R FAQ


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R FAQ

Frequently Asked Questions on R

Version 2.9.2009-07-24

ISBN 3-900051-08-9

Kurt Hornik



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1 Introduction

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


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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.


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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?).


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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}
     }


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1.4 Notation

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


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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.


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2 R Basics


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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”).


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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.


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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.


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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.


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2.5 How can R be installed?


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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.


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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.


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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.


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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.


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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 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.


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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().


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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.


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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.


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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.


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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?).


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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.


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3 R and S


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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.

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.


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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.


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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.


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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.


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3.3.2 Models

There are some differences in the modeling code, such as


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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.

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.


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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.


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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.


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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.


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5 R Add-On Packages


Next: , Previous: R Add-On Packages, Up: R Add-On Packages

5.1 Which add-on packages exist for R?


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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.


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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.


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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.


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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.


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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.


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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.


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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")


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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


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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.


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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.


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6 R and Emacs


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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.


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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.


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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


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7 R Miscellanea


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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.


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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.


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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.)


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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.


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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]


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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.


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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.


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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").


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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.


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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).


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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.)


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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.


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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.


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7.14 What are valid names?

When creating data frames using data.frame() or read.table(), R by default ensures that the variable names are syntactically valid. (The argument check.names to these functions controls whether variable names are checked and adjusted by make.names() if needed.)

To understand what names are “valid”, one needs to take into account that the term “name” is used in several different (but related) ways in the language:

  1. A syntactic name is a string the parser interprets as this type of expression. It consists of letters, numbers, and the dot and (for version of R at least 1.9.0) underscore characters, and starts with either a letter or a dot not followed by a number. Reserved words are not syntactic names.
  2. An object name is a string associated with an object that is assigned in an expression either by having the object name on the left of an assignment operation or as an argument to the assign() function. It is usually a syntactic name as well, but can be any non-empty string if it is quoted (and it is always quoted in the call to assign()).
  3. An argument name is what appears to the left of the equals sign when supplying an argument in a function call (for example, f(trim=.5)). Argument names are also usually syntactic names, but again can be anything if they are quoted.
  4. An element name is a string that identifies a piece of an object (a component of a list, for example.) When it is used on the right of the ‘$’ operator, it must be a syntactic name, or quoted. Otherwise, element names can be any strings. (When an object is used as a database, as in a call to eval() or attach(), the element names become object names.)
  5. Finally, a file name is a string identifying a file in the operating system for reading, writing, etc. It really has nothing much to do with names in the language, but it is traditional to call these strings file “names”.


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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.


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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).


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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)


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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.


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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.


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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).


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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.


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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.


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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).


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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.


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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).


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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.


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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.


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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.


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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.


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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.


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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.


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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).


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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


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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)
     }


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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.


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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.


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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.


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8 R Programming


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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.


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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?.


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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.


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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.


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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.


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9 R Bugs


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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.


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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.


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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 ...