R FAQ
R FAQ
Frequently Asked Questions on R
Version 2.9.2009-07-24
ISBN 3-900051-08-9
Kurt Hornik
1 Introduction
This document contains answers to some of the most frequently asked
questions about R.
1.1 Legalese
This document is copyright © 1998–2009 by Kurt
Hornik.
This document is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published
by the Free Software Foundation; either version 2, or (at your option)
any later version.
This document is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
A copy of the GNU General Public License is available via WWW
at
http://www.gnu.org/copyleft/gpl.html.
You can also obtain it by writing to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, U.S.A.
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.
You can also obtain the R FAQ from the doc/FAQ
subdirectory of a CRAN site (see What is CRAN?).
1.3 Citing this document
In publications, please refer to this FAQ as Hornik
(2009), “The R FAQ”, and give the above,
official URL and the ISBN 3-900051-08-9:
@Misc{,
author = {Kurt Hornik},
title = {The {R} {FAQ}},
year = {2009},
note = {{ISBN} 3-900051-08-9},
url = {http://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
}
1.4 Notation
Everything should be pretty standard. ‘R>’ is used for the R
prompt, and a ‘$’ for the shell prompt (where applicable).
1.5 Feedback
Feedback via email to [email protected] 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” and the
“R for Mac OS X FAQ. If you have information on Macintosh or Windows
systems that you think should be added to this document, please let me
know.
2 R Basics
2.1 What is R?
R is a system for statistical computation and graphics. It consists of
a language plus a run-time environment with graphics, a debugger, access
to certain system functions, and the ability to run programs stored in
script files.
The design of R has been heavily influenced by two existing languages:
Becker, Chambers & Wilks' S (see What is S?) and Sussman's
Scheme.
Whereas the resulting language is very similar in appearance to S, the
underlying implementation and semantics are derived from Scheme.
See What are the differences between R and S?, for further details.
The core of R is an interpreted computer language which allows branching
and looping as well as modular programming using functions. Most of the
user-visible functions in R are written in R. It is possible for the
user to interface to procedures written in the C, C++, or FORTRAN
languages for efficiency. The R distribution contains functionality for
a large number of statistical procedures. Among these are: linear and
generalized linear models, nonlinear regression models, time series
analysis, classical parametric and nonparametric tests, clustering and
smoothing. There is also a large set of functions which provide a
flexible graphical environment for creating various kinds of data
presentations. Additional modules (“add-on packages”) are available
for a variety of specific purposes (see R Add-On Packages).
R was initially written by Ross Ihaka
and Robert Gentleman at the
Department of Statistics of the University of Auckland in Auckland, New
Zealand. In addition, a large group of individuals has contributed to R
by sending code and bug reports.
Since mid-1997 there has been a core group (the “R Core Team”) who can
modify the R source code archive. The group currently consists of Doug
Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik,
Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin
Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley,
Duncan Temple Lang, Luke Tierney, and Simon Urbanek.
R has a home page at http://www.R-project.org/. It is
free software
distributed under a GNU-style
copyleft, and an
official part of the GNU project
(“GNU S”).
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,
i386-freebsd,
rs6000-ibm-aix, and sparc-sun-solaris.
If you know about other platforms, please drop us a note.
2.3 What is the current version of R?
The current released version is 2.9.1. Based on this
`major.minor.patchlevel' numbering scheme, there are two development
versions of R, a patched version of the current release (`r-patched')
and one working towards the next minor or eventually major (`r-devel')
releases of R, respectively. Version r-patched is for bug fixes mostly.
New features are typically introduced in r-devel.
2.4 How can R be obtained?
Sources, binaries and documentation for R can be obtained via CRAN,
the “Comprehensive R Archive Network” (see What is CRAN?).
Sources are also available via https://svn.R-project.org/R/, the
R Subversion repository, but currently not via anonymous rsync (nor
CVS).
Tarballs with daily snapshots of the r-devel and r-patched development
versions of R can be found at
ftp://ftp.stat.math.ethz.ch/Software/R.
2.5 How can R be installed?
2.5.1 How can R be installed (Unix)
If R is already installed, it can be started by typing R at the
shell prompt (of course, provided that the executable is in your path).
If binaries are available for your platform (see Are there Unix binaries for R?), you can use these, following the instructions that
come with them.
Otherwise, you can compile and install R yourself, which can be done
very easily under a number of common Unix platforms (see What machines does R run on?). The file INSTALL that comes with the
R distribution contains a brief introduction, and the “R Installation
and Administration” guide (see What documentation exists for R?)
has full details.
Note that you need a FORTRAN compiler or perhaps f2c in
addition to a C compiler to build R. Also, you need Perl version 5 to
build the R object documentations. (If this is not available on your
system, you can obtain a PDF version of the object reference manual via
CRAN.)
In the simplest case, untar the R source code, change to the directory
thus created, and issue the following commands (at the shell prompt):
$ ./configure
$ make
If these commands execute successfully, the R binary and a shell script
front-end called R are created and copied to the bin
directory. You can copy the script to a place where users can invoke
it, for example to /usr/local/bin. In addition, plain text help
pages as well as HTML and LaTeX versions of the documentation are
built.
Use make dvi to create DVI versions of the R manuals, such as
refman.dvi (an R object reference index) and R-exts.dvi,
the “R Extension Writers Guide”, in the doc/manual
subdirectory. These files can be previewed and printed using standard
programs such as xdvi and dvips. You can also use
make pdf to build PDF (Portable Document Format) version of the
manuals, and view these using e.g. Acrobat. Manuals written in the
GNU Texinfo system can also be converted to info files
suitable for reading online with Emacs or stand-alone GNU
Info; use make info to create these versions (note that this
requires Makeinfo version 4.5).
Finally, use make check to find out whether your R system works
correctly.
You can also perform a “system-wide” installation using make
install. By default, this will install to the following directories:
- ${prefix}/bin
- the front-end shell script
- ${prefix}/man/man1
- the man page
- ${prefix}/lib/R
- all the rest (libraries, on-line help system, ...). This is the “R
Home Directory” (R_HOME) of the installed system.
In the above, prefix
is determined during configuration
(typically /usr/local) and can be set by running
configure with the option
$ ./configure --prefix=/where/you/want/R/to/go
(E.g., the R executable will then be installed into
/where/you/want/R/to/go/bin.)
To install DVI, info and PDF versions of the manuals, use make
install-dvi, make install-info and make install-pdf,
respectively.
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 2000 and later (including 64-bit versions of Windows)
on ix86 and x86_64 chips. The Windows version of R was created by Robert
Gentleman and Guido Masarotto, and is now being developed and maintained
by Duncan Murdoch and
Brian D. Ripley.
For most installations the Windows installer program will be the easiest
tool to use.
See the “R for Windows FAQ” for more details.
2.5.3 How can R be installed (Macintosh)
The bin/macosx directory of a CRAN site contains a standard
Apple installer package inside a disk image named R.dmg. Once
downloaded and executed, the installer will install the current
non-developer release of R. RAqua is a native Mac OS X Darwin version
of R with a R.app Mac OS X GUI. Inside
bin/macosx/powerpc/contrib/x.y there are prebuilt
binary packages (for powerpc version of Mac OS X) to be used with RAqua
corresponding to the “x.y” release of R. The installation
of these packages is available through the “Package” menu of the R.app
GUI. This port of R for Mac OS X is maintained by
Stefano Iacus. The
“R for Mac OS X FAQ has more details.
The bin/macos directory of a CRAN site contains bin-hexed
(hqx) and stuffit (sit) archives for a base distribution
and a large number of add-on packages of R 1.7.1 to run under Mac OS 8.6
to Mac OS 9.2.2. This port of R for Macintosh is no longer supported.
2.6 Are there Unix binaries for R?
The bin/linux directory of a CRAN site contains the following
packages.
| CPU | Versions | Provider
|
---|
Debian | i386/amd64 | etch-cran | Johannes Ranke
|
| i386 | lenny-cran | Johannes Ranke
|
Red Hat | i386/x86_64 | fedora8/fedora9/fedora10 | Martyn Plummer
|
| i386/x86_64 | el4/el5 | Bob Kinney
|
SuSE | i586/x86_64 | 10.3/11.0/11.1 | Detlef Steuer
|
Ubuntu | i386 | dapper/gutsy/hardy/intrepid | Vincent Goulet
|
| amd64 | dapper/gutsy/hardy/intrepid | Michael Rutter
|
Debian packages, maintained by Dirk Eddelbuettel and Doug Bates, have
long been part of the Debian distribution, and can be accessed through
APT, the Debian package maintenance tool. Use e.g. apt-get
install r-base r-recommended
to install the R environment and
recommended packages. If you also want to build R packages from source,
also run apt-get install r-base-dev
to obtain the additional
tools required for this. So-called “backports” of the current R
packages for at least the stable distribution of Debian are
provided by Johannes Ranke, and available from CRAN. See
http://CRAN.R-project.org/bin/linux/debian/README for details on R
Debian packages and installing the backports, which should also be
suitable for other Debian derivatives. Native backports for Ubuntu are
provided by Vincent Goulet and Michael Rutter.
On SUSE, you can set up an installation source for R within Yast by
setting (e.g.)
Protocol: HTTP
Server name: software.openSUSE.org
Directory: /download/home:/dsteuer/openSUSE_10.2/
With this setting, online updates will check for new versions of R.
The bin/solaris directory of a CRAN site contains binary
packages for Solaris on the SPARC and x64 platforms, provided by Mithun
Sridharan.
No other binary distributions are currently publically available via
CRAN.
A “live” Linux distribution with a particular focus on R is
Quantian, which provides a directly bootable and self-configuring
“Live DVD” containing numerous applications of interests to scientists
and researchers, including several hundred CRAN and Bioconductor
packages, the “ESS” extensions for Emacs, the “JGR” Java
GUI for R, the Ggobi visualization tool as well as several
other R interfaces. The Quantian website at
http://dirk.eddelbuettel.com/quantian/ contains more details as
well download information.
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 How can R be installed?. An up-to-date HTML
version is always available for web browsing at
http://stat.ethz.ch/R-manual/.
Printed copies of the R reference manual for some version(s) are
available from Network Theory Ltd, at
http://www.network-theory.co.uk/R/base/. For each set of manuals
sold, the publisher donates USD 10 to the R Foundation (see What is the R Foundation?).
The R distribution also comes with the following manuals.
- “An Introduction to R” (R-intro)
includes information on data types, programming elements, statistical
modeling and graphics. This document is based on the “Notes on
S-Plus” by Bill Venables and David Smith.
- “Writing R Extensions” (R-exts)
currently describes the process of creating R add-on packages, writing R
documentation, R's system and foreign language interfaces, and the R
API.
- “R Data Import/Export” (R-data)
is a guide to importing and exporting data to and from R.
- “The R Language Definition” (R-lang),
a first version of the “Kernighan & Ritchie of R”, explains
evaluation, parsing, object oriented programming, computing on the
language, and so forth.
- “R Installation and Administration” (R-admin).
- “R Internals” (R-ints)
is a guide to R's internal structures.
(Added in R 2.4.0.)
An annotated bibliography (BibTeX format) of R-related publications
can be found at
http://www.R-project.org/doc/bib/R.bib
Books on R by R Core Team members include
John M. Chambers (2008), “Software for Data Analysis: Programming with
R”. Springer, New York, ISBN 978-0-387-75935-7,
http://stat.stanford.edu/~jmc4/Rbook/.
Peter Dalgaard (2008), “Introductory Statistics with R”, 2nd edition.
Springer, ISBN 978-0-387-79053-4,
http://www.biostat.ku.dk/~pd/ISwR.html.
Robert Gentleman (2008), “R Programming for Bioinformatics”. Chapman
& Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7,
http://www.bioconductor.org/pub/RBioinf/.
Stefano M. Iacus (2008), “Simulation and Inference for Stochastic
Differential Equations: With R Examples”. Springer, New York, ISBN
978-0-387-75838-1.
Deepayan Sarkar (2007), “Lattice: Multivariate Data Visualization with
R”. Springer, New York, ISBN 978-0-387-75968-5.
W. John Braun and Duncan J. Murdoch (2007), “A First Course in
Statistical Programming with R”. Cambridge University Press,
Cambridge, ISBN 978-0521872652.
P. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN:
1-584-88486-X,
http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.
William N. Venables and Brian D. Ripley (2002), “Modern Applied
Statistics with S” (4th edition). Springer, ISBN 0-387-95457-0,
http://www.stats.ox.ac.uk/pub/MASS4/.
Jose C. Pinheiro and Douglas M. Bates (2000), “Mixed-Effects Models in
S and S-Plus”. Springer, ISBN 0-387-98957-0.
Last, but not least, Ross' and Robert's experience in designing and
implementing R is described in Ihaka & Gentleman (1996), “R: A Language
for Data Analysis and Graphics”,
Journal of Computational and Graphical Statistics, 5, 299–314.
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 = 2009,
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org}
}
Citation strings (or BibTeX entries) for R and R packages can also be
obtained by citation()
.
2.9 What mailing lists exist for R?
Thanks to Martin Maechler, 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 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 [email protected].
Send email to [email protected] to send a message to
everyone on the R-help mailing list. Subscription and posting to the
other lists is done analogously, with ‘R-help’ replaced by
‘R-announce’, ‘R-packages’, and ‘R-devel’, respectively.
Note that the R-announce and R-packages lists are gatewayed into R-help.
Hence, you should subscribe to either of them only in case you are not
subscribed to R-help.
It is recommended that you send mail to R-help rather than only to the R
Core developers (who are also subscribed to the list, of course). This
may save them precious time they can use for constantly improving R, and
will typically also result in much quicker feedback for yourself.
Of course, in the case of bug reports it would be very helpful to have
code which reliably reproduces the problem. Also, make sure that you
include information on the system and version of R being used. See
R Bugs for more details.
See http://www.R-project.org/mail.html for more information on
the R mailing lists.
The R Core Team can be reached at [email protected]
for comments and reports.
Many of the R project's mailing lists are also available via
Gmane, from which they can be read with a web
browser, using an NNTP news reader, or via RSS feeds. See
http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r. for
the available mailing lists, and http://www.gmane.org/rss.php for
details on RSS feeds.
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 Wirtschaftsuniversität Wien, Austria, can
be found at the URL
http://CRAN.R-project.org/
Daily mirrors are available at URLs including
http://cran.at.R-project.org/
| (WU Wien, Austria)
|
http://cran.au.R-project.org/
| (PlanetMirror, Australia)
|
http://cran.br.R-project.org/
| (Universidade Federal do Paraná, Brazil)
|
http://cran.ch.R-project.org/
| (ETH Zürich, Switzerland)
|
http://cran.dk.R-project.org/
| (SunSITE, Denmark)
|
http://cran.es.R-project.org/
| (Spanish National Research Network, Madrid, Spain)
|
http://cran.fr.R-project.org/
| (INRA, Toulouse, France)
|
http://cran.pt.R-project.org/
| (Universidade do Porto, Portugal)
|
http://cran.uk.R-project.org/
| (U of Bristol, United Kingdom)
|
http://cran.za.R-project.org/
| (Rhodes U, South Africa)
|
See http://CRAN.R-project.org/mirrors.html for a complete list of
mirrors. Please use the CRAN site closest to you to reduce network
load.
From CRAN, you can obtain the latest official release of R, daily
snapshots of R (copies of the current source trees), as gzipped and
bzipped tar files, a wealth of additional contributed code, as well as
prebuilt binaries for various operating systems (Linux, Mac OS Classic,
Mac OS X, and MS Windows). CRAN also provides access to
documentation on R, existing mailing lists and the R Bug Tracking
system.
To “submit” to CRAN, simply upload to
ftp://CRAN.R-project.org/incoming/ and send an email to
[email protected]. 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-2, GPL-3, BSD, Artistic,
...) in your submission.
Please always use the URL of the master site when referring to
CRAN.
2.11 Can I use R for commercial purposes?
R is released under the
GNU General Public License (GPL) version 2. 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:
The license must not restrict anyone from making use of the program in a
specific field of endeavor. For example, it may not restrict the
program from being used in a business, or from being used for genetic
research.
It is also explicitly stated in clause 0 of the GPL, which says in part
Activities other than copying, distribution and modification are not
covered by this License; they are outside its scope. The act of running
the Program is not restricted, and the output from the Program is
covered only if its contents constitute a work based on the Program.
Most add-on packages, including all recommended ones, also explicitly
allow commercial use in this way. A few packages are restricted to
“non-commercial use”; you should contact the author to clarify whether
these may be used or seek the advice of your legal counsel.
None of the discussion in this section constitutes legal advice. The R
Core Team does not provide legal advice under any circumstances.
2.12 Why is R named R?
The name is partly based on the (first) names of the first two R authors
(Robert Gentleman and Ross Ihaka), and partly a play on the name of the
Bell Labs language `S' (see What is S?).
2.13 What is the R Foundation?
The R Foundation is a not for profit organization working in the public
interest. It was founded by the members of the R Core Team in order to
provide support for the R project and other innovations in statistical
computing, provide a reference point for individuals, institutions or
commercial enterprises that want to support or interact with the R
development community, and to hold and administer the copyright of R
software and documentation. See
http://www.R-project.org/foundation/ for more information.
3 R and S
3.1 What is S?
S is a very high level language and an environment for data analysis and
graphics. In 1998, the Association for Computing Machinery
(ACM) presented its Software System Award to John M. Chambers,
the principal designer of S, for
the S system, which has forever altered the way people analyze,
visualize, and manipulate data ...
S is an elegant, widely accepted, and enduring software system, with
conceptual integrity, thanks to the insight, taste, and effort of John
Chambers.
The evolution of the S language is characterized by four books by John
Chambers and coauthors, which are also the primary references for S.
- Richard A. Becker and John M. Chambers (1984), “S. An Interactive
Environment for Data Analysis and Graphics,” Monterey: Wadsworth and
Brooks/Cole.
This is also referred to as the “Brown Book”, and of historical
interest only.
- Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), “The New
S Language,” London: Chapman & Hall.
This book is often called the “Blue Book”, and introduced what
is now known as S version 2.
- John M. Chambers and Trevor J. Hastie (1992), “Statistical Models in
S,” London: Chapman & Hall.
This is also called the “White Book”, and introduced S version
3, which added structures to facilitate statistical modeling in S.
- John M. Chambers (1998), “Programming with Data,” New York: Springer,
ISBN 0-387-98503-4
(http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/).
This “Green Book” describes version 4 of S, a major revision of
S designed by John Chambers to improve its usefulness at every stage of
the programming process.
See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html
for further information on “Stages in the Evolution of S”.
There is a huge amount of user-contributed code for S, available at the
S Repository at CMU.
3.2 What is S-Plus?
S-Plus is a value-added version of S sold by Insightful Corporation.
Based on the S language, S-Plus provides functionality in a wide
variety of areas, including robust regression, modern non-parametric
regression, time series, survival analysis, multivariate analysis,
classical statistical tests, quality control, and graphics drivers.
Add-on modules add additional capabilities.
See the Insightful S-Plus page for further information.
3.3 What are the differences between R and S?
We can regard S as a language with three current implementations or
“engines”, the “old S engine” (S version 3; S-Plus 3.x and 4.x),
the “new S engine” (S version 4; S-Plus 5.x and above), and R.
Given this understanding, asking for “the differences between R and S”
really amounts to asking for the specifics of the R implementation of
the S language, i.e., the difference between the R and S engines.
For the remainder of this section, “S” refers to the S engines and not
the S language.
3.3.1 Lexical scoping
Contrary to other implementations of the S language, R has adopted an
evaluation model in which nested function definitions are lexically
scoped. This is analogous to the evalutation model in Scheme.
This difference becomes manifest when free variables occur in a
function. Free variables are those which are neither formal parameters
(occurring in the argument list of the function) nor local variables
(created by assigning to them in the body of the function). In S, the
values of free variables are determined by a set of global variables
(similar to C, there is only local and global scope). In R, they are
determined by the environment in which the function was created.
Consider the following function:
cube <- function(n) {
sq <- function() n * n
n * sq()
}
Under S, sq()
does not “know” about the variable n
unless it is defined globally:
S> cube(2)
Error in sq(): Object "n" not found
Dumped
S> n <- 3
S> cube(2)
[1] 18
In R, the “environment” created when cube()
was invoked is
also looked in:
R> cube(2)
[1] 8
As a more “interesting” real-world problem, suppose you want to write
a function which returns the density function of the r-th order
statistic from a sample of size n from a (continuous)
distribution. For simplicity, we shall use both the cdf and pdf of the
distribution as explicit arguments. (Example compiled from various
postings by Luke Tierney.)
The S-Plus documentation for call()
basically suggests the
following:
dorder <- function(n, r, pfun, dfun) {
f <- function(x) NULL
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
PF <- call(substitute(pfun), as.name("x"))
DF <- call(substitute(dfun), as.name("x"))
f[[length(f)]] <-
call("*", con,
call("*", call("^", PF, r - 1),
call("*", call("^", call("-", 1, PF), n - r),
DF)))
f
}
Rather tricky, isn't it? The code uses the fact that in S,
functions are just lists of special mode with the function body as the
last argument, and hence does not work in R (one could make the idea
work, though).
A version which makes heavy use of substitute()
and seems to work
under both S and R is
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
list(PF = substitute(pfun), DF = substitute(dfun),
a = r - 1, b = n - r, K = con)))
}
(the eval()
is not needed in S).
However, in R there is a much easier solution:
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
}
}
This seems to be the “natural” implementation, and it works because
the free variables in the returned function can be looked up in the
defining environment (this is lexical scope).
Note that what you really need is the function closure, i.e., the
body along with all variable bindings needed for evaluating it. Since
in the above version, the free variables in the value function are not
modified, you can actually use it in S as well if you abstract out the
closure operation into a function MC()
(for “make closure”):
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
MC(function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
},
list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
}
Given the appropriate definitions of the closure operator, this works in
both R and S, and is much “cleaner” than a substitute/eval solution
(or one which overrules the default scoping rules by using explicit
access to evaluation frames, as is of course possible in both R and S).
For R, MC()
simply is
MC <- function(f, env) f
(lexical scope!), a version for S is
MC <- function(f, env = NULL) {
env <- as.list(env)
if (mode(f) != "function")
stop(paste("not a function:", f))
if (length(env) > 0 && any(names(env) == ""))
stop(paste("not all arguments are named:", env))
fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
fargs <- c(fargs, env)
if (any(duplicated(names(fargs))))
stop(paste("duplicated arguments:", paste(names(fargs)),
collapse = ", "))
fbody <- f[length(f)]
cf <- c(fargs, fbody)
mode(cf) <- "function"
return(cf)
}
Similarly, most optimization (or zero-finding) routines need some
arguments to be optimized over and have other parameters that depend on
the data but are fixed with respect to optimization. With R scoping
rules, this is a trivial problem; simply make up the function with the
required definitions in the same environment and scoping takes care of
it. With S, one solution is to add an extra parameter to the function
and to the optimizer to pass in these extras, which however can only
work if the optimizer supports this.
Nested lexically scoped functions allow using function closures and
maintaining local state. A simple example (taken from Abelson and
Sussman) is obtained by typing demo("scoping") at the R prompt.
Further information is provided in the standard R reference “R: A
Language for Data Analysis and Graphics” (see What documentation exists for R?) and in Robert Gentleman and Ross Ihaka (2000), “Lexical
Scope and Statistical Computing”,
Journal of Computational and Graphical Statistics, 9, 491–508.
Nested lexically scoped functions also imply a further major difference.
Whereas S stores all objects as separate files in a directory somewhere
(usually .Data under the current directory), R does not. All
objects in R are stored internally. When R is started up it grabs a
piece of memory and uses it to store the objects. R performs its own
memory management of this piece of memory, growing and shrinking its
size as needed. Having everything in memory is necessary because it is
not really possible to externally maintain all relevant “environments”
of symbol/value pairs. This difference also seems to make R
faster than S.
The down side is that if R crashes you will lose all the work for the
current session. Saving and restoring the memory “images” (the
functions and data stored in R's internal memory at any time) can be a
bit slow, especially if they are big. In S this does not happen,
because everything is saved in disk files and if you crash nothing is
likely to happen to them. (In fact, one might conjecture that the S
developers felt that the price of changing their approach to persistent
storage just to accommodate lexical scope was far too expensive.)
Hence, when doing important work, you might consider saving often (see
How can I save my workspace?) to safeguard against possible
crashes. Other possibilities are logging your sessions, or have your R
commands stored in text files which can be read in using
source()
.
Note: If you run R from within Emacs (see R and Emacs), you can save the
contents of the interaction buffer to a file and conveniently manipulate
it using ess-transcript-mode
, as well as save source copies of
all functions and data used.
3.3.2 Models
There are some differences in the modeling code, such as
- Whereas in S, you would use
lm(y ~ x^3)
to regress y
on
x^3
, in R, you have to insulate powers of numeric vectors (using
I()
), i.e., you have to use lm(y ~ I(x^3))
.
- The glm family objects are implemented differently in R and S. The same
functionality is available but the components have different names.
- Option
na.action
is set to "na.omit"
by default in R,
but not set in S.
- Terms objects are stored differently. In S a terms object is an
expression with attributes, in R it is a formula with attributes. The
attributes have the same names but are mostly stored differently.
- Finally, in R
y ~ x + 0
is an alternative to y ~ x - 1
for
specifying a model with no intercept. Models with no parameters at all
can be specified by y ~ 0
.
3.3.3 Others
Apart from lexical scoping and its implications, R follows the S
language definition in the Blue and White Books as much as possible, and
hence really is an “implementation” of S. There are some intentional
differences where the behavior of S is considered “not clean”. In
general, the rationale is that R should help you detect programming
errors, while at the same time being as compatible as possible with S.
Some known differences are the following.
- In R, if
x
is a list, then x[i] <- NULL
and x[[i]]
<- NULL
remove the specified elements from x
. The first of
these is incompatible with S, where it is a no-op. (Note that you can
set elements to NULL
using x[i] <- list(NULL)
.)
- In S, the functions named
.First
and .Last
in the
.Data directory can be used for customizing, as they are executed
at the very beginning and end of a session, respectively.
In R, the startup mechanism is as follows. Unless --no-environ
was given on the command line, R searches for site and user files to
process for setting environment variables. Then, R searches for a
site-wide startup profile unless the command line option
--no-site-file was given. This code is loaded in package
base. Then, unless --no-init-file was given, R
searches for a user profile file, and sources it into the user
workspace. It then loads a saved image of the user workspace from
.RData in case there is one (unless --no-restore-data or
--no-restore were specified). Next, a function .First()
is run if found on the search path. Finally, function .First.sys
in the base package is run. When terminating an R session, by
default a function .Last
is run if found on the search path,
followed by .Last.sys
. If needed, the functions .First()
and .Last()
should be defined in the appropriate startup
profiles. See the help pages for .First
and .Last
for
more details.
- In R,
T
and F
are just variables being set to TRUE
and FALSE
, respectively, but are not reserved words as in S and
hence can be overwritten by the user. (This helps e.g. when you have
factors with levels "T"
or "F"
.) Hence, when writing code
you should always use TRUE
and FALSE
.
- In R,
dyn.load()
can only load shared objects, as created
for example by R CMD SHLIB.
- In R,
attach()
currently only works for lists and data frames,
but not for directories. (In fact, attach()
also works for R
data files created with save()
, which is analogous to attaching
directories in S.) Also, you cannot attach at position 1.
- Categories do not exist in R, and never will as they are deprecated now
in S. Use factors instead.
- In R,
For()
loops are not necessary and hence not supported.
- In R,
assign()
uses the argument envir= rather than
where= as in S.
- The random number generators are different, and the seeds have different
length.
- R passes integer objects to C as
int *
rather than long *
as in S.
- R has no single precision storage mode. However, as of version 0.65.1,
there is a single precision interface to C/FORTRAN subroutines.
- By default,
ls()
returns the names of the objects in the current
(under R) and global (under S) environment, respectively. For example,
given
x <- 1; fun <- function() {y <- 1; ls()}
then fun()
returns "y"
in R and "x"
(together with
the rest of the global environment) in S.
- R allows for zero-extent matrices (and arrays, i.e., some elements of
the
dim
attribute vector can be 0). This has been determined a
useful feature as it helps reducing the need for special-case tests for
empty subsets. For example, if x
is a matrix, x[, FALSE]
is not NULL
but a “matrix” with 0 columns. Hence, such objects
need to be tested for by checking whether their length()
is zero
(which works in both R and S), and not using is.null()
.
- Named vectors are considered vectors in R but not in S (e.g.,
is.vector(c(a = 1:3))
returns FALSE
in S and TRUE
in R).
- Data frames are not considered as matrices in R (i.e., if
DF
is a
data frame, then is.matrix(DF)
returns FALSE
in R and
TRUE
in S).
- R by default uses treatment contrasts in the unordered case, whereas S
uses the Helmert ones. This is a deliberate difference reflecting the
opinion that treatment contrasts are more natural.
- In R, the argument of a replacement function which corresponds to the
right hand side must be named ‘value’. E.g.,
f(a) <- b
is
evaluated as a <- "f<-"(a, value = b)
. S always takes the last
argument, irrespective of its name.
- In S,
substitute()
searches for names for substitution in the
given expression in three places: the actual and the default arguments
of the matching call, and the local frame (in that order). R looks in
the local frame only, with the special rule to use a “promise” if a
variable is not evaluated. Since the local frame is initialized with
the actual arguments or the default expressions, this is usually
equivalent to S, until assignment takes place.
- In S, the index variable in a
for()
loop is local to the inside
of the loop. In R it is local to the environment where the for()
statement is executed.
- In S,
tapply(simplify=TRUE)
returns a vector where R returns a
one-dimensional array (which can have named dimnames).
- In S(-Plus) the C locale is used, whereas in R the current
operating system locale is used for determining which characters are
alphanumeric and how they are sorted. This affects the set of valid
names for R objects (for example accented chars may be allowed in R) and
ordering in sorts and comparisons (such as whether
"aA" < "Bb"
is
true or false). From version 1.2.0 the locale can be (re-)set in R by
the Sys.setlocale()
function.
- In S,
missing(
arg)
remains TRUE
if arg is
subsequently modified; in R it doesn't.
- From R version 1.3.0,
data.frame
strips I()
when creating
(column) names.
- In R, the string
"NA"
is not treated as a missing value in a
character variable. Use as.character(NA)
to create a missing
character value.
- R disallows repeated formal arguments in function calls.
- In S,
dump()
, dput()
and deparse()
are essentially
different interfaces to the same code. In R from version 2.0.0, this is
only true if the same control
argument is used, but by default it
is not. By default dump()
tries to write code that will evaluate
to reproduce the object, whereas dput()
and deparse()
default to options for producing deparsed code that is readable.
- In R, indexing a vector, matrix, array or data frame with
[
using
a character vector index looks only for exact matches (whereas [[
and $
allow partial matches). In S, [
allows partial
matches.
- S has a two-argument version of
atan
and no atan2
. A call
in S such as atan(x1, x2)
is equivalent to R's atan2(x1,
x2)
. However, beware of named arguments since S's atan(x = a, y
= b)
is equivalent to R's atan2(y = a, x = b)
with the meanings
of x
and y
interchanged. (R used to have undocumented
support for a two-argument atan
with positional arguments, but
this has been withdrawn to avoid further confusion.)
- Numeric constants with no fractional and exponent (i.e., only integer)
part are taken as integer in S-Plus 6.x or later, but as double in R.
There are also differences which are not intentional, and result from
missing or incorrect code in R. The developers would appreciate hearing
about any deficiencies you may find (in a written report fully
documenting the difference as you see it). Of course, it would be
useful if you were to implement the change yourself and make sure it
works.
3.4 Is there anything R can do that S-Plus cannot?
Since almost anything you can do in R has source code that you could
port to S-Plus with little effort there will never be much you can do
in R that you couldn't do in S-Plus if you wanted to. (Note that
using lexical scoping may simplify matters considerably, though.)
R offers several graphics features that S-Plus does not, such as finer
handling of line types, more convenient color handling (via palettes),
gamma correction for color, and, most importantly, mathematical
annotation in plot texts, via input expressions reminiscent of TeX
constructs. See the help page for plotmath
, which features an
impressive on-line example. More details can be found in Paul Murrell
and Ross Ihaka (2000), “An Approach to Providing Mathematical
Annotation in Plots”, Journal of Computational and Graphical Statistics, 9,
582–599.
3.5 What is R-plus?
For a very long time, there was no such thing.
XLSolutions Corporation is
currently beta testing a commercially supported version of R named R+
(read R plus).
REvolution Computing has
released
REvolution R, an enterprise-class statistical analysis system based on
R, suitable for deployment in professional, commercial and regulated
environments.
Random Technologies
offers RStat, an enterprise-strength statistical computing environment which
combines R with enterprise-level validation, documentation, software
support, and consulting services, as well as related R-based products.
See also
http://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R
for pointers to commercialized versions of R.
4 R Web Interfaces
Rweb is developed and maintained by
Jeff Banfield. The
Rweb Home Page provides access
to all three versions of Rweb—a simple text entry form that returns
output and graphs, a more sophisticated Javascript version that provides
a multiple window environment, and a set of point and click modules that
are useful for introductory statistics courses and require no knowledge
of the R language. All of the Rweb versions can analyze Web accessible
datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed
explanation of the different versions of Rweb and an overview of how
Rweb works, was published in the Journal of Statistical Software
(http://www.jstatsoft.org/v04/i01/).
Ulf Bartel has developed
R-Online, a simple on-line programming environment for R which
intends to make the first steps in statistical programming with R
(especially with time series) as easy as possible. There is no need for
a local installation since the only requirement for the user is a
JavaScript capable browser. See http://osvisions.com/r-online/
for more information.
Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix
user input and code, allowing the HTML author to do anything from
load in data sets to enter most of the commands for users without
writing CGI scripts. Graphical output was possible in PostScript or GIF
formats and the executed code was presented to the user for revision.
However, it is not clear if the project is still active.
Currently, a modified version of Rcgi by
Mai Zhou (actually, two versions: one with
(bitmap) graphics and one without) as well as the original code are
available from http://www.ms.uky.edu/~statweb/.
CGI-based web access to R is also provided at
http://hermes.sdu.dk/cgi-bin/go/. There are many additional
examples of web interfaces to R which basically allow to submit R code
to a remote server, see for example the collection of links available from
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.
David Firth has written
CGIwithR, an R add-on package available from CRAN. It
provides some simple extensions to R to facilitate running R scripts
through the CGI interface to a web server, and allows submission of data
using both GET and POST methods. It is easily installed using Apache
under Linux and in principle should run on any platform that supports R
and a web server provided that the installer has the necessary security
permissions. David's paper “CGIwithR: Facilities for Processing Web
Forms Using R” was published in the Journal of Statistical Software
(http://www.jstatsoft.org/v08/i10/). The package is now
maintained by Duncan Temple Lang and
has a web page at http://www.omegahat.org/CGIwithR/.
Rpad, developed and actively maintained
by Tom Short, provides a sophisticated environment which combines some
of the features of the previous approaches with quite a bit of
Javascript, allowing for a GUI-like behavior (with sortable
tables, clickable graphics, editable output), etc.
Jeff Horner is working on the R/Apache Integration Project which embeds
the R interpreter inside Apache 2 (and beyond). A tutorial and
presentation are available from the project web page at
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.
Rserve 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 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 by Steve Chen (though
it is unclear if this project is still active) is somewhat similar to
the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and
provides both a web interface to R and a set of pre-specified analyses
that need no R code input.
webbioc is “an integrated web
interface for doing microarray analysis using several of the
Bioconductor packages” and is designed to be installed at local sites
as a shared computing resource.
Finally, Rwui is a web application to
to create user-friendly web interfaces for R scripts. All code for the
web interface is created automatically. There is no need for the user
to do any extra scripting or learn any new scripting techniques.
5 R Add-On Packages
5.1 Which add-on packages exist for R?
5.1.1 Add-on packages in R
The R distribution comes with the following packages:
- base
- Base R functions (and datasets before R 2.0.0).
- datasets
- Base R datasets (added in R 2.0.0).
- grDevices
- Graphics devices for base and grid graphics (added in R 2.0.0).
- graphics
- R functions for base graphics.
- grid
- A rewrite of the graphics layout capabilities, plus some support for
interaction.
- methods
- Formally defined methods and classes for R objects, plus other
programming tools, as described in the Green Book.
- splines
- Regression spline functions and classes.
- stats
- R statistical functions.
- stats4
- Statistical functions using S4 classes.
- tcltk
- Interface and language bindings to Tcl/Tk GUI elements.
- tools
- Tools for package development and administration.
- utils
- R utility functions.
These “base packages” were substantially reorganized in R 1.9.0. The
former base was split into the four packages base,
graphics, stats, and utils. Packages
ctest, eda, modreg, mva,
nls, stepfun and ts were merged into
stats, package lqs returned to the recommended package
MASS, and package mle moved to stats4.
5.1.2 Add-on packages from CRAN
The following packages are available from the CRAN src/contrib
area. (Packages denoted as Recommended are to be included in all
binary distributions of R.)
- ADGofTest
- Anderson-Darling GoF test.
- ADaCGH
- Analysis of data from aCGH experiments.
- AER
- Functions, data sets, examples and vignettes for the book “Applied
Econometrics with R” by Christian Kleiber and Achim Zeileis, 2008,
Springer-Verlag, New York.
- AIGIS
- Areal Interpolation for GIS data.
- AIS
- Tools to look at the data (“Ad Inidicia Spectata”).
- ALS
- Multivariate curve resolution alternating least squares (MCR-ALS).
- AMORE
- A MORE flexible neural network package, providing the TAO robust neural
network algorithm.
- AcceptanceSampling
- Creation and evaluation of acceptance sampling plans,
- AdMit
- Adaptive mixture of Student t distributions.
- AdaptFit
- Adaptive semiparametic regression.
- AlgDesign
- Algorithmic experimental designs. Calculates exact and approximate
theory experimental designs for D, A, and I criteria.
- Amelia
- Amelia II: a program for missing data.
- AnalyzeFMRI
- Functions for I/O, visualisation and analysis of functional Magnetic
Resonance Imaging (fMRI) datasets stored in the ANALYZE format.
- Animal
- Analyze time-coded animal behavior data.
- AquaEnv
- An integrated development toolbox for aquatic chemical model
generation.
- ArDec
- Time series autoregressive decomposition.
- BACCO
- Bayesian Analysis of Computer Code Output, a former bundle now replaced
by the indidual packages approximator, calibrator, and
emulator.
- BAMD
- Bayesian association model for genomic data with missing covariates.
- BARD
- Better Automated ReDistricting.
- BAS
- Bayesian model averaging using Bayesian Adaptive Sampling.
- BAYSTAR
- Bayesian analysis of threshold autoregressive models.
- BB
- Barzilai-Borwein spectral methods for solving nonlinear system of
equations, and for optimizing nonlinear objective functions subject to
simple constraints.
- BCE
- Bayesian Composition Estimator for sample (taxonomic) composition from
biomarker data.
- BGSIMD
- Block Gibbs Sampler with Incomplete Multinomial Distribution.
- BHH2
- Functions and data sets reproducing some examples in “Statistics for
Experimenters II” by G. E. P. Box, J. S. Hunter, and W. C. Hunter,
2005, John Wiley and Sons.
- BLCOP
- Black-Litterman and copula-opinion pooling frameworks.
- BMA
- Bayesian Model Averaging for linear models, generalizable linear models
and survival models (Cox regression).
- BMN
- Approximate and exact methods for pairwise binary markov networks.
- BPHO
- Bayesian Prediction with High-order Interactions.
- BaM
- Functions and datasets for “Bayesian Methods: A Social and Behavioral
Sciences Approach” (2nd edition) by Jeff Gill, 2007, CRC Press.
- BayHaz
- Functions for Bayesian Hazard rate estimation.
- BayesDA
- Functions and data sets for the book “Bayesian Data Analysis” by
A. Gelman, J. B. Carlin, H. S. Stern and D. B. Rubin, 2003, Chapman &
Hall/CRC.
- BayesTree
- Bayesian methods for tree based models.
- BayesValidate
- Bayesian software validation using posterior quantiles.
- BayesX
- Utilities accompanying the BayesX software for Bayesian Inference in
structured additive regression models.
- Bchron
- Create chronologies based on radiocarbon and non-radiocarbon dated
depths.
- Bhat
- Functions for general likelihood exploration (MLE, MCMC, CIs).
- BiasedUrn
- Biased urn model distributions.
- BioIDMapper
- Mapping between BioIDs.
- Biodem
- A number of functions for biodemographycal analysis.
- BiodiversityR
- GUI for biodiversity and community ecology analysis.
- BiplotGUI
- Interactive biplots in R.
- Bolstad
- Functions and data sets for the book “Introduction to Bayesian
Statistics” by W. M. Bolstad, 2004, John Wiley and Sons.
- BootCL
- Bootstrapping test for chromosomal localization.
- BootPR
- Bootstrap prediction intervals and bias-corrected forecasting.
- BradleyTerry
- Specify and fit the Bradley-Terry model and structured versions.
- Brobdingnag
- Very large numbers in R.
- BSDA
- Data sets for the book “Basic Statistics and Data Analysis” by
L. J. Kitchens, 2003, Duxbury.
- BSagri
- Statistical methods for safety assessment in agricultural field trials.
- BsMD
- Bayes screening and model discrimination follow-up designs.
- CADFtest
- Hansen's Covariate-Augmented Dickey-Fuller (CADF) test.
- CADStat
- A GUI to several statistical methods for biological
inferences.
- CCA
- Canonical correlation analysis.
- CDFt
- Statistical downscaling through CDF transform.
- CDNmoney
- Components of Canadian monetary aggregates.
- CGIwithR
- Facilities for the use of R to write CGI scripts.
- CHNOSZ
- Chemical thermodynamics and activity diagrams.
- CHsharp
- Choi and Hall clustering in 3d.
- CORREP
- Multivariate correlation estimation.
- COZIGAM
- Constrained Zero-Inflated Generalized Additive Model.
- CPE
- Concordance probability estimates in survival analysis.
- CTFS
- The CTFS large plot forest dynamics analyses.
- CTT
- Classical Test Theory functions.
- CVThresh
- Level-dependent Cross-Validation Thresholding.
- Cairo
- Graphics device using cairographics library for creating high-quality
PNG, PDF, SVG, PostScript output and interactive display devices such as
X11.
- CalciOMatic
- Automatic calcium imaging analysis.
- CarbonEL
- Carbon Event Loop.
- CellularAutomaton
- One-dimensional cellular automata.
- ChainLadder
- Mack- and Munich-chain-ladder methods for insurance claims reserving.
- CircStats
- Circular Statistics, from “Topics in Circular Statistics” by S. Rao
Jammalamadaka and A. SenGupta, 2001, World Scientific.
- ClinicalRobustPriors
- Robust Bayesian priors in clinical trials.
- CoCo
- Graphical modeling for contingency tables using CoCo.
- ComPairWise
- Compare phylogenetic or population genetic data alignments.
- CombMSC
- Combined Model Selection Criteria.
- CompetingRiskFrailty
- Competing risk model with frailties for right censored survival data.
- Containers
- Object-oriented data structures including stack, queue, and binary
search tree.
- ConvCalendar
- Converts dates between calendars.
- ConvergenceConcepts
- Seeing convergence concepts in action.
- CoxBoost
- Cox survival models by likelihood based boosting.
- CreditMetrics
- Functions for calculating the CreditMetrics risk model.
- CvM2SL1Test
- Cramer-von Mises two sample tests, L1 version.
- CvM2SL2Test
- Cramer-von Mises two sample tests.
- DAAG
- Various data sets used in examples and exercises in “Data Analysis and
Graphics Using R” by John H. Maindonald and W. John Brown, 2003.
- DAAGbio
- Data sets and functions, for demonstrations with expression arrays.
- DAAGxtras
- Data sets and functions additional to DAAG.
- DAKS
- Data Analysis and Knowledge Spaces.
- DBI
- A common database interface (DBI) class and method definitions. All
classes in this package are virtual and need to be extended by the
various DBMS implementations.
- DCluster
- A set of functions for the detection of spatial clusters of diseases
using count data.
- DEA
- Data Envelopment Analysis.
- DEoptim
- Differential Evolution Optimization.
- DICOM
- Import and manipulate medical imaging data using the Digital Imaging and
Communications in Medicine (DICOM) Standard.
- DPpackage
- Semiparametric Bayesian analysis using Dirichlet process priors.
- DSpat
- Spatial modelling for distance sampling data.
- DTK
- Dunnett-Tukey-Kramer: pairwise multiple comparison test adjusted for
unequal variances and unequal sample sizes.
- Daim
- Diagnostic accuracy of classification models.
- Davies
- Functions for the Davies quantile function and the Generalized Lambda
distribution.
- Deducer
- An intuitive graphical data analysis system for use with JGR.
- Defaults
- Create global function defaults.
- Depela
- Semiparametric estimation of copula models.
- DescribeDisplay
- R interface to the DescribeDisplay GGobi plugin.
- Design
- Regression modeling, testing, estimation, validation, graphics,
prediction, and typesetting by storing enhanced model design attributes
in the fit. Design is a collection of about 180 functions that assist
and streamline modeling, especially for biostatistical and epidemiologic
applications. It also contains new functions for binary and ordinal
logistic regression models and the Buckley-James multiple regression
model for right-censored responses, and implements penalized maximum
likelihood estimation for logistic and ordinary linear models. Design
works with almost any regression model, but it was especially written to
work with logistic regression, Cox regression, accelerated failure time
models, ordinary linear models, and the Buckley-James model.
- Devore5
- Data sets and sample analyses from “Probability and Statistics for
Engineering and the Sciences (5th ed)” by Jay L. Devore, 2000, Duxbury.
- Devore6
- Data sets and sample analyses from “Probability and Statistics for
Engineering and the Sciences (6th ed)” by Jay L. Devore, 2003, Duxbury.
- Devore7
- Data sets and sample analyses from “Probability and Statistics for
Engineering and the Sciences (7th ed)” by Jay L. Devore, 2008, Thomson.
- DiagnosisMed
- Diagnostic test accuracy evaluation for medical professionals.
- DierckxSpline
- R companion to “Curve and Surface Fitting with Splines” by Paul
Dierckx, 1993, Oxford University Press.
- DiversitySampler
- Functions for re-sampling a community matrix to compute Shannon's
Diversity index at different sampling levels.
- DoE.base
- Full factorials, orthogonal arrays and base utilities for DoE packages.
- EDR
- Estimation of the effective dimension reduction (EDR) space.
- EMC
- Evolutionary Monte Carlo (EMC) algorithm.
- EMCC
- Evolutionary Monte Carlo (EMC) methods for clustering.
- EMD
- Empirical mode decomposition and Hilbert spectral analysis.
- EMJumpDiffusion
- EM algorithm for jump diffusion processes.
- ETC
- Tests and simultaneous confidence intervals for equivalence to control.
- EVER
- Estimation of Variance by Efficient Replication.
- EbayesThresh
- Empirical Bayes thresholding and related methods.
- Ecdat
- Data sets from econometrics textbooks.
- EffectiveDose
- Estimate the effective dose level for quantal bioassay data by
nonparametric techniques.
- ElectroGraph
- Enhanced routines for plotting and analyzing valued relational data.
- 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.
- EnQuireR
- Questionnaires.
- EngrExpt
- Data sets from the book “Introductory Statistics for Engineering
Experimentation” by Peter Nelson, Marie Coffin and Karen Copeland
(2003), Elsevier, with sample code.
- Epi
- Statistical analysis in epidemiology, with functions for demographic and
epidemiological analysis in the Lexis diagram.
- ExPD2D
- Exact computation of bivariate projection depth.
- FAiR
- Factor Analysis in R, using genetic algorithms.
- FBN
- FISH Based Normalization and copy number inference of SNP microarray
data.
- FD
- Measuring functional diversity (FD) from multiple traits.
- FEST
- Identification of family relations using linked markers.
- FGN
- Fractional Gaussian Noise model fitting.
- FITSio
- FITS (Flexible Image Transport System) utilities.
- FKBL
- Fuzzy Knowledge Base Learning.
- FKF
- Fast Kalman Filter.
- FRB
- Fast and Robust Bootstrap.
- FSelector
- Selecting attributes.
- FTICRMS
- Analysis of Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry
data.
- FactoClass
- Combination of factorial methods and cluster analysis.
- FactoMineR
- Factor analysis and data mining with R.
- Fahrmeir
- Data from the book “Multivariate Statistical Modelling Based
on Generalized Linear Models” by Ludwig Fahrmeir and Gerhard Tutz
(1994), Springer.
- FieldSim
- Random fields simulations.
- FinTS
- Companion to the book “Analysis of Financial Time Series” (2nd
edition) by Ruey Tsay (2005), Wiley.
- FitAR
- Subset AR model fitting.
- Flury
- Data sets from from “A First Course in Multivariate Statistics” by
Bernard Flury (1997), Springer.
- Formula
- Infrastructure for extended formulas.
- FrF2
- Analysis of fractional factorial designs with 2-level factors.
- FracSim
- Simulation of one- and two-dimensional fractional and multifractional
Levy motions.
- FunCluster
- Functional profiling of cDNA microarray expression data.
- FunNet
- Functional analysis of gene co-expression networks.
- G1DBN
- Dynamic Bayesian Network inference using 1st order conditional
dependencies.
- GAMBoost
- Generalized additive models by likelihood based boosting.
- GDD
- Platform and X11 independent device for creating bitmaps (png, gif and
jpeg) using the GD graphics library.
- GEOmap
- Topographic and geologic mapping.
- GExMap
- Functions for the analysis of genomic distribution of genes lists
produced by transcriptomic studies.
- GFMaps
- Visualization of high-throughput genetic or proteomic experiments.
- GLDEX
- Fit RS and FMKL generalised lambda distributions using discretized and
maximum likelihood methods.
- GOFSN
- Goodness-Of-Fit tests for the family of Skew-Normal models.
- GOSim
- Computation of functional similarities between GO terms and gene
products.
- GPArotation
- Gradient Projection Algorithm rotation for factor analysis.
- GRRGI
- Gauge R and R Confidence Intervals.
- GRASS
- An interface between the GRASS geographical information system and R,
based on starting R from within the GRASS environment and chosen
LOCATION_NAME and MAPSET. Wrapper and helper functions are provided for
a range of R functions to match the interface metadata structures.
- GSA
- Gene set analysis.
- GSM
- Gamma Shape Mixture.
- GenABEL
- Genome-wide SNP association analysis.
- GenKern
- Functions for generating and manipulating generalised binned kernel
density estimates.
- GeneCycle
- Identification of periodically expressed genes.
- GeneF
- Generalized F-statistics.
- GeneNT
- Relevance or Dependency network and signaling pathway discovery.
- GeneNet
- Modeling and inferring gene networks.
- GeneReg
- Infer gene regulatory networks with time delay using time course gene
expression data.
- Geneland
- MCMC inference from individual genetic data based on a spatial
statistical model.
- GeoXp
- Interactive exploratory spatial data analysis.
- GillespieSSA
- Gillespie's Stochastic Simulation Algorithm (SSA).
- GridR
- Executes functions on remote hosts, clusters or grids.
- GroupSeq
- Computations related to group-seqential boundaries.
- HAPim
- Methods for QTL detection and fine mapping.
- HFWutils
- Utilities by H. Felix Wittmann: Excel connections, string matching, and
passing by reference.
- HH
- Support software for “Statistical Analysis and Data Display” by
Richard M. Heiberger and Burt Holland, Springer, 2005.
- HI
- Simulation from distributions supported by nested hyperplanes.
- HSAUR
- Functions, data sets, analyses and examples from the book “A Handbook
of Statistical Analyses Using R” by Brian S. Everitt and Torsten
Hothorn (2006), Chapman & Hall/CRC.
- HSAUR2
- Functions, data sets, analyses and examples from the second edition of
the book “A Handbook of Statistical Analyses Using R” by Brian
S. Everitt and Torsten Hothorn (2008), Chapman & Hall/CRC.
- HTMLUtils
- Facilitate automated HTML report creation.
- HWEBayes
- Bayesian investigation of Hardy-Weinberg Equilibrium.
- HadoopStreaming
- Utilities for using R scripts in Hadoop streaming.
- Haplin
- Analyzing case-parent triad and/or case-control data with SNP
haplotypes.
- HaploSim
- Simulate haplotypes through meioses.
- HardyWeinberg
- Graphical tests for Hardy-Weinberg equilibrium.
- HiddenMarkov
- Hidden Markov Models.
- Hmisc
- Functions useful for data analysis, high-level graphics, utility
operations, functions for computing sample size and power, importing
datasets, imputing missing values, advanced table making, variable
clustering, character string manipulation, conversion of S objects to
LaTeX code, recoding variables, and bootstrap repeated measures
analysis.
- HybridMC
- Implementation of the Hybrid Monte Carlo and Multipoint Hybrid Monte
Carlo sampling techniques.
- HydroMe
- Estimation of soil hydraulic parameters from experimental data.
- HyperbolicDist
- Basic functions for the hyperbolic distribution: probability density
function, distribution function, quantile function, a routine for
generating observations from the hyperbolic, and a function for fitting
the hyperbolic distribution to data.
- IBrokers
- R API to Interactive Brokers Trader Workstation.
- ICE
- Iterated Conditional Expectation: kernel estimators for
interval-censored data.
- ICEinfer
- Incremental Cost-Effectiveness (ICE) statistical inference (from
two unbiased samples).
- ICS
- ICS/ICA computation based on two scatter matrices.
- ICSNP
- Tools for multivariate nonparametrics.
- IDPmisc
- Utilities from the Institute of Data Analyses and Process Design,
IDP/ZHW.
- ISA
- Functions to support “Introduzione alla Statistica Applicata con esempi
in R” by Federico M. Stefanini, Pearson Education Milano, 2007.
- ISOcodes
- ISO language, territory, currency, script and character codes.
- ISwR
- Data sets for “Introductory Statistics with R” by Peter Dalgaard,
2002, Springer.
- Icens
- Functions for computing the NPMLE for censored and truncated data.
- Iso
- Functions to perform isotonic regression.
- IsoGene
- Testing for monotonic relationship between gene expression and doses in
a microarray experiment.
- JADE
- JADE and ICA performance criteria.
- JGR
- Java Gui for R.
- JM
- Joint Modeling of longitudinal and survival data.
- JavaGD
- Java Graphics Device.
- JointGLM
- Joint modeling of mean and dispersion through two interlinked GLM's.
Defunct in favor of JointModeling.
- JointModeling
- Joint modeling of mean and dispersion.
- JudgeIt
- Calculates bias, responsiveness, and other characteristics of two-party
electoral systems, with district-level electoral and other data.
- KMsurv
- Data sets and functions for “Survival Analysis, Techniques for Censored
and Truncated Data” by Klein and Moeschberger, 1997, Springer.
- Kendall
- Kendall rank correlation and Mann-Kendall trend test.
- KernSmooth
- Functions for kernel smoothing (and density estimation) corresponding to
the book “Kernel Smoothing” by M. P. Wand and M. C. Jones, 1995.
Recommended.
- LDheatmap
- Heat maps of linkage disequilibrium measures.
- LDtests
- Exact tests for linkage disequilibrium and Hardy-Weinberg equilibrium.
- LIM
- Linear Inverse Model examples and solution methods.
- LIStest
- Longest increasing subsequence independence test.
- LLAhclust
- Hierarchical clustering of variables or objects based on the likelihood
linkage analysis method.
- LMGene
- Date transformation and identification of differentially expressed genes
in gene expression arrays.
- LambertW
- Lambert W parameter estimation.
- LearnBayes
- Functions for Learning Bayesian Inference.
- LearnEDA
- Functions for Learning Exploratory Data Analysis.
- Lmoments
- Estimation of L-moments and the parameters of normal and Cauchy
polynomial quantile mixtures.
- LogConcDEAD
- Maximum likelihood estimation of a log-concave density.
- LogicReg
- Routines for Logic Regression.
- LoopAnalyst
- A collection of tools to conduct Levins' Loop Analysis.
- LowRankQP
- Low Rank Quadratic Programming: QP problems where the hessian is
represented as the product of two matrices.
- MASS
- Functions and datasets from the main package of Venables and Ripley,
“Modern Applied Statistics with S”. Contained in the VR
bundle. Recommended.
- MAclinical
- Class prediction based on microarray data and clinical parameters.
- MAMSE
- Calculation of Minimum Averaged Mean Squared Error (MAMSE) weights.
- MBA
- Multilevel B-spline Approximation.
- MBESS
- Methods for the Behavioral, Educational, and Social Sciences.
- MCAPS
- Weather and air pollution data, risk estimates, and other information
from the Medicare Air Pollution Study (MCAPS) of 204 U.S. counties,
1999–2002.
- MCE
- Tools for evaluating Monte Carlo Error.
- MCMCglmm
- MCMC Generalized Linear Mixed Models.
- MCMCpack
- Markov chain Monte Carlo (MCMC) package: functions for posterior
simulation for a number of statistical models.
- MCPAN
- Multiple comparisons using normal approximation.
- MCPMod
- Design and analysis of dose-finding studies.
- MChtest
- Monte Carlo hypothesis tests.
- MEMSS
- Data sets and sample analyses from “Mixed-effects Models in S and
S-PLUS” by J. Pinheiro and D. Bates, 2000, Springer.
- MFDA
- Model Based Functional Data Analysis.
- MIfuns
- Pharmacometric tools for data preparation, analysis, simulation, and
reporting.
- MKLE
- Maximum kernel likelihood estimation.
- MKmisc
- Miscellaneous Functions from M. Kohl.
- MLDA
- Methylation Linear Discriminant Analysis (MLDA).
- MLDS
- Maximum Likelihood Difference Scaling.
- MLEcens
- Computation of the MLE for bivariate (interval) censored data.
- MMG
- Mixture Model on Graphs.
- MMIX
- Model mixing and model selection methods for linear or logistic models.
- MNP
- Fitting Bayesian Multinomial Probit models via Markov chain Monte Carlo.
Along with the standard Multinomial Probit model, it can also fit models
with different choice sets for each observation and complete or partial
ordering of all the available alternatives.
- MPV
- Data sets from the book “Introduction to Linear Regression Analysis”
by D. C. Montgomery, E. A. Peck, and C. G. Vining, 2001, John Wiley and
Sons.
- MSBVAR
- Bayesian vector autoregression models, impulse responses and
forecasting.
- MSVAR
- Markov Switching VAR.
- MarkedPointProcess
- Non-parametric analysis of the marks of marked point processes.
- MasterBayes
- Maximum likelihood and Markov chain Monte Carlo methods for pedigree
reconstruction, analysis and simulation.
- MatchIt
- Select matched samples of the original treated and control groups with
similar covariate distributions.
- Matching
- Multivariate and propensity score matching with formal tests of balance.
- Matrix
- A Matrix package. Recommended for R 2.9.0 or later.
- Metabonomic
- GUI for metabonomic analysis.
- MiscPsycho
- Miscellaneous Psychometrics.
- MixSim
- Simulating data to study performance of clustering algorithms.
- ModelMap
- Random forest and stochastic gradient boosting models for building
detailed prediction maps.
- MultEq
- Equivalence tests and simultaneous confidence intervals for multiple
endpoints.
- Multiclasstesting
- Performance of N-ary classification testing.
- NADA
- Methods described in “Nondetects And Data Analysis: Statistics for
Censored Environmental Data” by Dennis R. Helsel, 2004, John Wiley and
Sons.
- NISTnls
- A set of test nonlinear least squares examples from NIST, the
U.S. National Institute for Standards and Technology.
- NMMAPSlite
- U.S. National Morbidity, Mortality, and Air Pollution Study data lite.
- NMRS
- NMR spectroscopy.
- NORMT3
- Evaluates complex erf, erfc and density of sum of Gaussian and Student's
t.
- NRAIA
- Data sets with sample code from “Nonlinear Regression Analysis and Its
Applications” by Doug Bates and Donald Watts, 1988, Wiley.
- NeatMap
- Non-clustered heatmap alternatives.
- NestedCohort
- Survival analysis for cohorts with missing covariate information.
- NetIndices
- Estimates network indices, including trophic structure of foodwebs.
- OAIHarvester
- Harvest metadata using the Open Archives Initiative Protocol for
Metadata Harvesting (OAI-PMH) version 2.0.
- OPE
- Fit an outer-product emlator to the multivariate evaluations of a
computer model.
- ORMDR
- Odds ratio based multivactor-dimensionality reduction method for
detecting gene-gene interactions.
- Oarray
- Arrays with arbitrary offsets.
- Oncotree
- Estimation of oncogenetic trees.
- OrdFacReg
- Least squares, logistic, and Cox regression with ordered predictors.
- OrdMonReg
- Compute least squares estimates of one bounded or two ordered antitonic
regression curves.
- PASWR
- Data and functions for the book “Probability and Statistics with R” by
M. D. Ugarte, A. F. Militino and A. T. Arnholt, 2008, Chapman &
Hall/CRC.
- PBSddesolve
- Solver for delay differential equations.
- PBSmapping
- Software evolved from fisheries research conducted at the Pacific
Biological Station (PBS) in Nanaimo, British Columbia, Canada. Draws
maps and implements other GIS procedures.
- PBSmodelling
- Software to facilitate the design, testing, and operation of computer
models.
- PCS
- Calculate the Probability of Correct Selection.
- PET
- Simulation and reconstruction of PET images.
- PHYLOGR
- Manipulation and analysis of phylogenetically simulated data sets (as
obtained from PDSIMUL in package PDAP) and phylogenetically-based
analyses using GLS.
- PK
- Estimation of pharmacokinetic parameters.
- PKfit
- A nonlinear regression (including a genetic algorithm) program designed
to deal with curve fitting for pharmacokinetics.
- PKtools
- Unified computational interfaces for pop PK.
- PMA
- Penalized Multivariate Analysis.
- POT
- Generalized Pareto distribution and Peaks Over Threshold.
- PSAgraphics
- Propensity Score Analysis Graphics.
- PSM
- Non-linear mixed-effects modeling using stochastic differential
equations.
- PTAk
- A multiway method to decompose a tensor (array) of any order, as a
generalisation of SVD also supporting non-identity metrics and
penalisations. Also includes some other multiway methods.
- PairViz
- Visualization using Eulerian tours and Hamiltonian decompositions.
- Peaks
- Spectrum manipulation: background estimation, Markov smoothing,
deconvolution and peaks search functions.
- PearsonICA
- Independent component analysis using score functions from the Pearson
system.
- PerformanceAnalytics
- Econometric tools for performance and risk analysis.
- PhViD
- Pharmacovigilance signal detection methods extended to the multiple
comparison setting.
- PhySim
- Phylogenetic tree simulation.
- PolynomF
- Univariate polynomials.
- Pomic
- Pattern oriented modeling information criterion.
- PredictiveRegression
- Prediction intervals for three basic statistical models.
- PresenceAbsence
- Presence-absence model evaluation.
- ProfessR
- Programs to determine student grades and create examinations from
question banks.
- PtProcess
- Time dependent point process modeling.
- PwrGSD
- Power in a Group Sequential Design.
- QCA
- Qualitative Comparative Analysis for crisp sets.
- QCAGUI
- QCA Graphical User Interface.
- QRMlib
- Code to examine Quantitative Risk Management concepts.
- QuantPsyc
- Quantitative Psychology tools.
- R.cache
- Fast and light-weight caching of objects.
- R.filesets
- Easy handling of and access to files organized in structured
directories.
- R.huge
- Methods for accessing huge amounts of data.
- R.matlab
- Read and write of MAT files together with R-to-Matlab connectivity.
- R.methodsS3
- Utility functions for defining S3 methods.
- R.oo
- R object-oriented programming with or without references.
- R.rsp
- R server pages.
- R.utils
- Utility classes and methods useful when programming in R and developing
R packages.
- R2HTML
- Functions for exporting R objects & graphics in an HTML document.
- R2PPT
- R Interface to Microsoft PowerPoint using rcom.
- R2WinBUGS
- Running WinBUGS from R: call a BUGS model, summarize inferences and
convergence in a table and graph, and save the simulations in arrays for
easy access in R.
- R2jags
- Call JAGS from R.
- RArcInfo
- Functions to import Arc/Info V7.x coverages and data.
- RBGL
- Interface to the boost C++ graph library.
- RBloomberg
- Fetch data from a Bloomberg API using COM.
- RColorBrewer
- ColorBrewer palettes for drawing nice maps shaded according to a
variable.
- RDS
- Respondent-Driven Sampling.
- RDieHarder
- R interface to the dieharder random number generator test suite.
- REQS
- R/EQS interface.
- REEMtree
- Regression trees with random effects for longitudinal (panel) data.
- RExcelInstaller
- Integration of R and Excel under MS Windows.
- RFA
- Regional Frequency Analysis.
- RFOC
- Graphics for spherical distributions and earthquake focal mechanisms.
- RFreak
- An R interface to a modified version of the Free Evolutionary Algorithm
Kit FrEAK.g
- RGtk2
- Facilities for programming graphical interfaces using Gtk (the Gimp Tool
Kit) version 2.
- RGrace
- Mouse/menu driven interactive plotting application.
- RGraphics
- Data and functions from the book “R Graphics” by Paul Murrell, 2005,
Chapman & Hall/CRC.
- RHRV
- Heart rate variability analysis of ECG data.
- RHmm
- Hidden Markov Model simulations and estimations.
- RII
- Estimation of the relative index of inequality for interval-censored
data using natural cubic splines.
- RImageJ
- R bindings for the ImageJ Java based image processing and analysis
platform.
- RItools
- Randomization inference tools.
- RJDBC
- Access to databases through the JDBC interface.
- RJaCGH
- Reversible Jump MCMC for the analysis of CGH arrays.
- RKEA
- R/KEA interface for extracting keyphrases from text documents.
- RLMM
- A genotype calling algorithm for Affymetrix SNP arrays.
- RLRsim
- Exact (Restricted) Likelihood Ratio tests for mixed and additive
models.
- RLadyBug
- Analysis of infectious diseases using stochastic epidemic models.
- RM2
- Revenue management and pricing.
- RMTstat
- Distributions and statistics from Random Matrix Theory.
- RMySQL
- An interface between R and the MySQL database system.
- RNetCDF
- An interface to Unidata's NetCDF library functions (version 3) and
furthermore access to Unidata's udunits calendar conversions.
- ROCR
- Visualizing the performance of scoring classifiers.
- RODBC
- An ODBC database interface.
- ROptEst
- Optimally robust estimation.
- ROptEstOld
- Optimally robust estimation, old version.
- ROptEstTS
- Optimally robust estimation for regression-type models.
- ROracle
- Oracle Database Interface driver for R. Uses the ProC/C++ embedded SQL.
- RPMG
- Poor Man's Gui: create interactive R analysis sessions.
- RPostgreSQL
- R interface to the PostgreSQL database system.
- RPyGeo
- ArcGIS Geoprocessing in R via Python.
- RQDA
- Qualitative Data Analysis.
- RQuantLib
- Provides access to (some) of the QuantLib functions from within R;
currently limited to some Option pricing and analysis functions. The
QuantLib project aims to provide a comprehensive software framework for
quantitative finance.
- RSAGA
- SAGA geoprocessing and terrain analysis in R.
- RSEIS
- Seismic time series analysis tools.
- RSQLite
- Database Interface R driver for SQLite. Embeds the SQLite database
engine in R.
- RScaLAPACK
- An interface to ScaLAPACK functions from R.
- RSVGTipsDevice
- An R SVG graphics device with dynamic tips and hyperlinks.
- RSeqMeth
- analysis of Sequenom EpiTYPER data.
- RSiena
- Simulation Investigation for Empirical Network Analysis.
- RSiteSearch
- Alternative interfaces to RSiteSearch.
- RSurvey
- Analysis of spatially distributed data.
- RSvgDevice
- A graphics device for R that uses the new w3.org XML standard for
Scalable Vector Graphics.
- RTOMO
- Visualization for seismic tomography.
- RTisean
- R interface to Tisean algorithms.
- RUnit
- Functions implementing a standard Unit Testing framework, with
additional code inspection and report generation tools.
- RWeka
- An R interface to Weka, a rich collection of machine learning algorithms
for data mining tasks.
- RWinEdt
- A plug in for using WinEdt as an editor for R.
- RXshrink
- Maximum Likelihood Shrinkage via Ridge or Least Angle Regression.
- RadioSonde
- A collection of programs for reading and plotting SKEW-T,log p diagrams
and wind profiles for data collected by radiosondes (the typical weather
balloon-borne instrument).
- RandVar
- Implementation of random variables by means of S4 classes and methods.
- RandomFields
- Creating random fields using various methods.
- RankAggreg
- Weighted rank aggregation.
- RaschSampler
- Sampling binary matrices with fixed margins.
- Ratings
- Model-based ratings figures.
- Rcapture
- Loglinear models in capture-recapture experiments.
- Rcmdr
- A platform-independent basic-statistics GUI (graphical user
interface) for R, based on the tcltk package.
- RcmdrPlugin.Export
- Graphically export objects to LaTeX or HTML.
- RcmdrPlugin.FactoMineR
- Rcmdr plug-in for the FactoMineR package.
- RcmdrPlugin.HH
- Rcmdr support for the introductory course at Temple University.
- RcmdrPlugin.IPSUR
- Rcmdr plugin for “Introduction to Probability and Statistics Using R”.
- RcmdrPlugin.SurvivalT
- Rcmdr survival plug-in.
- RcmdrPlugin.TeachingDemos
- Rcmdr Teaching Demos plug-in.
- RcmdrPlugin.epack
- Rcmdr epack demos plug-in.
- RcmdrPlugin.orloca
- Rcmdr orloca plug-in.
- RcmdrPlugin.qcc
- Rcmdr qcc plug-in.
- RcmdrPlugin.survival
- Rcmdr plugin for the survival package.
- Rcplex
- R interface to CPLEX solvers for linear, quadratic, and (linear and
quadratic) mixed integer programs.
- Rcpp
- R/C++ interface library and package template.
- Rcsdp
- R interface to the CSDP semidefinite programming library.
- ReacTran
- Reactive transport modelling in 1D, 2D and 3D.
- Read.isi
- Access old data saved in fixed-width format based on ISI-formatted
codebooks.
- ReadImages
- Functions for reading JPEG and PNG files.
- Reliability
- Functions for estimating parameters in software reliability models.
- ResearchMethods
- Using GUIs to help teach statistics to non-statistics students.
- ResistorArray
- Electrical properties of resistor networks.
- Rfwdmv
- Forward Search for Multivariate Data.
- Rglpk
- R/GNU Linear Programming Kit interface.
- RgoogleMaps
- Overlays on Google map tiles in R.
- RiboSort
- Classification and analysis of microbial community profiles.
- Rigroup
- Provides small integer group functions.
- Rlab
- Functions and data sets for the NCSU ST370 class.
- Rlabkey
- Data retrieval from a Labkey database.
- Rlsf
- Interface to the LSF queuing system.
- Rmpi
- An interface (wrapper) to MPI (Message-Passing Interface)
APIs. It also provides an interactive R slave environment in
which distributed statistical computing can be carried out.
- RobAStBase
- Base classes and functions for robust asymptotic statistics.
- RobLox
- Optimally robust influence curves for location and scale.
- RobRex
- Optimally robust influence curves for regression and scale.
- Rpad
- Utility functions for the Rpad workbook-style interface.
- Rsac
- Seismic tools for R.
- Rserve
- A socket server (TCP/IP or local sockets) which allows binary requests
to be sent to R.
- Rsge
- Interface to the SGE cluster/grid queuing system.
- Rsundials
- SUite of Nonlinear DIfferential ALgebraic equations Solvers in R.
- Rsymphony
- An R interface to the SYMPHONY mixed integer linear program (MILP)
solver.
- RthroughExcelWorkbooksInstaller
- Excel workbooks supporting statistics courses using “R through Excel”.
- Runuran
- Interface to the UNU.RAN library for Universal Non-Uniform RANdom
variate generators.
- Rvelslant
- Downhole seismic analysis.
- Rwave
- An environment for the time-frequency analysis of 1-D signals (and
especially for the wavelet and Gabor transforms of noisy signals), based
on the book “Practical Time-Frequency Analysis: Gabor and Wavelet
Transforms with an Implementation in S” by Rene Carmona, Wen L. Hwang
and Bruno Torresani, 1998, Academic Press.
- Ryacas
- An R interfaces to the yacas computer algebra system.
- RxCEcolInf
- R x C Ecological Inference with optional incorporation of survey
information.
- SASPECT
- Significant AnalysiS of PEptide CounTs.
- SASmixed
- Data sets and sample linear mixed effects analyses corresponding to the
examples in “SAS System for Mixed Models” by R. C. Littell,
G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.
- SASxport
- Read and write SAS XPORT files.
- SDDA
- Stepwise Diagonal Discriminant Analysis.
- SDaA
- Functions and data sets from “Sampling: Design and Analysis” by
S. Lohr, 1999, Duxbury.
- SEL
- Semiparametric elicitation.
- SEMModComp
- Model Comparisons for SEM.
- SGCS
- Spatial Graph based Clustering Summaries for spatial point patterns.
- SGP
- Student growth percentile and percentile growth projection/trajectory
functions.
- SHARE
- SNP-Haplotype Adaptive REgression.
- SIN
- A SINful approach to selection of Gaussian Graphical Markov Models.
- SLmisc
- Miscellaneous Functions for analysis of gene expression data at SIRS-Lab
GmbH.
- SMC
- Sequential Monte Carlo (SMC) Algorithm.
- SMIR
- Companion to “Statistical Modelling in R” by Murray Aitkin, Brian
Francis, John Hinde and Ross Darnell, 2009, Oxford University Press.
- SMPracticals
- Data sets and a few functions for use with the practicals outlined in
Appendix A of the book “Statistical Models” by Anthony Davison, 2003,
Cambridge University Press.
- SMVar
- Structural Model for Variances to detect differentially expressed
genes.
- SNPMaP
- SNP Microarrays and Pooling in R.
- SNPMaP.cdm
- Annotation for SNP microarrays and pooling in R.
- SNPassoc
- SNP-based whole genome association studies.
- SNPmaxsel
- Maximally selected statistics for SNP data.
- SQLiteMap
- Manage vector graphical maps using SQLite.
- SQLiteDF
- Stores data frames and matrices in SQLite tables.
- SRPM
- Shared Reproducibility Package Management.
- STAR
- Spike Train Analysis with R.
- ScottKnott
- Multiple comparison test of means using the clustering method of Scott &
Knott.
- SemiPar
- Functions for semiparametric regression analysis, to complement the
book “Semiparametric Regression” by R. Ruppert, M. P. Wand, and
R. J. Carroll, 2003, Cambridge University Press.
- SenSrivastava
- Collection of datasets from “Regression Analysis, Theory, Methods and
Applications” by A. Sen and M. Srivastava, 1990, Springer.
- SensoMineR
- Sensory data analysis.
- SeqKnn
- Sequential KNN imputation.
- SharedHT2
- Shared Hotelling T^2 test for small sample microarray
experiments.
- SiZer
- Significant Zero crossings.
- SigWinR
- SigWin-detector implementation in R.
- SimComp
- Simultaneous Comparisons for multiple endpoints.
- SimHap
- A comprehensive modeling framework for epidemiological outcomes and a
multiple-imputation approach to haplotypic analysis of population-based
data.
- SimpleTable
- Bayesian inference and sensitivity analysis for causal effects from
2 x 2 and 2 x 2 x K tables in the presence of unmeasured
confounding.
- Snowball
- Snowball stemmers.
- SoDA
- Utilities and examples from the book “Software for Data Analysis:
Programming with R” by John Chambers, Springer, 2008.
- SoPhy
- Soil Physics Tools: simulation of water flux and solute transport in
soil.
- SparseM
- Basic linear algebra for sparse matrices.
- SpatialExtremes
- Modeling spatial extremes.
- SpatialNP
- Multivariate nonparametric methods based on spatial signs and ranks.
- SpectralGEM
- Discovering genetic ancestry using spectral graph theory.
- SpherWave
- Spherical Wavelets and SW-based spatially adaptive methods.
- StatDA
- Statistical analysis for environmental data, a companion to the book
“Statistical Data Analysis Explained: Applied Environmental Statistics
with R” by C. Reimann, P. Filzmoser, R. G. Garrett, and R. Dutter,
2008, John Wiley and Sons.
- StatDataML
- Read and write StatDataML.
- StatFingerprints
- Processing and statistical analysis of molecular fingerprint profiles.
- StatMatch
- Functions to perform statistical matching between two data sources.
- Stem
- Spatio-temporal models in R.
- StreamMetabolism
- Calculation of single station metabolism from diurnal oxygen curves.
- SubpathwayMiner
- Annotation and identification of metabolic sub-pathways and pathways.
- SuppDists
- Ten distributions supplementing those built into R (Inverse Gauss,
Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho,
maximum F ratio, the Pearson product moment correlation coefficiant,
Johnson distributions, normal scores and generalized hypergeometric
distributions).
- SweaveListingUtils
- Utilities for Sweave together with TeX listings package.
- SwissAir
- Air quality data of Switzerland for one year in 30 min resolution.
- SyNet
- Inference and analysis of sympatry networks.
- Synth
- Causal inference using the synthetic control group method.
- TIMP
- A problem solving environment for fitting superposition models.
- TRAMPR
- Terminal Restriction Fragment Length Polymorphism (TRFLP) Analysis and
Matching Package for R.
- TRIANG
- Discrete triangular distributions.
- TSA
- Functions and datasets detailed in the book “Time Series Analysis
With Applications in R” (3rd edition) by Jonathan Cryer and Kung-Sik
Chan, 2008, Springer.
- TSHRC
- Two Stage Hazard Rate Comparison.
- TSMySQL
- Time Series Database Interface extensions for MySQL.
- TSP
- Traveling Salesperson Problem (TSP).
- TSPostgreSQL
- Time Series Database Interface extensions for PostgreSQL.
- TSSQLite
- Time Series Database Interface extensions for SQLite.
- TSdbi
- Time Series Database Interface.
- TSfame
- Time Series Database Interface extensions for fame.
- TShistQuote
- Time Series Database Interface interface for get.hist.quote.
- TSodbc
- Time Series Database Interface extensions for ODBC.
- TSpadi
- Connect to a time series database (e.g., Fame) via PADI (Protocol for
Application Database Interface), using the TSdbi
infrastructure.
- TTR
- Technical Trading Rules.
- TWIX
- Trees WIth eXtra splits.
- TeachingDemos
- A set of demonstration functions that can be used in a classroom to
demonstrate statistical concepts, or on your own to better understand
the concepts or the programming.
- TeachingSampling
- Sampling designs and parameter estimation in finite populations.
- TinnR
- Resources for the Tinn-R GUI/Editor for R.
- TraMineR
- Sequences and trajectories mining for social scientists.
- TripleR
- Social Relation Model (SRM) analyses for single round-robin groups.
- TwoWaySurvival
- Additive two-way hazards modeling of right censored survival data.
- TwslmSpikeWeight
- Normalization of cDNA microarray data with the two-way semilinear model
(TW-SLM).
- UNF
- Tools for creating universal numeric fingerprints for data.
- USPS
- Unsupervised and Supervised methods of Propensity Score adjustment for
bias.
- Umacs
- Universal MArkov Chain Sampler.
- UsingR
- Data sets to accompany the textbook “Using R for Introductory
Statistics” by J. Verzani, 2005, Chapman & Hall/CRC.
- VDCutil
- Utilities supporting VDC, an open source digital library system for
quantitative data.
- VGAM
- Vector Generalized Linear and Additive Models.
- VIM
- Visualization and Imputation of Missing Values.
- VLMC
- Functions, classes & methods for estimation, prediction, and simulation
(bootstrap) of VLMC (Variable Length Markov Chain) models.
- VaR
- Methods for calculation of Value at Risk (VaR).
- VarianceGamma
- The variance gamma distribution.
- VhayuR
- R interface to the Vhayu Velocity high volume fast financial market data
archival and analysis products.
- WINRPACK
- Reads in WIN pickfile and waveform files.
- WWGbook
- Functions and datasets for the book “Linear Mixed Models: A Practical
Guide Using Statistical Software” by B. West, K. Welch, and A. Galecki,
2006, Chapman & Hall/CRC.
- WhatIf
- Software for evaluating counterfactuals.
- WilcoxCV
- Wilcoxon-based variable selection in cross-validation.
- WriteXLS
- Cross-platform Perl based R function to create Excel 2003 (XLS) files.
- XML
- Tools for reading XML documents and DTDs.
- XReg
- Extreme regression.
- YaleToolkit
- Data exploration tools from Yale University.
- YourCast
- YourCast: time series cross-sectional forecasts.
- ZIGP
- Zero Inflated Generalized Poisson (ZIGP) regression models.
- Zelig
- Everyone's statistical software: an easy-to-use program that can
estimate, and help interpret the results of, an enormous range of
statistical models.
- aCGH.Spline
- Robust spline interpolation for dual color array comparative genomic
hybridisation data.
- aaMI
- Mutual information for protein sequence alignments.
- abind
- Combine multi-dimensional arrays.
- accuracy
- A suite of tools designed to test and improve the accuracy of
statistical computation.
- acepack
- ACE (Alternating Conditional Expectations) and AVAS (Additivity and
VAriance Stabilization for regression) methods for selecting regression
transformations.
- actuar
- Functions related to actuarial science applications.
- ada
- Performs boosting algorithms for a binary response.
- adabag
- Adaboost.M1 and Bagging.
- adapt
- Adaptive quadrature in up to 20 dimensions.
- ade4
- Multivariate data analysis and graphical display.
- ade4TkGUI
- Tcl/Tk Graphical User Interface for ade4.
- adegenet
- Genetic data handling for multivariate analysis using ade4.
- adehabitat
- A collection of tools for the analysis of habitat selection by animals.
- adimpro
- Adaptive smoothing of digital images.
- adk
- Anderson-Darling K-sample test and combinations of such tests.
- adlift
- Adaptive Wavelet transforms for signal denoising.
- ads
- Spatial point patterns analysis.
- afc
- Calculate the Generalized Discrimination Score (also known as Two
Alternatives Forced Choice Score, 2AFC).
- agce
- Analysis of growth curve experiments.
- agreement
- Analyze the agreement between two measurement methods.
- agricolae
- Statistical procedures for agricultural research.
- agsemisc
- Miscellaneous plotting and utility functions.
- akima
- Linear or cubic spline interpolation for irregularly gridded data.
- allelic
- A fast, unbiased and exact allelic exact test.
- alphahull
- Generalization of the convex hull of a sample of points in the plane.
- alr3
- Methods and data to accompany the textbook “Applied Linear Regression”
by S. Weisberg, 2005, Wiley.
- amap
- Another Multidimensional Analysis Package.
- amei
- Adaptive Management of Epidemiological Interventions.
- anacor
- Simple and Canonical Correspondence Analysis.
- analogue
- Analogue methods for palaeoecology.
- anapuce
- Tools for microarray data analysis.
- anchors
- Statistical analysis of surveys with anchoring vignettes.
- animation
- Demonstrate animations in statistics.
- anm
- Analog model for statistical/empirical downscaling.
- aod
- Analysis of Overdispersed Data.
- apTreeshape
- Analyses of phylogenetic treeshape.
- ape
- Analyses of Phylogenetics and Evolution, providing functions for reading
and plotting phylogenetic trees in parenthetic format (standard Newick
format), analyses of comparative data in a phylogenetic framework,
analyses of diversification and macroevolution, computing distances from
allelic and nucleotide data, reading nucleotide sequences from GenBank
via internet, and several tools such as Mantel's test, computation of
minimum spanning tree, or the population parameter theta based on
various approaches.
- aplpack
- Another PLot PACKage: stem.leaf, bagplot, faces, spin3R, ....
- approximator
- Bayesian prediction of complex computer codes.
- apsrtable
- American Political Science Review style table formatting.
- archetypes
- Archetypal analysis.
- argosfilter
- Argos locations filter.
- arm
- Data Analysis using Regression and Multilevel/hierarchical models.
- aroma.apd
- A probe-level data file format used by aroma.affymetrix.
- aroma.core
- Support package for aroma.affymetrix et al.
- arrayImpute
- Missing imputation for microarray data.
- arrayMissPattern
- Exploratory analysis of missing patterns for microarray data.
- ars
- Adaptive Rejection Sampling.
- arules
- Mining association rules and frequent itemsets with R.
- arulesNBMiner
- Mining NB-frequent itemsets and NB-precise rules.
- arulesSequences
- Mining frequent sequences.
- ascii
- Export R objects to asciidoc or txt2tags.
- ash
- David Scott's ASH routines for 1D and 2D density estimation.
- aspace
- Estimating centrographic statistics and computational geometries from
spatial point patterns.
- aspect
- Aspects of multivariables.
- assist
- A suite of functions implementing smoothing splines.
- aster
- Functions and datasets for Aster modeling (forest graph exponential
family conditional or unconditional canonical statistic models for life
history trait modeling).
- asympTest
- Asymptotic testing.
- asypow
- A set of routines that calculate power and related quantities utilizing
asymptotic likelihood ratio methods.
- audio
- Audio interface for R.
- automap
- Automatic interpolation.
- asuR
- Functions and data sets for a lecture in “Advanced Statistics using
R”.
- aws
- Functions to perform adaptive weights smoothing.
- aylmer
- A generalization of Fisher's exact test.
- backfitRichards
- Backfitted independent values of Richards curves.
- backtest
- Exploring portfolio-based hypotheses about financial instruments.
- bark
- Bayesian Additive Regression Kernels.
- bayesCGH
- Bayesian analysis of array CGH data.
- bayesGARCH
- Bayesian estimation of the GARCH(1,1) model with Student's t
innovations.
- bayesSurv
- Bayesian survival regression with flexible error and (later on also
random effects) distributions.
- bayesclust
- Tests/searches for significant clusters in genetic data.
- bayescount
- Bayesian analysis of count distributions with JAGS.
- bayesm
- Bayes Inference for Marketing/Micro-econometrics.
- bayesmix
- Bayesian mixture models of univariate Gaussian distributions using JAGS.
- bbmle
- Modifications and extensions of stats4 MLE code.
- bcp
- Bayesian Change Point based on the Barry and Hartigan product partition
model.
- beanplot
- Visualization via beanplots.
- bear
- Bioavability and bioequivalence data analysis with crossover design.
- benchden
- 28 benchmark densities from Berlinet/Devroye (1994).
- bentcableAR
- Bent-cable regression for independent data or autoregressive time
series.
- betaper
- Distance decay of similarity among biological inventories in the face of
taxonomic uncertainty.
- betareg
- Beta regression for modeling rates and proportions.
- bethel
- Sample size according to Bethel's procedure.
- bs
- Utilities for the Birnbaum-Saunders distribution.
- biOps
- Basic image operations and image processing.
- biOpsGUI
- GUI for Basic image operations.
- biclust
- BiCluster algorithms.
- bicreduc
- Reduction algorithm for the NPMLE for the distribution function of
bivariate interval-censored data.
- bifactorial
- Inferences for bi- and trifactorial trial designs.
- biglm
- Linear regression for data too large to fit in memory.
- bigmemory
- Manage massive matrices in R using C++, with UNIX support for shared
memory.
- bim
- Bayesian interval mapping diagnostics: functions to interpret QTLCart
and Bmapqtl samples.
- binGroup
- Evaluation and experimental design for binomial group testing.
- binMto
- Asymptotic simultaneous confidence intervals for many-to-one comparisons
of proportions.
- binarySimCLF
- Simulate correlated binary data.
- bindata
- Generation of correlated artificial binary data.
- binom
- Binomial confidence intervals for several parameterizations.
- binomSamSize
- Confidence intervals and sample size determination for a binomial
proportion under simple random sampling and pooled sampling.
- bio.infer
- Compute biological inferences.
- biopara
- Self-contained parallel system for R.
- bipartite
- Visualises bipartite networks and calculates some ecological indices.
- birch
- Dealing with very large datasets using BIRCH.
- bise
- Auxiliary functions for phenological data analysis.
- bit
- A class for vectors of 1-bit booleans.
- bitops
- Functions for Bitwise operations on integer vectors.
- bivpois
- Bivariate Poisson models using the EM algorithm.
- blighty
- Function for drawing the coastline of the United Kingdom.
- blockTools
- Block, randomly assign, and diagnose potential problems between units in
randomized experiments.
- blockmodeling
- Generalized and classical blockmodeling of valued networks.
- blockrand
- Randomization for block random clinical trials.
- bmd
- Benchmark dose analysis for dose-response data.
- bnlearn
- Bayesian network structure learning.
- boa
- Bayesian Output Analysis Program for MCMC.
- 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.
- bootRes
- Bootstrapped response and correlation functions.
- bootStepAIC
- Model selection by bootstrapping the
stepAIC()
procedure.
- bootspecdens
- Bootstrap for testing equality of spectral densities.
- bootstrap
- Software (bootstrap, cross-validation, jackknife), data and errata for
the book “An Introduction to the Bootstrap” by B. Efron and
R. Tibshirani, 1993, Chapman and Hall.
- bpca
- Biplot of multivariate data based on Principal Components Analysis.
- bqtl
- QTL mapping toolkit for inbred crosses and recombinant inbred lines.
Includes maximum likelihood and Bayesian tools.
- brainwaver
- Basic wavelet analysis of multivariate time series with a vizualisation
and parametrization using graph theory.
- brew
- Templating framework for report generation.
- brglm
- Bias-reduction in binomial-response GLMs.
- bspec
- Bayesian inference on the (discrete) power spectrum of time series.
- bvls
- The Stark-Parker algorithm for bounded-variable least squares.
- bvpSolve
- Solvers for boundary value problems of ordinary differential equations.
- ca
- Simple, multiple and joint Correspondence Analysis.
- caMassClass
- Processing and Classification of protein mass spectra (SELDI) data.
- caTools
- Miscellaneous utility functions, including reading/writing ENVI binary
files, a LogitBoost classifier, and a base64 encoder/decoder.
- cacheSweave
- Tools for caching Sweave computations.
- cacher
- Tools for caching and distributing statistical analyses.
- cairoDevice
- Loadable CAIRO/GTK device driver.
- calib
- Statistical tool for calibration of plate based bioassays.
- calibrate
- Calibration of biplot axes.
- calibrator
- Bayesian calibration of complex computer codes.
- candisc
- Generalized canonical discriminant analysis.
- canvas
- R graphics device targeting the HTML canvas element.
- car
- Companion to Applied Regression, containing functions for applied
regession, linear models, and generalized linear models, with an
emphasis on regression diagnostics, particularly graphical diagnostic
methods.
- caret
- Classification and REgression Training.
- caretLSF
- Classification and REgression Training, LSF style.
- caretNWS
- Classification and REgression Training in parallel using NetworkSpaces.
- catmap
- Case-control and TDT meta-analysis package.
- catspec
- Special models for categorical variables.
- cba
- Clustering for Business Analytics, including implementations of Proximus
and Rock.
- ccgarch
- Conditional Correlation GARCH models.
- cclust
- Convex clustering methods, including k-means algorithm, on-line
update algorithm (Hard Competitive Learning) and Neural Gas algorithm
(Soft Competitive Learning) and calculation of several indexes for
finding the number of clusters in a data set.
- ccems
- Combinatorially Complex Equilibrium Model Selection.
- cellVolumeDist
- Functions to fit cell volume distributions and thereby estimate cell
growth rates and division times.
- celsius
- Retrieve Affymetrix microarray measurements and metadata from Celsius.
- cem
- The coarsened exact matching algorithm (and many extensions).
- cfa
- Analysis of configuration frequencies.
- cggd
- Continuous Generalized Gradient Descent.
- cgh
- Analysis of microarray comparative genome hybridisation data using the
Smith-Waterman algorithm.
- cghFLasso
- Hot spot detecting for CGH array data with fused lasso regression.
- chplot
- Augmented convex hull plots: informative and nice plots for grouped
bivariate data.
- changeLOS
- Change in length of hospital stay (LOS).
- cheb
- Discrete linear Chebyshev approximation.
- chemCal
- Calibration functions for analytical chemistry.
- chemometrics
- Companion to the book “Introduction to Multivariate Statistical
Analysis in Chemometrics” by K. Varmuza and P. Filzmoser, CRC Press, to
appear.
- choplump
- Choplump tests (permutation tests for comparing two groups with some
positive but many zero responses).
- chron
- A package for working with chronological objects (times and dates).
- cir
- Nonparametric estimation of monotone functions via isotonic regression
and centered isotonic regression.
- circular
- Circular statistics, from “Topics in Circular Statistics” by Rao
Jammalamadaka and A. SenGupta, 2001, World Scientific.
- clValid
- Statistical and biological validation of clustering results.
- clac
- Clust Along Chromosomes, a method to call gains/losses in CGH array
data.
- class
- Functions for classification (k-nearest neighbor and LVQ).
Contained in the VR bundle. Recommended.
- classGraph
- Construct graph of S4 class hierarchies.
- classInt
- Choose univariate class intervals for mapping or other graphics
purposes.
- classifly
- Explore classification models in high dimensions.
- clim.pact
- Climate analysis and downscaling for monthly and daily data.
- climatol
- Functions to fill missing data in climatological (monthly) series and to
test their homogeneity, plus functions to draw wind-rose and
Walter&Lieth diagrams.
- clinfun
- Utilities for clinical study design and data analyses.
- clinsig
- Functions for calculating clinical significance.
- clue
- CLUster Ensembles.
- clues
- Clustering method based on local shrinking.
- clustTool
- GUI for clustering data with spatial information.
- cluster
- Functions for cluster analysis. Recommended.
- clusterGeneration
- Random cluster generation (with specified degree of separation).
- clusterRepro
- Reproducibility of gene expression clusters.
- clusterSim
- Searching for optimal clustering procedure for a data set.
- clusterfly
- Explore clustering interactively using R and GGobi.
- clustvarsel
- Variable selection for model-based clustering.
- clv
- Cluster validation techniques.
- cmm
- Categorical Marginal Models.
- cmprsk
- Estimation, testing and regression modeling of subdistribution functions
in competing risks.
- cmprskContin
- Continuous mark-specific relative risks for two groups.
- cmrutils
- Miscellaneous functions from the Center for the Mathematical Research,
Stankin, Moskow.
- cobs99
- Constrained B-splines: outdated 1999 version.
- cobs
- Constrained B-splines: qualitatively constrained (regression) smoothing
via linear programming and sparse matrices.
- cocorresp
- Co-correspondence analysis ordination methods for community ecology.
- coda
- Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)
simulations.
- codetools
- Code analysis tools. Recommended for R 2.5.0 or later.
- coin
- COnditional INference procedures for the general independence problem
including two-sample, K-sample, correlation, censored, ordered
and multivariate problems.
- colbycol
- Read big text files column by column.
- colorRamp
- Builds single and double gradient color maps.
- colorspace
- Mapping between assorted color spaces.
- combinat
- Combinatorics utilities.
- compHclust
- Complementary hierarchical clustering.
- compOverlapCorr
- Comparing overlapping correlation coefficients.
- compare
- Comparing objects for differences.
- compoisson
- Conway-Maxwell-Poisson distribution.
- compositions
- Functions for the consistent analysis of compositional data (e.g.,
portions of substances) and positive numbers (e.g., concentrations).
- concor
- Concordance, providing “SVD by blocks”.
- concord
- Measures of concordance and reliability.
- conf.design
- A series of simple tools for constructing and manipulating confounded
and fractional factorial designs.
- connectedness
- Find disconnected sets for two-way classification.
- contfrac
- Continued fractions.
- contrast
- A collection of contrast methods.
- convexHaz
- Nonparametric MLE/LSE of convex hazard.
- copas
- Statistical methods to model and adjust for bias in meta-analysis.
- copula
- Classes of commonly used copulas (including elliptical and Archimedian),
and methods for density, distribution, random number generators, and
plotting.
- corcounts
- Generate correlated count random variables.
- corpcor
- Efficient estimation of covariance and (partial) correlation.
- corpora
- Utility functions for the statistical analysis of corpus frequency
data.
- corrgram
- Plot a correlogram.
- corrperm
- Permutation tests of correlation with repeated measurements.
- countrycode
- Convert country names and coding schemes.
- covRobust
- Robust covariance estimation via nearest neighbor cleaning.
- coxphf
- Cox regression with Firth's penalized likelihood.
- coxphw
- Weighted estimation for Cox regression.
- coxrobust
- Robust Estimation in the Cox proportional hazards regression model.
- cramer
- Routine for the multivariate nonparametric Cramer test.
- crank
- Functions for completing and recalculating rankings.
- crawl
- (C)orrelated (RA)ndom (W)alk (L)ibrary for fitting continuous-time
correlated random walk models for animal movement data.
- crossdes
- Functions for the construction and randomization of balanced carryover
balanced designs, to check given designs for balance, and for simulation
studies on the validity of two randomization procedures.
- crosshybDetector
- Detection of cross-hybridization events in microarray experiments.
- crq
- Quantile regression for randomly censored data.
- cslogistic
- Likelihood and posterior analysis of conditionally specified logistic
regression models.
- cts
- Continuous time autoregressive models and the Kalman filter.
- ctv
- Server-side and client-side tools for CRAN task views.
- curvetest
- Test the equality of two curves, or one curve with 0.
- cwhmisc
- Miscellaneous functions by Christian W. Hoffmann.
- cyclones
- Cyclone identification.
- data.table
- Extension of data frames to allow subscripting by expressions evaluated
within the frame.
- dataframes2xls
- Write data frames to .xls files.
- date
- Functions for dealing with dates. The most useful of them accepts a
vector of input dates in any of the forms ‘8/30/53’,
‘30Aug53’, ‘30 August 1953’, ..., ‘August 30 53’, or
any mixture of these.
- dblcens
- Calculates the NPMLE of the survival distribution for doubly censored
data.
- ddesolve
- Solver for Delay Differential Equations.
- ddst
- Data driven smooth Neyman test.
- deSolve
- General solvers for ordinary differential equations (ODE) and for
differential algebraic equations (DAE).
- deal
- Bayesian networks with continuous and/or discrete variables can be
learned and compared from data.
- debug
- Debugger for R functions, with code display, graceful error recovery,
line-numbered conditional breakpoints, access to exit code, flow
control, and full keyboard input.
- degreenet
- Models for skewed count distributions relevant to networks.
- deldir
- Calculates the Delaunay triangulation and the Dirichlet or Voronoi
tesselation (with respect to the entire plane) of a planar point set.
- delt
- Estimation of multivariate densities with adaptive histograms.
- demogR
- Analysis of age-structured demographic models.
- denpro
- Visualization of multivariate density functions and estimates with level
set trees and shape trees, and visualization of multivariate data with
tail trees.
- denstrip
- Density strips and other methods for compactly illustrating
distributions.
- depmix
- Dependent Mixture Models: fit (multi-group) mixtures of latent Markov
models on mixed categorical and continuous (time series) data.
- depmixS4
- Dependent Mixture Models: fit latent (hidden) Markov models on mixed
categorical and continuous (time series) data.
- depth
- Depth functions tools for multivariate analysis.
- descr
- Functions to describe weighted categorical variables, and to facilitate
the character encoding conversion of objects.
- desirability
- Desirabiliy function optimization and ranking.
- dfcrm
- Dose-finding by the continual reassessment method.
- dglm
- Double generalized linear models.
- diagram
- Functions for visualising simple graphs (networks) and plotting flow
diagrams.
- diamonds
- Functions for illustrating aperture-4 diamond partitions in the plane,
or on the surface of an octahedron or icosahedron, for use as analysis
or sampling grids.
- dice
- Calculate probabilities of various dice-rolling events.
- dichromat
- Color schemes for dichromats: collapse red-green distinctions to
simulate the effects of colour-blindness.
- diffractometry
- Baseline identification and peak decomposition for x-ray
diffractograms.
- diffusionMap
- Diffusion map method of data parametrization.
- digeR
- GUI for analyzing 2D DIGE data.
- digest
- Two functions for the creation of “hash” digests of arbitrary R
objects using the md5 and sha-1 algorithms permitting easy comparison of
R language objects.
- diptest
- Compute Hartigan's dip test statistic for unimodality.
- dirichlet
- Dirichlet model of consumer buying behavior for marketing research.
- dirmult
- Estimation of Dirichlet-Multinomial distribution.
- diseasemapping
- Calculate SMRs from population and case data.
- dispmod
- Functions for modelling dispersion in GLMs.
- distr
- An object orientated implementation of distributions and some additional
functionality.
- distrDoc
- Documentation for packages distr, distrEx,
distrSim, and distrTEst.
- distrEx
- Extensions of package distr.
- distrMod
- Object orientated implementation of probability models based on
distr and distrEx.
- distrSim
- Simulation classes based on package distr.
- distrTEst
- Estimation and Testing classes based on package distr.
- distrTeach
- Extensions of distr for teaching stochastics/statistics in
secondary school.
- distributions
- Probability distributions based on TI-83 Plus.
- divagis
- Tools for quality checks of georeferenced plant species accessions.
- diveMove
- Dive analysis and calibration.
- dlm
- Maximum likelihood and Bayesian analysis of Dynamic Linear Models.
- dlmap
- Detection Localization Mapping for QTL.
- dlnm
- Distributed Lag Non-linear Models.
- doBy
- Facilities for groupwise computations.
- doMC
- Foreach parallel adaptor for the multicore package.
- dplR
- Dendrochronology Program Library in R.
- dr
- Functions, methods, and datasets for fitting dimension reduction
regression, including pHd and inverse regression methods SIR and SAVE.
- drc
- Non-linear regression analysis for multiple curves with focus on
concentration-response, dose-response and time-response curves.
- drm
- Regression and association models for clustered categorical responses.
- drfit
- Dose-response data evaluation.
- dse
- Dynamic System Estimation, a multivariate time series package bundle.
Contains dse1 (the base system, including multivariate ARMA and
state space models) and dse2 (extensions for evaluating
estimation techniques, forecasting, and for evaluating forecasting
model).
- dti
- DTI (Diffusion Tensor Image) analysis.
- dtt
- Discrete Trigonometric Transforms.
- dtw
- Dynamic Time Warping algorithms.
- dyad
- Analysis of dyadic observational data.
- dyn
- Time series regression.
- dynCorr
- Dynamic correlation.
- dynGraph
- Interactive visualization of data frames and factorial planes.
- dynamicGraph
- Interactive graphical tool for manipulating graphs.
- dynamicTreeCut
- Methods for detection of clusters in hierarchical clustering
dendrograms.
- dynamo
- Estimation, simulation, regularization and prediction of univariate
dynamic models including ARMA, ARMA-GARCH, ACD, and MEM.
- dynlm
- Dynamic linear models and time series regression.
- e1071
- Miscellaneous functions used at the Department of Statistics at TU Wien
(E1071), including moments, short-time Fourier transforms, Independent
Component Analysis, Latent Class Analysis, support vector machines, and
fuzzy clustering, shortest path computation, bagged clustering, and some
more.
- eRm
- Estimating extended Rasch models.
- earth
- Earth: multivariate adaptive regression spline models.
- eba
- Fitting and testing probabilistic choice models, especially the BTL,
elimination-by-aspects (EBA), and preference tree (Pretree) models.
- ecespa
- Functions and data for spatial point pattern analysis.
- eco
- Fitting Bayesian models of ecological inference in 2 by 2 tables.
- ecodist
- Dissimilarity-based functions for ecological analysis.
- ecolMod
- Figures, data sets and examples from the book “A Practical Guide to
Ecological Modelling — Using R as a Simulation Platform” by Karline
Soetaert and Peter M. J. Herman, 2008, Springer.
- effects
- Graphical and tabular effect displays, e.g., of interactions, for linear
and generalised linear models.
- eha
- A package for survival and event history analysis.
- eiPack
- Ecological inference and higher-dimension data management.
- eigenmodel
- Semiparametric factor and regression models for symmetric relational
data.
- elasticnet
- Elastic net regularization and variable selection.
- elec
- Functions for statistical election audits.
- ellipse
- Package for drawing ellipses and ellipse-like confidence regions.
- elliptic
- A suite of elliptic and related functions including Weierstrass and
Jacobi forms.
- elrm
- Exact Logistic Regression via MCMC.
- emdbook
- Data sets and auxiliary functions for “Ecological Models and Data” by
Ben Bolker (work in progress).
- emme2
- Functions to read from and write to an EMME/2 databank.
- empiricalBayes
- A bundle for dealing with extreme multiple testing problems by
estimating local false discovery rates. Contains packages
localFDR and HighProbability.
- emplik
- Empirical likelihood ratio for means/quantiles/hazards from possibly
right censored data.
- emplik2
- Empirical likelihood test (two-sample, censored data).
- emu
- Interface to the Emu speech database system.
- emulator
- Bayesian emulation of computer programs.
- endogMNP
- Fitting Multinomial Probit Models with Endogenous selection.
- energy
- E-statistics (energy) tests for comparing distributions: multivariate
normality, Poisson test, multivariate k-sample test for equal
distributions, hierarchical clustering by e-distances.
- ensembleBMA
- Probabilistic forecasting using Bayesian Model Averaging of ensembles
using a mixture of normal distributions.
- entropy
- Entropy estimation.
- epiR
- Functions for analyzing epidemiological data.
- epibasix
- Elementary functions for epidemiological analysis.
- epicalc
- Epidemiological calculator.
- epitools
- Basic tools for applied epidemiology.
- eqtl
- Tools for analyzing eQTL experiments.
- equivalence
- Tests and graphics for assessing tests of equivalence.
- ergm
- An integrated set of tools to analyze and simulate networks based on
exponential-family random graph models (ERGM).
- estout
- Stores model estimates and format them as LaTeX table.
- etm
- Empirical Transition Matrix.
- evd
- Functions for extreme value distributions. Extends simulation,
distribution, quantile and density functions to univariate, bivariate
and (for simulation) multivariate parametric extreme value
distributions, and provides fitting functions which calculate maximum
likelihood estimates for univariate and bivariate models.
- evdbayes
- Functions for the bayesian analysis of extreme value models, using MCMC
methods.
- evir
- Extreme Values in R: Functions for extreme value theory, which may be
divided into the following groups; exploratory data analysis, block
maxima, peaks over thresholds (univariate and bivariate), point
processes, gev/gpd distributions.
- exact2x2
- Exact conditional tests and confidence intervals for 2 x 2
tables.
- exactLoglinTest
- Monte Carlo exact tests for log-linear models.
- exactRankTests
- Computes exact p-values and quantiles using an implementation of
the Streitberg/Roehmel shift algorithm.
- exactmaxsel
- Exact methods for maximally selected statistics for binary response
variables.
- exams
- Automatic generation of simple (statistical) exams.
- experiment
- Designing and analyzing randomized experiments.
- expert
- Modeling of data using expert opinion.
- extRemes
- Extreme value toolkit.
- ez
- Easy analysis and visualization of factorial experiments.
- fArma
- The Rmetrics module for “ARMA Time Series Modelling”.
- fAsianOptions
- The Rmetrics module for “Option Valuation”.
- fAssets
- The Rmetrics module for “Assets Selection and Modelling”.
- fBasics
- The Rmetrics module for “Markets and Basic Statistics”.
- fBonds
- The Rmetrics module for “Bonds and Interest Rate Models”.
- fCalendar
- The Rmetrics module for “Chronological and Calendarical Objects”.
- fCopulae
- The Rmetrics module for “Dependence Structures with Copulas”.
- fEcofin
- The Rmetrics module for “Economic and Financial Data Sets”.
- fExoticOptions
- The Rmetrics module for “Option Valuation”.
- fExtremes
- The Rmetrics module for “Extreme Financial Market Data”.
- fGarch
- The Rmetrics module for “Autoregressive Conditional Heteroskedastic
Modelling”.
- fImport
- The Rmetrics module for “Chronological and Calendarical Objects”.
- fMultivar
- The Rmetrics module for “Multivariate Market Analysis”.
- fNonlinear
- The Rmetrics module for “Nonlinear and Chaotic Time Series Modelling”.
- fOptions
- The Rmetrics module for “Basics of Option Valuation”.
- fPortfolio
- The Rmetrics module for “Portfolio Selection and Optimization”.
- fRegression
- The Rmetrics module for “Regression Based Decision and Prediction”.
- fSeries
- The Rmetrics module for “Financial Time Series Objects”.
- fTrading
- The Rmetrics module for “Technical Trading Analysis”.
- fUnitRoots
- The Rmetrics module for “The Dynamical Process Behind Markets”.
- fUtilities
- The Rmetrics module for “Rmetrics Function Utilities”.
- fame
- Interface for FAME time series database.
- far
- Modelization for Functional AutoRegressive processes.
- faraway
- Functions and datasets for books by Julian Faraway.
- fast
- Implementation of the Fourier Amplitute Sensitivity Test (FAST).
- fastICA
- Implementation of FastICA algorithm to perform Independent Component
Analysis (ICA) and Projection Pursuit.
- fbati
- Gene by environment interaction tests.
- fda
- Functional Data Analysis: analysis of data where the basic observation
is a function of some sort.
- fdim
- Functions for calculating fractal dimension.
- fdrtool
- Estimation and control of (local) False Discovery Rates.
- feature
- Feature significance for multivariate kernel density estimation.
- fechner
- Fechnerian scaling of discrete object sets.
- ff
- Flat file database designed for large vectors and multi-dimensional
arrays.
- ffmanova
- Fifty-fifty MANOVA.
- fgac
- Families of Generalized Archimedean Copulas.
- fgui
- Function GUI.
- fields
- A collection of programs for curve and function fitting with an emphasis
on spatial data. The major methods implemented include cubic and thin
plate splines, universal Kriging and Kriging for large data sets. The
main feature is that any covariance function implemented in R can be
used for spatial prediction.
- filehash
- Simple file-based hash table.
- filehashSQLite
- Simple key-value database using SQLite as the backend.
- financial
- Solving financial problems in R.
- fingerprint
- Functions to operate on binary fingerprint data.
- fishmethods
- Fisheries methods and models.
- fit4NM
- Platform for NONMEM.
- fitdistrplus
- Fit parametric distributions to non-censored or censored data.
- flashClust
- Implementation of optimal hierarchical clustering.
- flexclust
- Flexible cluster algorithms.
- flexmix
- Flexible Mixture Modeling: a general framework for finite mixtures of
regression models using the EM algorithm.
- flubase
- Baseline of mortality free of influenza epidemics.
- fmri
- Functions for the analysis of fMRI experiments.
- foba
- Forward, backward, and foba sparse learning algorithms for ridge
regression.
- foreach
- Foreach looping construct.
- forecasting
- A bundle with functions and datasets for forecasting. Contains
forecast (time series forecasting), fma (data sets
from the book “Forecasting: Methods and Applications” by Makridakis,
Wheelwright & Hyndman, 1998), and Mcomp (data from the
M-competitions).
- foreign
- Functions for reading and writing data stored by statistical software
like Minitab, S, SAS, SPSS, Stata, Systat, etc. Recommended.
- forensic
- Statistical methods in forensic genetics.
- fork
- Functions for handling multiple processes: simple wrappers around the
Unix process management API calls.
- fortunes
- R fortunes.
- forward
- Forward search approach to robust analysis in linear and generalized
linear regression models.
- fossil
- Palaeoecological and palaeogeographical analysis tools.
- fpc
- Fixed point clusters, clusterwise regression and discriminant plots.
- fpca
- Restricted MLE for Functional Principal Components Analysis.
- fpow
- Compute the non-centrality parameter of the non-central F
distribution.
- fracdiff
- Maximum likelihood estimation of the parameters of a fractionally
differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied
Statistics, 1989).
- fractal
- Insightful fractal time series modeling and analysis.
- fractalrock
- Generate fractal time series with non-normal returns distribution.
- frailtypack
- Fit a shared gamma frailty model and Cox proportional hazards model
using a Penalized Likelihood on the hazard function.
- freqMAP
- Frequency Moving Average Plots (MAP) of multinomial data by a continuous
covariate.
- frontier
- Maximum likelihood estimation of stochastic frontier production and cost
functions.
- fso
- Fuzzy set ordination.
- ftnonpar
- Features and strings for nonparametric regression.
- fts
- Fast operations for time series objects via an interface to tslib (a
C++ time series library).
- futile
- A collection of utility functions to expedite software development.
- fuzzyFDR
- Exact calculation of fuzzy decision rules for multiple testing.
- fuzzyOP
- Fuzzy numbers and the main mathematical operations on these.
- fuzzyRankTests
- Fuzzy rank tests and confidence intervals.
- fxregime
- Frankel-Wei regression and structural change tools for estimating,
testing, dating and monitoring (de facto) exchange rate regimes.
- g.data
- Create and maintain delayed-data packages (DDP's).
- gPdtest
- Bootstrap goodness-of-fit test for the generalized Pareto distribution.
- gRain
- Probability propagation in graphical independence networks.
- gRbase
- A package for graphical modelling in R. Defines S4 classes for
graphical meta data and graphical models, and illustrates how
hierarchical log-linear models may be implemented and combined with
dynamicGraph.
- gRc
- Inference in graphical Gaussian models with edge and vertex symmetries.
- gWidgets
- gWidgets API for building toolkit-independent, interactive
GUIs.
- gWidgetsRGtk2
- Toolkit implementation of gWidgets for RGtk2.
- gWidgetsWWW
- Toolkit implementation of gWidgets for www.
- gWidgetsrJava
- Toolkit implementation of gWidgets for rJava.
- gWidgetstcltk
- Toolkit implementation of gWidgets for tcltk.
- gafit
- Genetic algorithm for curve fitting.
- gam
- Functions for fitting and working with Generalized Additive Models, as
described in chapter 7 of the White Book, and in “Generalized Additive
Models” by T. Hastie and R. Tibshirani (1990).
- gamair
- Data sets used in the book “Generalized Additive Models: An
Introduction with R” by S. Wood (2006).
- gamlss
- Functions to fit Generalized Additive Models for Location Scale and
Shape.
- gamlss.cens
- A GAMLSS add on package for censored data.
- gamlss.data
- Data for GAMLSS models.
- gamlss.dist
- Extra distributions for GAMLSS modeling.
- gamlss.mx
- A GAMLSS add on package for fitting mixture distributions.
- gamlss.nl
- A GAMLSS add on package for fitting non linear parametric models.
- gamlss.tr
- A GAMLSS add on for generating and fitting truncated (gamlss.family)
distributions.
- gap
- Genetic analysis package for both population and family data.
- gbev
- Gradient Boosted regression trees with Errors-in-Variables.
- gbm
- Generalized Boosted Regression Models: implements extensions to Freund
and Schapire's AdaBoost algorithm and J. Friedman's gradient boosting
machine. Includes regression methods for least squares, absolute loss,
logistic, Poisson, Cox proportional hazards partial likelihood, and
AdaBoost exponential loss.
- gbs
- Generalized Birnbaum-Saunders distributions.
- gcExplorer
- Graphical cluster explorer.
- gcl
- Compute a fuzzy rules or tree classifier from data.
- gclus
- Clustering Graphics. Orders panels in scatterplot matrices and parallel
coordinate displays by some merit index.
- gcmrec
- Parameters estimation of the general semiparametric model for recurrent
event data proposed by Peña and Hollander.
- gdata
- Various functions to manipulate data.
- gee
- An implementation of the Liang/Zeger generalized estimating equation
approach to GLMs for dependent data.
- geepack
- Generalized estimating equations solver for parameters in mean, scale,
and correlation structures, through mean link, scale link, and
correlation link. Can also handle clustered categorical responses.
- geiger
- Analysis of evolutionary diversification.
- genalg
- R based genetic algorithm for binary and floating point chromosomes.
- gene2pathway
- Prediction of KEGG pathway membership for individual genes based on
InterPro domain signatures.
- genetics
- Classes and methods for handling genetic data. Includes classes to
represent genotypes and haplotypes at single markers up to multiple
markers on multiple chromosomes, and functions for allele frequencies,
flagging homo/heterozygotes, flagging carriers of certain alleles,
computing disequlibrium, testing Hardy-Weinberg equilibrium, ...
- geoR
- Functions to perform geostatistical data analysis including model-based
methods.
- geoRglm
- Functions for inference in generalised linear spatial models.
- geomapdata
- Data for topographic and geologic mapping.
- geometry
- Mesh generation and surface tesselation, based on the Qhull library.
- geonames
- Interface to www.geonames.org web service.
- geozoo
- Definition of geometric objects and display via rggobi.
- getopt
- C-like getopt behavior for R scripts.
- ggm
- Functions for defining directed acyclic graphs and undirected graphs,
finding induced graphs and fitting Gaussian Markov models.
- ggplot2
- An implementation of the Grammar of Graphics in R.
- ghyp
- Univariate and multivariate generalized hyperbolic distributions.
- giRaph
- Data structures and algorithms for computations on graphs.
- gibbs.met
- Naive Gibbs sampling with Metropolis steps.
- glasso
- Graphical lasso.
- gld
- Basic functions for the generalised (Tukey) lambda distribution.
- glmc
- Fitting Generalized Linear Models subject to Constraints.
- glmmAK
- Generalized Linear Mixed Models.
- glmmBUGS
- Generalised Linear Mixed Models with WinBUGS.
- glmmML
- A Maximum Likelihood approach to generalized linear models with random
intercept.
- glmnet
- Lasso and elastic-net regularized generalized linear models.
- glmpath
- L1 regularization path for Generalized Linear Models.
- glmulti
- GLM model selection and multimodel inference made easy.
- glpk
- Interface to the GNU Linear Programming Kit (GLPK).
- gmaps
- Wrapper and auxiliary functions for the maps package to work
with the grid graphics system.
- gmm
- Generalized Method of Moments.
- gmodels
- Various functions to manipulate models.
- gmp
- Arithmetic “without limitations” using the GNU Multiple
Precision library.
- gmt
- Interface between the GMT 4.0 map-making software and R.
- gnm
- Functions to specify and fit generalized nonlinear models, including
models with multiplicative interaction terms such as the UNIDIFF model
from sociology and the AMMI model from crop science.
- goalprog
- Weighted and lexicographical goal programming and optimization.
- gof
- Model-diagnostics based on cumulative residuals.
- gogarch
- Generalized Orthogonal GARCH (GO-GARCH) models.
- gpclib
- General polygon clipping routines for R based on Alan Murta's C
library.
- gpls
- Classification using generalized partial least squares for two-group and
multi-group (more than 2 group) classification.
- gplots
- Various functions to draw plots.
- gputools
- A few GPU-enabled data mining functions.
- grImport
- Importing vector graphics.
- grade
- Binary grading functions.
- granova
- Graphical Analysis of Variance.
- graph
- Handling of graph data structures.
- graphicsQC
- Quality Control for graphics in R.
- grasp
- Generalized Regression Analysis and Spatial Predictions for R.
- gregmisc
- Miscellaneous functions written/maintained by Gregory R. Warnes.
- gridBase
- Integration of base and grid graphics.
- grnnR
- A Generalized Regression Neural Network.
- grouped
- Regression models for grouped and coarse data, under the Coarsened At
Random assumption.
- grplasso
- Fit user specified models with group lasso penalty.
- grpreg
- Regularization paths for regression models with grouped covariates.
- gsDesign
- Group sequential designs.
- gsarima
- functions for Generalized SARIMA time series simulation.
- gsl
- Wrapper for special functions of the Gnu Scientific Library (GSL).
- gss
- A comprehensive package for structural multivariate function estimation
using smoothing splines.
- gstat
- multivariable geostatistical modelling, prediction and simulation.
Includes code for variogram modelling; simple, ordinary and universal
point or block (co)kriging, sequential Gaussian or indicator
(co)simulation, and map plotting functions.
- gsubfn
- Miscellaneous string utilities.
- gtm
- Generative topographic mapping.
- gtools
- Various functions to help manipulate data.
- gumbel
- Functions for the Gumbel-Hougaard copula.
- gvlma
- Global Validation of Linear Models Assumptions.
- hacks
- Some convenience functions.
- hapassoc
- Likelihood inference of trait associations with SNP haplotypes and other
attributes using the EM Algorithm.
- haplo.ccs
- Estimate haplotype relative risks in case-control data.
- haplo.stats
- Statistical analysis of haplotypes with traits and covariates when
linkage phase is ambiguous.
- hapsim
- Haplotype data simulation.
- hash
- Implements hash/associated arrays/dictionaries.
- hbim
- Hill/Bliss Independence Model for combination vaccines.
- hbmem
- Hierarchial Bayesian analysis of recognition memory.
- hddplot
- Use known groups in high-dimensional data to derive scores for plots.
- hdeco
- Hierarchical DECOmposition of entropy for categorical map comparisons.
- hdf5
- Interface to the NCSA HDF5 library.
- hdrcde
- Highest Density Regions and Conditional Density Estimation.
- heatmap.plus
- Heatmap with sensible behavior.
- helloJavaWorld
- A demonstration how to interface to a jar file that resides inside an R
package.
- heplots
- Visualizing tests in multivariate linear models.
- hett
- Functions for the fitting and summarizing of heteroscedastic
t-regression.
- hexView
- Viewing binary files.
- hexbin
- Hexagonal binning routines.
- hier.part
- Hierarchical Partitioning: variance partition of a multivariate data
set.
- hierfstat
- Estimation of hierarchical F-statistics from haploid or diploid genetic
data with any numbers of levels in the hierarchy, and tests for the
significance of each F and variance components.
- hints
- Provide hints on what to do next.
- hlr
- Hidden logistic regression.
- hmm.discnp
- Hidden Markov models with discrete non-parametric observation
distributions.
- hoa
- A bundle of packages for higher order likelihood-based inference.
Contains cond for approximate conditional inference for
logistic and loglinear models, csampling for conditional
simulation in regression-scale models, marg for approximate
marginal inference for regression-scale models, and nlreg for
higher order inference for nonlinear heteroscedastic models.
- homals
- Homogeneity Analysis (HOMALS) package with optional Tcl/Tk interface.
- homtest
- Homogeneity tests for regional frequency analysis.
- hopach
- Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).
- hot
- Computation on micro-arrays.
- howmany
- A lower bound for the number of correct rejections.
- hsmm
- Hidden Semi Markov Models.
- httpRequest
- Implements HTTP Request protocols (GET, POST, and multipart POST
requests).
- hwde
- Models and tests for departure from Hardy-Weinberg equilibrium and
independence between loci.
- hwriter
- Easy-to-use and versatile functions to output R objects in HTML
format.
- hybridHclust
- Hybrid hierarchical clustering via mutual clusters.
- hydrogeo
- Groundwater data presentation and interpretation.
- hydrosanity
- Graphical user interface for exploring hydrological time series.
- hyperdirichlet
- Routines for the hyperdirichlet distribution.
- hypergeo
- The hypergeometric function over the whole complex plane.
- ibdreg
- Regression methods for IBD linkage with covariates.
- ic.infer
- Inequality constrained inference in linear normal situations.
- ic50
- Evaluation of compound screens.
- icomp
- Calculates the ICOMP criterion and its variations.
- identity
- Jacquard condensed coefficients of identity.
- ifa
- Independent Factor Analysis.
- ifs
- Iterated Function Systems distribution function estimator.
- ifultools
- Insightful research tools.
- ig
- Robust and classical versions of the inverse Gaussian distribution.
- igraph
- Routines for simple graphs.
- iid.test
- Testing whether data is independent and identically distributed.
- imprProbEst
- Minimum distance estimation in an imprecise probability model.
- impute
- Imputation for microarray data (currently KNN only).
- imputeMDR
- Multifactor Dimensionality Reduction (MDR) analysis for imcomplete data.
- ineq
- Inequality, concentration and poverty measures, and Lorenz curves
(empirical and theoretic).
- inetwork
- Network analysis and plotting.
- influence.ME
- Tools for recognizing influential data in mixed models.
- infotheo
- Information-theoretic tools.
- inline
- Inline C/C++ function calls from R.
- intcox
- Implementation of the Iterated Convex Minorant Algorithm for the Cox
proportional hazard model for interval censored event data.
- integrOmics
- Integrate Omics data project.
- intervals
- Tools for working with points and intervals.
- introgress
- Analysis of introgression of genotypes between divergent, hybridizing
lineages.
- iplots
- Interactive graphics for R.
- ipptoolbox
- Uncertainty quantification and propagation in the framework of
Dempster-Shafer theory and imprecise probabilities.
- ipred
- Improved predictive models by direct and indirect bootstrap aggregation
in classification and regression as well as resampling based estimators
of prediction error.
- irr
- Coefficients of Interrater Reliability and Agreement for quantitative,
ordinal and nominal data.
- irtProb
- Utilities and probability distributions related to multidimensional
person Item Response Models (IRT).
- irtoys
- Simple interface to the estimation and plotting of IRT models.
- isa2
- The Iterative Signature Algorithm for finding modules in an input
matrix.
- 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.
- isotone
- Active set and generalized PAVA for isotone optimization.
- iterators
- Iterator construct.
- its
- An S4 class for handling irregular time series.
- ivivc
- In vitro in vivo correlation (IVIVC) modeling.
- jit
- Just-in-time compiler.
- jointDiag
- Joint approximate diagonalization of a set of square matrices.
- kappalab
- The “laboratory for capacities”, an S4 tool box for capacity (or
non-additive measure, fuzzy measure) and integral manipulation on a
finite setting.
- kerfdr
- Semi-parametric kernel-based approach to local fdr estimations.
- kernelPop
- Spatially explicit population genetic simulations.
- kernlab
- Kernel-based machine learning methods including support vector machines.
- kin.cohort
- Analysis of kin-cohort studies.
- kinship
- Mixed-effects Cox models, sparse matrices, and modeling data from large
pedigrees.
- kknn
- Weighted k-nearest neighbors classification and regression.
- klaR
- Miscellaneous functions for classification and visualization developed
at the Department of Statistics, University of Dortmund.
- klin
- Linear equations with Kronecker structure.
- kmi
- Kaplan-Meier multiple imputation for the analysis of cumulative
incidence functions in the competing risks setting.
- kml
- K-Means for Longitudinal data.
- knnTree
- Construct or predict with k-nearest-neighbor classifiers, using
cross-validation to select k, choose variables (by forward or
backwards selection), and choose scaling (from among no scaling, scaling
each column by its SD, or scaling each column by its MAD). The finished
classifier will consist of a classification tree with one such
k-nn classifier in each leaf.
- knncat
- Nearest-neighbor classification with categorical variables.
- knnflex
- A more flexible k-NN.
- knorm
- Microarray data from multiple biologically interrelated experiments.
- kohonen
- Supervised and unsupervised self-organising maps.
- ks
- Kernel smoothing: bandwidth matrices for kernel density estimators and
kernel discriminant analysis for bivariate data.
- kst
- Knowledge Space Theory: a set-theoretical framework which proposes
mathematical formalisms to operationalize knowledge structures in a
particular domain.
- kza
- Kolmogorov-Zurbenko Adpative filter for locating change points in a time
series.
- kzft
- Kolmogorov-Zurbenko Fourier Transform and application.
- kzs
- Kolmogorov-Zurbenko Spline.
- labdsv
- Laboratory for Dynamic Synthetic Vegephenomenology.
- labeltodendro
- Convert labels or tables to a dendrogram.
- labstatR
- Functions for the book “Laboratorio di statistica con R” by
S. M. Iacus and G. Masarotto, 2002, McGraw-Hill. Function names and
documentation in Italian.
- laercio
- Tests to compare means.
- lago
- LAGO for rare target detection.
- lancet.iraqmortality
- Surveys of Iraq mortality published in The Lancet.
- languageR
- Data sets and functions for the book “Analyzing Linguistic Data: A
practical introduction to statistics” by R. H. Baayen, 2007, Cambridge:
Cambridge University Press.
- lars
- Least Angle Regression, Lasso and Forward Stagewise: efficient
procedures for fitting an entire lasso sequence with the cost of a
single least squares fit.
- laser
- Likelihood Analysis of Speciation/Extinction Rates from phylogenies.
- lasso2
- Routines and documentation for solving regression problems while
imposing an L1 constraint on the estimates, based on the algorithm of
Osborne et al. (1998).
- latentnet
- Latent position and cluster models for statistical networks.
- latentnetHRT
- Latent position and cluster models for statistical networks,
implementing the original specification by Handcock, Raftery and Tantrum.
- lattice
- Lattice graphics, an implementation of Trellis Graphics functions.
Recommended.
- latticeExtra
- Generic functions and standard methods for Trellis-based displays.
- latticist
- A Lattice-based tool for exploratory visualization.
- lawstat
- Statistical tests widely utilized in biostatistics, public policy and
law.
- lazy
- Lazy learning for local regression.
- ldDesign
- Design of experiments for detection of linkage disequilibrium.
- lcd
- Learn Chain graphs via Decomposition.
- lcda
- Latent Class Discriminant Analysis.
- lda.cv
- Cross-validation for linear discriminant analysis.
- ldbounds
- Lan-DeMets method for group sequential boundaries.
- leaps
- A package which performs an exhaustive search for the best subsets of a
given set of potential regressors, using a branch-and-bound algorithm,
and also performs searches using a number of less time-consuming
techniques.
- lga
- Tools for Linear Grouping Analysis (LGA).
- lgtdl
- A set of methods for longitudinal data objects.
- lhs
- Latin Hypercube Samples.
- limSolve
- Solving linear inverse models.
- linprog
- Solve linear programming/linear optimization problems by using the
simplex algorithm.
- ljr
- Logistic Joinpoint Regression.
- lme4
- Fit linear and generalized linear mixed-effects models.
- lmec
- Linear mixed-effects models with censored responses.
- lmeSplines
- Fit smoothing spline terms in Gaussian linear and nonlinear
mixed-effects models.
- lmm
- Linear mixed models.
- lmodel2
- Model II simple linear regression.
- lmom
- L-moments.
- lmomRFA
- Regional Frequency Analysis using L-moments.
- lmomco
- L-moments and L-comoments.
- lmtest
- A collection of tests on the assumptions of linear regression models
from the book “The linear regression model under test” by W. Kraemer
and H. Sonnberger, 1986, Physica.
- lnMLE
- Marginally specified logistic normal models for longitudinal binary
data.
- localdepth
- Simplicial, Mahalanobis and ellipsoid local and global depth.
- locfdr
- Computation of local false discovery rates.
- locfit
- Local Regression, likelihood and density estimation.
- locpol
- Kernel local polynomial regression.
- lodplot
- Assorted plots of location score versus genetic map position.
- logcondens
- Estimate a log-concave probability density from i.i.d. observations.
- logilasso
- Analysis of sparse contingency tables with penalization approaches.
- logistf
- Firth's bias reduced logistic regression approach with penalized profile
likelihood based confidence intervals for parameter estimates.
- loglognorm
- Double log normal distribution functions.
- logregperm
- Inference in Logistic Regression using permutation tests.
- logspline
- Logspline density estimation.
- lokern
- Kernel regression smoothing with adaptive local or global plug-in
bandwidth selection.
- longRPart
- Recursive partitioning of longitudinal data using mixed-effects models.
- longitudinal
- Analysis of multiple time course data.
- longitudinalData
- Tools for longitudinal data.
- longmemo
- Datasets and Functionality from the textbook “Statistics for
Long-Memory Processes” by J. Beran, 1994, Chapman & Hall.
- lpSolve
- Functions that solve general linear/integer problems, assignment
problems, and transportation problems via interfacing Lp_solve.
- lpSolveAPI
- An R interface to the lp_solve library API.
- lpc
- Lassoed principal components for testing significance of features.
- lpridge
- Local polynomial (ridge) regression.
- lsa
- Latent Semantic Analysis.
- lspls
- LS-PLS (least squares — partial least squares) models.
- lss
- Accelerated failure time model to right censored data based on
least-squares principle.
- ltm
- Analysis of multivariate Bernoulli data using latent trait models
(including the Rasch model) under the Item Response Theory approach.
- ltsa
- Linear Time Series Analysis.
- luca
- Likelihood Under Covariate Assumptions (LUCA).
- lvplot
- Letter-value box plots.
- mAr
- Estimation of multivariate AR models through a computationally efficient
stepwise least-squares algorithm.
- mFilter
- Miscellenous time series filters.
- maanova
- Analysis of N-dye Micro Array experiments using mixed model
effect. Contains anlysis of variance, permutation and bootstrap,
cluster and consensus tree.
- magic
- A variety of methods for creating magic squares of any order greater
than 2, and various magic hypercubes.
- mapLD
- Linkage Disequilibrium mapping.
- mapReduce
- Flexible mapReduce algorithm for parallel computation.
- 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.
- mar1s
- Multiplicative AR(1) with seasonal processes.
- marelac
- Datasets, constants, conversion factors, utilities for the marine and
lacustrine sciences.
- marginTree
- Margin trees for high-dimensional classification.
- marginalmodelplots
- Marginal model plots for linear and generalized linear models.
- markerSearchPower
- Power calculation for marker detection strategies in genome-wide
association studies.
- mathgraph
- Tools for constructing and manipulating objects from a class of directed
and undirected graphs.
- matlab
- Emulate MATLAB code using R.
- matrixStats
- Methods that apply to rows and columns of a matrix.
- matrixcalc
- Collection of functions for matrix differential calculus.
- maxLik
- Tools for Maximum Likelihood Estimation.
- maxstat
- Maximally selected rank and Gauss statistics with several p-value
approximations.
- mblm
- Median-based Linear models, using Theil-Sen single or Siegel repeated
medians.
- mboost
- Gradient boosting for fitting generalized linear, additive and
interaction models.
- mc2d
- Tools for two-dimensional Monte-Carlo simulations.
- mcclust
- Process an MCMC sample of clusterings.
- mcgibbsit
- Warnes and Raftery's MCGibbsit MCMC diagnostic.
- mclust
- Model-based clustering and normal mixture modeling including Bayesian
regularization.
- mclust02
- Model-based cluster analysis: the 2002 version of MCLUST.
- mcmc
- Functions for Markov Chain Monte Carlo (MCMC).
- mco
- Multi criteria optimization algorithms.
- mcsm
- Functions for Monte Carlo methods.
- mda
- Code for mixture discriminant analysis (MDA), flexible discriminant
analysis (FDA), penalized discriminant analysis (PDA), multivariate
additive regression splines (MARS), adaptive back-fitting splines
(BRUTO), and penalized regression.
- meboot
- Maximum entropy bootstrap for time series.
- medAdherence
- Medication Adherence: commonly used definitions.
- mediation
- Causal mediation analysis.
- mefa
- Faunistic count data handling and reporting.
- meifly
- Interactive model exploration using GGobi.
- memisc
- Miscellaneous Tools for data management, simulation, and presentation of
estimates.
- merror
- Accuracy and precision of measurements.
- meta
- Fixed and random effects meta-analysis, with functions for tests of
bias, forest and funnel plot.
- metaMA
- Meta-analysis for MicroArrays.
- metacor
- Meta-analysis of correlation coefficients.
- metafor
- Meta-analysis.
- mfp
- Multiple Fractional Polynomials.
- mgcv
- Routines for GAMs and other genralized ridge regression problems with
multiple smoothing parameter selection by GCV or UBRE.
Recommended.
- mhsmm
- Parameter estimation and prediction for multiple hidden Markov and
semi-Markov models.
- mi
- Missing-data imputation and model checking.
- micEcon
- Tools for microeconomic analysis and microeconomic modelling.
- mice
- Multivariate Imputation by Chained Equations.
- mimR
- An R interface to MIM for graphical modeling in R.
- minet
- Mutual Information NETwork.
- minpack.lm
- R interface for two functions from the MINPACK least squares
optimization library, solving the nonlinear least squares problem by a
modification of the Levenberg-Marquardt algorithm.
- minxent
- Entropy optimization distribution under constraints.
- misc3d
- A collection of miscellaneous 3d plots, including rgl-based isosurfaces.
- mirf
- Multiple Imputation and Random Forests for unobservable phase,
high-dimensional data.
- mitools
- Tools to perform analyses and combine results from multiple-imputation
datasets.
- mix
- Estimation/multiple imputation programs for mixed categorical and
continuous data.
- mixAK
- Mixture of methods including mixtures.
- mixPHM
- Mixtures of proportional hazard models.
- mixRasch
- Estimation of mixture Rasch models.
- mixdist
- Finite mixture distribution models.
- mixer
- Random graph clustering via estimation of Erdo''s-Rényi mixtures.
- mixfdr
- Compute false discovery rates and effect sizes using normal mixtures.
- mixlow
- Assessing drug synergism/antagonism.
- mixreg
- Functions to fit mixtures of regressions.
- mixstock
- Mixed stock analysis functions.
- mixtools
- Tools for mixture models.
- mlCopulaSelection
- Copula selection and fitting using maximum likelihood.
- mlbench
- A collection of artificial and real-world machine learning benchmark
problems, including the Boston housing data.
- mlegp
- Maximum Likelihood Estimates of Gaussian Processes.
- mlmRev
- Examples from Multilevel Modelling Software Review.
- mlogit
- Estimation of the multinomial logit model with choice specific
variables.
- mmcm
- Modified Maximum Contrast Method.
- mmlcr
- Mixed-mode latent class regression (also known as mixed-mode mixture
model regression or mixed-mode mixture regression models) which can
handle both longitudinal and one-time responses.
- mnormt
- The multivariate normal and t distributions.
- moc
- Fits a variety of mixtures models for multivariate observations with
user-difined distributions and curves.
- modTempEff
- Model temperature effects using time series data.
- modeest
- Mode estimation and Chernoff distribution.
- modehunt
- Multiscale analysis for density functions.
- modeltools
- A collection of tools to deal with statistical models.
- moduleColor
- Methods for color labeling, calculation of eigengenes, and merging of
closely related modules.
- mokken
- Mokken Scale Analysis for test and questionnaire data.
- mombf
- Moment and inverse moment Bayes factors.
- moments
- Moments, skewness, kurtosis and related tests.
- monomvn
- Estimation for multivariate normal data with monotone missingness.
- monreg
- Estimation of monotone regression and variance functions in
nonparametric models.
- monoProc
- Strictly monotone smoothing procedure.
- moonsun
- Basic astronomical calculations.
- mpm
- Spectral map analysis.
- mprobit
- Multivariate probit model for binary/ordinal response.
- mra
- Analysis of capture-recapture data.
- mratios
- Inferences for ratios of coefficients in the general linear model.
- mrdrc
- Model-robust concentration-response analysis.
- mrt
- Data sets and functions for the book “Modern Regression Techniques
Using R” by D. B. Wright and K. London, 2009, Sage Publications.
- msBreast
- Protein mass spectra dataset from a breast cancer study.
- msDilution
- Protein mass spectra dataset from a dilution experiment.
- msProcess
- Tools for protein mass spectra processing including data preparation,
denoising, noise estimation, baseline correction, intensity
normalization, peak detection, peak alignment, peak quantification, and
various functionalities for data ingestion/conversion, mass calibration,
data quality assessment, and protein mass spectra simulation.
- msProstate
- Protein mass spectra dataset from a prostate cancer study.
- msm
- Functions for fitting continuous-time Markov multi-state models to
categorical processes observed at arbitrary times, optionally with
misclassified responses, and covariates on transition or
misclassification rates.
- mstate
- Data preparation, estimation and prediction in multi-state models.
- muRL
- Mailmerge using R, LaTeX, and the web.
- muS2RC
- S-plus to R Compatibility for package muStat.
- muStat
- Prentice rank sum test and McNemar test.
- muUtil
- Utility functions for package muStat.
- muhaz
- Hazard function estimation in survival analysis.
- multcomp
- Multiple comparison procedures for the one-way layout.
- multcompView
- Visualizations of paired comparisons.
- multic
- Quantitative linkage analysis tools using the variance components
approach.
- multicore
- Parallel processing of R code on machines with multiple cores or CPUs.
- multilevel
- Analysis of multilevel data by organizational and social psychologists.
- multinomRob
- Overdispersed multinomial regression using robust (LQD and tanh)
estimation.
- multipol
- Utilities to manipulate multivariate polynomials.
- multmod
- Testing of multiple outcomes.
- multtest
- Resampling-based multiple hypothesis testing.
- muscor
- Multi-stage Convex Relaxation.
- mvShapiroTest
- Generalized Shapiro-Wilk test for multivariate normality.
- mvbutils
- Utilities by Mark V. Bravington for project organization, editing and
backup, sourcing, documentation (formal and informal), package
preparation, macro functions, and more.
- mvgraph
- Multivariate interactive visualization.
- mvna
- Nelson-Aalen estimator of the cumulative hazard in multistate models.
- mvnmle
- ML estimation for multivariate normal data with missing values.
- mvnormtest
- Generalization of the Shapiro-Wilk test for multivariate variables.
- mvoutlier
- Multivariate outlier detection based on robust estimates of location and
covariance structure.
- mvpart
- Multivariate partitioning.
- mvtBinaryEP
- Generate correlated binary data based on the method of Emrich and
Piedmonte.
- mvtnorm
- Multivariate normal and t distributions.
- mvtnormpcs
- Multivariate Normal and T Distribution functions of Dunnett
(1989).
- nFDR
- Nonparametric Estimate of FDR Based on Bernstein polynomials.
- nFactors
- Non-graphical solution to the Cattell Scree Test.
- ncdf
- Interface to Unidata netCDF data files.
- ncf
- Spatial nonparametric covariance functions.
- ncomplete
- Functions to perform the regression depth method (RDM) to binary
regression to approximate the minimum number of observations that can be
removed such that the reduced data set has complete separation.
- negenes
- Estimating the number of essential genes in a genome on the basis of
data from a random transposon mutagenesis experiment, through the use of
a Gibbs sampler.
- netmodels
- Tools for the study of scale free and small world networks.
- network
- Tools to create and modify network objects, which can represent a range
of relational data types.
- networksis
- Simulate bipartite graphs with fixed marginals through sequential
importance sampling.
- neural
- RBF and MLP neural networks with graphical user interface.
- neuralnet
- Training of neural networks.
- nice
- Get or set UNIX priority (niceness) of running R process.
- nleqslv
- Solve systems of non-linear equations.
- nlme
- Fit and compare Gaussian linear and nonlinear mixed-effects models.
Recommended.
- nlmeODE
- Combine the nlme and odesolve packages for
mixed-effects modelling using differential equations.
- nlrwr
- Non-linear regression with R.
- nls2
- Non-linear regression with brute force.
- nlstools
- Tools for nonlinear regression diagnostics.
- nlt
- A nondecimated lifting transform for signal denoising.
- nlts
- (Non)linear time series analysis.
- nltm
- NonLinear Transformation Models for survival analysis.
- nnet
- Software for single hidden layer perceptrons (“feed-forward neural
networks”), and for multinomial log-linear models. Contained in the
VR bundle. Recommended.
- nnls
- The Lawson-Hanson NNLS algorithm for non-negative least squares.
- noia
- Implementation of the Natural and Orthogonal InterAction (NOIA) model.
- nonbinROC
- ROC-type analysis for non-binary gold standards.
- nor1mix
- One-dimensional normal mixture models classes, for, e.g., density
estimation or clustering algorithms research and teaching; providing the
widely used Marron-Wand densities.
- normwm.test
- Normality and white noise testing.
- normalp
- A collection of utilities for normal of order p distributions
(General Error Distributions).
- nortest
- Five omnibus tests for the composite hypothesis of normality.
- noverlap
- Functions to perform the regression depth method (RDM) to binary
regression to approximate the amount of overlap, i.e., the minimal
number of observations that need to be removed such that the reduced
data set has no longer overlap.
- np
- Nonparametric kernel smoothing methods for mixed datatypes.
- nparcomp
- Nonparametric relative contrast effects.
- npde
- Normalized prediction distribution errors for nonlinear mixed-effect
models.
- nplplot
- Plotting non-parametric LOD scores from multiple input files.
- npmc
- Nonparametric Multiple Comparisons: provides simultaneous rank test
procedures for the one-way layout without presuming a certain
distribution.
- nsRFA
- Non-supervised Regional Frequency Analysis.
- numDeriv
- Accurate numerical derivatives.
- nws
- Functions for NetWorkSpaces and Sleigh.
- obliqueTree
- Oblique trees for classification data.
- obsSens
- Sensitivity analysis for observational studies.
- oc
- Optimal Classification roll call analysis.
- oce
- Analysis of oceanographic data.
- odesolve
- An interface for the Ordinary Differential Equation (ODE) solver lsoda.
ODEs are expressed as R functions.
- odfWeave
- Sweave processing of Open Document Format (ODF) files.
- odfWeave.survey
- Support for odfWeave on the survey package.
- ofw
- Optimal Feature Weighting algorithm.
- onemap
- Analysis of molecular marker data from non-model systems to
simultaneously estimate linkage and linkage phases (genetic map
construction).
- onion
- A collection of routines to manipulate and visualize quaternions and
octonions.
- openNLP
- An interface to openNLP, a
collection of natural language processing tools including a sentence
detector, tokenizer, pos-tagger, shallow and full syntactic parser, and
named-entity detector, using the Maxent Java package for training and using maximum entropy models.
- openNLPmodels.en
- English models for openNLP.
- openNLPmodels.es
- Spanish models for openNLP.
- opentick
- Interface to opentick real time and historical market data.
- operators
- Additional binary operators for R.
- optBiomarker
- Estimates optimal number of biomarkers for two-group classification
based on microarray data.
- optmatch
- Functions to perform optimal matching, particularly full matching.
- orientlib
- Representations, conversions and display of orientation SO(3) data.
- orloca
- Operations Research LOCational Analysis models.
- orloca.es
- Spanish version of orloca package.
- orth
- Multivariate logistic regressions using orthogonalized residuals.
- orthogonalsplinebasis
- Orthogonal B-spline basis functions.
- orthopolynom
- Functions for orthogonal and orthonormal polynomials.
- ouch
- Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses.
- outliers
- A collection of some tests commonly used for identifying outliers.
- oz
- Functions for plotting Australia's coastline and state boundaries.
- p3state.msm
- Analyzing survival data from illness-death model.
- pARccs
- Estimation of partial attributable risks from case-control data.
- pack
- Create and manipulate raw vectors.
- packS4
- Toy example of S4 package illustrating the book “Petit Manuel de
Programmation Orientee Objet sous R”.
- packClassic
- Illustrate the tutorial “S4: From Idea To Package”.
- packdep
- Mapping dependencies among R packages.
- pairwiseCI
- Calculate and plot unadjusted confidence intervals for two sample
comparisons.
- paleoTS
- Modeling evolution in paleontological time-series.
- paltran
- Functions for paleolimnology.
- pamm
- Power analysis for random effects in mixed models.
- pamr
- Pam: Prediction Analysis for Microarrays.
- pan
- Multiple imputation for multivariate panel or clustered data.
- panel
- Functions and datasets for fitting models to Panel data.
- papply
- Parallel apply function using MPI.
- paran
- Horn's test of principal components/factors.
- parcor
- Regularized estimation of partial correlation matrices.
- partDSA
- Partitioning using Deletion, Substitution, and Addition moves.
- partitions
- Additive partitions of integers.
- party
- Unbiased recursive partitioning in a conditional inference framework.
- pastecs
- Package for Analysis of Space-Time Ecological Series.
- pbatR
- Frontend to PBAT to run within R.
- pcaPP
- Robust PCA by Projection Pursuit.
- pcalg
- Standard and robust estimation of the skeleton (ugraph) of a Directed
Acyclic Graph (DAG) via the PC algorithm.
- pcse
- Panel-Corrected Standard Error estimation.
- pcurve
- Fits a principal curve to a numeric multivariate dataset in arbitrary
dimensions. Produces diagnostic plots. Also calculates Bray-Curtis and
other distance matrices and performs multi-dimensional scaling and
principal component analyses.
- pear
- Periodic Autoregression Analysis.
- pec
- Prediction Error Curves for survival models.
- pedigree
- Pedigree functions.
- pedigreemm
- Pedigree-based mixed-effects models.
- pegas
- Population and Evolutionary Genetics Analysis System.
- penalized
- Penalized estimation in generalized linear models.
- penalizedSVM
- Feature selection SVM using penalty functions.
- peperr
- Parallelised Estimation of Prediction ERRor.
- permax
- Functions intended to facilitate certain basic analyses of DNA array
data, especially with regard to comparing expression levels between two
types of tissue.
- permtest
- Permutation test to compare variability within and distance between two
groups.
- perturb
- Perturbation analysis for evaluating collinearity.
- pga
- An ensemble method for variable selection by carrying out Darwinian
evolution in parallel universes.
- pgam
- Poisson-Gamma Additive Models.
- pgirmess
- Functions for analysis and display of ecological and spatial data.
- pgs
- Precision of Geometric Sampling.
- phangorn
- Phylogenetic analysis in R.
- pheno
- Some easy-to-use functions for time series analyses of (plant-)
phenological data sets.
- phmm
- Proportional Hazards with Mixed Model.
- phpSerialize
- Serialize R to PHP associative array.
- picante
- Tools for integrating phylogenies and ecology.
- pinktoe
- Converts S trees to HTML/Perl files for interactive tree traversal.
- pixmap
- Functions for import, export, plotting and other manipulations of
bitmapped images.
- plRasch
- Log linear by linear asscociation models.
- plan
- Tools for project planning.
- playwith
- A GUI for interactive plots using GTK+.
- plink
- Separate calibration linking methods.
- plm
- Linear models for panel data.
- plotSEMM
- Graphing nonlinear latent variable interactions in SEMM.
- plotpc
- Plot principal component histograms around a scatter plot.
- plotrix
- Various useful functions for enhancing plots.
- plugdensity
- Kernel density estimation with global bandwidth selection via
“plug-in”.
- pls
- Partial Least Squares Regression (PLSR) and Principal Component
Regression (PCR).
- plsgenomics
- PLS analyses for genomics.
- plspm
- Partial Least Squares Path Modeling.
- plus
- Penalized Linear Unbiased Selection.
- plyr
- Tools for splitting, applying and combining data.
- pmg
- Poor Man's GUI.
- pmml
- Generate Predictive Modelling Markup Language (PMML) for various
models.
- poLCA
- POlytomous variable Latent Class Analysis.
- poilog
- Poisson lognormal and bivariate Poisson lognormal distribution.
- polspline
- Routines for the polynomial spline fitting routines hazard regression,
hazard estimation with flexible tails, logspline, lspec, polyclass, and
polymars, by C. Kooperberg and co-authors.
- polyapost
- Simulating from the Polya posterior.
- polycor
- Polychoric and polyserial correlations.
- polydect
- One-dimension jump position detection using one-sided polynomial
kernels.
- polynom
- A collection of functions to implement a class for univariate polynomial
manipulations.
- pomp
- Partially-observed Markov processes.
- popbio
- Construction and analysis of matrix population models.
- popgen
- Statistical and POPulation GENetics.
- poplab
- Population Lab, a tool for constructing a virtual electronic population
evolving over time.
- portfolio
- Classes for analyzing and implementing portfolios.
- portfolioSim
- Framework for simulating equity portfolio strategies.
- powell
- Optimizes a function using Powell's UObyQA algorithm.
- powerGWASinteraction
- Power calculations for interactions for GWAS.
- powerSurvEpi
- Power and sample size calculation for survival analysis of
epidemiological studies.
- powerpkg
- Power analyses for the affected sib pair and the TDT design.
- ppc
- Sample classification of protein mass spectra by peak probabilty
contrasts.
- ppls
- Penalized Partial Least Squares.
- pps
- Functions to select samples using PPS (probability proportional to size)
sampling, for stratified simple random sampling, and to compute joint
inclusion probabilities for Sampford's method of PPS sampling.
- prabclus
- Distance based parametric bootstrap tests for clustering, mainly thought
for presence-absence data (clustering of species distribution maps).
Jaccard and Kulczynski distance measures, clustering of MDS scores, and
nearest neighbor based noise detection.
- predbayescor
- Classification rule based on Bayesian naive Bayes models with feature
selection bias corrected.
- predmixcor
- Classification rule based on Bayesian mixture models with feature
selection bias corrected.
- prefmod
- Utilities to fit paired comparison models for preferences.
- prettyR
- Pretty descriptive stats.
- prim
- Patient Rule Induction Method (PRIM).
- primer
- Functions and data for the book “A Primer of Ecology with R” by
M. H. H. Stevens, 2009, Springer.
- princurve
- Fits a principal curve to a matrix of points in arbitrary dimension.
- prob
- Elementary probability on finite sample spaces.
- prodlim
- Product limit estimation.
- profileModel
- Tools for profiling inference functions for various model classes.
- profr
- Alternative display for profiling information.
- proftools
- Profile output processing tools for R.
- proj4
- A simple interface to the PROJ.4 cartographic projections library.
- proptest
- Tests of the proportional hazards assumption in the Cox model.
- proto
- An object oriented system using prototype or object-based (rather than
class-based) object oriented ideas.
- proxy
- Distance and similarity measures.
- pscl
- R in the Political Science Computational Laboratory, Stanford
University.
- pseudo
- Pseudo-observations.
- pspearman
- Spearman's rank correlation test.
- pspline
- Smoothing splines with penalties on order m derivatives.
- psy
- Various procedures used in psychometry: Kappa, ICC, Cronbach alpha,
screeplot, PCA and related methods.
- psych
- Procedures for personality and psychological research.
- psychometric
- Applied psychometric theory: functions useful for correlation theory,
meta-analysis (validity-generalization), reliability, item analysis,
inter-rater reliability, and classical utility.
- psyphy
- Functions for analyzing psychophysical data in R.
- pwr
- Basic functions for power analysis.
- pwt
- The Penn World Table providing purchasing power parity and national
income accounts converted to international prices for 168 countries for
some or all of the years 1950–2000.
- pvclust
- Hierarchical clustering with p-value.
- qAnalyst
- Variables and attributes control charts.
- qcc
- Quality Control Charts. Shewhart quality control charts for continuous,
attribute and count data. Cusum and EWMA charts. Operating
characteristic curves. Process capability analysis. Pareto chart and
cause-and-effect chart.
- qdg
- Infer QTL-directed Dependency Graphs for phenotype networks.
- qgen
- Quantitative Genetics using R.
- qlspack
- Quasi least squares package.
- qp
- q-order partial correlation graph search algorithm.
- qpcR
- Modelling and analysis of real-time PCR data.
- qtl
- Analysis of experimental crosses to identify QTLs.
- qtlDesign
- Tools for the design of QTL experiments.
- qtlbim
- QTL Bayesian Interval Mapping.
- qtlbook
- Datasets for the book “A guide to QTL Mapping with R/qtl” by by Karl
W. Broman and Saunak Sen, 2009, Springer.
- quadprog
- For solving quadratic programming problems.
- qualV
- Qualitative methods for the validation of models.
- quantchem
- Quantitative chemical analysis: calibration and evaluation of results.
- quantmod
- Quantitative financial modelling framework.
- quantreg
- Quantile regression and related methods.
- quantregForest
- Quantile Regression Forests, a tree-based ensemble method for estimation
of conditional quantiles.
- qvalue
- Q-value estimation for false discovery rate control.
- qvcalc
- Functions to compute quasi-variances and associated measures of
approximation error.
- r2lUniv
- R to LaTeX Univariate: perform basic analysis and generate
corresponding LaTeX code.
- rJava
- Low-level R to Java interface. Allows creation of objects, calling
methods and accessing fields.
- rPorta
- An R interface to PORTA, a collection of routines for analyzing
polytopes and polyhedra.
- rSymPy
- R interface to SymPy computer algebra system.
- race
- Implementation of some racing methods for the empirical selection of the
best.
- rainbow
- Rainbow plots, functional bagplot, and functional HDR boxplot.
- rake
- Raking survey datasets by re-weighting.
- ramps
- Bayesian geostatistical modeling of Gaussian processes using a
reparameterized and marginalized posterior sampling (RAMPS) algorithm.
- randaes
- Random number generator based on AES cipher.
- random
- True random numbers using random.org.
- randomLCA
- Random effects Latent Class Analysis.
- randomSurvivalForest
- Ishwaran and Kogalur's random survival forest.
- randomForest
- Breiman's random forest classifier.
- randtoolbox
- Toolbox for pseudo and quasi random number generation.
- rankhazard
- Rank-hazard plots.
- rankreg
- Rank regression estimator for the AFT model with right censored data.
- rateratio.test
- Exact rate ratio test.
- rattle
- A graphical user interface for data mining in R using GTK.
- rbenchmark
- Benchmarking of arbitrary R code.
- rbounds
- Perform Rosenbaum bounds sensitivity tests for matched data.
- rbugs
- Functions to prepare files needed for running BUGS in batch mode, and
running BUGS from R. Support for Linux systems with Wine is emphasized.
- rcdd
- C Double Description for R, an interface to the CDD computational
geometry library.
- rcdk
- Interface to the CDK libraries, a Java framework for cheminformatics.
- rcdklibs
- CDK libraries packaged for R.
- rcom
- R COM Client Interface and internal COM Server.
- rcompgen
- Completion generator for R. Recommended for R 2.5.0 or 2.6.0.
- rconifers
- Interface to the CONIFERS forest growth model.
- rda
- Shrunken Centroids Regularized Discriminant Analysis.
- rdetools
- Relevant Dimension Estimation (RDE) in feature spaces.
- realized
- Realized variance toolkit.
- ref
- Functions for creating references, reading from and writing ro
references and a memory efficient refdata type that transparently
encapsulates matrices and data frames.
- regress
- Fitting Gaussian linear models where the covariance structure is a
linear combination of known matrices by maximising the residual log
likelihood. Can be used for multivariate models and random effects
models.
- regsubseq
- Detect and test regular sequences and subsequences.
- regtest
- Regression testing.
- rela
- Item analysis with standard errors.
- relaimpo
- RELAtive IMPOrtance of regressors in linear models.
- relations
- Data structures for k-ary relations with arbitrary domains,
predicate functions, and fitters for consensus relations.
- relax
- Functions for report writing, presentation, and programming.
- relaxo
- Relaxed Lasso.
- reldist
- Functions for the comparison of distributions, including nonparametric
estimation of the relative distribution PDF and CDF and numerical
summaries as described in “Relative Distribution Methods in the Social
Sciences” by Mark S. Handcock and Martina Morris, 1999, Springer.
- relimp
- Functions to facilitate inference on the relative importance of
predictors in a linear or generalized linear model.
- relsurv
- Various functions for regression in relative survival.
- remMap
- Regularized multivariate regression for identifying master predictors.
- repolr
- Repeated measures proportional odds logistic regression.
- reporttools
- Generate LaTeX tables of descriptive statistics
- reshape
- Flexibly reshape data.
- resper
- Sampling from restricted permutations.
- reweight
- Adjustment of survey respondent weights.
- rgcvpack
- R interface for GCVPACK Fortran package.
- rgdal
- Provides bindings to Frank Warmerdam's Geospatial Data Abstraction
Library (GDAL).
- rgenoud
- R version of GENetic Optimization Using Derivatives.
- rggobi
- Interface between R and GGobi.
- rgl
- 3D visualization device system (OpenGL).
- rgr
- The GSC (Geological Survey of Canada) applied geochemistry EDA package.
- rgrs
- Functions to make R usage in social sciences easier (documentation in
french).
- rhosp
- Side effect risks in hospital: simulation and estimation.
- richards
- Richards curves.
- rimage
- Functions for image processing, including Sobel filter, rank filters,
fft, histogram equalization, and reading JPEG files.
- rindex
- Indexing for R.
- ripa
- R Image Processing and Analysis.
- risksetROC
- Riskset ROC curve estimation from censored survival data.
- rjacobi
- Jacobi polynomials and Gauss-Jacobi quadrature related operations.
- rjags
- Bayesian graphical models via an interface to the JAGS MCMC library.
- rjson
- JSON (JavaScript Object Notation) for R.
- rlecuyer
- R interface to RNG with multiple streams.
- rmeta
- Functions for simple fixed and random effects meta-analysis for
two-sample comparison of binary outcomes.
- rmetasim
- An interface between R and the metasim simulation engine. Facilitates
the use of the metasim engine to build and run individual based
population genetics simulations.
- rngwell19937
- WELL19937a random number generator.
- robCompositions
- Robust estimation for compositional data.
- robfilter
- Robust time series filters.
- robust
- Insightful robust package.
- robustX
- eXperimental eXtraneous eXtraordinary ... functionality for robust
statistics.
- robustbase
- Basic Robust Statistics.
- rootSolve
- Nonlinear root finding, equilibrium and steady-state analysis of
ordinary differential equations.
- roxygen
- A Doxygen-like in-source documentation system for Rd, collation,
namespace and callgraphs.
- rpanel
- Simple interactive controls for R using the tcltk package.
- rpart
- Recursive PARTitioning and regression trees. Recommended.
- rpartOrdinal
- Ordinal classification tree functions.
- rpubchem
- R interface to the PubChem collection.
- rpvm
- R interface to PVM (Parallel Virtual Machine). Provides interface to
PVM APIs, and examples and documentation for its use.
- rqmcmb2
- Markov chain marginal bootstrap for quantile regression.
- rrcov
- Functions for robust location and scatter estimation and robust
regression with high breakdown point.
- rrp
- Random Recursive Partitioning.
- rscproxy
- A portable C-style interface to R (StatConnector).
- rsm
- Response-Surface Models.
- rsprng
- Provides interface to SPRNG (Scalable Parallel Random Number Generators)
APIs, and examples and documentation for its use.
- rstream
- Unified object oriented interface for multiple independent streams of
random numbers from different sources.
- rtiff
- Read TIFF format images and return them as pixmap objects.
- rtv
- Random Time Variable objects.
- runjags
- Run Bayesian MCMC models in the BUGS syntax using JAGS.
- rv
- Simulation-based random variable object class.
- rwm
- R Workspace Manager.
- rwt
- Rice Wavelet Toolbox wrapper, providing a set of functions for
performing digital signal processing.
- s20x
- Stats 20x functions.
- sabreR
- Provide SABRE functionality (analysis of multi-process random effect
response data) from within R.
- sac
- Semiparametric empirical likelihood ratio based test of changepoint with
one-change or epidemic alternatives with data-based model diagnostic.
- safeBinaryRegression
- Safe binary regression.
- sampfling
- Implements a modified version of the Sampford sampling algorithm. Given
a quantity assigned to each unit in the population, samples are drawn
with probability proportional to te product of the quantities of the
units included in the sample.
- sampleSelection
- Estimation of sample selection models.
- sampling
- A set of tools to select and to calibrate samples.
- samr
- Significance Analysis of Microarrays.
- sandwich
- Model-robust standard error estimators for time series and longitudinal
data.
- sapa
- Insightful Spectral Analysis for Physical Applications.
- sbgcop
- Semiparametric Bayesian Gaussian copula estimation.
- sca
- Simple Component Analysis.
- scaleboot
- Approximately unbiased p-values via multiscale bootstrap.
- scape
- functions to import and plot results from statistical catch-at-age
models, used in fisheries stock assessments.
- scapeMCMC
- Markov-chain Monte Carlo diagnostic plots, accompanying the
scape package.
- scatterplot3d
- Plots a three dimensional (3D) point cloud perspectively.
- schoolmath
- Functions and datasets for math used in school.
- sciplot
- Scientific graphing functions for factorial designs.
- scout
- Scout method for covariance-regularized regression.
- scrime
- Tools for the analysis of high-dimensional data developed/implemented at
the group “Statistical Complexity Reduction In Molecular Epidemiology”
(SCRIME), with main focus on SNP data.
- scuba
- Scuba diving calculations and decompression models.
- sda
- Shrinkage Discriminant Analysis.
- sdcMicro
- Statistical Disclosure Control methods for the generation of public and
scientific use files.
- sdcTable
- Statistical Disclosure Control for tabular data.
- sddpack
- SemiDiscrete Decomposition.
- sde
- Simulation and inference for Stochastic Differential Equations.
- sdtalt
- Signal Detection Theory measures and ALTernatives.
- sdtoolkit
- Scenario discovery tools to support robust decision making.
- seacarb
- Calculates parameters of the seawater carbonate system.
- seas
- Detailed seasonal plots of temperature and precipitation data.
- seewave
- Time wave analysis and graphical representation.
- segclust
- Segmentation and segmentation/clustering.
- segmented
- Functions to estimate break-points of segmented relationships in
regression models (GLMs).
- selectiongain
- Calculate the gain from a model selection.
- sem
- Functions for fitting general linear Structural Equation Models (with
observed and unobserved variables) by the method of maximum likelihood
using the RAM approach.
- sendmailR
- Send email from inside R.
- sendplot
- Tool for sending interactive plots.
- sensR
- Thurstonian models for sensory discrimination.
- sensitivity
- Sensitivity analysis.
- seqinr
- Exploratory data analysis and data visualization for biological sequence
(DNA and protein) data.
- seqmon
- Sequential monitoring of clinical trials.
- seriation
- Infrastructure for seriation.
- session
- Functions for interacting with, saving and restoring R sessions.
- setRNG
- Set (normal) random number generator and seed.
- sets
- Data structures and basic operations for ordinary sets, and
generalizations such as fuzzy sets, multisets, and fuzzy multisets.
- sfsmisc
- Utilities from Seminar fuer Statistik ETH Zurich.
- sgeostat
- An object-oriented framework for geostatistical modeling.
- shape
- Functions for plotting graphical shapes.
- shapefiles
- Functions to read and write ESRI shapefiles.
- shapes
- Routines for the statistical analysis of shapes, including procrustes
analysis, displaying shapes and principal components, testing for mean
shape difference, thin-plate spline transformation grids and edge
superimposition methods.
- siar
- Stable Isotope Analysis in R.
- sigma2tools
- Test of hypothesis about sigma2.
- signal
- A set of generally Matlab/Octave-compatible signal processing
functions.
- signalextraction
- Real-time signal extraction (Direct Filter Approach).
- simba
- Functions for similarity calculation of binary data.
- simco
- Import Structure files and deduce similarity coefficients from them.
- simecol
- SIMulation of ECOLogical (and other) dynamic systems.
- simctest
- Sequential (or Safe) Implementation of Monte Carlo TESTs with uniformly
bounded resampling risk.
- simex
- SIMEX and MCSIMEX algorithms for measurement error models.
- similarityRichards
- Similarity of Richards curves.
- simone
- Statistical Inference for MOdular NEtworks (SIMoNe).
- simpleboot
- Simple bootstrap routines.
- singlecase
- Tests for single case studies in neuropsychology.
- sisus
- Stable Isotope Sourcing Using Sampling.
- skewt
- Density, distribution function, quantile function and random generation
for the skewed t distribution of Fernandez and Steel.
- slam
- Sparse Lightweight Arrays and Matrices.
- sm
- Software linked to the book “Applied Smoothing Techniques for Data
Analysis: The Kernel Approach with S-Plus Illustrations” by
A. W. Bowman and A. Azzalini, 1997, Oxford University Press.
- sma
- Functions for exploratory (statistical) microarray analysis.
- smacof
- Multidimensional scaling based on stress minimization by means of
majorization (smacof).
- smatr
- (Standardized) Major Axis estimation and Testing Routines.
- smoothSurv
- Survival regression with smoothed error distribution.
- smoothtail
- Smooth estimation of generalized Pareto distribution shape parameter.
- sn
- Functions for manipulating skew-normal probability distributions and for
fitting them to data, in the scalar and the multivariate case.
- sna
- A range of tools for social network analysis, including node and
graph-level indices, structural distance and covariance methods,
structural equivalence detection, p* modeling, and network
visualization.
- snow
- Simple Network of Workstations: support for simple parallel computing in
R.
- snowFT
- Fault Tolerant Simple Network of Workstations.
- snowfall
- Wrapper around snow for easier development of parallel R
programs.
- snp.plotter
- Plots of p-values using single SNP and/or haplotype data.
- snpXpert
- Tools to analyze SNP data.
- som
- Self-Organizing Maps (with application in gene clustering).
- sound
- A sound interface for R: Basic functions for dealing with .wav
files and sound samples.
- sp
- A package that provides classes and methods for spatial data, including
utility functions for plotting data as maps, spatial selection, amd much
more.
- spBayes
- Fit Gaussian models with potentially complex hierarchical error
structures by Markov chain Monte Carlo (MCMC).
- space
- Sparse PArtial Correlation Estimation.
- spam
- SPArse Matrix algebra.
- sparseLDA
- Sparse Linear Discriminant Analysis for gaussians and mixture of
gaussians models.
- spatcounts
- Fit spatial CAR count regression models using MCMC.
- spatclus
- Arbitrarily shaped multiple spatial cluster detection for case event
data.
- spatgraphs
- Graphs for 2-d point patterns.
- spatial
- Functions for kriging and point pattern analysis from “Modern Applied
Statistics with S” by W. Venables and B. Ripley. Contained in the
VR bundle. Recommended.
- spatialCovariance
- Computation of spatial covariance matrices for data on rectangles using
one dimensional numerical integration and analytic results.
- spatialkernel
- Nonparameteric estimation of spatial segregation in a multivariate point
process.
- spatialsegregation
- Segregation measures for multitype spatial point patterns.
- spatstat
- Data analysis and modelling of two-dimensional point patterns, including
multitype points and spatial covariates.
- spc
- Statistical Process Control: evaluation of control charts by means of
the zero-state, steady-state ARL (Average Run Length), setting up
control charts for given in-control ARL, and plotting of the related
figures.
- spcosa
- SPatial COverage SAmpling.
- spdep
- A collection of functions to create spatial weights matrix objects from
polygon contiguities, from point patterns by distance and tesselations,
for summarising these objects, and for permitting their use in spatial
data analysis; a collection of tests for spatial autocorrelation,
including global Moran's I and Geary's C, local Moran's I, saddlepoint
approximations for global and local Moran's I; and functions for
estimating spatial simultaneous autoregressive (SAR) models. (Was
formerly the three packages: spweights, sptests, and
spsarlm.)
- spe
- Stochastic Proximity Embedding.
- spectralGP
- Approximate Gaussian processes using the Fourier basis.
- speff2trial
- Semiparametric efficient estimation for a two-sample treatment effect.
- spgrass6
- Interface between the GRASS 6.0 geographical information system and R.
- spgwr
- Geographically weighted regression.
- splancs
- Spatial and space-time point pattern analysis functions.
- spls
- Sparse Partial Least Squares (SPLS) regression.
- splus2R
- Insightful package providing missing S-PLUS functionality in R.
- spssDDI
- Read SPSS system files and produce valid DDI version 3.0 documents.
- spsurvey
- Spatial survey design and analysis.
- spuRs
- Functions and datasets from the book “An Introduction to Scientific
Programming and Simulation Using R” by O. Jones, R. Maillardet and
A. Robinson, 2009, CRC Press.
- sqldf
- Perform SQL selects on R data frames.
- ssanv
- Sample Size Adjusted for Nonadherence or Variability of input
parameters.
- ssize.fdr
- Sample size calculations for microarray experiments.
- sspir
- State SPace models In R.
- sspline
- Smoothing splines on the sphere.
- st
- Shrinkage t statistic.
- staRt
- Inferenza classica con TI-83 Plus.
- stab
- Data analysis of drug stability.
- startupmsg
- Utilities for start-up messages.
- stashR
- A Set of Tools for Administering SHared Repositories.
- statmod
- Miscellaneous biostatistical modelling functions.
- statnet
- Software tools for the statistical modeling of network data.
- stepPlr
- L2 penalized logistic regression with a stepwise variable selection.
- stepwise
- A stepwise approach to identifying recombination breakpoints in a
sequence alignment.
- stinepack
- Stineman interpolation package.
- stochasticGEM
- Fitting Stochastic General Epidemic Models.
- stochmod
- Learning and inference algorithms for a variety of probabilistic
models.
- stream.net
- Building and analyzing binary stream networks.
- strucchange
- Various tests on structural change in linear regression models.
- subplex
- The subplex algorithm for unconstrained optimization.
- subselect
- A collection of functions which assess the quality of variable subsets
as surrogates for a full data set, and search for subsets which are
optimal under various criteria.
- sudoku
- Sudoku puzzle solver.
- sugaR
- Plots to help optimizing diabetes therapy.
- supclust
- Methodology for supervised grouping of predictor variables.
- superpc
- Supervised principal components.
- surv2sample
- Two-sample tests for survival analysis.
- survBayes
- Fits a proportional hazards model to time to event data by a Bayesian
approach.
- survcomp
- Performance assessment and comparison for survival analysis.
- surveillance
- Outbreak detection algorithms for surveillance data.
- survey
- Summary statistics, generalized linear models, and general maximum
likelihood estimation for stratified, cluster-sampled, unequally
weighted survey samples.
- surveyNG
- Complex survey samples: database interface, sparse matrices.
- survival
- Functions for survival analysis, including penalised likelihood.
Recommended.
- survivalROC
- Time-dependent ROC curve estimation from censored survival data.
- survrec
- Survival analysis for recurrent event data.
- svDialogs
- SciViews GUI API: dialog boxes.
- svGUI
- SciViews GUI API: functions to manage GUI
clients.
- svIDE
- SciViews GUI API: functions to interact with
external IDE/code editors.
- svMisc
- SciViews GUI API: miscellaneous functions.
- svSocket
- SciViews GUI API socket server.
- svSweave
- SciViews GUI API: Sweave functions.
- svUnit
- SciViews GUI API: unit testing.
- svWidgets
- SciViews GUI API: widgets and windows.
- svcR
- A support vector machine technique for clustering.
- svcm
- 2d and 3d Space-Varying Coefficient Models.
- svmpath
- Computes the entire regularization path for the two-class svm classifier
with essentialy the same cost as a single SVM fit.
- systemfit
- Contains functions for fitting simultaneous systems of equations using
Ordinary Least Sqaures (OLS), Two-Stage Least Squares (2SLS), and
Three-Stage Least Squares (3SLS).
- taskPR
- Task-Parallel R package.
- tawny
- Various portfolio optimization strategies, including random matrix
theory and shrinkage estimators.
- tcltk2
- A series of widgets and functions to supplement tcltk.
- tdist
- Computes the distribution of a linear combination of independent
Student's t variables.
- tdm
- A tool for Therapeutic Drug Monitoring.
- tdthap
- Transmission/disequilibrium tests for extended marker haplotypes.
- tensor
- Tensor product of arrays.
- tensorA
- Advanced tensors arithmetic with named indices.
- termstrc
- Term structure and credit spread estimation.
- tframe
- Time Frame coding kernel: functions for writing code that is independent
of the way time is represented.
- tframePlus
- Time Frame coding kernel extensions.
- tgp
- Bayesian regression and adaptive sampling with Treed Gaussian Process
models.
- tiger
- TIme series of Grouped ERrors.
- tileHMM
- Hidden Markov Models for ChIP-on-Chip analysis.
- time
- Time tracking for developers.
- timeDate
- The Rmetrics module for “Chronological and Calendarical Objects”.
- timeSeries
- The Rmetrics module for “Financial Time Series Objects”.
- timereg
- Code and data sets for the book “Dynamic Regression Models for Survival
Data” by T. Martinussen and T. Scheike, 2006, Springer Verlag, plus
more recent developments.
- timsac
- TIMe Series Analysis and Control package.
- tis
- Time indexes and time indexed series.
- titan
- Titration analysis for mass spectrometry data.
- titecrm
- TIme-To-Event Continual Reassessment Method and calibration tools.
- tkrgl
- TK widget tools for rgl package.
- tkrplot
- Simple mechanism for placing R graphics in a Tk widget.
- tlemix
- Trimmed maximum likelihood estimation for robust fitting of finite
mixture models.
- tlnise
- Two-level normal independent sampling estimation.
- tm
- A framework for text mining applications within R.
- tmvtnorm
- Truncated multivariate normal distribution.
- tnet
- Analysis of weighted and longitudinal networks.
- tolerance
- Functions for calculating tolerance intervals.
- topmodel
- An R implementation of TOPMODEL.
- tossm
- Testing Of Spatial Structure Methods.
- tpr
- Temporal Process Regression.
- trackObjs
- Track objects.
- tractor.base
- Basic TractoR (tractography with R) functions for working with magnetic
images.
- tradeCosts
- Post-trade analysis of transaction costs.
- tree
- Classification and regression trees.
- treelet
- Treelet: a novel construction of multi-scale bases that extends wavelets
to non-smooth signals.
- triangle
- Standard distribution functions for the triangle distribution.
- trimcluster
- Cluster analysis with trimming.
- trip
- Spatial analysis of animal track data.
- tripEstimation
- Metropolis sampler and supporting functions for estimating animal
movement from archival tags and satellite fixes.
- tripack
- A constrained two-dimensional Delaunay triangulation package.
- truncgof
- Goodness-of-fit tests allowing for left truncated data.
- truncnorm
- Truncated normal distribution.
- truncreg
- Truncated regression models.
- trust
- Local optimization using two derivatives and trust regions.
- tsDyn
- Time series analysis based on dynamical systems theory.
- tsModel
- Time series modeling for air pollution and health.
- tseries
- Package for time series analysis with emphasis on non-linear modelling.
- tseriesChaos
- Routines for the analysis of non-linear time series.
- tsfa
- Time Series Factor Analysis.
- tslars
- Least angle regression for time series analysis.
- tuneR
- Collection of tools to analyze music, handle wave files, transcription,
etc.
- tutoR
- Student-friendly package to mask common functions.
- twang
- Toolkit for Weighting and Analysis of Nonequivalent Groups.
- tweedie
- Maximum likelihood computations for Tweedie exponential family models.
- twitteR
- R based Twitter client.
- twslm
- A two-way semilinear model for normalization and analysis of cDNA
microarray data.
- ucminf
- Unconstrained nonlinear optimization via UCMINF.
- udunits
- Interface to Unidata's routines to convert units.
- ump
- Uniformly Most Powerful tests.
- unbalhaar
- Function estimation via Unbalanced Haar wavelets.
- uncompress
- For uncompressing .Z files.
- uniCox
- Univarate shrinkage prediction in the Cox model.
- untb
- Ecological drift under the UNTB (Unified Neutral Theory of
Biodiversity).
- urca
- Unit root and cointegration tests for time series data.
- urn
- Functions for sampling without replacement (simulated urns).
- vabayelMix
- Variational Bayesian mixture model.
- varSelRF
- Variable selection using random forests.
- varmixt
- Mixture model on the variance for the analysis of gene expression data.
- vars
- VAR modeling.
- vbmp
- Variational Bayesian Multinomial Probit Regression.
- vcd
- Functions and data sets based on the book “Visualizing Categorical
Data” by Michael Friendly.
- vegan
- Various help functions for vegetation scientists and community
ecologists.
- verification
- Utilities for verification of discrete and probabilistic forecasts.
- vioplot
- Violin plots, which are a combination of a box plot and a kernel density
plot.
- vowels
- Vowel manipulation, normalization, and plotting.
- vrmlgen
- Create plots, charts and graphs for 3D data visualization as VRML
files.
- vrtest
- Variance ratio tests for weak-form market efficiency.
- wasim
- Tools for data processing and visualization of results of the WASIM-ETH
hydrological model.
- waved
- WaveD transform in R.
- wavelets
- Functions for computing wavelet filters, wavelet transforms and
multiresolution analyses.
- waveslim
- Basic wavelet routines for time series analysis.
- wavethresh
- Software to perform 1-d and 2-d wavelet statistics and transforms.
- wccsom
- SOM networks for comparing patterns with peak shifts.
- wgaim
- Whole Genome Average Interval Mapping for QTL detection using mixed
models.
- wikibooks
- Functions and datasets for the German WikiBook “GNU R”.
- wle
- Robust statistical inference via a weighted likelihood approach.
- wmtsa
- Insightful Wavelet Methods for Time Series Analysis.
- wnominate
- WNOMINATE roll call analysis software.
- wombsoft
- Wombling computation.
- wordnet
- WordNet interface.
- write.snns
- Function for exporting data to SNNS (Stuttgart Neural Network Simulator)
pattern files.
- x12
- A wrapper function and GUI for the X12 binaries under windows.
- xgobi
- Interface to the XGobi and XGvis programs for graphical data analysis.
- xtable
- Export data to LaTeX and HTML tables.
- xterm256
- Support for xterm256 escape sequences.
- xts
- Extensible time series.
- yaImpute
- Performs popular nearest neighbor routines.
- yacca
- Yet Another Canonical Correlation Analysis package.
- yaml
- Methods to convert R to YAML and back.
- yest
- Gaussian independence models.
- yhat
- Interpreting regression effects.
- zipfR
- Statistical models for word frequency distributions.
- zoeppritz
- Zoeppritz equations: calculate and plot scattering coefficients of
seismic waves when they interact at an interface between two layers.
- zoo
- A class with methods for totally ordered indexed observations such as
irregular time series.
- zyp
- Zhang & Yue-Pilon approach to determining trends in climate data.
See CRAN src/contrib/PACKAGES for more information.
Some CRAN packages that do not build out of the box on Windows,
require additional software, or are shipping third party libraries for
Windows cannot be made available on CRAN in form of a Windows binary
packages. Nevertheless, some of these packages are available at the
“CRAN extras” repository at http://www.stats.ox.ac.uk/pub/RWin/
kindly provided by Brian D. Ripley. Note that this repository is a
default repository for recent versions of R for Windows.
There used to be a CRAN src/contrib/Devel directory with
packages still “under development” or depending on features only
present in the current development versions of R. This area is no
longer provided, with packages formerly in this area either in the
regular package area or the archive src/contrib/Archive.
5.1.3 Add-on packages from Omegahat
The Omegahat Project for Statistical Computing 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.
- CodeDepends
- Analysis of R code for reproducible research and code view.
- Combinations
- Compute the combinations of choosing r items from n
elements.
- FlashMXML
- A simple Flash graphics device for R.
- IDocs
- Infrastructure for interactive documents.
- IDynDocs
- Interactive and dynamic Documents with XML/XSL.
- OOP
- OOP style classes and methods for R and S-Plus. Object references and
class-based method definition are supported in the style of languages
such as Java and C++.
- RCSS
- Facilities for reading and working with CSS files in R.
- RCurl
- Allows one to compose HTTP requests to fetch URIs, post forms, etc., and
process the results returned by the Web server.
- RDA
- Read RDA files using R.
- 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.
- RExcelXML
- Tools for working with Excel XML documents.
- REmbeddedPostgres
- Allows R functions and objects to be used to implement SQL functions —
per-record, aggregate and trigger functions.
- REventLoop
- An abstract event loop mechanism that is toolkit independent and can be
used to to replace the R event loop.
- RGdkPixbuf
- S language functions to access the facilities in the GdkPixbuf library
for manipulating images.
- RGnumeric
- A plugin for the Gnumeric spreadsheet that allows R functions to be
called from cells within the sheet, automatic recalculation, etc.
- RGoogleDocs
- Initial, elementary interface to Google's Document API.
- RGraphicsDevice
- A framework for developing R graphics devices entirely in R.
- 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.
- RJSONIO
- Serialize R objects to JSON (JavaScript Object Notation).
- RJavaDevice
- A graphics device for R that uses Java components and graphics.
APIs.
- RKML
- Simple tools for creating KML displays from R.
- RMatlab
- A bi-directional interface between R and Matlab.
- RObjectTables
- The C and S code allows one to define R objects to be used as elements
of the search path with their own semantics and facilities for reading
and writing variables. The objects implement a simple interface via R
functions (either methods or closures) and can access external data,
e.g., in other applications, languages, formats, ...
- RQt
- Bindings to the Qt meta data and connect mechanisms.
- RRuby
- An initial exploration in calling Ruby from R.
- 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.
- RUbigraph
- Interface to Ubigraph server via XML-RPC.
- RXLisp
- An interface to call XLisp-Stat functions from within R.
- Rcartogram
- An interface to Mark Newman's cartogram software.
- Rcompression
- In-memory decompression for GNU zip and bzip2 formats.
- Rcrypt
- An interface to the crypt(3) C function.
- Rexif
- Extract meta-information from JPEG files.
- Rflickr
- R interface to Flickr API.
- Rlibstree
- Suffix Trees in R via the libstree C library.
- Rstem
- Interface to Snowball implementation of Porter's word stemming
algorithm.
- RwxDevice
- R graphics device using wxWidgets.
- RwxWidgets
- Facilities to program GUIs using wxWidgets in R.
- SASXML
- Example for reading XML files in SAS 8.2 manner.
- SJava
- An interface from R to Java to create and call Java objects and
methods.
- SLanguage
- Functions and C support utilities to support S language programming
that can work in both R and S-Plus.
- SNetscape
- Plugin for Netscape and JavaScript.
- SSOAP
- A client interface to SOAP (Simple Object Access Protocol) servers from
within S.
- SVGAnnotation
- Tools for post-processing SVG plots created in R.
- SWinRegistry
- Provides access from within R to read and write the Windows registry.
- SWinTypeLibs
- Provides ways to extract type information from type libraries and/or
DCOM objects that describes the methods, properties, etc., of an
interface.
- SXalan
- Process XML documents using XSL functions implemented in R and
dynamically substituting output from R.
- Slcc
- Parses C source code, allowing one to analyze and automatically generate
interfaces from S to that code, including the table of S-accessible
native symbols, parameter count and type information, S constructors
from C objects, call graphs, etc.
- Sxslt
- An extension module for libxslt, the XML-XSL document translator,
that allows XSL functions to be implemented via R functions.
- XML
- Tools for reading XML documents and DTDs.
- XMLRPC
- RPC via XML.
- XMLSchema
- R facilities to read XML schema.
- Zillow
- Simple interface to Zillow.com's house price estimate API.
5.1.4 Add-on packages from Bioconductor
The Bioconductor Project produces an
open source software framework that will assist biologists and
statisticians working in bioinformatics, with primary emphasis on
inference using DNA microarrays. A CRAN style R package
repository is available via http://www.bioconductor.org/.
The following R packages are contained in the current release of
Bioconductor, with more packages under development.
- ABarray
- Microarray QA and statistical data analysis for Applied Biosystems
Genome Survey Micorarray (AB1700) gene expression data.
- ACME
- Algorithms for Calculating Microarray Enrichment (ACME).
- AffyCompatible
- Affymetrix GeneChip software compatibility
- AffyExpress
- Affymetrix quality assessment and analysis tool.
- AffyTiling
- Easy extraction of individual probes in Affymetrix tiling arrays.
- AnnotationDbi
- Annotation DataBase Interface.
- BAC
- Bayesian Analysis of Chip-chip experiment.
- BCRANK
- Predicting binding site consensus from ranked DNA sequences.
- BGmix
- Bayesian models for differential gene expression.
- BSgenome
- Infrastructure for Biostrings-based genome data packages.
- BioMVCClass
- Model-View-Controller (MVC) classes that use Biobase.
- Biobase
- Object-oriented representation and manipulation of genomic data (S4
class structure).
- BiocCaseStudies
- Support for the Bioconductor Case Studies monograph.
- Biostrings
- Class definitions and generics for biological sequences along with
pattern matching algorithms.
- BufferedMatrix
- Microarray analysis methods that use BufferedMatrix objects.
- BufferedMatrixMethods
- A matrix data storage object held in temporary files.
- CALIB
- Calibration model for estimating absolute expression levels from
microarray data.
- CAMERA
- Collection of annotation related methods for mass spectrometry data.
- CGHcall
- Calling aberrations for array CGH tumor profiles.
- CORREP
- Multivariate correlation estimation and statistical inference.
- Category
- A collection of tools for performing category analysis.
- CoCiteStats
- A collection of software tools for dealing with co-citation data.
- DAVIDQuery
- Retrieval from the DAVID bioinformatics data resource into R.
- DEDS
- Differential Expression via Distance Summary for microarray data.
- DNAcopy
- Segments DNA copy number data using circular binary segmentation to
detect regions with abnormal copy number.
- DynDoc
- Functionality to create and interact with dynamic documents, vignettes,
and other navigable documents.
- EBImage
- Image processing and image analysis toolkit.
- EBarrays
- Empirical Bayes tools for the analysis of replicated microarray data
across multiple conditions.
- GEOmetadb
- A compilation of metadata from NCBI GEO.
- GEOquery
- Get data from NCBI Gene Expression Omnibus (GEO).
- GGBase
- Infrastructure for genetics of gene expression.
- GGtools
- Software and data for genetical genomics.
- GLAD
- Gain and Loss Analysis of DNA.
- GOSemSim
- GO-terms Semantic Similarity measures.
- GOstats
- Tools for manipulating GO and microarrays.
- GSEABase
- Gene set enrichment data structures and methods.
- GSEAlm
- Linear model toolset for Gene Set Enrichment Analysis.
- GeneMeta
- A collection of meta-analysis tools for analyzing high throughput
experimental data.
- GeneR
- Package manipulating nucleotidic sequences (Embl, Fasta, GenBank).
- GeneRfold
- R for genes and sequences, using viennaRNA package (fold).
- GeneRegionScan
- Analysis of Affymetrix data within discrete regions of the genome.
- GeneSelectMMD
- Gene selection based on the marginal distributions of gene profiles that
characterized by a mixture of three-component multivariate
distributions.
- GeneSelector
- GeneSelector.
- GeneSpring
- Functions and class definitions to be able to read and write GeneSpring
specific data objects and convert them to Bioconductor objects.
- GeneTraffic
- GeneTraffic R integration functions.
- GeneticsBase
- Classes and functions for handling genetic data.
- GeneticsDesign
- Functions for designing genetics studies.
- GeneticsPed
- Pedigree and genetic relationship functions.
- GenomeGraphs
- Plotting genomic information from Ensembl.
- GlobalAncova
- Calculates a global test for differential gene expression between
groups.
- GraphAT
- Graph theoretic Association Tests.
- GraphAlignment
- GraphAlignment.
- HEM
- Heterogeneous Error Model for analysis of microarray data.
- Harshlight
- A “corrective make-up” program for microarray chips.
- Heatplus
- A heat map displaying covariates and coloring clusters.
- Icens
- Functions for computing the NPMLE for censored and truncated data.
- KEGGSOAP
- Client-side SOAP access KEGG.
- KEGGgraph
- A graph approach to KEGG PATHWAY.
- LBE
- Estimation of the false discovery rate.
- LMGene
- Analysis of microarray data using a linear model and glog data
transformation.
- LPE
- Significance analysis of microarray data with small number of replicates
using the Local Pooled Error (LPE) method.
- MANOR
- Micro-Array NORmalization.
- MCRestimate
- Misclassification error estimation with cross-validation.
- MLInterfaces
- Uniform interfaces to machine learning code for the exprSet class from
Bioconductor.
- MVCClass
- Model-View-Controller (MVC) classes.
- MantelCorr
- Compute Mantel Cluster Correlations.
- MassSpecWavelet
- Mass spectrum processing by wavelet-based algorithms.
- MeasurementError.cor
- Two-stage measurement error model for correlation estimation with
smaller bias than the usual sample correlation.
- MergeMaid
- Cross-study comparison of gene expression array data.
- Mfuzz
- Soft clustering of time series gene expression data.
- MiPP
- Misclassification Penalized Posterior Classification.
- OCplus
- Operating characteristics plus sample size and local fdr for microarray
experiments.
- OLIN
- Optimized Local Intensity-dependent Normalisation of two-color
microarrays.
- OLINgui
- Graphical user interface for OLIN.
- OrderedList
- Similarities of ordered gene lists.
- OutlierD
- Outlier detection using quantile regression on the M-A scatterplots of
high-throughput data.
- PAnnBuilder
- Protein annotation data package builder.
- PCpheno
- Phenotypes and cellular organizational units.
- PGSEA
- Parametric Gene Set Enrichment Analysis.
- PROcess
- Ciphergen SELDI-TOF processing.
- RBGL
- An interface between the graph package and the Boost graph libraries,
allowing for fast manipulation of graph objects in R.
- RBioinf
- Support for R for Bioinformatics monograph.
- RLMM
- A genotype calling algorithm for Affymetrix SNP arrays.
- RMAGEML
- Functionality to handle MAGEML documents.
- ROC
- Receiver Operating Characteristic (ROC) approach for identifying genes
that are differentially expressed in two types of samples.
- RWebServices
- Expose R functions as web services through Java/Axis/Apache.
- RankProd
- Rank Product method for identifying differentially expressed genes.
- RbcBook1
- Support for Springer monograph on Bioconductor.
- Rdbi
- Generic framework for database access in R.
- RdbiPgSQL
- Methods for accessing data stored in PostgreSQL tables.
- Rdisop
- Decomposition of isotopic patterns.
- RefPlus
- Functions for pre-processing Affymetrix data using the RMA+ and the
RMA++ methods.
- Resourcerer
- Read annotation data from TIGR Resourcerer or convert the annotation
data into Bioconductor data package.
- Rgraphviz
- An interface with Graphviz for plotting graph objects in R.
- Ringo
- R Investigation of NimbleGen Oligoarrays..
- Rintact
- Interface to EBI Intact protein interaction data base.
- Rmagpie
- Micro-array gene-expression-based program in error rate estimation.
- Rredland
- Interface to redland RDF utilities.
- Rtreemix
- Mutagenetic trees mixture models.
- Ruuid
- Creates Universally Unique ID values (UUIDs) in R.
- SAGx
- Statistical Analysis of the GeneChip.
- SBMLR
- Systems Biology Markup Language (SBML) interface and biochemical system
analysis tools.
- SLGI
- Synthetic Lethal Genetic Interaction.
- SLqPCR
- Functions for analysis of real-time quantitative PCR data at SIRS-Lab
GmbH.
- SMAP
- A Segmental Maximum A Posteriori approach to array-CGH copy number
profiling.
- SNPchip
- Classes and methods for high throughput SNP chip data.
- SPIA
- Signaling Pathway Impact Analysis using combined evidence of pathway
over-representation and unusual signaling perturbations.
- SSPA
- Sample Size and Power Analysis for microarray data.
- ScISI
- In Silico Interactome.
- SemSim
- Gene ontology-based semantic similarity measures.
- TargetSearch
- Analysis of GC-MS metabolite profiling data.
- TypeInfo
- Optional type specification prototype.
- VanillaICE
- Methods for fitting Hidden Markov Models to SNP chip data.
- XDE
- A Bayesian hierarchical model for cross-study analysis of differential
gene expression.
- aCGH
- Classes and functions for Array Comparative Genomic Hybridization data.
- adSplit
- Annotation-driven clustering.
- affxparser
- Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR).
- affy
- Methods for Affymetrix Oligonucleotide Arrays.
- affyPLM
- For fitting Probe Level Models.
- affyPara
- Parallelized preprocessing methods for Affymetrix Oligonucleotide
Arrays.
- affyQCReport
- QC Report Generation for affyBatch objects.
- affycomp
- Graphics toolbox for assessment of Affymetrix expression measures.
- affycoretools
- Functions useful for those doing repetitive analyses.
- affyio
- Tools for parsing Affymetrix data files.
- affylmGUI
- Graphical User Interface for affy analysis using package limma.
- affypdnn
- Probe Dependent Nearest Neighbors (PDNN) for the affy package.
- altcdfenvs
- Utilities to handle cdfenvs.
- annaffy
- Functions for handling data from Bioconductor Affymetrix annotation data
packages.
- annotate
- Associate experimental data in real time to biological metadata from web
databases such as GenBank, LocusLink and PubMed. Process and store
query results. Generate HTML reports of analyses.
- annotationTools
- Annotate microarrays and perform cross-species gene expression analyses
using flat file databases.
- apComplex
- Estimate protein complex membership using AP-MS protein data.
- aroma.affymetrix
- Analysis of large Affymetrix microarray data sets.
- aroma.apd
- A probe-level data file format used by aroma.affymetrix
(deprecated).
- aroma.light
- Light-weight methods for normalization and visualization of microarray
data using only basic R data types.
- arrayQuality
- Performing print-run and array level quality assessment.
- arrayQualityMetrics
- Quality metrics on ExpressionSets.
- beadarray
- Quality control and low-level analysis of BeadArrays.
- beadarraySNP
- Normalization and reporting of Illumina SNP bead arrays.
- betr
- Identify differentially expressed genes in microarray time-course data.
- bgafun
- A method to identify specifity determining residues in protein
families.
- bgx
- Bayesian Gene eXpression.
- bioDist
- A collection of software tools for calculating distance measures.
- biocDatasets
- Synthetic datasets for bioconductor.
- biocGraph
- Graph examples and use cases in Bioinformatics.
- biocViews
- Categorized views of R package repositories.
- biomaRt
- Interface to BioMart databases (e.g., Ensembl)
- bridge
- Bayesian Robust Inference for Differential Gene Expression.
- cellHTS
- Analysis of cell-based screens.
- cellHTS2
- Analysis of cell-based screens — revised version of cellHTS.
- cghMCR
- Find chromosome regions showing common gains/losses.
- clusterStab
- Compute cluster stability scores for microarray data.
- codelink
- Manipulation of Codelink Bioarrays data.
- convert
- Convert Microarray Data Objects.
- copa
- Functions to perform cancer outlier profile analysis.
- cosmo
- Supervised detection of conserved motifs in DNA sequences.
- cosmoGUI
- GUI for constructing constraint sets used by the cosmo
package.
- crlmm
- Genotype calling (CRLMM) and copy number analysis tool for Affymetrix
SNP 5.0 and 6.0 and Illumina arrays.
- 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.
- dyebias
- Methods to correct for gene-specific dye bias.
- ecolitk
- Metadata and tools to work with E. coli.
- edd
- Expression density diagnostics: graphical methods and pattern
recognition algorithms for distribution shape classification.
- exonmap
- High level analysis of Affymetrix exon array data.
- explorase
- GUI for exploratory data analysis of systems biology data.
- externalVector
- Vector objects for R with external storage.
- factDesign
- A set of tools for analyzing data from factorial designed microarray
experiments. The functions can be used to evaluate appropriate tests of
contrast and perform single outlier detection.
- fbat
- Family Based Association Tests for genetic data.
- fdrame
- FDR Adjustments of Microarray Experiments (FDR-AME).
- flagme
- Analysis of metabolomics GC/MS data.
- flowClust
- Clustering for flow cytometry.
- flowCore
- Basic structures for flow cytometry data.
- flowFlowJo
- Tools for extracting information from a FlowJo workspace and working
with the data in the flowCore paradigm.
- flowQ
- Qualitiy control for flow cytometry.
- flowStats
- Statistical methods for the analysis of flow cytometry data.
- flowUtils
- Utilities for flow cytometry.
- flowViz
- Visualization for flow cytometry.
- gaga
- GaGa hierarchical model for microarray data analysis.
- gaggle
- Broadcast data between R and Java bioinformatics programs.
- gcrma
- Background adjustment using sequence information.
- genArise
- A tool for dual color microarray data.
- gene2pathway
- Prediction of KEGG pathway membership for individual genes based on
InterPro domain signatures.
- 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.
- genomeIntervals
- Operations on genomic intervals.
- globaltest
- Testing globally whether a group of genes is significantly related to
some clinical variable of interest.
- goProfiles
- Statistical analysis of functional profiles.
- goTools
- Functions for description/comparison of oligo ID list using the Gene
Ontology database.
- gpls
- Classification using generalized partial least squares for two-group and
multi-group classification.
- graph
- Classes and tools for creating and manipulating graphs within R.
- hexbin
- Binning functions, in particular hexagonal bins for graphing.
- hopach
- Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).
- hypergraph
- Capabilities for representing and manipulating hypergraphs.
- idiogram
- Plotting genomic data by chromosomal location.
- impute
- Imputation for microarray data (currently KNN only).
- keggorth
- Graph support for KO, KEGG Orthology.
- lapmix
- Laplace mixture model in microarray experiments.
- limma
- Linear models for microarray data.
- limmaGUI
- Graphical User Interface for package limma.
- logicFS
- Identification of SNP interactions.
- lumi
- BeadArray specific methods for Illumina microarrays.
- maCorrPlot
- Visualize artificial correlation in microarray data.
- maDB
- Microarray database and utility functions for microarray analysis.
- maSigPro
- Significant gene expression profile differeneces in time course
microarray data.
- maanova
- Tools for analyzing micro array experiments.
- macat
- MicroArray Chromosome Analysis Tool.
- made4
- Multivariate analysis of microarray data using ADE4.
- maigesPack
- Functions to handle cDNA microarray data, including several methods of
data analysis.
- makePlatformDesign
- Creates the Platform Design environments (PDenvs) required by
oligo.
- makecdfenv
- Two functions. One reads a Affymetrix chip description file (CDF) and
creates a hash table environment containing the location/probe set
membership mapping. The other creates a package that automatically loads
that environment.
- marray
- Exploratory analysis for two-color spotted microarray data.
- matchprobes
- Tools for sequence matching of probes on arrays.
- mdqc
- Mahalanobis Distance Quality Control for microarrays.
- metaArray
- Integration of microarray data for meta-analysis.
- metahdep
- Hierarchical dependence in meta-analysis.
- multtest
- Multiple testing procedures for controlling the family-wise error rate
(FWER) and the false discovery rate (FDR). Tests can be based on
t- or F-statistics for one- and two-factor designs, and
permutation procedures are available to estimate adjusted
p-values.
- nem
- Nested Effects Models to reconstruct phenotypic hierarchies.
- nnNorm
- Spatial and intensity based normalization of cDNA microarray data based
on robust neural nets.
- nudge
- Normal Uniform Differential Gene Expression detection.
- occugene
- Functions for multinomial occupancy distribution.
- oligo
- Oligonucleotide arrays.
- oligoClasses
- Classes for high-throughput SNP arrays.
- oneChannelGUI
- Extend the capabilities of affylmGUI.
- ontoTools
- Graphs and sparse matrices for working with ontologies; formal objects
for nomenclatures with provenance management.
- pamr
-
Pam: Prediction Analysis for Microarrays.
- panp
- Presence-Absence calls from Negative strand matching Probesets.
- pathRender
- Render molecular pathways.
- pcaMethods
- A collection of PCA methods.
- pcot2
- Principal coordinates and Hotelling's T-square method.
- pdInfoBuilder
- Platform design information package builder.
- pdmclass
- CLASSification of microarray samples using Penalized Discriminant
Methods.
- pgUtils
- Utility functions for PostgreSQL databases.
- pickgene
- Adaptive gene picking for microarray expression data analysis.
- pkgDepTools
- Package dependency tools.
- plgem
- Power Law Global Error Model.
- plier
- Implements the Affymetrix PLIER (Probe Logarithmic Error Intensity
Estimate) algorithm.
- plw
- Probe level Locally moderated Weighted t-tests.
- ppiStats
- Protein-Protein Interaction Statistical package.
- prada
- Tools for analyzing and navigating data from high-throughput phenotyping
experiments based on cellular assays and fluorescent detection.
- preprocessCore
- A collection of pre-processing functions.
- puma
- Propagating Uncertainty in Microarray Analysis.
- qpcrNorm
- Data-driven normalization strategies for high-throughput qPCR data.
- qpgraph
- Reverse engineering of molecular regulatory networks with qp-graphs.
- quantsmooth
- Quantile smoothing and genomic visualization of array data.
- qvalue
- Q-value estimation for false discovery rate control.
- rHVDM
- Hidden Variable Dynamic Modeling.
- rMAT
- R implementation from MAT program to normalize and analyze tiling arrays
and ChIP-chip data.
- rama
- Robust Analysis of MicroArrays: robust estimation of cDNA microarray
intensities with replicates using a Bayesian hierarchical model.
- rbsurv
- Robust likelihood-based survival modeling with microarray data.
- reb
- Regional Expression Biases.
- rflowcyt
- Statistical tools and data structures for analytic flow cytometry.
- rsbml
- R support for SBML, using libsbml.
- rtracklayer
- R interface to genome browsers and their annotation tracks.
- safe
- Significance Analysis of Function and Expression.
- sagenhaft
- Functions for reading and comparing SAGE (Serial Analysis of Gene
Expression) libraries.
- seqLogo
- Sequence logos for DNA sequence alignments.
- sigPathway
- Pathway analysis.
- siggenes
- Identifying differentially expressed genes and estimating the False
Discovery Rate (FDR) with both the Significance Analysis of Microarrays
(SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).
- simpleaffy
- Very simple high level analysis of Affymetrix data.
- simulatorAPMS
- Computationally simulates the AP-MS technology.
- sizepower
- Sample size and power calculation in microrarray studies.
- snapCGH
- Segmentation, normalization and processing of aCGH data.
- snpMatrix
- The snp.matrix and X.snp.matrix classes.
- spikeLI
- Affymetrix Spike-in Langmuir Isotherm data analysis tool.
- spkTools
- Methods for spike-in arrays.
- splicegear
- A set of tools to work with alternative splicing.
- splots
- Visualization routines for high throughput screens.
- spotSegmentation
- Microarray spot segmentation and gridding for blocks of microarray
spots.
- sscore
- S-score algorithm for Affymetrix oligonucleotide microarrays.
- ssize
- Estimate microarry sample size.
- stam
- STructured Analysis of Microarray data.
- stepNorm
- Stepwise normalization functions for cDNA microarrays.
- tilingArray
- Analysis of tiling arrays.
- timecourse
- Statistical analysis for developmental microarray time course data.
- tkWidgets
- Widgets in Tcl/Tk that provide functionality for Bioconductor packages.
- topGO
- Enrichment analysis for Gene Ontology.
- tspair
- Top scoring pairs for microarray classification.
- twilight
- Estimation of local false discovery rate.
- vbmp
- Variational Bayesian Multinomial Probit regression.
- vsn
- Calibration and variance stabilizing transformations for both Affymetrix
and cDNA array data.
- weaver
- Tools and extensions for processing Sweave documents.
- webbioc
- Integrated web interface for doing microarray analysis using several of
the Bioconductor packages.
- widgetInvoke
- Evaluation widgets for functions.
- widgetTools
- Tools for creating Tcl/Tk widgets, i.e., small-scale graphical user
interfaces.
- xcms
- LC/MS and GC/MS data analysis: framework for processing and
visualization of chromatographically separated mass spectral data.
- xps
- Methods for processing and analysis of Affymetrix Oligonucleotide
Arrays.
- yaqcaffy
- Affymetrix expression data quality control and reproducibility
analysis.
5.1.5 Other add-on packages
Jim Lindsey has written a collection
of R packages for nonlinear regression and repeated measurements,
consisting of event (event history procedures and models),
gnlm (generalized nonlinear regression models), growth
(multivariate normal and elliptically-contoured repeated measurements
models), repeated (non-normal repeated measurements models),
rmutil (utilities for nonlinear regression and repeated
measurements), and stable (probability functions and
generalized regression models for stable distributions). All analyses
in the new edition of his book “Models for Repeated Measurements”
(1999, Oxford University Press) were carried out using these packages.
Jim has also started dna, a package with procedures for the
analysis of DNA sequences. Jim's packages can be obtained from
http://popgen.unimaas.nl/~jlindsey/rcode.html.
More code has been posted to the R-help mailing list, and can be
obtained from the mailing list archive.
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 in your library search path (see the help page for
.libPaths()
for details on how the search path is determined).
To install to another tree (e.g., your private one), use
$ R CMD INSTALL -l lib /path/to/pkg_version.tar.gz
where lib gives the path to the library tree to install to.
Even more conveniently, you can install and automatically update
packages from within R if you have access to repositories such as
CRAN. See the help page for available.packages()
for more
information.
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
Matrix Sparse and Dense Matrix Classes and Methods
base The R Base package
boot Bootstrap R (S-Plus) Functions (Canty)
class Functions for Classification
cluster Functions for clustering (by Rousseeuw et al.)
codetools Code Analysis Tools for R
datasets The R Datasets Package
foreign Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat,
dBase, ...
grDevices The R Graphics Devices and Support for Colours and Fonts
graphics The R Graphics Package
grid The Grid Graphics Package
lattice Lattice Graphics
methods Formal Methods and Classes
mgcv GAMs with GCV/AIC/REML smoothness estimation and GAMMs
by PQL
nlme Linear and Nonlinear Mixed Effects Models
nnet Feed-forward Neural Networks and Multinomial Log-Linear
Models
rpart Recursive Partitioning
spatial Functions for Kriging and Point Pattern Analysis
splines Regression Spline Functions and Classes
stats The R Stats Package
stats4 Statistical functions using S4 Classes
survival Survival analysis, including penalised likelihood
tcltk Tcl/Tk Interface
tools Tools for Package Development
utils The R Utils Package
|
You can “load” the installed package pkg by
library(pkg)
You can then find out which functions it provides by typing one of
library(help = pkg)
help(package = pkg)
You can unload the loaded package pkg by
detach("package:pkg")
5.4 How can add-on packages be removed?
Use
$ R CMD REMOVE pkg_1 ... pkg_n
to remove the packages pkg_1, ..., pkg_n from the
library tree rooted at the first directory given in R_LIBS if this
is set and non-null, and from the default library otherwise. (Versions
of R prior to 1.3.0 removed from the default library by default.)
To remove from library lib, do
$ R CMD REMOVE -l lib pkg_1 ... pkg_n
5.5 How can I create an R package?
A package consists of a subdirectory containing the files
DESCRIPTION and INDEX, and the subdirectories R,
data, demo, exec, inst, man,
src, and tests (some of which can be missing). Optionally
the package can also contain script files configure and
cleanup which are executed before and after installation.
See section “Creating R packages” in Writing R Extensions, for
details. This manual is included in the R distribution, see What documentation exists for R?, and gives information on package
structure, the configure and cleanup mechanisms, and on automated
package checking and building.
R version 1.3.0 has added the function package.skeleton()
which
will set up directories, save data and code, and create skeleton help
files for a set of R functions and datasets.
See What is CRAN?, for information on uploading a package to CRAN.
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 What is S?); some
of the nonlinear modeling code is not there yet.
The R Developer Page 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.
6 R and Emacs
6.1 Is there Emacs support for R?
There is an Emacs package called ESS (“Emacs Speaks
Statistics”) which provides a standard interface between statistical
programs and statistical processes. It is intended to provide
assistance for interactive statistical programming and data analysis.
Languages supported include: S dialects (R, S 3/4, and S-Plus
3.x/4.x/5.x/6.x/7.x), LispStat dialects (XLispStat, ViSta), SAS, Stata,
and BUGS.
ESS grew out of the need for bug fixes and extensions to
S-mode 4.8 (which was a GNU Emacs interface to S/S-Plus
version 3 only). The current set of developers desired support for
XEmacs, R, S4, and MS Windows. In addition, with new modes being
developed for R, Stata, and SAS, it was felt that a unifying interface
and framework for the user interface would benefit both the user and the
developer, by helping both groups conform to standard Emacs usage. The
end result is an increase in efficiency for statistical programming and
data analysis, over the usual tools.
R support contains code for editing R source code (syntactic indentation
and highlighting of source code, partial evaluations of code, loading
and error-checking of code, and source code revision maintenance) and
documentation (syntactic indentation and highlighting of source code,
sending examples to running ESS process, and previewing),
interacting with an inferior R process from within Emacs (command-line
editing, searchable command history, command-line completion of R object
and file names, quick access to object and search lists, transcript
recording, and an interface to the help system), and transcript
manipulation (recording and saving transcript files, manipulating and
editing saved transcripts, and re-evaluating commands from transcript
files).
The latest stable version of ESS are available via CRAN or
the ESS web page. 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
[email protected].
Please send bug reports and suggestions on ESS to
[email protected]. The easiest way to do this from is
within Emacs by typing M-x ess-submit-bug-report or using the
[ESS] or [iESS] pulldown menus.
6.2 Should I run R from within Emacs?
Yes, definitely. Inferior R mode provides a readline/history
mechanism, object name completion, and syntax-based highlighting of the
interaction buffer using Font Lock mode, as well as a very convenient
interface to the R help system.
Of course, it also integrates nicely with the mechanisms for editing R
source using Emacs. One can write code in one Emacs buffer and send
whole or parts of it for execution to R; this is helpful for both data
analysis and programming. One can also seamlessly integrate with a
revision control system, in order to maintain a log of changes in your
programs and data, as well as to allow for the retrieval of past
versions of the code.
In addition, it allows you to keep a record of your session, which can
also be used for error recovery through the use of the transcript mode.
To specify command line arguments for the inferior R process, use
C-u M-x R for starting R.
6.3 Debugging R from within Emacs
To debug R “from within Emacs”, there are several possibilities. To
use the Emacs GUD (Grand Unified Debugger) library with the recommended
debugger GDB, type M-x gdb and give the path to the R
binary as argument. At the gdb prompt, set
R_HOME and other environment variables as needed (using e.g.
set env R_HOME /path/to/R/, but see also below), and start the
binary with the desired arguments (e.g., run --quiet).
If you have ESS, you can do C-u M-x R <RET> - d
<SPC> g d b <RET> to start an inferior R process with arguments
-d gdb.
A third option is to start an inferior R process via ESS
(M-x R) and then start GUD (M-x gdb) giving the R binary
(using its full path name) as the program to debug. Use the program
ps to find the process number of the currently running R
process then use the attach
command in gdb to attach it to that
process. One advantage of this method is that you have separate
*R*
and *gud-gdb*
windows. Within the *R*
window
you have all the ESS facilities, such as object-name
completion, that we know and love.
When using GUD mode for debugging from within Emacs, you may find it
most convenient to use the directory with your code in it as the current
working directory and then make a symbolic link from that directory to
the R binary. That way .gdbinit can stay in the directory with
the code and be used to set up the environment and the search paths for
the source, e.g. as follows:
set env R_HOME /opt/R
set env R_PAPERSIZE letter
set env R_PRINTCMD lpr
dir /opt/R/src/appl
dir /opt/R/src/main
dir /opt/R/src/nmath
dir /opt/R/src/unix
7 R Miscellanea
7.1 How can I set components of a list to NULL?
You can use
x[i] <- list(NULL)
to set component i
of the list x
to NULL
, similarly
for named components. Do not set x[i]
or x[[i]]
to
NULL
, because this will remove the corresponding component from
the list.
For dropping the row names of a matrix x
, it may be easier to use
rownames(x) <- NULL
, similarly for column names.
7.2 How can I save my workspace?
save.image()
saves the objects in the user's .GlobalEnv
to
the file .RData in the R startup directory. (This is also what
happens after q("yes").) Using save.image(
file)
one
can save the image under a different name.
7.3 How can I clean up my workspace?
To remove all objects in the currently active environment (typically
.GlobalEnv
), you can do
rm(list = ls(all = TRUE))
(Without all = TRUE, only the objects with names not starting
with a ‘.’ are removed.)
7.4 How can I get eval() and D() to work?
Strange things will happen if you use eval(print(x), envir = e)
or D(x^2, "x")
. The first one will either tell you that
"x
" is not found, or print the value of the wrong x
.
The other one will likely return zero if x
exists, and an error
otherwise.
This is because in both cases, the first argument is evaluated in the
calling environment first. The result (which should be an object of
mode "expression"
or "call"
) is then evaluated or
differentiated. What you (most likely) really want is obtained by
“quoting” the first argument upon surrounding it with
expression()
. For example,
R> D(expression(x^2), "x")
2 * x
Although this behavior may initially seem to be rather strange, is
perfectly logical. The “intuitive” behavior could easily be
implemented, but problems would arise whenever the expression is
contained in a variable, passed as a parameter, or is the result of a
function call. Consider for instance the semantics in cases like
D2 <- function(e, n) D(D(e, n), n)
or
g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
g(a * b)
See the help page for deriv()
for more examples.
7.5 Why do my matrices lose dimensions?
When a matrix with a single row or column is created by a subscripting
operation, e.g., row <- mat[2, ]
, it is by default turned into a
vector. In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is created by subscripting it will be coerced into a 2 x 3 x 4
array, losing the unnecessary dimension. After much discussion this has
been determined to be a feature.
To prevent this happening, add the option drop = FALSE to the
subscripting. For example,
rowmatrix <- mat[2, , drop = FALSE] # creates a row matrix
colmatrix <- mat[, 2, drop = FALSE] # creates a column matrix
a <- b[1, 1, 1, drop = FALSE] # creates a 1 x 1 x 1 array
The drop = FALSE option should be used defensively when
programming. For example, the statement
somerows <- mat[index, ]
will return a vector rather than a matrix if index
happens to
have length 1, causing errors later in the code. It should probably be
rewritten as
somerows <- mat[index, , drop = FALSE]
7.6 How does autoloading work?
R has a special environment called .AutoloadEnv
. Using
autoload(name, pkg), where name and
pkg are strings giving the names of an object and the package
containing it, stores some information in this environment. When R
tries to evaluate name, it loads the corresponding package
pkg and reevaluates name in the new package's
environment.
Using this mechanism makes R behave as if the package was loaded, but
does not occupy memory (yet).
See the help page for autoload()
for a very nice example.
7.7 How should I set options?
The function options()
allows setting and examining a variety of
global “options” which affect the way in which R computes and displays
its results. The variable .Options
holds the current values of
these options, but should never directly be assigned to unless you want
to drive yourself crazy—simply pretend that it is a “read-only”
variable.
For example, given
test1 <- function(x = pi, dig = 3) {
oo <- options(digits = dig); on.exit(options(oo));
cat(.Options$digits, x, "\n")
}
test2 <- function(x = pi, dig = 3) {
.Options$digits <- dig
cat(.Options$digits, x, "\n")
}
we obtain:
R> test1()
3 3.14
R> test2()
3 3.141593
What is really used is the global value of .Options
, and
using options(OPT = VAL) correctly updates it. Local copies of
.Options
, either in .GlobalEnv
or in a function
environment (frame), are just silently disregarded.
7.8 How do file names work in Windows?
As R uses C-style string handling, ‘\’ is treated as an escape
character, so that for example one can enter a newline as ‘\n’.
When you really need a ‘\’, you have to escape it with another
‘\’.
Thus, in filenames use something like "c:\\data\\money.dat"
. You
can also replace ‘\’ by ‘/’ ("c:/data/money.dat"
).
7.9 Why does plotting give a color allocation error?
On an X11 device, plotting sometimes, e.g., when running
demo("image")
, results in “Error: color allocation error”.
This is an X problem, and only indirectly related to R. It occurs when
applications started prior to R have used all the available colors.
(How many colors are available depends on the X configuration; sometimes
only 256 colors can be used.)
One application which is notorious for “eating” colors is Netscape.
If the problem occurs when Netscape is running, try (re)starting it with
either the -no-install (to use the default colormap) or the
-install (to install a private colormap) option.
You could also set the colortype
of X11()
to
"pseudo.cube"
rather than the default "pseudo"
. See the
help page for X11()
for more information.
7.10 How do I convert factors to numeric?
It may happen that when reading numeric data into R (usually, when
reading in a file), they come in as factors. If f
is such a
factor object, you can use
as.numeric(as.character(f))
to get the numbers back. More efficient, but harder to remember, is
as.numeric(levels(f))[as.integer(f)]
In any case, do not call as.numeric()
or their likes directly for
the task at hand (as as.numeric()
or unclass()
give the
internal codes).
7.11 Are Trellis displays implemented in R?
The recommended package lattice (which is based on another
recommended package, grid) provides graphical functionality
that is compatible with most Trellis commands.
You could also look at coplot()
and dotchart()
which might
do at least some of what you want. Note also that the R version of
pairs()
is fairly general and provides most of the functionality
of splom()
, and that R's default plot method has an argument
asp
allowing to specify (and fix against device resizing) the
aspect ratio of the plot.
(Because the word “Trellis” has been claimed as a trademark we do not
use it in R. The name “lattice” has been chosen for the R
equivalent.)
7.12 What are the enclosing and parent environments?
Inside a function you may want to access variables in two additional
environments: the one that the function was defined in (“enclosing”),
and the one it was invoked in (“parent”).
If you create a function at the command line or load it in a package its
enclosing environment is the global workspace. If you define a function
f()
inside another function g()
its enclosing environment
is the environment inside g()
. The enclosing environment for a
function is fixed when the function is created. You can find out the
enclosing environment for a function f()
using
environment(f)
.
The “parent” environment, on the other hand, is defined when you
invoke a function. If you invoke lm()
at the command line its
parent environment is the global workspace, if you invoke it inside a
function f()
then its parent environment is the environment
inside f()
. You can find out the parent environment for an
invocation of a function by using parent.frame()
or
sys.frame(sys.parent())
.
So for most user-visible functions the enclosing environment will be the
global workspace, since that is where most functions are defined. The
parent environment will be wherever the function happens to be called
from. If a function f()
is defined inside another function
g()
it will probably be used inside g()
as well, so its
parent environment and enclosing environment will probably be the same.
Parent environments are important because things like model formulas
need to be evaluated in the environment the function was called from,
since that's where all the variables will be available. This relies on
the parent environment being potentially different with each invocation.
Enclosing environments are important because a function can use
variables in the enclosing environment to share information with other
functions or with other invocations of itself (see the section on
lexical scoping). This relies on the enclosing environment being the
same each time the function is invoked. (In C this would be done with
static variables.)
Scoping is hard. Looking at examples helps. It is particularly
instructive to look at examples that work differently in R and S and try
to see why they differ. One way to describe the scoping differences
between R and S is to say that in S the enclosing environment is
always the global workspace, but in R the enclosing environment
is wherever the function was created.
7.13 How can I substitute into a plot label?
Often, it is desired to use the value of an R object in a plot label,
e.g., a title. This is easily accomplished using paste()
if the
label is a simple character string, but not always obvious in case the
label is an expression (for refined mathematical annotation). In such a
case, either use parse()
on your pasted character string or use
substitute()
on an expression. For example, if ahat
is an
estimator of your parameter a of interest, use
title(substitute(hat(a) == ahat, list(ahat = ahat)))
(note that it is ‘==’ and not ‘=’). Sometimes bquote()
gives a more compact form, e.g.,
title(bquote(hat(a) = .(ahat)))
where subexpressions enclosed in ‘.()’ are replaced by their
values.
There are more worked examples in the mailing list achives.
7.14 What are valid names?
When creating data frames using data.frame()
or
read.table()
, R by default ensures that the variable names are
syntactically valid. (The argument check.names to these
functions controls whether variable names are checked and adjusted by
make.names()
if needed.)
To understand what names are “valid”, one needs to take into account
that the term “name” is used in several different (but related) ways
in the language:
- 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.
- 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()
).
- 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.
- 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.)
- Finally, a file name is a string identifying a file in the
operating system for reading, writing, etc. It really has nothing much
to do with names in the language, but it is traditional to call these
strings file “names”.
7.15 Are GAMs implemented in R?
Package gam from CRAN implements all the Generalized
Additive Models (GAM) functionality as described in the GAM chapter of
the White Book. In particular, it implements backfitting with both
local regression and smoothing splines, and is extendable. There is a
gam()
function for GAMs in package mgcv, but it is not
an exact clone of what is described in the White Book (no lo()
for example). Package gss can fit spline-based GAMs too. And
if you can accept regression splines you can use glm()
. For
gaussian GAMs you can use bruto()
from package mda.
7.16 Why is the output not printed when I source() a file?
Most R commands do not generate any output. The command
1+1
computes the value 2 and returns it; the command
summary(glm(y~x+z, family=binomial))
fits a logistic regression model, computes some summary information and
returns an object of class "summary.glm"
(see How should I write summary methods?).
If you type ‘1+1’ or ‘summary(glm(y~x+z, family=binomial))’ at
the command line the returned value is automatically printed (unless it
is invisible()
), but in other circumstances, such as in a
source()
d file or inside a function it isn't printed unless you
specifically print it.
To print the value use
print(1+1)
or
print(summary(glm(y~x+z, family=binomial)))
instead, or use source(
file, echo=TRUE)
.
7.17 Why does outer() behave strangely with my function?
As the help for outer()
indicates, it does not work on arbitrary
functions the way the apply()
family does. It requires functions
that are vectorized to work elementwise on arrays. As you can see by
looking at the code, outer(x, y, FUN)
creates two large vectors
containing every possible combination of elements of x
and
y
and then passes this to FUN
all at once. Your function
probably cannot handle two large vectors as parameters.
If you have a function that cannot handle two vectors but can handle two
scalars, then you can still use outer()
but you will need to wrap
your function up first, to simulate vectorized behavior. Suppose your
function is
foo <- function(x, y, happy) {
stopifnot(length(x) == 1, length(y) == 1) # scalars only!
(x + y) * happy
}
If you define the general function
wrapper <- function(x, y, my.fun, ...) {
sapply(seq(along = x), FUN = function(i) my.fun(x[i], y[i], ...))
}
then you can use outer()
by writing, e.g.,
outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)
7.18 Why does the output from anova() depend on the order of factors in the model?
In a model such as ~A+B+A:B
, R will report the difference in sums
of squares between the models ~1
, ~A
, ~A+B
and
~A+B+A:B
. If the model were ~B+A+A:B
, R would report
differences between ~1
, ~B
, ~A+B
, and
~A+B+A:B
. In the first case the sum of squares for A
is
comparing ~1
and ~A
, in the second case it is comparing
~B
and ~B+A
. In a non-orthogonal design (i.e., most
unbalanced designs) these comparisons are (conceptually and numerically)
different.
Some packages report instead the sums of squares based on comparing the
full model to the models with each factor removed one at a time (the
famous `Type III sums of squares' from SAS, for example). These do not
depend on the order of factors in the model. The question of which set
of sums of squares is the Right Thing provokes low-level holy wars on
R-help from time to time.
There is no need to be agitated about the particular sums of squares
that R reports. You can compute your favorite sums of squares quite
easily. Any two models can be compared with anova(
model1,
model2)
, and drop1(
model1)
will show the sums of
squares resulting from dropping single terms.
7.19 How do I produce PNG graphics in batch mode?
Under a Unix-alike, if your installation supports the
type="cairo"
option to the png()
device there should be no
problems, and the default settings should just work. This option is not
available for versions of R prior to 2.7.0, or without support for
cairo. From R 2.7.0 png()
by default uses the Quartz device
on Mac OS X, and that too works in batch mode.
Earlier versions of the png()
device uses the X11 driver, which is
a problem in batch mode or for remote operation. If you have Ghostscript
you can use bitmap()
, which produces a PostScript or PDF file then
converts it to any bitmap format supported by Ghostscript. On some
installations this produces ugly output, on others it is perfectly
satisfactory. Many systems now come with Xvfb from
X.Org (possibly as an optional
install), which is an X11 server that does not require a screen; and there
is the GDD package from CRAN, which produces PNG, JPEG and GIF
bitmaps without X11.
7.20 How can I get command line editing to work?
The Unix command-line interface to R can only provide the inbuilt
command line editor which allows recall, editing and re-submission of
prior commands provided that the GNU readline library is
available at the time R is configured for compilation. Note that the
`development' version of readline including the appropriate headers is
needed: users of Linux binary distributions will need to install
packages such as libreadline-dev
(Debian) or
readline-devel
(Red Hat).
7.21 How can I turn a string into a variable?
If you have
varname <- c("a", "b", "d")
you can do
get(varname[1]) + 2
for
a + 2
or
assign(varname[1], 2 + 2)
for
a <- 2 + 2
or
eval(substitute(lm(y ~ x + variable),
list(variable = as.name(varname[1]))))
for
lm(y ~ x + a)
At least in the first two cases it is often easier to just use a list,
and then you can easily index it by name
vars <- list(a = 1:10, b = rnorm(100), d = LETTERS)
vars[["a"]]
without any of this messing about.
7.22 Why do lattice/trellis graphics not work?
The most likely reason is that you forgot to tell R to display the
graph. Lattice functions such as xyplot()
create a graph object,
but do not display it (the same is true of ggplot2 graphics,
and Trellis graphics in S-Plus). The print()
method for the
graph object produces the actual display. When you use these functions
interactively at the command line, the result is automatically printed,
but in source()
or inside your own functions you will need an
explicit print()
statement.
7.23 How can I sort the rows of a data frame?
To sort the rows within a data frame, with respect to the values in one
or more of the columns, simply use order()
(e.g.,
DF[order(DF$a, DF[["b"]]), ]
to sort the data frame DF
on
columns named a
and b
).
7.24 Why does the help.start() search engine not work?
The browser-based search engine in help.start()
utilizes a Java
applet. In order for this to function properly, a compatible version of
Java must installed on your system and linked to your browser, and both
Java and JavaScript need to be enabled in your browser.
There have been a number of compatibility issues with versions of Java
and of browsers.
For further details please consult section “Enabling search in HTML
help” in R Installation and Administration. This manual is
included in the R distribution, see What documentation exists for R?, and its HTML version is linked from the HTML
search page.
7.25 Why did my .Rprofile stop working when I updated R?
Did you read the NEWS file? For functions that are not in the
base package you need to specify the correct package namespace,
since the code will be run before the packages are loaded. E.g.,
ps.options(horizontal = FALSE)
help.start()
needs to be
grDevices::ps.options(horizontal = FALSE)
utils::help.start()
(graphics::ps.options(horizontal = FALSE)
in R 1.9.x).
7.26 Where have all the methods gone?
Many functions, particularly S3 methods, are now hidden in namespaces.
This has the advantage that they cannot be called inadvertently with
arguments of the wrong class, but it makes them harder to view.
To see the code for an S3 method (e.g., [.terms
) use
getS3method("[", "terms")
To see the code for an unexported function foo()
in the namespace
of package "bar"
use bar:::foo
. Don't use these
constructions to call unexported functions in your own code—they are
probably unexported for a reason and may change without warning.
7.27 How can I create rotated axis labels?
To rotate axis labels (using base graphics), you need to use
text()
, rather than mtext()
, as the latter does not
support par("srt")
.
## Increase bottom margin to make room for rotated labels
par(mar = c(7, 4, 4, 2) + 0.1)
## Create plot with no x axis and no x axis label
plot(1 : 8, xaxt = "n", xlab = "")
## Set up x axis with tick marks alone
axis(1, labels = FALSE)
## Create some text labels
labels <- paste("Label", 1:8, sep = " ")
## Plot x axis labels at default tick marks
text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1,
labels = labels, xpd = TRUE)
## Plot x axis label at line 6 (of 7)
mtext(1, text = "X Axis Label", line = 6)
When plotting the x axis labels, we use srt = 45
for text
rotation angle, adj = 1
to place the right end of text at the
tick marks, and xpd = TRUE
to allow for text outside the plot
region. You can adjust the value of the 0.25
offset as required
to move the axis labels up or down relative to the x axis. See
?par
for more information.
Also see Figure 1 and associated code in Paul Murrell (2003),
“Integrating grid Graphics Output with Base Graphics Output”,
R News, 3/2, 7–12.
7.28 Why is read.table() so inefficient?
By default, read.table()
needs to read in everything as character
data, and then try to figure out which variables to convert to numerics
or factors. For a large data set, this takes condiderable amounts of
time and memory. Performance can substantially be improved by using the
colClasses
argument to specify the classes to be assumed for the
columns of the table.
7.29 What is the difference between package and library?
A package is a standardized collection of material extending R,
e.g. providing code, data, or documentation. A library is a
place (directory) where R knows to find packages it can use (i.e., which
were installed). R is told to use a package (to “load” it and
add it to the search path) via calls to the function library
.
I.e., library()
is employed to load a package from libraries
containing packages.
See R Add-On Packages, for more details. See also Uwe Ligges (2003),
“R Help Desk: Package Management”, R News, 3/3,
37–39.
7.30 I installed a package but the functions are not there
To actually use the package, it needs to be loaded using
library()
.
See R Add-On Packages and What is the difference between package and library? for more information.
7.31 Why doesn't R think these numbers are equal?
The only numbers that can be represented exactly in R's numeric type
are integers and fractions whose denominator is a power of 2. Other
numbers have to be rounded to (typically) 53 binary digits accuracy. As a
result, two floating point numbers will not reliably be equal unless they
have been computed by the same algorithm, and not always even then. For
example
R> a <- sqrt(2)
R> a * a == 2
[1] FALSE
R> a * a - 2
[1] 4.440892e-16
The function all.equal()
compares two objects using a numeric
tolerance of .Machine$double.eps ^ 0.5
. If you want much greater
accuracy than this you will need to consider error propagation
carefully.
For more information, see e.g. David Goldberg (1991), “What Every
Computer Scientist Should Know About Floating-Point Arithmetic”,
ACM Computing Surveys, 23/1, 5–48, also available via
http://docs.sun.com/source/806-3568/ncg_goldberg.html.
To quote from “The Elements of Programming Style” by Kernighan and
Plauger:
10.0 times 0.1 is hardly ever 1.0.
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).
7.33 Why are powers of negative numbers wrong?
You are probably seeing something like
R> -2^2
[1] -4
and misunderstanding the precedence rules for expressions in R.
Write
R> (-2)^2
[1] 4
to get the square of -2.
The precedence rules are documented in ?Syntax
, and to see how R
interprets an expression you can look at the parse tree
R> as.list(quote(-2^2))
[[1]]
`-`
[[2]]
2^2
7.34 How can I save the result of each iteration in a loop into a separate file?
One way is to use paste()
(or sprintf()
) to concatenate a
stem filename and the iteration number while file.path()
constructs the path. For example, to save results into files
result1.rda, ..., result100.rda in the subdirectory
Results of the current working directory, one can use
for(i in 1:100) {
## Calculations constructing "some_object" ...
fp <- file.path("Results", paste("result", i, ".rda", sep = ""))
save(list = "some_object", file = fp)
}
7.35 Why are p-values not displayed when using lmer()?
Doug Bates has kindly provided an extensive response in a post to the
r-help list, which can be reviewed at
https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html.
7.36 Why are there unwanted borders, lines or grid-like artifacts when viewing a plot saved to a PS or PDF file?
This can occur when using functions such as polygon()
,
filled.contour()
, image()
or other functions which may
call these internally. In the case of polygon()
, you may observe
unwanted borders between the polygons even when setting the
border
argument to NA
or "transparent"
.
The source of the problem is the PS/PDF viewer when the plot is
anti-aliased. The details for the solution will be different depending
upon the viewer used, the operating system and may change over time.
For some common viewers, consider the following:
- Acrobat Reader (cross platform)
- There are options in Preferences to enable/disable text smoothing, image
smoothing and line art smoothing.
Disable line art smoothing.
- Preview (Mac OS X)
- There is an option in Preferences to enable/disable anti-aliasing of
text and line art.
Disable this option.
- GSview (cross platform)
- There are settings for Text Alpha and Graphics Alpha.
Change Graphics Alpha from 4 bits to 1 bit to disable graphic
anti-aliasing.
- gv (Linux/Unix X)
- There is an option to enable/disable anti-aliasing.
Disable this option.
- Evince (Linux/GNOME)
- There is not an option to disable anti-aliasing in this viewer.
- Okular (Linux/KDE)
- There is not an option in the GUI to enable/disable anti-aliasing.
From a console command line, use:
$ kwriteconfig --file okularpartrc --group 'Dlg Performance' \
--key TextAntialias Disabled
Then restart Okular. Change the final word to ‘Enabled’ to restore
the original setting.
7.37 Why does backslash behave strangely inside strings?
This question most often comes up in relation to file names (see
How do file names work in Windows?) but it also happens that
people complain that they cannot seem to put a single ‘\’ character
into a text string unless it happens to be followed by certain other
characters.
To understand this, you have to distinguish between character strings
and representations of character strings. Mostly, the
representation in R is just the string with a single or double quote at
either end, but there are strings that cannot be represented that way,
e.g., strings that themselves contains the quote character. So
> str <- "This \"text\" is quoted"
> str
[1] "This \"text\" is quoted"
> cat(str, "\n")
This "text" is quoted
The escape sequences ‘\"’ and ‘\n’ represent a double
quote and the newline character respectively. Printing text strings,
using print()
or by typing the name at the prompt will use the
escape sequences too, but the cat()
function will display the
string as-is. Notice that ‘"\n"’ is a one-character string, not
two; the backslash is not actually in the string, it is just generated
in the printed representation.
> nchar("\n")
[1] 1
> substring("\n", 1, 1)
[1] "\n"
So how do you put a backslash in a string? For this, you have to
escape the escape character. I.e., you have to double the backslash.
as in
> cat("\\n", "\n")
\n
Some functions, particularly those involving regular expression
matching, themselves use metacharacters, which may need to be escaped by
the backslash mechanism. In those cases you may need a quadruple
backslash to represent a single literal one.
In versions of R up to 2.4.1 an unknown escape sequence like ‘\p’
was quietly interpreted as just ‘p’. Current versions of R emit a
warning.
8 R Programming
8.1 How should I write summary methods?
Suppose you want to provide a summary method for class "foo"
.
Then summary.foo()
should not print anything, but return an
object of class "summary.foo"
, and you should write a
method print.summary.foo()
which nicely prints the summary
information and invisibly returns its object. This approach is
preferred over having summary.foo()
print summary information and
return something useful, as sometimes you need to grab something
computed by summary()
inside a function or similar. In such
cases you don't want anything printed.
8.2 How can I debug dynamically loaded code?
Roughly speaking, you need to start R inside the debugger, load the
code, send an interrupt, and then set the required breakpoints.
See section “Finding entry points in dynamically loaded code” in
Writing R Extensions. This manual is included in the R
distribution, see What documentation exists for R?.
8.3 How can I inspect R objects when debugging?
The most convenient way is to call R_PV
from the symbolic
debugger.
See section “Inspecting R objects when debugging” in Writing R
Extensions.
8.4 How can I change compilation flags?
Suppose you have C code file for dynloading into R, but you want to use
R CMD SHLIB
with compilation flags other than the default ones
(which were determined when R was built).
Starting with R 2.1.0, users can provide personal Makevars configuration
files in $HOME/.R to override the default flags.
See section “Add-on packages” in R Installation and
Administration.
For earlier versions of R, you could change the file
R_HOME/etc/Makeconf to reflect your preferences, or (at
least for systems using GNU Make) override them by the
environment variable MAKEFLAGS.
See section “Creating shared objects” in Writing R Extensions.
8.5 How can I debug S4 methods?
Use the trace()
function with argument signature=
to add
calls to the browser or any other code to the method that will be
dispatched for the corresponding signature. See ?trace
for
details.
9 R Bugs
9.1 What is a bug?
If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like “disk full”), then it is certainly a bug. If you call
.C()
, .Fortran()
, .External()
or .Call()
(or
.Internal()
) yourself (or in a function you wrote), you can
always crash R by using wrong argument types (modes). This is not a
bug.
Taking forever to complete a command can be a bug, but you must make
certain that it was really R's fault. Some commands simply take a long
time. If the input was such that you know it should have been
processed quickly, report a bug. If you don't know whether the command
should take a long time, find out by looking in the manual or by asking
for assistance.
If a command you are familiar with causes an R error message in a case
where its usual definition ought to be reasonable, it is probably a bug.
If a command does the wrong thing, that is a bug. But be sure you know
for certain what it ought to have done. If you aren't familiar with the
command, or don't know for certain how the command is supposed to work,
then it might actually be working right. For example, people sometimes
think there is a bug in R's mathematics because they don't understand
how finite-precision arithmetic works. Rather than jumping to
conclusions, show the problem to someone who knows for certain.
Unexpected results of comparison of decimal numbers, for example
0.28 * 100 != 28
or 0.1 + 0.2 != 0.3
, are not a bug.
See Why doesn't R think these numbers are equal?, for more details.
Finally, a command's intended definition may not be best for statistical
analysis. This is a very important sort of problem, but it is also a
matter of judgment. Also, it is easy to come to such a conclusion out
of ignorance of some of the existing features. It is probably best not
to complain about such a problem until you have checked the
documentation in the usual ways, feel confident that you understand it,
and know for certain that what you want is not available. If you are
not sure what the command is supposed to do after a careful reading of
the manual this indicates a bug in the manual. The manual's job is to
make everything clear. It is just as important to report documentation
bugs as program bugs. However, we know that the introductory
documentation is seriously inadequate, so you don't need to report this.
If the online argument list of a function disagrees with the manual, one
of them must be wrong, so report the bug.
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
(e.g., http://en.wikipedia.org/wiki/Reproducibility) the problem,
preferably the simplest one you have found.
Invoking R with the --vanilla option may help in isolating a
bug. This ensures that the site profile and saved data files are not
read.
Before you actually submit a bug report, you should check whether the
bug has already been reported and/or fixed. First, try the “Search
Existing Reports” facility in the Bug Tracking page at
http://bugs.R-project.org/. Second, consult
https://svn.R-project.org/R/trunk/NEWS, which records changes that
will appear in the next release of R, including some bug fixes
that do not appear in Bug Tracking. (Windows users should additionally
consult
https://svn.R-project.org/R/trunk/src/gnuwin32/CHANGES.)
Third, if possible try the current r-patched or r-devel version of R.
If a bug has already been reported or fixed, please do not submit
further bug reports on it. Finally, check carefully whether the bug is
with R, or a contributed package. Bug reports on contributed packages
should be sent first to the package maintainer, and only submitted to
the R-bugs repository by package maintainers, mentioning the package in
the subject line.
On Unix systems a bug report can be generated using the function
bug.report()
. This automatically includes the version
information and sends the bug to the correct address. Alternatively the
bug report can be emailed to [email protected] 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
[email protected].
Corrections to and comments on message translation should be sent to the
last translator (listed at the top of the appropriate ‘.po’ file)
or to the translation team as listed at
http://developer.R-project.org/TranslationTeams.html.
10 Acknowledgments
Of course, many many thanks to Robert and Ross for the R system, and to
the package writers and porters for adding to it.
Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano
Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian
D. Ripley, Anthony Rossini, and Andreas Weingessel for their comments
which helped me improve this FAQ.
More to come soon ...