*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.

*roblm*
     Robust regression estimators.

*rpart*
     Recursive PARTitioning and regression trees.  _Recommended_.

*rpart.permutation*
     Permutation tests of rpart models.

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

*rqmcmb2*
     Markov chain marginal bootstrap for quantile regression.

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

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

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

*rv*
     Simulation-based random variable object class.

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

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

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

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

*samr*
     Significance Analysis of Microarrays.

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

*sca*
     Simple Component Analysis.

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

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

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

*seacarb*
     Calculates parameters of the seawater carbonate system.

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

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

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

*seqmon*
     Sequential monitoring of clinical trials.

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

*setRNG*
     Set (normal) random number generator and seed.

*sfsmisc*
     Utilities from Seminar fuer Statistik ETH Zurich.

*sgeostat*
     An object-oriented framework for geostatistical modeling.

*shapefiles*
     Functions to read and write ESRI shapefiles.

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

*simex*
     SIMEX and MCSIMEX algorithms for measurement error models.

*simpleboot*
     Simple bootstrap routines.

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

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

*sma*
     Functions for exploratory (statistical) microarray analysis.

*smoothSurv*
     Survival regression with smoothed error distribution.

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

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

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

*snowFT*
     Fault Tolerant Simple Network of Workstations.

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

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

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

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

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

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

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

*spe*
     Stochastic Proximity Embedding.

*spectralGP*
     Approximate Gaussian processes using the Fourier basis.

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

*splancs*
     Spatial and space-time point pattern analysis functions.

*sspir*
     State SPace models In R.

*statmod*
     Miscellaneous biostatistical modelling functions.

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

*strucchange*
     Various tests on structural change in linear regression models.

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

*supclust*
     Methodology for supervised grouping of predictor variables.

*superpc*
     Supervised principal components.

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

*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.

*survival*
     Functions for survival analysis, including penalised likelihood.
     _Recommended_.

*survrec*
     Survival analysis for recurrent event data.

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

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

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

*taskPR*
     Task-Parallel R package.

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

*tdthap*
     Transmission/disequilibrium tests for extended marker haplotypes.

*tensor*
     Tensor product of arrays.

*time*
     Time tracking for developers.

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

*tlnise*
     Two-level normal independent sampling estimation.

*tree*
     Classification and regression trees.

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

*tripack*
     A constrained two-dimensional Delaunay triangulation package.

*trust*
     Local optimization using two derivatives and trust regions.

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

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

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

*tweedie*
     Maximum likelihood computations for Tweedie exponential family models.

*udunits*
     Interface to Unidata's routines to convert units.

*ump*
     Uniformly Most Powerful tests.

*urca*
     Unit root and cointegration tests for time series data.

*urn*
     Functions for sampling without replacement (simulated urns).

*uroot*
     Unit root tests and graphics for seasonal time series.

*vabayelMix*
     Variational Bayesian mixture model.

*varSelRF*
     Variable selection using random forests.

*vardiag*
     Interactive variogram diagnostics.

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

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

*vegan*
     Various help functions for vegetation scientists and community
     ecologists.

*verification*
     Utilities for verification of discrete and probabilistic forecasts.

*verify*
     Construction of test suites using verify objects.

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

*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.

*wle*
     Robust statistical inference via a weighted likelihood approach.

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

*xtable*
     Export data to LaTeX and HTML tables.

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

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

See CRAN `src/contrib/PACKAGES' for more information.

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

*GLMMGibbs*
     Generalised Linear Mixed Models by Gibbs sampling.

*RPgSQL*
     Provides methods for accessing data stored in PostgreSQL tables.

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

*ensemble*
     Ensembles of tree classifiers.

*runStat*
     Running median and mean.

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

5.1.3 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.

*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.

*Rexif*
     Extract meta-information from JPEG files.

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

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

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

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

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

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

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

*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.

*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.

*Rlibstree*
     Suffix Trees in R via the libstree C library.

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

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

*SASXML*
     Example for reading XML files in SAS 8.2 manner.

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

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

*SNetscape*
     Plugin for Netscape and JavaScript.

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

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

*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.

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

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

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

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

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

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

*Category*
     A collection of tools for performing category analysis.

*ChromoViz*
     Draw gene expression profile onto chromosome using SVG.

*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.

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

*GLAD*
     Gain and Loss Analysis of DNA.

*GOstats*
     Tools for manipulating GO and microarrays.

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

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

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

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

*GeneTraffic*
     GeneTraffic R integration functions.

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

*GraphAT*
     Graph theoretic Association Tests.

*HEM*
     Heterogeneous Error Model for analysis of microarray data.

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

*KEGGSOAP*
     Client-side SOAP access KEGG.

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

*MANOR*
     Micro-Array NORmalization.

*MCRestimate*
     Misclassification error estimation with cross-validation.

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

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

*MergeMaid*
     Cross-study comparison of gene expression array data.

*MiPP*
     Misclassification Penalized Posterior Classification.

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

*OLINgui*
     Graphical user interface for *OLIN*.

*PROcess*
     Ciphergen SELDI-TOF processing.

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

*RMAGEML*
     Functionality to handle MAGEML documents.

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

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

*RankProd*
     Rank Product method for identifying differentially expressed genes.

*RdbiPgSQL*
     Methods for accessing data stored in PostgreSQL tables.

*Rdbi*
     Generic framework for database access in R.

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

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

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

*SAGElyzer*
     Locates genes based on SAGE tags.

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

*SNAData*
     Data from the book "Social Network Analysis" by Wasserman & Faust,
     1999.

*aCGH*
     Classes and functions for Array Comparative Genomic Hybridization data.

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

*affy*
     Methods for Affymetrix Oligonucleotide Arrays.

*affyQCReport*
     QC Report Generation for affyBatch objects.

*affyPLM*
     For fitting Probe Level Models.

*affycomp*
     Graphics toolbox for assessment of Affymetrix expression measures.

*affydata*
     Affymetrix data for demonstration purposes.

*affylmGUI*
     Graphical User Interface for affy analysis using package *limma*.

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

*altcdfenvs*
     Utilities to handle cdfenvs.

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

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

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

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

*arrayQuality*
     Performing print-run and array level quality assessment.

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

*bioDist*
     A collection of software tools for calculating distance measures.

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

*bridge*
     Bayesian Robust Inference for Differential Gene Expression.

*clusterStab*
     Compute cluster stability scores for microarray data.

*convert*
     Convert Microarray Data Objects.

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

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

*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.

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

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

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

*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.

*gff3Plotter*
     Plotting data of experiments on the genomic layout.

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

*goCluster*
     Analysis of clustering results in conjunction with annotation data.

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

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

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

*hexbin*
     Binning functions, in particular hexagonal bins for graphing.

*hopach*
     Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).

*hypergraph*
     Capabilities for representing and manipulating hypergraphs.

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

*ideogram*
     Plotting genomic data by chromosomal location.

*impute*
     Imputation for microarray data (currently KNN only).

*limma*
     Linear models for microarray data.

*limmaGUI*
     Graphical User Interface for package *limma*.

*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.

*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.

*mmgmos*
     Multi-chip Modified Gamma Model of Oligonucleotide Signal.

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

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

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

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

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

*pamr*
     Pam: Prediction Analysis for Microarrays.

*pdmclass*
     CLASSification of microarray samples using Penalized Discriminant
     Methods.

*pgUtils*
     Utility functions for PostgreSQL databases.

*pickgene*
     Adaptive gene picking for microarray expression data analysis.

*plgem*
     Power Law Global Error Model.

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

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

*qvalue*
     Q-value estimation for false discovery rate control.

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

*reb*
     Regional Expression Biases.

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

*rfcdmin*
     Data sets for RFlowCyt examples.

*rflowcyt*
     Statistical tools and data structures for analytic flow cytometry.

*rhdf5*
     Support for large matrix manipulation using the HDF5 storage library.

*safe*
     Significance Analysis of Function and Expression.

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

*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.

*splicegear*
     A set of tools to work with alternative splicing.

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

*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.

*twilight*
     Estimation of local false discovery rate.

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

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

*widgetInvoke*
     Evaluation widgets for functions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

     library()

at the R prompt.

   This produces something like

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

          mystuff       My own R functions, nicely packaged but not documented

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

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

   You can "load" the installed package PKG by

     library(PKG)

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

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

   You can unload the loaded package PKG by

     detach("package:PKG")

5.4 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

5.5 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, *note 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.

   *Note What is CRAN?::, for information on uploading a package to CRAN.

5.6 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 *Note 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 <Kurt.Hornik@R-project.org>.

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

6.1 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.

6.2 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.

6.3 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

7 R Miscellanea
***************

7.1 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.

7.2 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.

7.3 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.)

7.4 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.

7.5 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]

7.6 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.

7.7 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.

7.8 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"').

7.9 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.

7.10 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).

7.11 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.)

7.12 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.

7.13 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.

7.14 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".

7.15 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*.

7.16 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"' (*note 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)'.

7.17 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)

7.18 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.

7.19 How do I produce PNG graphics in batch mode?
=================================================

Under Unix, the `png()' device uses the X11 driver, which is a problem in
batch mode or for remote operation.  If you have Ghostscript you can use
`bitmap()', which produces a PostScript file then converts it to any bitmap
format supported by ghostscript.  On some installations this produces ugly
output, on others it is perfectly satisfactory.  In theory one could also
use Xvfb from X.Org (http://www.x.org/Downloads.html), which provides an X
server with no display.

7.20 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).

7.21 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.

7.22 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.

7.23 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()'.

7.24 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, *note What documentation exists for R?::,
and its HTML version is linked from the HTML search page.

7.25 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).

7.26 Where have all the methods gone?
=====================================

Many functions, particularly S3 methods, are now hidden in namespaces.
This has the advantage that they cannot be called inadvertantly with
arguments of the wrong class, but it makes them harder to view.

   To see the code for an S3 method (e.g., `[.terms') use

     getS3method("[", "terms")

To see the code for an unexported function `foo()' in the namespace of
package `"bar"' use `bar:::foo'.  Don't use these constructions to call
unexported functions in your own code--they are probably unexported for a
reason and may change without warning.

7.27 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.

7.28 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.

7.29 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.

   *Note R Add-On Packages::, for more details.  See also Uwe Ligges (2003),
"R Help Desk: Package Management", _R News_, *3/3*, 37-39.

7.30 I installed a package but the functions are not there
==========================================================

To actually _use_ the package, it needs to be _loaded_ using `library()'.

   See *Note R Add-On Packages:: and *Note What is the difference between
package and library?:: for more information.

7.31 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'.

7.32 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).

8 R Programming
***************

8.1 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.

8.2 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,
*note What documentation exists for R?::.

8.3 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'.

8.4 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'.

8.5 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.

9 R Bugs
********

9.1 What is a bug?
==================

If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like "disk full"), then it is certainly a bug.  If you call
`.C()', `.Fortran()', `.External()' or `.Call()' (or `.Internal()')
yourself (or in a function you wrote), you can always crash R by using
wrong argument types (modes).  This is not a bug.

   Taking forever to complete a command can be a bug, but you must make
certain that it was really R's fault.  Some commands simply take a long
time.  If the input was such that you _know_ it should have been processed
quickly, report a bug.  If you don't know whether the command should take a
long time, find out by looking in the manual or by asking for assistance.

   If a command you are familiar with causes an R error message in a case
where its usual definition ought to be reasonable, it is probably a bug.
If a command does the wrong thing, that is a bug.  But be sure you know for
certain what it ought to have done.  If you aren't familiar with the
command, or don't know for certain how the command is supposed to work,
then it might actually be working right.  Rather than jumping to
conclusions, show the problem to someone who knows for certain.

   Finally, a command's intended definition may not be best for statistical
analysis.  This is a very important sort of problem, but it is also a
matter of judgment.  Also, it is easy to come to such a conclusion out of
ignorance of some of the existing features.  It is probably best not to
complain about such a problem until you have checked the documentation in
the usual ways, feel confident that you understand it, and know for certain
that what you want is not available.  If you are not sure what the command
is supposed to do after a careful reading of the manual this indicates a
bug in the manual.  The manual's job is to make everything clear.  It is
just as important to report documentation bugs as program bugs.  However,
we know that the introductory documentation is seriously inadequate, so you
don't need to report this.

   If the online argument list of a function disagrees with the manual, one
of them must be wrong, so report the bug.

9.2 How to report a bug
=======================

When you decide that there is a bug, it is important to report it and to
report it in a way which is useful.  What is most useful is an exact
description of what commands you type, starting with the shell command to
run R, until the problem happens.  Always include the version of R,
machine, and operating system that you are using; type `version' in R to
print this.

   The most important principle in reporting a bug is to report _facts_,
not hypotheses or categorizations.  It is always easier to report the
facts, but people seem to prefer to strain to posit explanations and report
them instead.  If the explanations are based on guesses about how R is
implemented, they will be useless; others will have to try to figure out
what the facts must have been to lead to such speculations.  Sometimes this
is impossible.  But in any case, it is unnecessary work for the ones trying
to fix the problem.

   For example, suppose that on a data set which you know to be quite large
the command

     R> data.frame(x, y, z, monday, tuesday)

never returns.  Do not report that `data.frame()' fails for large data
sets.  Perhaps it fails when a variable name is a day of the week.  If this
is so then when others got your report they would try out the
`data.frame()' command on a large data set, probably with no day of the
week variable name, and not see any problem.  There is no way in the world
that others could guess that they should try a day of the week variable
name.

   Or perhaps the command fails because the last command you used was a
method for `"["()' that had a bug causing R's internal data structures to
be corrupted and making the `data.frame()' command fail from then on.  This
is why others need to know what other commands you have typed (or read from
your startup file).

   It is very useful to try and find simple examples that produce
apparently the same bug, and somewhat useful to find simple examples that
might be expected to produce the bug but actually do not.  If you want to
debug the problem and find exactly what caused it, that is wonderful.  You
should still report the facts as well as any explanations or solutions.
Please include an example that reproduces the problem, preferably the
simplest one you have found.

   Invoking R with the `--vanilla' option may help in isolating a bug.
This ensures that the site profile and saved data files are not read.

   Before you actually submit a bug report, you should check whether the
bug has already been reported and/or fixed.  First, try the "Search
Existing Reports" facility in the Bug Tracking page at
`http://bugs.R-project.org/'.  Second, consult
`https://svn.R-project.org/R/trunk/NEWS', which records changes that will
appear in the _next_ release of R, including some bug fixes that do not
appear in Bug Tracking.  (Windows users should additionally consult
`https://svn.R-project.org/R/trunk/src/gnuwin32/CHANGES'.)  Third, if
possible try the current r-patched or r-devel version of R.  If a bug has
already been reported or fixed, please do not submit further bug reports on
it.  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'.

10 Acknowledgments
******************

Of course, many many thanks to Robert and Ross for the R system, and to the
package writers and porters for adding to it.

   Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano
Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D.
Ripley, Anthony Rossini, and Andreas Weingessel for their comments which
helped me improve this FAQ.

   More to some soon ...

