This is R-FAQ.info, produced by makeinfo version 4.8 from R-FAQ.texi.

INFO-DIR-SECTION Programming
START-INFO-DIR-ENTRY
* R FAQ: (R-FAQ).               The R statistical system FAQ.
END-INFO-DIR-ENTRY

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



File: R-FAQ.info,  Node: Top,  Next: Introduction,  Prev: (dir),  Up: (dir)

R FAQ
*****

* Menu:

* Introduction::
* R Basics::
* R and S::
* R Web Interfaces::
* R Add-On Packages::
* R and Emacs::
* R Miscellanea::
* R Programming::
* R Bugs::
* Acknowledgments::


File: R-FAQ.info,  Node: Introduction,  Next: R Basics,  Prev: Top,  Up: Top

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

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

* Menu:

* Legalese::
* Obtaining this document::
* Citing this document::
* Notation::
* Feedback::


File: R-FAQ.info,  Node: Legalese,  Next: Obtaining this document,  Prev: Introduction,  Up: Introduction

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.


File: R-FAQ.info,  Node: Obtaining this document,  Next: Citing this document,  Prev: Legalese,  Up: Introduction

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?::).


File: R-FAQ.info,  Node: Citing this document,  Next: Notation,  Prev: Obtaining this document,  Up: Introduction

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.


File: R-FAQ.info,  Node: Notation,  Next: Feedback,  Prev: Citing this document,  Up: Introduction

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

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


File: R-FAQ.info,  Node: Feedback,  Prev: Notation,  Up: Introduction

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.


File: R-FAQ.info,  Node: R Basics,  Next: R and S,  Prev: Introduction,  Up: Top

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

* Menu:

* What is R?::
* What machines does R run on?::
* What is the current version of R?::
* How can R be obtained?::
* How can R be installed?::
* Are there Unix binaries for R?::
* What documentation exists for R?::
* Citing R::
* What mailing lists exist for R?::
* What is CRAN?::
* Can I use R for commercial purposes?::
* Why is R named R?::
* What is the R Foundation?::


File: R-FAQ.info,  Node: What is R?,  Next: What machines does R run on?,  Prev: R Basics,  Up: 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").


File: R-FAQ.info,  Node: What machines does R run on?,  Next: What is the current version of R?,  Prev: What is R?,  Up: R Basics

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.


File: R-FAQ.info,  Node: What is the current version of R?,  Next: How can R be obtained?,  Prev: What machines does R run on?,  Up: R Basics

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.


File: R-FAQ.info,  Node: How can R be obtained?,  Next: How can R be installed?,  Prev: What is the current version of R?,  Up: R Basics

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'.


File: R-FAQ.info,  Node: How can R be installed?,  Next: Are there Unix binaries for R?,  Prev: How can R be obtained?,  Up: R Basics

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

* Menu:

* How can R be installed (Unix)::
* How can R be installed (Windows)::
* How can R be installed (Macintosh)::


File: R-FAQ.info,  Node: How can R be installed (Unix),  Next: How can R be installed (Windows),  Prev: How can R be installed?,  Up: 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.


File: R-FAQ.info,  Node: How can R be installed (Windows),  Next: How can R be installed (Macintosh),  Prev: How can R be installed (Unix),  Up: How can R be installed?

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.


File: R-FAQ.info,  Node: How can R be installed (Macintosh),  Prev: How can R be installed (Windows),  Up: How can R be installed?

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.


File: R-FAQ.info,  Node: Are there Unix binaries for R?,  Next: What documentation exists for R?,  Prev: How can R be installed?,  Up: R Basics

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.


File: R-FAQ.info,  Node: What documentation exists for R?,  Next: Citing R,  Prev: Are there Unix binaries for R?,  Up: R Basics

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'


File: R-FAQ.info,  Node: Citing R,  Next: What mailing lists exist for R?,  Prev: What documentation exists for R?,  Up: R Basics

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()'.


File: R-FAQ.info,  Node: What mailing lists exist for R?,  Next: What is CRAN?,  Prev: Citing R,  Up: R Basics

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.


File: R-FAQ.info,  Node: What is CRAN?,  Next: Can I use R for commercial purposes?,  Prev: What mailing lists exist for R?,  Up: R Basics

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.


File: R-FAQ.info,  Node: Can I use R for commercial purposes?,  Next: Why is R named R?,  Prev: What is CRAN?,  Up: R Basics

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.


File: R-FAQ.info,  Node: Why is R named R?,  Next: What is the R Foundation?,  Prev: Can I use R for commercial purposes?,  Up: R Basics

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?::).


File: R-FAQ.info,  Node: What is the R Foundation?,  Prev: Why is R named R?,  Up: R Basics

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.


File: R-FAQ.info,  Node: R and S,  Next: R Web Interfaces,  Prev: R Basics,  Up: Top

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

* Menu:

* What is S?::
* What is S-PLUS?::
* What are the differences between R and S?::
* Is there anything R can do that S-PLUS cannot?::
* What is R-plus?::


File: R-FAQ.info,  Node: What is S?,  Next: What is S-PLUS?,  Prev: R and S,  Up: 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.


File: R-FAQ.info,  Node: What is S-PLUS?,  Next: What are the differences between R and S?,  Prev: What is S?,  Up: R and S

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.


File: R-FAQ.info,  Node: What are the differences between R and S?,  Next: Is there anything R can do that S-PLUS cannot?,  Prev: What is S-PLUS?,  Up: R and S

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.

* Menu:

* Lexical scoping::
* Models::
* Others::


File: R-FAQ.info,  Node: Lexical scoping,  Next: Models,  Prev: What are the differences between R and S?,  Up: What are the differences between R and S?

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.


File: R-FAQ.info,  Node: Models,  Next: Others,  Prev: Lexical scoping,  Up: What are the differences between R and S?

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'.


File: R-FAQ.info,  Node: Others,  Prev: Models,  Up: What are the differences between R and S?

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.


File: R-FAQ.info,  Node: Is there anything R can do that S-PLUS cannot?,  Next: What is R-plus?,  Prev: What are the differences between R and S?,  Up: R and S

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.


File: R-FAQ.info,  Node: What is R-plus?,  Prev: Is there anything R can do that S-PLUS cannot?,  Up: R and S

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

There is no such thing.


File: R-FAQ.info,  Node: R Web Interfaces,  Next: R Add-On Packages,  Prev: R and S,  Up: Top

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.


File: R-FAQ.info,  Node: R Add-On Packages,  Next: R and Emacs,  Prev: R Web Interfaces,  Up: Top

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

* Menu:

* Which add-on packages exist for R?::
* How can add-on packages be installed?::
* How can add-on packages be used?::
* How can add-on packages be removed?::
* How can I create an R package?::
* How can I contribute to R?::


File: R-FAQ.info,  Node: Which add-on packages exist for R?,  Next: How can add-on packages be installed?,  Prev: R Add-On Packages,  Up: R Add-On Packages

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

* Menu:

* Add-on packages in R::
* Add-on packages from CRAN::
* Add-on packages from Omegahat::
* Add-on packages from Bioconductor::
* Other add-on packages::


File: R-FAQ.info,  Node: Add-on packages in R,  Next: Add-on packages from CRAN,  Prev: Which add-on packages exist for R?,  Up: 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*.


File: R-FAQ.info,  Node: Add-on packages from CRAN,  Next: Add-on packages from Omegahat,  Prev: Add-on packages in R,  Up: Which add-on packages exist for R?

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.


File: R-FAQ.info,  Node: Add-on packages from Omegahat,  Next: Add-on packages from Bioconductor,  Prev: Add-on packages from CRAN,  Up: Which add-on packages exist for R?

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.


File: R-FAQ.info,  Node: Add-on packages from Bioconductor,  Next: Other add-on packages,  Prev: Add-on packages from Omegahat,  Up: Which add-on packages exist for R?

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.


File: R-FAQ.info,  Node: Other add-on packages,  Prev: Add-on packages from Bioconductor,  Up: Which add-on packages exist for R?

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.


File: R-FAQ.info,  Node: How can add-on packages be installed?,  Next: How can add-on packages be used?,  Prev: Which add-on packages exist for R?,  Up: R Add-On Packages

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.)


File: R-FAQ.info,  Node: How can add-on packages be used?,  Next: How can add-on packages be removed?,  Prev: How can add-on packages be installed?,  Up: R Add-On Packages

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")


File: R-FAQ.info,  Node: How can add-on packages be removed?,  Next: How can I create an R package?,  Prev: How can add-on packages be used?,  Up: R Add-On Packages

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


File: R-FAQ.info,  Node: How can I create an R package?,  Next: How can I contribute to R?,  Prev: How can add-on packages be removed?,  Up: R Add-On Packages

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.

   *Note Creating R packages: (R-exts)Creating R packages, for details.

   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.


File: R-FAQ.info,  Node: How can I contribute to R?,  Prev: How can I create an R package?,  Up: R Add-On Packages

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>.


File: R-FAQ.info,  Node: R and Emacs,  Next: R Miscellanea,  Prev: R Add-On Packages,  Up: Top

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

* Menu:

* Is there Emacs support for R?::
* Should I run R from within Emacs?::
* Debugging R from within Emacs::


File: R-FAQ.info,  Node: Is there Emacs support for R?,  Next: Should I run R from within Emacs?,  Prev: R and Emacs,  Up: 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.


File: R-FAQ.info,  Node: Should I run R from within Emacs?,  Next: Debugging R from within Emacs,  Prev: Is there Emacs support for R?,  Up: R and Emacs

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.


File: R-FAQ.info,  Node: Debugging R from within Emacs,  Prev: Should I run R from within Emacs?,  Up: R and Emacs

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


File: R-FAQ.info,  Node: R Miscellanea,  Next: R Programming,  Prev: R and Emacs,  Up: Top

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

* Menu:

* How can I set components of a list to NULL?::
* How can I save my workspace?::
* How can I clean up my workspace?::
* How can I get eval() and D() to work?::
* Why do my matrices lose dimensions?::
* How does autoloading work?::
* How should I set options?::
* How do file names work in Windows?::
* Why does plotting give a color allocation error?::
* How do I convert factors to numeric?::
* Are Trellis displays implemented in R?::
* What are the enclosing and parent environments?::
* How can I substitute into a plot label?::
* What are valid names?::
* Are GAMs implemented in R?::
* Why is the output not printed when I source() a file?::
* Why does outer() behave strangely with my function?::
* Why does the output from anova() depend on the order of factors in the model?::
* How do I produce PNG graphics in batch mode?::
* How can I get command line editing to work?::
* How can I turn a string into a variable?::
* Why do lattice/trellis graphics not work?::
* How can I sort the rows of a data frame?::
* Why does the help.start() search engine not work?::
* Why did my .Rprofile stop working when I updated R?::
* Where have all the methods gone?::
* How can I create rotated axis labels?::
* Why is read.table() so inefficient?::
* What is the difference between package and library?::
* I installed a package but the functions are not there::
* Why doesn't R think these numbers are equal?::
* How can I capture or ignore errors in a long simulation?::


File: R-FAQ.info,  Node: How can I set components of a list to NULL?,  Next: How can I save my workspace?,  Prev: R Miscellanea,  Up: 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.


File: R-FAQ.info,  Node: How can I save my workspace?,  Next: How can I clean up my workspace?,  Prev: How can I set components of a list to NULL?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How can I clean up my workspace?,  Next: How can I get eval() and D() to work?,  Prev: How can I save my workspace?,  Up: R Miscellanea

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.)


File: R-FAQ.info,  Node: How can I get eval() and D() to work?,  Next: Why do my matrices lose dimensions?,  Prev: How can I clean up my workspace?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: Why do my matrices lose dimensions?,  Next: How does autoloading work?,  Prev: How can I get eval() and D() to work?,  Up: R Miscellanea

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]


File: R-FAQ.info,  Node: How does autoloading work?,  Next: How should I set options?,  Prev: Why do my matrices lose dimensions?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How should I set options?,  Next: How do file names work in Windows?,  Prev: How does autoloading work?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How do file names work in Windows?,  Next: Why does plotting give a color allocation error?,  Prev: How should I set options?,  Up: R Miscellanea

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"').


File: R-FAQ.info,  Node: Why does plotting give a color allocation error?,  Next: How do I convert factors to numeric?,  Prev: How do file names work in Windows?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How do I convert factors to numeric?,  Next: Are Trellis displays implemented in R?,  Prev: Why does plotting give a color allocation error?,  Up: R Miscellanea

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).


File: R-FAQ.info,  Node: Are Trellis displays implemented in R?,  Next: What are the enclosing and parent environments?,  Prev: How do I convert factors to numeric?,  Up: R Miscellanea

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.)


File: R-FAQ.info,  Node: What are the enclosing and parent environments?,  Next: How can I substitute into a plot label?,  Prev: Are Trellis displays implemented in R?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How can I substitute into a plot label?,  Next: What are valid names?,  Prev: What are the enclosing and parent environments?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: What are valid names?,  Next: Are GAMs implemented in R?,  Prev: How can I substitute into a plot label?,  Up: R Miscellanea

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".


File: R-FAQ.info,  Node: Are GAMs implemented in R?,  Next: Why is the output not printed when I source() a file?,  Prev: What are valid names?,  Up: R Miscellanea

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*.


File: R-FAQ.info,  Node: Why is the output not printed when I source() a file?,  Next: Why does outer() behave strangely with my function?,  Prev: Are GAMs implemented in R?,  Up: R Miscellanea

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)'.


File: R-FAQ.info,  Node: Why does outer() behave strangely with my function?,  Next: Why does the output from anova() depend on the order of factors in the model?,  Prev: Why is the output not printed when I source() a file?,  Up: R Miscellanea

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)


File: R-FAQ.info,  Node: Why does the output from anova() depend on the order of factors in the model?,  Next: How do I produce PNG graphics in batch mode?,  Prev: Why does outer() behave strangely with my function?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How do I produce PNG graphics in batch mode?,  Next: How can I get command line editing to work?,  Prev: Why does the output from anova() depend on the order of factors in the model?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How can I get command line editing to work?,  Next: How can I turn a string into a variable?,  Prev: How do I produce PNG graphics in batch mode?,  Up: R Miscellanea

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).


File: R-FAQ.info,  Node: How can I turn a string into a variable?,  Next: Why do lattice/trellis graphics not work?,  Prev: How can I get command line editing to work?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: Why do lattice/trellis graphics not work?,  Next: How can I sort the rows of a data frame?,  Prev: How can I turn a string into a variable?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How can I sort the rows of a data frame?,  Next: Why does the help.start() search engine not work?,  Prev: Why do lattice/trellis graphics not work?,  Up: R Miscellanea

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()'.


File: R-FAQ.info,  Node: Why does the help.start() search engine not work?,  Next: Why did my .Rprofile stop working when I updated R?,  Prev: How can I sort the rows of a data frame?,  Up: R Miscellanea

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.  *Note Enabling search in HTML help:
(R-admin)Enabling search in HTML help, for further details.


File: R-FAQ.info,  Node: Why did my .Rprofile stop working when I updated R?,  Next: Where have all the methods gone?,  Prev: Why does the help.start() search engine not work?,  Up: R Miscellanea

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).


File: R-FAQ.info,  Node: Where have all the methods gone?,  Next: How can I create rotated axis labels?,  Prev: Why did my .Rprofile stop working when I updated R?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: How can I create rotated axis labels?,  Next: Why is read.table() so inefficient?,  Prev: Where have all the methods gone?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: Why is read.table() so inefficient?,  Next: What is the difference between package and library?,  Prev: How can I create rotated axis labels?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: What is the difference between package and library?,  Next: I installed a package but the functions are not there,  Prev: Why is read.table() so inefficient?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: I installed a package but the functions are not there,  Next: Why doesn't R think these numbers are equal?,  Prev: What is the difference between package and library?,  Up: R Miscellanea

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.


File: R-FAQ.info,  Node: Why doesn't R think these numbers are equal?,  Next: How can I capture or ignore errors in a long simulation?,  Prev: I installed a package but the functions are not there,  Up: R Miscellanea

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'.


File: R-FAQ.info,  Node: How can I capture or ignore errors in a long simulation?,  Prev: Why doesn't R think these numbers are equal?,  Up: R Miscellanea

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).


File: R-FAQ.info,  Node: R Programming,  Next: R Bugs,  Prev: R Miscellanea,  Up: Top

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

* Menu:

* How should I write summary methods?::
* How can I debug dynamically loaded code?::
* How can I inspect R objects when debugging?::
* How can I change compilation flags?::
* How can I debug S4 methods?::


File: R-FAQ.info,  Node: How should I write summary methods?,  Next: How can I debug dynamically loaded code?,  Prev: R Programming,  Up: 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.


File: R-FAQ.info,  Node: How can I debug dynamically loaded code?,  Next: How can I inspect R objects when debugging?,  Prev: How should I write summary methods?,  Up: R Programming

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.

   *Note Finding entry points in dynamically loaded code:
(R-exts)Finding entry points.


File: R-FAQ.info,  Node: How can I inspect R objects when debugging?,  Next: How can I change compilation flags?,  Prev: How can I debug dynamically loaded code?,  Up: R Programming

8.3 How can I inspect R objects when debugging?
===============================================

The most convenient way is to call `R_PV' from the symbolic debugger.

   *Note Inspecting R objects when debugging: (R-exts)Inspecting R
objects.


File: R-FAQ.info,  Node: How can I change compilation flags?,  Next: How can I debug S4 methods?,  Prev: How can I inspect R objects when debugging?,  Up: R Programming

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.
*Note Add-on packages: (R-admin)Add-on packages.

   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'.  *Note Creating shared objects: (R-exts)Creating shared
objects.


File: R-FAQ.info,  Node: How can I debug S4 methods?,  Prev: How can I change compilation flags?,  Up: R Programming

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.


File: R-FAQ.info,  Node: R Bugs,  Next: Acknowledgments,  Prev: R Programming,  Up: Top

9 R Bugs
********

* Menu:

* What is a bug?::
* How to report a bug::


File: R-FAQ.info,  Node: What is a bug?,  Next: How to report a bug,  Prev: R Bugs,  Up: 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.


File: R-FAQ.info,  Node: How to report a bug,  Prev: What is a bug?,  Up: R Bugs

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'.


File: R-FAQ.info,  Node: Acknowledgments,  Prev: R Bugs,  Up: Top

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 ...



Local Variables:
coding: iso-8859-1
End:


Tag Table:
Node: Top300
Node: Introduction570
Node: Legalese871
Node: Obtaining this document1781
Node: Citing this document2392
Node: Notation2697
Node: Feedback2948
Node: R Basics3575
Node: What is R?4065
Node: What machines does R run on?6561
Node: What is the current version of R?7301
Node: How can R be obtained?7911
Node: How can R be installed?8534
Node: How can R be installed (Unix)8848
Node: How can R be installed (Windows)12072
Node: How can R be installed (Macintosh)12964
Node: Are there Unix binaries for R?14200
Node: What documentation exists for R?15992
Node: Citing R21070
Node: What mailing lists exist for R?21768
Node: What is CRAN?24905
Node: Can I use R for commercial purposes?27467
Node: Why is R named R?29396
Node: What is the R Foundation?29774
Node: R and S30448
Node: What is S?30719
Node: What is S-PLUS?32825
Node: What are the differences between R and S?33480
Node: Lexical scoping34286
Node: Models42036
Node: Others43079
Node: Is there anything R can do that S-PLUS cannot?51131
Node: What is R-plus?52233
Node: R Web Interfaces52412
Node: R Add-On Packages57471
Node: Which add-on packages exist for R?57847
Node: Add-on packages in R58248
Node: Add-on packages from CRAN59676
Node: Add-on packages from Omegahat134441
Node: Add-on packages from Bioconductor140433
Node: Other add-on packages154418
Node: How can add-on packages be installed?155588
Node: How can add-on packages be used?157544
Node: How can add-on packages be removed?159903
Node: How can I create an R package?160531
Node: How can I contribute to R?161430
Node: R and Emacs162537
Node: Is there Emacs support for R?162780
Node: Should I run R from within Emacs?165262
Node: Debugging R from within Emacs166420
Node: R Miscellanea168265
Node: How can I set components of a list to NULL?169874
Node: How can I save my workspace?170474
Node: How can I clean up my workspace?170941
Node: How can I get eval() and D() to work?171398
Node: Why do my matrices lose dimensions?172826
Node: How does autoloading work?174084
Node: How should I set options?174813
Node: How do file names work in Windows?175970
Node: Why does plotting give a color allocation error?176545
Node: How do I convert factors to numeric?177616
Node: Are Trellis displays implemented in R?178344
Node: What are the enclosing and parent environments?179293
Node: How can I substitute into a plot label?182043
Node: What are valid names?183089
Node: Are GAMs implemented in R?185325
Node: Why is the output not printed when I source() a file?186130
Node: Why does outer() behave strangely with my function?187199
Node: Why does the output from anova() depend on the order of factors in the model?188645
Node: How do I produce PNG graphics in batch mode?190222
Node: How can I get command line editing to work?191026
Node: How can I turn a string into a variable?191794
Node: Why do lattice/trellis graphics not work?192596
Node: How can I sort the rows of a data frame?193353
Node: Why does the help.start() search engine not work?193762
Node: Why did my .Rprofile stop working when I updated R?194539
Node: Where have all the methods gone?195252
Node: How can I create rotated axis labels?196069
Node: Why is read.table() so inefficient?197548
Node: What is the difference between package and library?198194
Node: I installed a package but the functions are not there199069
Node: Why doesn't R think these numbers are equal?199598
Node: How can I capture or ignore errors in a long simulation?200843
Node: R Programming201600
Node: How should I write summary methods?201937
Node: How can I debug dynamically loaded code?202719
Node: How can I inspect R objects when debugging?203217
Node: How can I change compilation flags?203647
Node: How can I debug S4 methods?204531
Node: R Bugs204915
Node: What is a bug?205078
Node: How to report a bug207441
Node: Acknowledgments211960

End Tag Table
