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

This document is copyright © 1998–2004 by Kurt Hornik.

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

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

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

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

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

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?).

In publications, please refer to this FAQ as Hornik
(2004), “The R FAQ”, and give the above,
*official* URL and the ISBN 3-900051-08-9.

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

Feedback via email to Kurt.Hornik@R-project.org is of course most welcome.

In particular, note that I do not have access to Windows or Macintosh systems. Features specific to the Windows and Mac OS X ports of R are described in the “R for Windows FAQ” 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.

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, and Luke Tierney.

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”).

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,
i386-freebsd, i386-sun-solaris, powerpc-apple-darwin, mips-sgi-irix,
rs6000-ibm-aix, hppa-hp-hpux, and sparc-sun-solaris.

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

The current released version is 2.0.1. 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.

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.

Next: How can R be installed (Windows), Previous: How can R be installed?, Up: How can R be installed?

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.

Next: How can R be installed (Macintosh), Previous: How can R be installed (Unix), Up: How can R be installed?

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

The bin/macosx directory of a CRAN site contains a standard
Apple installer package named RAqua.pkg.sit compressed in Aladdin
Stuffit format. Once downloaded, uncompressed 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 an Aqua GUI. Inside
bin/macosx/`x`.`y` there are prebuilt binary packages 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 RAqua 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.

The bin/linux directory of a CRAN site contains Mandrake 9.1/9.2/10.0 i386 packages by Michele Alzetta, Red Hat 8.x/9/Fedora1/Fedora2 i386 and Fedora1 x86_64 packages by Martyn Plummer and James Henstridge, respectively, SuSE 7.3/8.0/8.1/8.2 i386 and 9.0/9.1 i586 packages by Detlef Steuer, and VineLinux 2.6 i386 packages by Susunu Tanimura. Debian packages, maintained by Dirk Eddelbuettel, have long been part of the Debian distribution, and can be accessed through APT, the Debian package maintenance tool.

No other binary distributions are currently publically available.

Online documentation for most of the functions and variables in R
exists, and can be printed on-screen by typing `help(``name``)`
(or `?``name`) at the R prompt, where `name` is the name of
the topic help is sought for. (In the case of unary and binary
operators and control-flow special forms, the name may need to be be
quoted.)

This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?. An up-to-date HTML version is always available for web browsing at http://stat.ethz.ch/R-manual/.

Printed copies of the R reference manual for some version(s) are available from Network Theory Ltd, at http://www.network-theory.co.uk/R/base/. For each set of manuals sold, the publisher donates USD 10 to the R Foundation (see What is the R Foundation?).

The R distribution also comes with the following manuals.

- “An Introduction to R” (R-intro) includes information on data types, programming elements, statistical modeling and graphics. This document is based on the “Notes on S-Plus” by Bill Venables and David Smith.
- “Writing R Extensions” (R-exts) currently describes the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API.
- “R Data Import/Export” (R-data) is a guide to importing and exporting data to and from R.
- “The R Language Definition” (R-lang), a first version of the “Kernighan & Ritchie of R”, explains evaluation, parsing, object oriented programming, computing on the language, and so forth.
- “R Installation and Administration” (R-admin).

Books on R include

P. Dalgaard (2002), “Introductory Statistics with R”, Springer: New York, ISBN 0-387-95475-9.

http://www.biostat.ku.dk/~pd/ISwR.html.J. Fox (2002), “An R and S-Plus Companion to Applied Regression”, Sage Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2 (hardcover),

http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/.J. Maindonald and J. Braun (2003), “Data Analysis and Graphics Using R: An Example-Based Approach”, Cambridge University Press, ISBN 0-521-81336-0,

http://wwwmaths.anu.edu.au/~johnm/.S. M. Iacus and G. Masarotto (2002), “Laboratorio di statistica con R”, McGraw-Hill, ISBN 88-386-6084-0 (in Italian),

http://www.ateneonline.it/LibroAteneo.asp?item_id=1436.

The book

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

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

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

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

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

P. Spector (1994), “An introduction to S and S-Plus”, Duxbury Press.A. Krause and M. Olsen (2002), “The Basics of S-Plus” (Third Edition). Springer, ISBN 0-387-95456-2

The book

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

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

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

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

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

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

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

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

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 = 2004, note = {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()`

.

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,
enhancements and patches to the source code and documentation of R,
comparison and compatibility with S and S-Plus, and for the posting of
nice examples and benchmarks.
`R-devel`

- This list is for discussions about the future of R, proposals of new functionality, and pre-testing of new versions. It is meant for those who maintain an active position in the development of R.

Convenient access to information on these lists, subscription, and
archives is provided by the web interface at
http://stat.ethz.ch/mailman/listinfo/. One can also subscribe
(or unsubscribe) via email, e.g. to R-help by sending subscribe
(or unsubscribe) in the *body* of the message (not in the
subject!) to R-help-request@lists.R-project.org.

Send email to R-help@lists.R-project.org to send a message to everyone on the R-help mailing list. Subscription and posting to the other lists is done analogously, with R-help replaced by R-announce, R-packages, and R-devel, respectively. Note that the R-announce and R-packages lists are gatewayed into R-help. Hence, you should subscribe to either of them only in case you are not subscribed to R-help.

It is recommended that you send mail to R-help rather than only to the R Core developers (who are also subscribed to the list, of course). This may save them precious time they can use for constantly improving R, and will typically also result in much quicker feedback for yourself.

Of course, in the case of bug reports it would be very helpful to have code which reliably reproduces the problem. Also, make sure that you include information on the system and version of R being used. See R Bugs for more details.

Please read the posting guide *before* sending anything to any mailing list.

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

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

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

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

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

Daily mirrors are available at URLs including

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

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

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

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

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

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

R is released under the GNU General Public License (GPL). If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice.

It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (“No Discrimination Against Fields of Endeavor”) of the Open Source Definition:

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.

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?).

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.

S is a very high level language and an environment for data analysis and graphics. In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for

the S system, which has forever altered the way people analyze, visualize, and manipulate data ...S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers.

The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S.

- Richard A. Becker and John M. Chambers (1984), “S. An Interactive
Environment for Data Analysis and Graphics,” Monterey: Wadsworth and
Brooks/Cole.
This is also referred to as the “

*Brown Book*”, and of historical interest only. - Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), “The New
S Language,” London: Chapman & Hall.
This book is often called the “

*Blue Book*”, and introduced what is now known as S version 2. - John M. Chambers and Trevor J. Hastie (1992), “Statistical Models in
S,” London: Chapman & Hall.
This is also called the “

*White Book*”, and introduced S version 3, which added structures to facilitate statistical modeling in S. - John M. Chambers (1998), “Programming with Data,” New York: Springer,
ISBN 0-387-98503-4
(http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/).
This “

*Green Book*” describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.

See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html for further information on “Stages in the Evolution of S”.

There is a huge amount of user-contributed code for S, available at the S Repository at CMU.

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.

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.

Next: Models, Previous: What are the differences between R and S?, Up: What are the differences between R and S?

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.

There are some differences in the modeling code, such as

- Whereas in S, you would use
`lm(y ~ x^3)`

to regress`y`

on`x^3`

, in R, you have to insulate powers of numeric vectors (using`I()`

), i.e., you have to use`lm(y ~ I(x^3))`

. - The glm family objects are implemented differently in R and S. The same functionality is available but the components have different names.
- Option
`na.action`

is set to`"na.omit"`

by default in R, but not set in S. - Terms objects are stored differently. In S a terms object is an expression with attributes, in R it is a formula with attributes. The attributes have the same names but are mostly stored differently.
- Finally, in R
`y~x+0`

is an alternative to`y~x-1`

for specifying a model with no intercept. Models with no parameters at all can be specified by`y~0`

.

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

Some known differences are the following.

- In R, if
`x`

is a list, then`x[i] <- NULL`

and`x[[i]] <- NULL`

remove the specified elements from`x`

. The first of these is incompatible with S, where it is a no-op. (Note that you can set elements to`NULL`

using`x[i] <- list(NULL)`

.) - In S, the functions named
`.First`

and`.Last`

in the .Data directory can be used for customizing, as they are executed at the very beginning and end of a session, respectively.In R, the startup mechanism is as follows. R first sources the system startup file $R_HOME/library/base/R/Rprofile. Then, it searches for a site-wide startup profile unless the command line option --no-site-file was given. The name of this file is taken from the value of the R_PROFILE environment variable. If that variable is unset, the default is $R_HOME/etc/Rprofile.site ($R_HOME/etc/Rprofile in versions prior to 1.4.0). This code is loaded in package

**base**. Then, unless --no-init-file was given, R searches for a file called .Rprofile in the current directory or in the user's home directory (in that order) and sources it into the user workspace. It then loads a saved image of the user workspace from .RData in case there is one (unless --no-restore was specified). If needed, the functions`.First()`

and`.Last()`

should be defined in the appropriate startup profiles. - In R,
`T`

and`F`

are just variables being set to`TRUE`

and`FALSE`

, respectively, but are not reserved words as in S and hence can be overwritten by the user. (This helps e.g. when you have factors with levels`"T"`

or`"F"`

.) Hence, when writing code you should always use`TRUE`

and`FALSE`

. - In R,
`dyn.load()`

can only load*shared objects*, as created for example by`R CMD SHLIB`. - In R,
`attach()`

currently only works for lists and data frames, but not for directories. (In fact,`attach()`

also works for R data files created with`save()`

, which is analogous to attaching directories in S.) Also, you cannot attach at position 1. - Categories do not exist in R, and never will as they are deprecated now in S. Use factors instead.
- In R,
`For()`

loops are not necessary and hence not supported. - In R,
`assign()`

uses the argument envir= rather than where= as in S. - The random number generators are different, and the seeds have different length.
- R passes integer objects to C as
`int *`

rather than`long *`

as in S. - R has no single precision storage mode. However, as of version 0.65.1, there is a single precision interface to C/FORTRAN subroutines.
- By default,
`ls()`

returns the names of the objects in the current (under R) and global (under S) environment, respectively. For example, givenx <- 1; fun <- function() {y <- 1; ls()}

then

`fun()`

returns`"y"`

in R and`"x"`

(together with the rest of the global environment) in S. - R allows for zero-extent matrices (and arrays, i.e., some elements of
the
`dim`

attribute vector can be 0). This has been determined a useful feature as it helps reducing the need for special-case tests for empty subsets. For example, if`x`

is a matrix,`x[, FALSE]`

is not`NULL`

but a “matrix” with 0 columns. Hence, such objects need to be tested for by checking whether their`length()`

is zero (which works in both R and S), and not using`is.null()`

. - Named vectors are considered vectors in R but not in S (e.g.,
`is.vector(c(a = 1:3))`

returns`FALSE`

in S and`TRUE`

in R). - Data frames are not considered as matrices in R (i.e., if
`DF`

is a data frame, then`is.matrix(DF)`

returns`FALSE`

in R and`TRUE`

in S). - R by default uses treatment contrasts in the unordered case, whereas S uses the Helmert ones. This is a deliberate difference reflecting the opinion that treatment contrasts are more natural.
- In R, the argument of a replacement function which corresponds to the
right hand side must be named value. E.g.,
`f(a) <- b`

is evaluated as`a <- "f<-"(a, value = b)`

. S always takes the last argument, irrespective of its name. - In S,
`substitute()`

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

loop is local to the inside of the loop. In R it is local to the environment where the`for()`

statement is executed. - In S,
`tapply(simplify=TRUE)`

returns a vector where R returns a one-dimensional array (which can have named dimnames). - In S(-Plus) the C locale is used, whereas in R the current
operating system locale is used for determining which characters are
alphanumeric and how they are sorted. This affects the set of valid
names for R objects (for example accented chars may be allowed in R) and
ordering in sorts and comparisons (such as whether
`"aA" < "Bb"`

is true or false). From version 1.2.0 the locale can be (re-)set in R by the`Sys.setlocale()`

function. - In S,
`missing(`

`arg``)`

remains`TRUE`

if`arg`is subsequently modified; in R it doesn't. - From R version 1.3.0,
`data.frame`

strips`I()`

when creating (column) names. - In R, the string
`"NA"`

is not treated as a missing value in a character variable. Use`as.character(NA)`

to create a missing character value. - R disallows repeated formal arguments in function calls.
- In S,
`dump()`

,`dput()`

and`deparse()`

are essentially different interfaces to the same code. In R from version 2.0.0, this is only true if the same`control`

argument is used, but by default it is not. By default`dump()`

tries to write code that will evaluate to reproduce the object, whereas`dput()`

and`deparse()`

default to options for producing deparsed code that is readable.

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.

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.

There is no such thing.

**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.stat.ucla.edu/journals/jss/v04/i01/).

Ulf Bartel is working on
**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.

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. 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.

**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.

Next: Add-on packages from CRAN, Previous: Which add-on packages exist for R?, Up: Which add-on packages exist for 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.

Next: Add-on packages from Omegahat, Previous: Add-on packages in R, Up: Which add-on packages exist for R?

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.)

**AlgDesign**- Algorithmic experimental designs. Calculates exact and approximate
theory experimental designs for D, A, and I criteria.
**AnalyzeFMRI**- Functions for I/O, visualisation and analysis of functional Magnetic
Resonance Imaging (fMRI) datasets stored in the ANALYZE format.
**Bhat**- Functions for general likelihood exploration (MLE, MCMC, CIs).
**BradleyTerry**- Specify and fit the Bradley-Terry model and structured versions.
**BsMD**- Bayes screening and model discrimination follow-up designs.
**CDNmoney**- Components of Canadian monetary aggregates.
**CGIwithR**- Facilities for the use of R to write CGI scripts.
**CircStats**- Circular Statistics, from “Topics in Circular Statistics” by S. Rao
Jammalamadaka and A. SenGupta, 2001, World Scientific.
**CoCo**- Graphical modeling for contingency tables using CoCo.
**CoCoAn**- Constrained Correspondence Analysis.
**DAAG**- Various data sets used in examples and exercises in “Data Analysis and
Graphics Using R” by John H. Maindonald and W. John Brown, 2003.
**DBI**- A common database interface (DBI) class and method definitions. All
classes in this package are virtual and need to be extended by the
various DBMS implementations.
**DCluster**- A set of functions for the detection of spatial clusters of diseases
using count data.
**Davies**- Functions for the Davies quantile function and the Generalized Lambda
distribution.
**Design**- Regression modeling, testing, estimation, validation, graphics,
prediction, and typesetting by storing enhanced model design attributes
in the fit. Design is a collection of about 180 functions that assist
and streamline modeling, especially for biostatistical and epidemiologic
applications. It also contains new functions for binary and ordinal
logistic regression models and the Buckley-James multiple regression
model for right-censored responses, and implements penalized maximum
likelihood estimation for logistic and ordinary linear models. Design
works with almost any regression model, but it was especially written to
work with logistic regression, Cox regression, accelerated failure time
models, ordinary linear models, and the Buckley-James model.
**Devore5**- Data sets and sample analyses from “Probability and Statistics for
Engineering and the Sciences (5th ed)” by Jay L. Devore, 2000, Duxbury.
**Devore6**- Data sets and sample analyses from “Probability and Statistics for
Engineering and the Sciences (6th ed)” by Jay L. Devore, 2003, Duxbury.
**EMV**- Estimation of missing values in a matrix by a k-th nearest
neighboors algorithm.
**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.
**GenKern**- Functions for generating and manipulating generalised binned kernel
density estimates.
**GeneTS**- A package for analysing multiple gene expression time series data.
Currently, implements methods for cell cycle analysis and for inferring
large sparse graphical Gaussian models.
**HI**- Simulation from distributions supported by nested hyperplanes.
**HighProbability**- Estimation of the alternative hypotheses having frequentist or Bayesian
probabilities at least as great as a specified threshold, given a list
of p-values.
**Hmisc**- Functions useful for data analysis, high-level graphics, utility
operations, functions for computing sample size and power, importing
datasets, imputing missing values, advanced table making, variable
clustering, character string manipulation, conversion of S objects to
LaTeX code, recoding variables, and bootstrap repeated measures
analysis.
**HyperbolicDist**- Basic functions for the hyperbolic distribution: probability density
function, distribution function, quantile function, a routine for
generating observations from the hyperbolic, and a function for fitting
the hyperbolic distribution to data.
**ISwR**- Data sets for “Introductory Statistics with R” by Peter Dalgaard,
2002, Springer.
**Icens**- Functions for computing the NPMLE for censored and truncated data.
**KMsurv**- Data sets and functions for “Survival Analysis, Techniques for Censored
and Truncated Data” by Klein and Moeschberger, 1997, Springer.
**KernSmooth**- Functions for kernel smoothing (and density estimation) corresponding to
the book “Kernel Smoothing” by M. P. Wand and M. C. Jones, 1995.
*Recommended*. **MASS**- Functions and datasets from the main package of Venables and Ripley,
“Modern Applied Statistics with S”. Contained in the VR
bundle.
*Recommended*. **MCMCpack**- Markov chain Monte Carlo (MCMC) package: functions for posterior
simulation for a number of statistical models.
**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.
**Malmig**- An implementation of Malecot migration model together with a number of
related functions.
**Matching**- Multivariate and propensity score matching with formal tests of balance.
**Matrix**- A Matrix package.
**NADA**- Methods described in “Nondetects And Data Analysis: Statistics for
Censored Environmental Data” by Dennis R. Helsel, 2004, John Wiley and
Sons.
**NISTnls**- A set of test nonlinear least squares examples from NIST, the
U.S. National Institute for Standards and Technology.
**Oarray**- Arrays with arbitrary offsets.
**PBSmapping**- Software evolved from fisheries research conducted at the Pacific
Biological Station (PBS) in Nanaimo, British Columbia, Canada. Draws
maps and implements other GIS procedures.
**PHYLOGR**- Manipulation and analysis of phylogenetically simulated data sets (as
obtained from PDSIMUL in package PDAP) and phylogenetically-based
analyses using GLS.
**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.
**R2HTML**- Functions for exporting R objects & graphics in an HTML document.
**R2WinBUGS**- Running WinBUGS from R: call a BUGS model, summarize inferences and
convergence in a table and graph, and save the simulations in arrays for
easy access in R.
**RArcInfo**- Functions to import Arc/Info V7.x coverages and data.
**RColorBrewer**- ColorBrewer palettes for drawing nice maps shaded according to a
variable.
**RCurl**- Allows one to compose HTTP requests to fetch URIs, post forms, etc., and
process the results returned by the Web server.
**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.
**RODBC**- An ODBC database interface.
**ROracle**- Oracle Database Interface driver for R. Uses the ProC/C++ embedded SQL.
**RQuantLib**- Provides access to (some) of the QuantLib functions from within R;
currently limited to some Option pricing and analysis functions. The
QuantLib project aims to provide a comprehensive software framework for
quantitative finance.
**RSQLite**- Database Interface R driver for SQLite. Embeds the SQLite database
engine in R.
**RScaLAPACK**- An interface to ScaLAPACK functions from R.
**RSvgDevice**- A graphics device for R that uses the new w3.org XML standard for
Scalable Vector Graphics.
**RUnit**- Functions implementing a standard Unit Testing framework, with
additional code inspection and report generation tools.
**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).
**RandomFields**- Creating random fields using various methods.
**Rcmdr**- A platform-independent basic-statistics GUI (graphical user interface)
for R, based on the
**tcltk**package. **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.
**Rstem**- Interface to Snowball implementation of Porter's word stemming
algorithm.
**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.
**SASmixed**- Data sets and sample linear mixed effects analyses corresponding to the
examples in “SAS System for Mixed Models” by R. C. Littell,
G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.
**SIN**- A SINful approach to selection of Gaussian Graphical Markov Models.
**SenSrivastava**- Collection of datasets from “Regression Analysis, Theory, Methods and
Applications” by A. Sen and M. Srivastava, 1990, Springer.
**SoPhy**- Soil Physics Tools: simulation of water flux and solute transport in
soil.
**SparseLogReg**- Sparse logistic regression.
**SparseM**- Basic linear algebra for sparse matrices.
**StatDataML**- Read and write StatDataML.
**SuppDists**- Ten distributions supplementing those built into R (Inverse Gauss,
Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho,
maximum F ratio, the Pearson product moment correlation coefficiant,
Johnson distributions, normal scores and generalized hypergeometric
distributions).
**UNF**- Tools for creating universal numeric fingerprints for data.
**UsingR**- Data sets to accompany the textbook “Using R for Introductory
Statistics” by J. Verzani, 2005, Chapman & Hall/CRC.
**VLMC**- Functions, classes & methods for estimation, prediction, and simulation
(bootstrap) of VLMC (Variable Length Markov Chain) models.
**VaR**- Methods for calculation of Value at Risk (VaR).
**XML**- Facilities for reading XML documents and DTDs.
**Zelig**- Everyone's statistical software: an easy-to-use program that can
estimate, and help interpret the results of, an enormous range of
statistical models.
**abind**- Combine multi-dimensional arrays.
**accuracy**- A suite of tools designed to test and improve the accuracy of
statistical computation.
**acepack**- ACE (Alternating Conditional Expectations) and AVAS (Additivity and
VAriance Stabilization for regression) methods for selecting regression
transformations.
**adapt**- Adaptive quadrature in up to 20 dimensions.
**ade4**- Multivariate data analysis and graphical display.
**adehabitat**- A collection of tools for the analysis of habitat selection by animals.
**agce**- Analysis of growth curve experiments.
**akima**- Linear or cubic spline interpolation for irregularly gridded data.
**amap**- Another Multidimensional Analysis Package.
**anm**- Analog model for statistical/empirical downscaling.
**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.
**ash**- David Scott's ASH routines for 1D and 2D density estimation.
**assist**- A suite of functions implementing smoothing splines.
**asypow**- A set of routines that calculate power and related quantities utilizing
asymptotic likelihood ratio methods.
**aws**- Functions to perform adaptive weights smoothing.
**bayesSurv**- Bayesian survival regression with flexible error and (later on also
random effects) distributions.
**bayesmix**- Bayesian mixture models of univariate Gaussian distributions using JAGS.
**betareg**- Beta regression for modeling rates and proportions.
**bim**- Bayesian interval mapping diagnostics: functions to interpret QTLCart
and Bmapqtl samples.
**bindata**- Generation of correlated artificial binary data.
**blighty**- Function for drawing the coastline of the United Kingdom.
**boa**- Bayesian Output Analysis Program for MCMC.
**boolean**- Boolean logit and probit: a procedure for testing Boolean hypotheses.
**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*. **bqtl**- QTL mapping toolkit for inbred crosses and recombinant inbred lines.
Includes maximum likelihood and Bayesian tools.
**brlr**- Bias-reduced logistic regression: fits logistic regression models by
maximum penalized likelihood.
**car**- Companion to Applied Regression, containing functions for applied
regession, linear models, and generalized linear models, with an
emphasis on regression diagnostics, particularly graphical diagnostic
methods.
**cat**- Analysis of categorical-variable datasets with missing values.
**catspec**- Special models for categorical variables.
**cclust**- Convex clustering methods, including k-means algorithm, on-line
update algorithm (Hard Competitive Learning) and Neural Gas algorithm
(Soft Competitive Learning) and calculation of several indexes for
finding the number of clusters in a data set.
**cfa**- Analysis of configuration frequencies.
**chplot**- Augmented convex hull plots: informative and nice plots for grouped
bivariate data.
**chron**- A package for working with chronological objects (times and dates).
**circular**- Circular statistics, from “Topics in Circular Statistics” by Rao
Jammalamadaka and A. SenGupta, 2001, World Scientific.
**clac**- Clust Along Chromosomes, a method to call gains/losses in CGH array
data.
**class**- Functions for classification (k-nearest neighbor and LVQ).
Contained in the VR bundle.
*Recommended*. **classPP**- Projection Pursuit for supervised classification.
**clim.pact**- Climate analysis and downscaling for monthly and daily data.
**clines**- Calculates Contour Lines.
**cluster**- Functions for cluster analysis.
*Recommended*. **cmprsk**- Estimation, testing and regression modeling of subdistribution functions
in competing risks.
**cobs**- Constrained B-splines: qualitatively constrained (regression) smoothing
via linear programming.
**coda**- Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)
simulations.
**combinat**- Combinatorics utilities.
**concord**- Measures of concordance and reliability.
**conf.design**- A series of simple tools for constructing and manipulating confounded
and fractional factorial designs.
**covRobust**- Robust covariance estimation via nearest neighbor cleaning.
**cramer**- Routine for the multivariate nonparametric Cramer test.
**crossdes**- Functions for the construction and randomization of balanced carryover
balanced designs, to check given designs for balance, and for simulation
studies on the validity of two randomization procedures.
**cyclones**- Cyclone identification.
**date**- Functions for dealing with dates. The most useful of them accepts a
vector of input dates in any of the forms 8/30/53,
30Aug53, 30 August 1953, ..., August 30 53, or
any mixture of these.
**dblcens**- Calculates the NPMLE of the survival distribution for doubly censored
data.
**deal**- Bayesian networks with continuous and/or discrete variables can be
learned and compared from data.
**debug**- Debugger for R functions, with code display, graceful error recovery,
line-numbered conditional breakpoints, access to exit code, flow
control, and full keyboard input.
**deldir**- Calculates the Delaunay triangulation and the Dirichlet or Voronoi
tesselation (with respect to the entire plane) of a planar point set.
**diamonds**- Functions for illustrating aperture-4 diamond partitions in the plane,
or on the surface of an octahedron or icosahedron, for use as analysis
or sampling grids.
**dichromat**- Color schemes for dichromats: collapse red-green distinctions to
simulate the effects of colour-blindness.
**digest**- Two functions for the creation of “hash” digests of arbitrary R
objects using the md5 and sha-1 algorithms permitting easy comparison of
R language objects.
**diptest**- Compute Hartigan's dip test statistic for unimodality.
**dispmod**- Functions for modelling dispersion in GLMs.
**distr**- An object orientated implementation of distributions and some additional
functionality.
**dr**- Functions, methods, and datasets for fitting dimension reduction
regression, including pHd and inverse regression methods SIR and SAVE.
**drfit**- Dose-response data evaluation.
**dse**- Dynamic System Estimation, a multivariate time series package. Contains
**dse1**(the base system, including multivariate ARMA and state space models),**dse2**(extensions for evaluating estimation techniques, forecasting, and for evaluating forecasting model),**tframe**(functions for writing code that is independent of the representation of time). and**setRNG**(a mechanism for generating the same random numbers in S and R). **dynamicGraph**- Interactive graphical tool for manipulating graphs.
**e1071**- Miscellaneous functions used at the Department of Statistics at TU Wien
(E1071), including moments, short-time Fourier transforms, Independent
Component Analysis, Latent Class Analysis, support vector machines, and
fuzzy clustering, shortest path computation, bagged clustering, and some
more.
**eba**- Fitting and testing probabilistic choice models, especially the BTL,
elimination-by-aspects (EBA), and preference tree (Pretree) models.
**ebayesthresh**- Empirical Bayes thresholding and related methods.
**edci**- Edge Detection and Clustering in Images.
**effects**- Graphical and tabular effect displays, e.g., of interactions, for linear
and generalised linear models.
**eha**- A package for survival and event history analysis.
**ellipse**- Package for drawing ellipses and ellipse-like confidence regions.
**emme2**- Functions to read from and write to an EMME/2 databank.
**emplik**- Empirical likelihood ratio for means/quantiles/hazards from possibly
right censored data.
**energy**- E-statistics (energy) tests for comparing distributions: multivariate
normality, Poisson test, multivariate k-sample test for equal
distributions, hierarchical clustering by e-distances.
**epitools**- Basic tools for applied epidemiology.
**epsi**- Edge Preserving Smoothing for Images.
**evd**- Functions for extreme value distributions. Extends simulation,
distribution, quantile and density functions to univariate, bivariate
and (for simulation) multivariate parametric extreme value
distributions, and provides fitting functions which calculate maximum
likelihood estimates for univariate and bivariate models.
**evdbayes**- Functions for the bayesian analysis of extreme value models, using MCMC
methods.
**evir**- Extreme Values in R: Functions for extreme value theory, which may be
divided into the following groups; exploratory data analysis, block
maxima, peaks over thresholds (univariate and bivariate), point
processes, gev/gpd distributions.
**exactLoglinTest**- Monte Carlo exact tests for log-linear models.
**exactRankTests**- Computes exact p-values and quantiles using an implementation of
the Streitberg/Roehmel shift algorithm.
**fBasics**- The Rmetrics module for “Markets, basic statistics, and date and time
management”. Rmetrics is an environment and software collection for
teaching financial engineering and computational finance
(http://www.Rmetrics.org/).
**fExtremes**- The Rmetrics module for “Beyond the Sample, Dealing with Extreme
Values”.
**fOptions**- The Rmetrics module for “The Valuation of Options”.
**fSeries**- The Rmetrics module for “The Dynamical Process Behind Financial
Markets”.
**faraway**- Functions and datasets for books by Julian Faraway.
**fastICA**- Implementation of FastICA algorithm to perform Independent Component
Analysis (ICA) and Projection Pursuit.
**fda**- Functional Data Analysis: analysis of data where the basic observation
is a function of some sort.
**fdim**- Functions for calculating fractal dimension.
**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.
**flexmix**- Flexible Mixture Modeling: a general framework for finite mixtures of
regression models using the EM algorithm.
**foreign**- Functions for reading and writing data stored by statistical software
like Minitab, S, SAS, SPSS, Stata, Systat, etc.
*Recommended*. **fork**- Functions for handling multiple processes: simple wrappers around the
Unix process management API calls.
**fortunes**- R fortunes.
**forward**- Forward search approach to robust analysis in linear and generalized
linear regression models.
**fpc**- Fixed point clusters, clusterwise regression and discriminant plots.
**fracdiff**- Maximum likelihood estimation of the parameters of a fractionally
differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied
Statistics, 1989).
**ftnonpar**- Features and strings for nonparametric regression.
**g.data**- Create and maintain delayed-data packages (DDP's).
**gRbase**- A package for graphical modelling in R. Defines S4 classes for
graphical meta data and graphical models, and illustrates how
hierarchical log-linear models may be implemented and combined with
**dynamicGraph**. **gafit**- Genetic algorithm for curve fitting.
**gam**- Functions for fitting and working with Generalized Additive Models, as
described in chapter 7 of the White Book, and in “Generalized Additive
Models” by T. Hastie and R. Tibshirani (1990).
**gap**- Genetic analysis package for both population and family data.
**gbm**- Generalized Boosted Regression Models: implements extensions to Freund
and Schapire's AdaBoost algorithm and J. Friedman's gradient boosting
machine. Includes regression methods for least squares, absolute loss,
logistic, Poisson, Cox proportional hazards partial likelihood, and
AdaBoost exponential loss.
**gclus**- Clustering Graphics. Orders panels in scatterplot matrices and parallel
coordinate displays by some merit index.
**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.
**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.
**ggm**- Functions for defining directed acyclic graphs and undirected graphs,
finding induced graphs and fitting Gaussian Markov models.
**gld**- Basic functions for the generalised (Tukey) lambda distribution.
**gllm**- Routines for log-linear models of incomplete contingency tables,
including some latent class models via EM and Fisher scoring approaches.
**glmmML**- A Maximum Likelihood approach to generalized linear models with random
intercept.
**gmp**- Arithmetic “without limitations” using the GNU Multiple
Precision library.
**gpclib**- General polygon clipping routines for R based on Alan Murta's C
library.
**grasper**- Generalized Regression Analysis and Spatial Predictions for R.
**gregmisc**- Miscellaneous functions written/maintained by Gregory R. Warnes.
**gridBase**- Integration of base and grid graphics.
**gss**- A comprehensive package for structural multivariate function estimation
using smoothing splines.
**gstat**- multivariable geostatistical modelling, prediction and simulation.
Includes code for variogram modelling; simple, ordinary and universal
point or block (co)kriging, sequential Gaussian or indicator
(co)simulation, and map plotting functions.
**gtkDevice**- GTK graphics device driver that may be used independently of the R-GNOME
interface and can be used to create R devices as embedded components in
a GUI using a Gtk drawing area widget, e.g., using RGtk.
**hapassoc**- Likelihood inference of trait associations with SNP haplotypes and other
attributes using the EM Algorithm.
**haplo.score**- Score tests for association of traits with haplotypes when linkage phase
is ambiguous.
**haplo.stats**- Statistical analysis of haplotypes with traits and covariates when
linkage phase is ambiguous.
**hdf5**- Interface to the NCSA HDF5 library.
**hett**- Functions for the fitting and summarizing of heteroscedastic
t-regression.
**hier.part**- Hierarchical Partitioning: variance partition of a multivariate data
set.
**hierfstat**- Estimation of hierarchical F-statistics from haploid or diploid genetic
data with any numbers of levels in the hierarchy, and tests for the
significance of each F and variance components.
**homals**- Homogeneity Analysis (HOMALS) package with optional Tcl/Tk interface.
**hopach**- Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH).
**httpRequest**- Implements HTTP Request protocols (GET, POST, and multipart POST
requests).
**hwde**- Models and tests for departure from Hardy-Weinberg equilibrium and
independence between loci.
**ifs**- Iterated Function Systems distribution function estimator.
**impute**- Imputation for microarray data (currently KNN only).
**ineq**- Inequality, concentration and poverty measures, and Lorenz curves
(empirical and theoretic).
**intcox**- Implementation of the Iterated Convex Minorant Algorithm for the Cox
proportional hazard model for interval censored event data.
**ipred**- Improved predictive models by direct and indirect bootstrap aggregation
in classification and regression as well as resampling based estimators
of prediction error.
**ismev**- Functions to support the computations carried out in “An Introduction
to Statistical Modeling of Extreme Values;' by S. Coles, 2001, Springer.
The functions may be divided into the following groups; maxima/minima,
order statistics, peaks over thresholds and point processes.
**its**- An S4 class for handling irregular time series.
**kernlab**- Kernel-based machine learning methods including support vector machines.
**kinship**- Mixed-effects Cox models, sparse matrices, and modeling data from large
pedigrees.
**klaR**- Miscellaneous functions for classification and visualization developed
at the Department of Statistics, University of Dortmund.
**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.
**kza**- Kolmogorov-Zurbenko Adpative filter for locating change points in a time
series.
**labstatR**- Functions for the book “Laboratorio di statistica con R” by
S. M. Iacus and G. Masarotto, 2002, McGraw-Hill. Function names and
documentation in Italian.
**lars**- Least Angle Regression, Lasso and Forward Stagewise: efficient
procedures for fitting an entire lasso sequence with the cost of a
single least squares fit.
**lasso2**- Routines and documentation for solving regression problems while
imposing an L1 constraint on the estimates, based on the algorithm of
Osborne et al. (1998).
**lattice**- Lattice graphics, an implementation of Trellis Graphics functions.
*Recommended*. **lazy**- Lazy learning for local regression.
**ldDesign**- Design of experiments for detection of linkage disequilibrium,
**leaps**- A package which performs an exhaustive search for the best subsets of a
given set of potential regressors, using a branch-and-bound algorithm,
and also performs searches using a number of less time-consuming
techniques.
**lgtdl**- A set of methods for longitudinal data objects.
**limma**- LInear Models for MicroArray data.
**linprog**- Solve linear programming/linear optimization problems by using the
simplex algorithm.
**lme4**- Fit linear and generalized linear mixed-effects models.
**lmeSplines**- Fit smoothing spline terms in Gaussian linear and nonlinear
mixed-effects models.
**lmm**- Linear mixed models.
**lmtest**- A collection of tests on the assumptions of linear regression models
from the book “The linear regression model under test” by W. Kraemer
and H. Sonnberger, 1986, Physica.
**locfdr**- Computation of local false discovery rates.
**locfit**- Local Regression, likelihood and density estimation.
**logistf**- Firth's bias reduced logistic regression approach with penalized profile
likelihood based confidence intervals for parameter estimates.
**logspline**- Logspline density estimation.
**lokern**- Kernel regression smoothing with adaptive local or global plug-in
bandwidth selection.
**lpSolve**- Functions that solve general linear/integer problems, assignment
problems, and transportation problems via interfacing Lp_solve.
**lpridge**- Local polynomial (ridge) regression.
**mAr**- Estimation of multivariate AR models through a computationally efficient
stepwise least-squares algorithm.
**magic**- A variety of methods for creating magic squares of any order greater
than 2, and various magic hypercubes.
**mapdata**- Supplement to package
**maps**, providing the larger and/or higher-resolution databases. **mapproj**- Map Projections: converts latitude/longitude into projected coordinates.
**maps**- Draw geographical maps. Projection code and larger maps are in separate
packages.
**maptools**- Set of tools for manipulating and reading geographic data, in particular
ESRI shapefiles.
**maptree**- Functions with example data for graphing and mapping models from
hierarchical clustering and classification and regression trees.
**mathgraph**- Tools for constructing and manipulating objects from a class of directed
and undirected graphs.
**maxstat**- Maximally selected rank and Gauss statistics with several p-value
approximations.
**mcgibbsit**- Warnes and Raftery's MCGibbsit MCMC diagnostic.
**mclust**- Model-based cluster analysis: the 2002 version of MCLUST.
**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.
**meanscore**- Mean Score method for missing covariate data in logistic regression
models.
**merror**- Accuracy and precision of measurements.
**mfp**- Multiple Fractional Polynomials.
**mgcv**- Routines for GAMs and other genralized ridge regression problems with
multiple smoothing parameter selection by GCV or UBRE.
*Recommended*. **mimR**- An R interface to MIM for graphical modeling in R.
**minpack.lm**- R interface for two functions from the MINPACK least squares
optimization library, solving the nonlinear least squares problem by a
modification of the Levenberg-Marquardt algorithm.
**mitools**- Tools to perform analyses and combine results from multiple-imputation
datasets.
**mix**- Estimation/multiple imputation programs for mixed categorical and
continuous data.
**mixreg**- Functions to fit mixtures of regressions.
**mlbench**- A collection of artificial and real-world machine learning benchmark
problems, including the Boston housing data.
**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.
**mscalib**- Calibration and filtering of MALDI-TOF Peptide Mass Fingerprint data.
**msm**- Functions for fitting continuous-time Markov multi-state models to
categorical processes observed at arbitrary times, optionally with
misclassified responses, and covariates on transition or
misclassification rates.
**muhaz**- Hazard function estimation in survival analysis.
**multcomp**- Multiple comparison procedures for the one-way layout.
**multinomRob**- Overdispersed multinomial regression using robust (LQD and tanh)
estimation.
**mvbutils**- Utilities by Mark V. Bravington for project organization, editing and
backup, sourcing, documentation (formal and informal), package
preparation, macro functions, and more.
**mvnmle**- ML estimation for multivariate normal data with missing values.
**mvnormtest**- Generalization of the Shapiro-Wilk test for multivariate variables.
**mvoutlier**- Multivariate outlier detection based on robust estimates of location and
covariance structure.
**mvpart**- Multivariate partitioning.
**mvtnorm**- Multivariate normal and t distributions.
**ncdf**- Interface to Unidata netCDF data files.
**ncomplete**- Functions to perform the regression depth method (RDM) to binary
regression to approximate the minimum number of observations that can be
removed such that the reduced data set has complete separation.
**ncvar**- High-level R interface to netCDF datasets.
**negenes**- Estimating the number of essential genes in a genome on the basis of
data from a random transposon mutagenesis experiment, through the use of
a Gibbs sampler.
**nlme**- Fit and compare Gaussian linear and nonlinear mixed-effects models.
*Recommended*. **nlmeODE**- Combine the
**nlme**and**odesolve**packages for mixed-effects modelling using differential equations. **nlrq**- Nonlinear quantile regression routines.
*Defunct*. **nnet**- Software for single hidden layer perceptrons (“feed-forward neural
networks”), and for multinomial log-linear models. Contained in the
VR bundle.
*Recommended*. **nor1mix**- One-dimensional normal mixture models classes, for, e.g., density
estimation or clustering algorithms research and teaching; providing the
widely used Marron-Wand densities.
**norm**- Analysis of multivariate normal datasets with missing values.
**normalp**- A collection of utilities for normal of order p distributions
(General Error Distributions).
**nortest**- Five omnibus tests for the composite hypothesis of normality.
**noverlap**- Functions to perform the regression depth method (RDM) to binary
regression to approximate the amount of overlap, i.e., the minimal
number of observations that need to be removed such that the reduced
data set has no longer overlap.
**npmc**- Nonparametric Multiple Comparisons: provides simultaneous rank test
procedures for the one-way layout without presuming a certain
distribution.
**nprq**- Nonparametric quantile regression.
*Defunct*. **odesolve**- An interface for the Ordinary Differential Equation (ODE) solver lsoda.
ODEs are expressed as R functions.
**orientlib**- Representations, conversions and display of orientation SO(3) data.
**ouch**- Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses.
**oz**- Functions for plotting Australia's coastline and state boundaries.
**pamr**- Pam: Prediction Analysis for Microarrays.
**pan**- Multiple imputation for multivariate panel or clustered data.
**panel**- Functions and datasets for fitting models to Panel data.
**pastecs**- Package for Analysis of Space-Time Ecological Series.
**pcurve**- Fits a principal curve to a numeric multivariate dataset in arbitrary
dimensions. Produces diagnostic plots. Also calculates Bray-Curtis and
other distance matrices and performs multi-dimensional scaling and
principal component analyses.
**pear**- Periodic Autoregression Analysis.
**permax**- Functions intended to facilitate certain basic analyses of DNA array
data, especially with regard to comparing expression levels between two
types of tissue.
**pgam**- Poisson-Gamma Additive Models.
**pheno**- Some easy-to-use functions for time series analyses of (plant-)
phenological data sets.
**phyloarray**- Software to process data from phylogenetic or identification
microarrays.
**pinktoe**- Converts S trees to HTML/Perl files for interactive tree traversal.
**pixmap**- Functions for import, export, plotting and other manipulations of
bitmapped images.
**plotrix**- Various useful functions for enhancing plots.
**plugdensity**- Kernel density estimation with global bandwidth selection via
“plug-in”.
**pls.pcr**- Multivariate regression by PLS and PCR.
**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.
**polynom**- A collection of functions to implement a class for univariate polynomial
manipulations.
**ppc**- Sample classification of protein mass spectra by peak probabilty
contrasts.
**pps**- Functions to select samples using PPS (probability proportional to size)
sampling, for stratified simple random sampling, and to compute joint
inclusion probabilities for Sampford's method of PPS sampling.
**prabclus**- Distance based parametric bootstrap tests for clustering, mainly thought
for presence-absence data (clustering of species distribution maps).
Jaccard and Kulczynski distance measures, clustering of MDS scores, and
nearest neighbor based noise detection.
**princurve**- Fits a principal curve to a matrix of points in arbitrary dimension.
**pspline**- Smoothing splines with penalties on order m derivatives.
**psy**- Various procedures used in psychometry: Kappa, ICC, Cronbach alpha,
screeplot, PCA and related methods.
**pwt**- The Penn World Table providing purchasing power parity and national
income accounts converted to international prices for 168 countries for
some or all of the years 1950–2000.
**qcc**- Quality Control Charts. Shewhart quality control charts for continuous,
attribute and count data. Cusum and EWMA charts. Operating
characteristic curves. Process capability analysis. Pareto chart and
cause-and-effect chart.
**qtl**- Analysis of experimental crosses to identify QTLs.
**quadprog**- For solving quadratic programming problems.
**quantreg**- Quantile regression and related methods.
**qvalue**- Q-value estimation for false discovery rate control.
**qvcalc**- Functions to compute quasi-variances and associated measures of
approximation error.
**race**- Implementation of some racing methods for the empirical selection of the
best.
**randomForest**- Breiman's random forest classifier.
**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.
**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.
**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.
**rgdal**- Provides bindings to Frank Warmerdam's Geospatial Data Abstraction
Library (GDAL).
**rgenoud**- R version of GENetic Optimization Using Derivatives.
**rgl**- 3D visualization device system (OpenGL).
**rimage**- Functions for image processing, including Sobel filter, rank filters,
fft, histogram equalization, and reading JPEG files.
**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.
**rpart**- Recursive PARTitioning and regression trees.
*Recommended*. **rpart.permutation**- Permutation tests of rpart models.
**rpvm**- R interface to PVM (Parallel Virtual Machine). Provides interface to
PVM APIs, and examples and documentation for its use.
**rqmcmb2**- Markov chain marginal bootstrap for quantile regression.
**rrcov**- Functions for robust location and scatter estimation and robust
regression with high breakdown point.
**rsprng**- Provides interface to SPRNG (Scalable Parallel Random Number Generators)
APIs, and examples and documentation for its use.
**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.
**sandwich**- Model-robust standard error estimators for time series and longitudinal
data.
**sca**- Simple Component Analysis.
**scatterplot3d**- Plots a three dimensional (3D) point cloud perspectively.
**seacarb**- Calculates parameters of the seawater carbonate system.
**seao**- Simple Evolutionary Algorithm Optimization.
**seao.gui**- Simple Evolutionary Algorithm Optimization: graphical user interface.
**segmented**- Functions to estimate break-points of segmented relationships in
regression models (GLMs).
**sem**- Functions for fitting general linear Structural Equation Models (with
observed and unobserved variables) by the method of maximum likelihood
using the RAM approach.
**seqinr**- Exploratory data analysis and data visualization for biological sequence
(DNA and protein) data.
**seqmon**- Sequential monitoring of clinical trials.
**session**- Functions for interacting with, saving and restoring R sessions.
**setRNG**- Set (normal) random number generator and seed.
**sfsmisc**- Utilities from Seminar fuer Statistik ETH Zurich.
**sgeostat**- An object-oriented framework for geostatistical modeling.
**shapefiles**- Functions to read and write ESRI shapefiles.
**shapes**- Routines for the statistical analysis of shapes, including procrustes
analysis, displaying shapes and principal components, testing for mean
shape difference, thin-plate spline transformation grids and edge
superimposition methods.
**simpleboot**- Simple bootstrap routines.
**skewt**- Density, distribution function, quantile function and random generation
for the skewed t distribution of Fernandez and Steel.
**sm**- Software linked to the book “Applied Smoothing Techniques for Data
Analysis: The Kernel Approach with S-Plus Illustrations” by
A. W. Bowman and A. Azzalini, 1997, Oxford University Press.
**sma**- Functions for exploratory (statistical) microarray analysis.
**smoothSurv**- Survival regression with smoothed error distribution.
**sn**- Functions for manipulating skew-normal probability distributions and for
fitting them to data, in the scalar and the multivariate case.
**sna**- A range of tools for social network analysis, including node and
graph-level indices, structural distance and covariance methods,
structural equivalence detection, p* modeling, and network
visualization.
**snow**- Simple Network of Workstations: support for simple parallel computing in
R.
**snowFT**- Fault Tolerant Simple Network of Workstations.
**som**- Self-Organizing Maps (with application in gene clustering).
**sound**- A sound interface for R: Basic functions for dealing with .wav
files and sound samples.
**spatial**- Functions for kriging and point pattern analysis from “Modern Applied
Statistics with S” by W. Venables and B. Ripley. Contained in the
VR bundle.
*Recommended*. **spatialCovariance**- Computation of spatial covariance matrices for data on rectangles using
one dimensional numerical integration and analytic results.
**spatstat**- Data analysis and modelling of two-dimensional point patterns, including
multitype points and spatial covariates.
**spc**- Statistical Process Control: evaluation of control charts by means of
the zero-state, steady-state ARL (Average Run Length), setting up
control charts for given in-control ARL, and plotting of the related
figures.
**spdep**- A collection of functions to create spatial weights matrix objects from
polygon contiguities, from point patterns by distance and tesselations,
for summarising these objects, and for permitting their use in spatial
data analysis; a collection of tests for spatial autocorrelation,
including global Moran's I and Geary's C, local Moran's I, saddlepoint
approximations for global and local Moran's I; and functions for
estimating spatial simultaneous autoregressive (SAR) models. (Was
formerly the three packages:
**spweights**,**sptests**, and**spsarlm**.) **spe**- Stochastic Proximity Embedding.
**splancs**- Spatial and space-time point pattern analysis functions.
**statmod**- Miscellaneous biostatistical modelling functions.
**strucchange**- Various tests on structural change in linear regression models.
**subselect**- A collection of functions which assess the quality of variable subsets
as surrogates for a full data set, and search for subsets which are
optimal under various criteria.
**supclust**- Methodology for supervised grouping of predictor variables.
**superpc**- Supervised principal components.
**survey**- Summary statistics, generalized linear models, and general maximum
likelihood estimation for stratified, cluster-sampled, unequally
weighted survey samples.
**survival**- Functions for survival analysis, including penalised likelihood.
*Recommended*. **survrec**- Survival analysis for recurrent event data.
**svmpath**- Computes the entire regularization path for the two-class svm classifier
with essentialy the same cost as a single SVM fit.
**systemfit**- Contains functions for fitting simultaneous systems of equations using
Ordinary Least Sqaures (OLS), Two-Stage Least Squares (2SLS), and
Three-Stage Least Squares (3SLS).
**tapiR**- Tools for accessing (UK) parliamentary information in R.
**taskPR**- Task-Parallel R package.
**tensor**- Tensor product of arrays.
**tkrplot**- Simple mechanism for placing R graphics in a Tk widget.
**tree**- Classification and regression trees.
**treeglia**- Stem analysis functions for volume increment and carbon uptake
assessment from tree-rings.
**tripack**- A constrained two-dimensional Delaunay triangulation package.
**tseries**- Package for time series analysis with emphasis on non-linear modelling.
**tuneR**- Collection of tools to analyze music, handle wave files, transcription,
etc.
**tweedie**- Maximum likelihood computations for Tweedie exponential family models.
**twostage**- Functions for optimal design of two-stage-studies using the Mean Score
method.
**udunits**- Interface to Unidata's routines to convert units.
**urn**- Functions for sampling without replacement (simulated urns).
**urca**- Unit root and cointegration tests for time series data.
**vabayelMix**- Variational Bayesian mixture model.
**vardiag**- Interactive variogram diagnostics.
**vcd**- Functions and data sets based on the book “Visualizing Categorical
Data” by Michael Friendly.
**vegan**- Various help functions for vegetation scientists and community
ecologists.
**verification**- Utilities for verification of discrete and probabilistic forecasts.
**verify**- Construction of test suites using verify objects.
**vioplot**- Violin plots, which are a combination of a box plot and a kernel density
plot.
**waveslim**- Basic wavelet routines for time series analysis.
**wavethresh**- Software to perform 1-d and 2-d wavelet statistics and transforms.
**wle**- Robust statistical inference via a weighted likelihood approach.
**xgobi**- Interface to the XGobi and XGvis programs for graphical data analysis.
**xtable**- Export data to LaTeX and HTML tables.
**zoo**- A class with methods for totally ordered indexed observations such as irregular time series.

See CRAN src/contrib/PACKAGES for more information.

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

**Dopt**- Finding D-optimal experimental designs.
**GLMMGibbs**- Generalised Linear Mixed Models by Gibbs sampling.
**RPgSQL**- Provides methods for accessing data stored in PostgreSQL tables.
**dseplus**- Extensions to
**dse**, the Dynamic Systems Estimation multivariate time series package. Contains PADI, juice and monitoring extensions. **ensemble**- Ensembles of tree classifiers.
**rcom**- R COM Client Interface and internal COM Server.
**runStat**- Running median and mean.
**write.snns**- Function for writing a SNNS pattern file from a data frame or matrix.

Next: Add-on packages from Bioconductor, Previous: Add-on packages from CRAN, Up: Which add-on packages exist for R?

The src/contrib/Omegahat Directory of a CRAN site contains yet unreleased packages from the Omegahat Project for Statistical Computing. Currently, there are

**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.
**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++.
**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.
**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.
**RGtkViewers**- A collection of tools for viewing different S objects, databases, class
and widget hierarchies, S source file contents, etc.
**RJavaDevice**- A graphics device for R that uses Java components and graphics.
APIs.
**RObjectTables**- The C and S code allows one to define R objects to be used as elements
of the search path with their own semantics and facilities for reading
and writing variables. The objects implement a simple interface via R
functions (either methods or closures) and can access external data,
e.g., in other applications, languages, formats, ...
**RSMethods**- An implementation of S version 4 methods and classes for R, consistent
with the basic material in “Programming with Data” by John
M. Chambers, 1998, Springer NY.
**RSPerl**- An interface from R to an embedded, persistent Perl interpreter,
allowing one to call arbitrary Perl subroutines, classes and methods.
**RSPython**- Allows Python programs to invoke S functions, methods, etc., and S code
to call Python functionality.
**RXLisp**- An interface to call XLisp-Stat functions from within R.
**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.
**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.

Next: Other add-on packages, Previous: Add-on packages from Omegahat, Up: Which add-on packages exist for R?

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

**AnnBuilder**- Assemble and process genomic annotation data, from databases such as
GenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSC
Human Genome Project.
**Biobase**- Object-oriented representation and manipulation of genomic data (S4
class structure).
**Biostrings**- Class definitions and generics for biological sequences along with
pattern matching algorithms.
**DNAcopy**- Segments DNA copy number data using circular binary segmentation to
detect regions with abnormal copy number.
**DynDoc**- Functionality to create and interact with dynamic documents, vignettes,
and other navigable documents.
**EBarrays**- Empirical Bayes tools for the analysis of replicated microarray data
across multiple conditions.
**GOstats**- Tools for manipulating GO and microarrays.
**GeneSpring**- Functions and class definitions to be able to read and write GeneSpring
specific data objects and convert them to Bioconductor objects.
**GeneTS**-
A package for analysing multiple gene expression time series data.
Currently, implements methods for cell cycle analysis and for inferring
large sparse graphical Gaussian models.
**Icens**- Functions for computing the NPMLE for censored and truncated data.
**LPE**- Significance analysis of microarray data with small number of replicates
using the Local Pooled Error (LPE) method.
**MeasurementError.cor**- Two-stage measurement error model for correlation estimation with
smaller bias than the usual sample correlation.
**PROcess**- Ciphergen SELDI-TOF processing.
**RBGL**- An interface between the graph package and the Boost graph libraries,
allowing for fast manipulation of graph objects in R.
**RMAGEML**- Functionality to handle MAGEML documents.
**ROC**- Receiver Operating Characteristic (ROC) approach for identifying genes
that are differentially expressed in two types of samples.
**RSNPper**- Interface to chip.org::SNPper for SNP-related data.
**RdbiPgSQL**- Methods for accessing data stored in PostgreSQL tables.
**Rdbi**- Generic framework for database access in R.
**Resourcerer**- Read annotation data from TIGR Resourcerer or convert the annotation
data into Bioconductor data package.
**Rgraphviz**- An interface with Graphviz for plotting graph objects in R.
**Ruuid**- Creates Universally Unique ID values (UUIDs) in R.
**SAGElyzer**- Locates genes based on SAGE tags.
**SNAData**- Data from the book “Social Network Analysis” by Wasserman & Faust,
1999.
**aCGH**- Classes and functions for Array Comparative Genomic Hybridization data.
**affy**- Methods for Affymetrix Oligonucleotide Arrays.
**affyPLM**- For fitting Probe Level Models.
**affycomp**- Graphics toolbox for assessment of Affymetrix expression measures.
**affydata**- Affymetrix data for demonstration purposes.
**affylmGUI**- Graphical User Interface for affy analysis using package
**limma**. **affypdnn**- Probe Dependent Nearest Neighbors (PDNN) for the affy package.
**altcdfenvs**- Utilities to handle cdfenvs.
**annaffy**- Functions for handling data from Bioconductor Affymetrix annotation data
packages.
**annotate**- Associate experimental data in real time to biological metadata from web
databases such as GenBank, LocusLink and PubMed. Process and store
query results. Generate HTML reports of analyses.
**arrayMagic**- Utilities for quality control and processing for two-color cDNA
microarray data.
**arrayQuality**- Performing print-run and array level quality assessment.
**bim**-
Bayesian interval mapping diagnostics: functions to interpret QTLCart
and Bmapqtl samples.
**convert**- Convert Microarray Data Objects.
**ctc**- Tools to export and import Tree and Cluster to other programs.
**daMA**- Functions for the efficient design of factorial two-color microarray
experiments and for the statistical analysis of factorial microarray
data.
**ecolitk**- Metadata and tools to work with E. coli.
**edd**- Expression density diagnostics: graphical methods and pattern
recognition algorithms for distribution shape classification.
**exprExternal**- Implementation of exprSet using externalVectors.
**externalVector**- Basic class definitions and generics for external pointer based vector
objects for R.
**factDesign**- A set of tools for analyzing data from factorial designed microarray
experiments. The functions can be used to evaluate appropriate tests of
contrast and perform single outlier detection.
**gcrma**- Background adjustment using sequence information.
**genefilter**- Tools for sequentially filtering genes using a wide variety of filtering
functions. Example of filters include: number of missing value,
coefficient of variation of expression measures, ANOVA p-value,
Cox model p-values. Sequential application of filtering
functions to genes.
**geneplotter**- Graphical tools for genomic data, for example for plotting expression
data along a chromosome or producing color images of expression data
matrices.
**globaltest**- Testing globally whether a group of genes is significantly related to
some clinical variable of interest.
**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.
**impute**-
Imputation for microarray data (currently KNN only).
**limma**- Linear models for microarray data.
**limmaGUI**- Graphical User Interface for package
**limma**. **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.
**marrayClasses**- Class definitions for pre-normalized and normalized cDNA microarray
data. Basic methods for accessing/replacing, printing, and subsetting.
**marrayInput**- Functions for reading microarray data into R from different image
analysis output files, and probe and target description files. Widgets
are supplied to facilitate and automate data input and the creation of
microarray specific R objects for storing these data.
**marrayNorm**- Functions for location and scale normalization procedures based on
robust local regression.
**marrayPlots**- Functions for diagnostic plots for pre- and post-normalization cDNA
microarray intensity data: boxplots, scatter-plots, color images.
**marrayTools**- Miscellaneous functions used in the functional genomics core facility in
UCB and UCSF.
**matchprobes**- Tools for sequence matching of probes on arrays.
**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.
**ontoTools**- Graphs and sparse matrices for working with ontologies.
**pairseqsim**- Pairwise sequence alignment and scoring algorithms for global, local and
overlap alignment with affine gap penalty.
**pamr**-
Pam: Prediction Analysis for Microarrays.
**pickgene**- Adaptive gene picking for microarray expression data analysis.
**prada**- Tools for analyzing and navigating data from high-throughput phenotyping
experiments based on cellular assays and fluorescent detection.
**qvalue**- Q-value estimation for false discovery rate control.
**rama**- Robust Analysis of MicroArrays: robust estimation of cDNA microarray
intensities with replicates using a Bayesian hierarchical model.
**reposTools**- Tools for dealing with file repositories and allow users to easily
install, update, and distribute packages, vignettes, and other files.
**rhdf5**- Storage and retrieval of large datasets using the HDF5 library and file
format.
**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.
**splicegear**- A set of tools to work with alternative splicing.
**tkWidgets**- Widgets in Tcl/Tk that provide functionality for Bioconductor packages.
**vsn**- Calibration and variance stabilizing transformations for both Affymetrix
and cDNA array data.
**webbioc**- Integrated web interface for doing microarray analysis using several of
the Bioconductor packages.
**widgetTools**- Tools for creating Tcl/Tk widgets, i.e., small-scale graphical user interfaces.

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://www.luc.ac.be/~jlindsey/rcode.html.

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

Next: How can add-on packages be used?, Previous: Which add-on packages exist for R?, Up: R Add-On Packages

(Unix only.) The add-on packages on CRAN come as gzipped tar
files named `pkg``_`

`version``.tar.gz`

, which may in fact be
“bundles” containing more than one package. Provided that
tar and gzip are available on your system, type

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

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

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

$ R CMD INSTALL -llib/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 CRAN. See the
help page for `CRAN.packages()`

for more information.

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

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

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

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

your ~/.Renviron file. (Note that no `export`

statement is
needed or allowed in this file; see the on-line help for `Startup`

for more information.)

Next: How can add-on packages be removed?, Previous: How can add-on packages be installed?, Up: R Add-On Packages

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

library()

at the R prompt.

This produces something like

Packages in `/home/me/lib/R': mystuff My own R functions, nicely packaged but not documented Packages in `/usr/local/lib/R/library': KernSmooth Functions for kernel smoothing for Wand & Jones (1995) MASS Main Package of Venables and Ripley's MASS base The R Base package boot Bootstrap R (S-Plus) Functions (Canty) class Functions for Classification cluster Functions for clustering (by Rousseeuw et al.) datasets The R datasets Package foreign Read data stored by Minitab, S, SAS, SPSS, Stata, ... grDevices The R Graphics Devices and Support for Colours and Fonts graphics The R Graphics Package grid The Grid Graphics Package lattice Lattice Graphics methods Formal Methods and Classes mgcv GAMs with GCV smoothness estimation and GAMMs by REML/PQ nlme Linear and nonlinear mixed effects models nnet Feed-forward Neural Networks and Multinomial Log-Linear Models rpart Recursive partitioning spatial Functions for Kriging and Point Pattern Analysis splines Regression Spline Functions and Classes stats The R Stats Package stats4 Statistical functions using S4 classes survival Survival analysis, including penalised likelihood tcltk Tcl/Tk Interface tools Tools for Package Development utils The R Utils Package

You can “load” the installed package `pkg` by

library(pkg)

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

library(help =pkg) help(package =pkg)

You can unload the loaded package `pkg` by

detach("package:pkg")

Next: How can I create an R package?, Previous: How can add-on packages be used?, Up: R Add-On Packages

Use

$ R CMD REMOVEpkg_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 -llibpkg_1...pkg_n

Next: How can I contribute to R?, Previous: How can add-on packages be removed?, Up: R Add-On Packages

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.

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.

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 (S 3/4, S-Plus 3.x/4.x/5.x, and R), LispStat dialects (XLispStat, ViSta) and SAS. Stata and SPSS dialect (SPSS, PSPP) support is being examined for possible future implementation

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 ESS-help@stat.math.ethz.ch.

Please send bug reports and suggestions on ESS to
ESS-bugs@stat.math.ethz.ch. The easiest way to do this from is
within Emacs by typing `M-x ess-submit-bug-report` or using the
[ESS] or [iESS] pulldown menus.

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.

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

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.

Next: How can I clean up my workspace?, Previous: How can I set components of a list to NULL?, Up: R Miscellanea

`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.

Next: How can I get eval() and D() to work?, Previous: How can I save my workspace?, Up: R Miscellanea

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.)

Next: Why do my matrices lose dimensions?, Previous: How can I clean up my workspace?, Up: R Miscellanea

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.

Next: How does autoloading work?, Previous: How can I get eval() and D() to work?, Up: R Miscellanea

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]

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.

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.

Next: Why does plotting give a color allocation error?, Previous: How should I set options?, Up: R Miscellanea

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"`

).

Next: How do I convert factors to numeric?, Previous: How do file names work in Windows?, Up: R Miscellanea

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.

Next: Are Trellis displays implemented in R?, Previous: Why does plotting give a color allocation error?, Up: R Miscellanea

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).

Next: What are the enclosing and parent environments?, Previous: How do I convert factors to numeric?, Up: R Miscellanea

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.)

Next: How can I substitute into a plot label?, Previous: Are Trellis displays implemented in R?, Up: R Miscellanea

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.

Next: What are valid names?, Previous: What are the enclosing and parent environments?, Up: R Miscellanea

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.

Next: Are GAMs implemented in R?, Previous: How can I substitute into a plot label?, Up: R Miscellanea

When creating data frames using `data.frame()`

or
`read.table()`

, R by default ensures that the variable names are
syntactically valid. (The argument check.names to these
functions controls whether variable names are checked and adjusted by
`make.names()`

if needed.)

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

- A
*syntactic name*is a string the parser interprets as this type of expression. It consists of letters, numbers, and the dot and (for version of R at least 1.9.0) underscore characters, and starts with either a letter or a dot not followed by a number. Reserved words are not syntactic names. - An
*object name*is a string associated with an object that is assigned in an expression either by having the object name on the left of an assignment operation or as an argument to the`assign()`

function. It is usually a syntactic name as well, but can be any non-empty string if it is quoted (and it is always quoted in the call to`assign()`

). - An
*argument name*is what appears to the left of the equals sign when supplying an argument in a function call (for example,`f(trim=.5)`

). Argument names are also usually syntactic names, but again can be anything if they are quoted. - An
*element name*is a string that identifies a piece of an object (a component of a list, for example.) When it is used on the right of the $ operator, it must be a syntactic name, or quoted. Otherwise, element names can be any strings. (When an object is used as a database, as in a call to`eval()`

or`attach()`

, the element names become object names.) - Finally, a
*file name*is a string identifying a file in the operating system for reading, writing, etc. It really has nothing much to do with names in the language, but it is traditional to call these strings file “names”.

Next: Why is the output not printed when I source() a file?, Previous: What are valid names?, Up: R Miscellanea

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**.

Next: Why does outer() behave strangely with my function?, Previous: Are GAMs implemented in R?, Up: R Miscellanea

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)`

.

Next: Why does the output from anova() depend on the order of factors in the model?, Previous: Why is the output not printed when I source() a file?, Up: R Miscellanea

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)

Next: How do I produce PNG graphics in batch mode?, Previous: Why does outer() behave strangely with my function?, Up: R Miscellanea

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.

Next: How can I get command line editing to work?, Previous: Why does the output from anova() depend on the order of factors in the model?, Up: R Miscellanea

Under Unix, the `png()`

device uses the X11 driver, which is a
problem in batch mode or for remote operation. If you have Ghostscript
you can use `bitmap()`

, which produces a PostScript file then
converts it to any bitmap format supported by ghostscript. On some
installations this produces ugly output, on others it is perfectly
satisfactory. In theory one could also use Xvfb from
X.Org, which provides an X server
with no display.

Next: How can I turn a string into a variable?, Previous: How do I produce PNG graphics in batch mode?, Up: R Miscellanea

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).

Next: Why do lattice/trellis graphics not work?, Previous: How can I get command line editing to work?, Up: R Miscellanea

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.

Next: How can I sort the rows of a data frame?, Previous: How can I turn a string into a variable?, Up: R Miscellanea

The most likely reason is that you forgot to tell R to display the
graph. Lattice functions such as `xyplot()`

create a graph object,
but do not display it (the same is true of Trellis graphics in
S-Plus). The `print()`

method for the graph object produces the
actual display. When you use these functions interactively at the
command line, the result is automatically printed, but in
`source()`

or inside your own functions you will need an explicit
`print()`

statement.

Next: Why does the help.start() search engine not work?, Previous: Why do lattice/trellis graphics not work?, Up: R Miscellanea

To sort the rows within a data frame, with respect to the values in one
or more of the columns, simply use `order()`

.

Next: Why did my .Rprofile stop working when I updated R?, Previous: How can I sort the rows of a data frame?, Up: R Miscellanea

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.

Next: Where have all the methods gone?, Previous: Why does the help.start() search engine not work?, Up: R Miscellanea

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).

Next: How can I create rotated axis labels?, Previous: Why did my .Rprofile stop working when I updated R?, Up: R Miscellanea

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

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

) use

getS3method("[", "terms")

To see the code for an unexported function `foo()`

in the namespace
of package `"bar"`

use `bar:::foo`

. Don't use these
constructions to call unexported functions in your own code—they are
probably unexported for a reason and may change without warning.

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”,
*RNews*, **3/2**, 7–12.

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.

Next: How can I inspect R objects when debugging?, Previous: How should I write summary methods?, Up: R Programming

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?.

Next: How can I change compilation flags?, Previous: How can I debug dynamically loaded code?, Up: R Programming

The most convenient way is to call `R_PV`

from the symbolic
debugger.

See section “Inspecting R objects when debugging” in Writing R Extensions.

Next: How can I debug S4 methods?, Previous: How can I inspect R objects when debugging?, Up: R Programming

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). 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.

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.

If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like “disk full”), then it is certainly a bug. If you call
`.C()`

, `.Fortran()`

, `.External()`

or `.Call()`

(or
`.Internal()`

) yourself (or in a function you wrote), you can
always crash R by using wrong argument types (modes). This is not a
bug.

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

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

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

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

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

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

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

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

never returns. Do not report that `data.frame()`

fails for large
data sets. Perhaps it fails when a variable name is a day of the week.
If this is so then when others got your report they would try out the
`data.frame()`

command on a large data set, probably with no day of
the week variable name, and not see any problem. There is no way in the
world that others could guess that they should try a day of the week
variable name.

Or perhaps the command fails because the last command you used was a
method for `"["()`

that had a bug causing R's internal data
structures to be corrupted and making the `data.frame()`

command
fail from then on. This is why others need to know what other commands
you have typed (or read from your startup file).

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

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

On Unix systems a bug report can be generated using the function
`bug.report()`

. This automatically includes the version
information and sends the bug to the correct address. Alternatively the
bug report can be emailed to R-bugs@R-project.org or submitted
to the Web page at http://bugs.R-project.org/.

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.

There is a section of the bug repository for suggestions for enhancements for R labelled wishlist. Suggestions can be submitted in the same ways as bugs, but please ensure that the subject line makes clear that this is for the wishlist and not a bug report, for example by starting with Wishlist:.

Comments on and suggestions for the Windows port of R should be sent to R-windows@R-project.org.

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

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

More to some soon ...