]> R FAQ Introduction This document contains answers to some of the most frequently asked questions about R. Legalese This document is copyright © 1998–2007 by Kurt Hornik. This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version. This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A copy of the GNU General Public License is available via WWW at http://www.gnu.org/copyleft/gpl.html. You can also obtain it by writing to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, U.S.A. Obtaining this document The latest version of this document is always available from http://CRAN.R-project.org/doc/FAQ/ From there, you can obtain versions converted to plain ASCII text, DVI, GNU info, HTML, PDF, PostScript as well as the Texinfo source used for creating all these formats using the GNU Texinfo system. You can also obtain the R FAQ from the doc/FAQ subdirectory of a CRAN site (see ). Citing this document In publications, please refer to this FAQ as Hornik (2007), “The R FAQ”, and give the above, official URL and the ISBN 3-900051-08-9: @Misc{, author = {Kurt Hornik}, title = {The {R} {FAQ}}, year = {2007}, note = {{ISBN} 3-900051-08-9}, url = {http://CRAN.R-project.org/doc/FAQ/R-FAQ.html} } Notation Everything should be pretty standard. ‘R>’ is used for the R prompt, and a ‘$’ for the shell prompt (where applicable). Feedback Feedback via email to 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 Basics What is R? R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S (see ) 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 , 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 was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. In addition, a large group of individuals has contributed to R by sending code and bug reports. Since mid-1997 there has been a core group (the “R Core Team”) who can modify the R source code archive. The group currently consists of Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley, Duncan Temple Lang, Luke Tierney, and Simon Urbanek. R has a home page at http://www.R-project.org/. It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (“GNU S”). What machines does R run on? R is being developed for the Unix, Windows and Mac families of operating systems. Support for Mac OS Classic ended with R 1.7.1. The current version of R will configure and build under a number of common Unix platforms including cpu-linux-gnu for the i386, alpha, arm, hppa, ia64, m68k, mips/mipsel, powerpc, s390, sparc (e.g., http://buildd.debian.org/build.php?&pkg=r-base), and x86_64 CPUs, powerpc-apple-darwin, mips-sgi-irix, rs6000-ibm-aix, and sparc-sun-solaris. If you know about other platforms, please drop us a note. What is the current version of R? The current released version is 2.5.1. 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. How can R be obtained? Sources, binaries and documentation for R can be obtained via CRAN, the “Comprehensive R Archive Network” (see ). 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. How can R be installed? How can R be installed (Unix) If R is already installed, it can be started by typing R at the shell prompt (of course, provided that the executable is in your path). If binaries are available for your platform (see ), 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 ). The file INSTALL that comes with the R distribution contains a brief introduction, and the “R Installation and Administration” guide (see ) 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 “RHome 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. How can R be installed (Windows) The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, ME, NT4, 2000, and XP (at least) on Intel and clones (but not on other platforms). The Windows version of R was created by Robert Gentleman and Guido Masarotto, and is now being developed and maintained by Duncan Murdoch 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. How can R be installed (Macintosh) The bin/macosx directory of a CRAN site contains a standard Apple installer package inside a disk image named R.dmg. Once downloaded and executed, the installer will install the current non-developer release of R. RAqua is a native Mac OS X Darwin version of R with a R.app Mac OS X GUI. Inside bin/macosx/powerpc/contrib/x.y there are prebuilt binary packages (for powerpc version of Mac OS X) to be used with RAqua corresponding to the “x.y” release of R. The installation of these packages is available through the “Package” menu of the R.app GUI. This port of R for Mac OS X is maintained by Stefano Iacus. The “R for Mac OS X FAQ has more details. The bin/macos directory of a CRAN site contains bin-hexed (hqx) and stuffit (sit) archives for a base distribution and a large number of add-on packages of R 1.7.1 to run under Mac OS 8.6 to Mac OS 9.2.2. This port of R for Macintosh is no longer supported. Are there Unix binaries for R? The bin/linux directory of a CRAN site contains the following packages. CPUVersionsProvider Debiani386stable/oldstableJohannes Ranke amd64stable/oldstableJohannes Ranke Red Hati386FC3/FC4/FC5/FC6Martyn Plummer x86_64FC3Brian Ripley x86_64FC4/FC5/FC6Martyn Plummer i386Enterprise LinuxMatthew P. Cox x86_64Enterprise LinuxMatthew P. Cox SuSEi3867.3/8.0/8.1/8.2Detlef Steuer i5869.0/9.1/9.2/9.3/10.0/10.1Detlef Steuer x86_649.2/9.3/10.0/10.1Detlef Steuer Ubuntui386dapper/edgy/feistyVincent Goulet VineLinuxi3863.2Susumu Tanimura Debian packages, maintained by Dirk Eddelbuettel and Doug Bates, have long been part of the Debian distribution, and can be accessed through APT, the Debian package maintenance tool. Use e.g. apt-get install r-base r-recommended to install the R environment and recommended packages. If you also want to build R packages from source, also run apt-get install r-base-dev to obtain the additional tools required for this. So-called “backports” of the current R packages for the stable distribution of Debian are provided by Johannes Ranke, and available from CRAN. Simply add the line deb http://CRAN.R-project.org/bin/linux/debian stable/ (feel free to use a CRAN mirror instead of the master) to the file /etc/apt/sources.list, and install as usual. More details on installing and administering R on Debian Linux can be found at http://CRAN.R-project.org/bin/linux/debian/README. These backports should also be suitable for other Debian derivatives. Native backports for Ubuntu are provided by Vincent Goulet. No other binary distributions are currently publically available via CRAN. A “live” Linux distribution with a particular focus on R is Quantian, which provides a directly bootable and self-configuring “Live DVD” containing numerous applications of interests to scientists and researchers, including several hundred CRAN and Bioconductor packages, the “ESS” extensions for Emacs, the “JGR” Java GUI for R, the Ggobi visualization tool as well as several other R interfaces. The Quantian website at http://dirk.eddelbuettel.com/quantian/ contains more details as well download information. What documentation exists for R? Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(name) (or ?name) at the R prompt, where name is the name of the topic help is sought for. (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.) This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via &latex;, see . 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 ). The R distribution also comes with the following manuals. “An Introduction to R” (R-intro)includes information on data types, programming elements, statisticalmodeling and graphics. This document is based on the “Notes onS-Plus” by Bill Venables and David Smith. “Writing R Extensions” (R-exts)currently describes the process of creating R add-on packages, writing Rdocumentation, R's system and foreign language interfaces, and the RAPI. “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”, explainsevaluation, parsing, object oriented programming, computing on thelanguage, and so forth. “R Installation and Administration” (R-admin). “R Internals” (R-ints)is a guide to R's internal structures.(Added in R 2.4.0.) Books on R include P. Dalgaard (2002), “Introductory Statistics with R”, Springer: New York, ISBN 0-387-95475-9, http://www.biostat.ku.dk/~pd/ISwR.html. J. Fox (2002), “An R and S-Plus Companion to Applied Regression”, Sage Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2 (hardcover), http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/. J. Maindonald and J. Braun (2003), “Data Analysis and Graphics Using R: An Example-Based Approach”, Cambridge University Press, ISBN 0-521-81336-0, http://wwwmaths.anu.edu.au/~johnm/. S. M. Iacus and G. Masarotto (2002), “Laboratorio di statistica con R”, McGraw-Hill, ISBN 88-386-6084-0 (in Italian), http://www.ateneonline.it/LibroAteneo.asp?item_id=1436. P. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN: 1-584-88486-X, http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html. The book W. N. Venables and B. D. Ripley (2002), “Modern Applied Statistics with S. Fourth Edition”. Springer, ISBN 0-387-95457-0 has a home page at http://www.stats.ox.ac.uk/pub/MASS4/ providing additional material. Its companion is W. N. Venables and B. D. Ripley (2000), “S Programming”. Springer, ISBN 0-387-98966-8 and provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-Plus and the Open Source R data analysis software systems. See http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ for more information. In addition to material written specifically or explicitly for R, documentation for S/S-Plus (see ) can be used in combination with this FAQ (see ). Introductory books include P. Spector (1994), “An introduction to S and S-Plus”, Duxbury Press. A. Krause and M. Olsen (2005), “The Basics of S-Plus” (Fourth Edition). Springer, ISBN 0-387-26109-5. The book J. C. Pinheiro and D. M. Bates (2000), “Mixed-Effects Models in S and S-Plus”, Springer, ISBN 0-387-98957-0 provides a comprehensive guide to the use of the nlme package for linear and nonlinear mixed-effects models. As an example of how R can be used in teaching an advanced introductory statistics course, see D. Nolan and T. Speed (2000), “Stat Labs: Mathematical Statistics Through Applications”, Springer Texts in Statistics, ISBN 0-387-98974-9 This integrates theory of statistics with the practice of statistics through a collection of case studies (“labs”), and uses R to analyze the data. More information can be found at http://www.stat.Berkeley.EDU/users/statlabs/. Last, but not least, Ross' and Robert's experience in designing and implementing R is described in Ihaka & Gentleman (1996), “R: A Language for Data Analysis and Graphics”, Journal of Computational and Graphical Statistics, 5, 299–314. An annotated bibliography (Bib&tex; 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 Citing R To cite R in publications, use @Manual{, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = 2007, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org} } Citation strings (or Bib&tex; entries) for R and R packages can also be obtained by citation(). What mailing lists exist for R? Thanks to Martin Maechler, there are four mailing lists devoted to R. R-announce A moderated list for major announcements about the development of R andthe availability of new code. R-packages A moderated list for announcements on the availability of new orenhanced contributed packages. R-help The `main' R mailing list, for discussion about problems and solutionsusing R, announcements (not covered by `R-announce' and `R-packages')about the development of R and the availability of new code. R-devel This list is for questions and discussion about code development in R. Please read the posting guide before sending anything to any mailing list. Note in particular that R-help is intended to be comprehensible to people who want to use R to solve problems but who are not necessarily interested in or knowledgeable about programming. Questions likely to prompt discussion unintelligible to non-programmers (e.g., questions involving C or C++) should go to R-devel. Convenient access to information on these lists, subscription, and archives is provided by the web interface at http://stat.ethz.ch/mailman/listinfo/. One can also subscribe (or unsubscribe) via email, e.g. to R-help by sending ‘subscribe’ (or ‘unsubscribe’) in the body of the message (not in the subject!) to . Send email to 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 for more details. See http://www.R-project.org/mail.html for more information on the R mailing lists. The R Core Team can be reached at for comments and reports. Many of the R project's mailing lists are also available via Gmane, from which they can be read with a web browser, using an NNTP news reader, or via RSS feeds. See http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r. for the available mailing lists, and http://www.gmane.org/rss.php for details on RSS feeds. What is CRAN? The “Comprehensive R Archive Network” (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries. The CRAN master site at Wirtschaftsuniversität Wien, Austria, can be found at the URL http://CRAN.R-project.org/ Daily mirrors are available at URLs including http://cran.at.R-project.org/(WU Wien, Austria) http://cran.au.R-project.org/(PlanetMirror, Australia) http://cran.br.R-project.org/(Universidade Federal 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 . 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. 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. Can I use R for commercial purposes? R is released under the GNU General Public License (GPL). If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice. It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (“No Discrimination Against Fields of Endeavor”) of the Open Source Definition: 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. Why is R named R? The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language `S' (see ). What is the R Foundation? The R Foundation is a not for profit organization working in the public interest. It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing, provide a reference point for individuals, institutions or commercial enterprises that want to support or interact with the R development community, and to hold and administer the copyright of R software and documentation. See http://www.R-project.org/foundation/ for more information. R and S What is S? S is a very high level language and an environment for data analysis and graphics. In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for the S system, which has forever altered the way people analyze, visualize, and manipulate data … S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers. The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S. Richard A. Becker and John M. Chambers (1984), “S. An InteractiveEnvironment for Data Analysis and Graphics,” Monterey: Wadsworth andBrooks/Cole.This is also referred to as the “Brown Book”, and of historicalinterest only. Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), “The NewS Language,” London: Chapman & Hall.This book is often called the “Blue Book”, and introduced whatis now known as S version 2. John M. Chambers and Trevor J. Hastie (1992), “Statistical Models inS,” London: Chapman & Hall.This is also called the “White Book”, and introduced S version3, 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 ofS designed by John Chambers to improve its usefulness at every stage ofthe 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. What is S-Plus? S-Plus is a value-added version of S sold by Insightful Corporation. Based on the S language, S-Plus provides functionality in a wide variety of areas, including robust regression, modern non-parametric regression, time series, survival analysis, multivariate analysis, classical statistical tests, quality control, and graphics drivers. Add-on modules add additional capabilities. See the Insightful S-Plus page for further information. What are the differences between R and S? We can regard S as a language with three current implementations or “engines”, the “old S engine” (S version 3; S-Plus 3.x and 4.x), the “new S engine” (S version 4; S-Plus 5.x and above), and R. Given this understanding, asking for “the differences between R and S” really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines. For the remainder of this section, “S” refers to the S engines and not the S language. Lexical scoping Contrary to other implementations of the S language, R has adopted an evaluation model in which nested function definitions are lexically scoped. This is analogous to the evalutation model in Scheme. This difference becomes manifest when free variables occur in a function. Free variables are those which are neither formal parameters (occurring in the argument list of the function) nor local variables (created by assigning to them in the body of the function). In S, the values of free variables are determined by a set of global variables (similar to C, there is only local and global scope). In R, they are determined by the environment in which the function was created. Consider the following function: cube <- function(n) { sq <- function() n * n n * sq() } Under S, sq() does not “know” about the variable n unless it is defined globally: S> cube(2) Error in sq(): Object "n" not found Dumped S> n <- 3 S> cube(2) [1] 18 In R, the “environment” created when cube() was invoked is also looked in: R> cube(2) [1] 8 As a more “interesting” real-world problem, suppose you want to write a function which returns the density function of the r-th order statistic from a sample of size n from a (continuous) distribution. For simplicity, we shall use both the cdf and pdf of the distribution as explicit arguments. (Example compiled from various postings by Luke Tierney.) The S-Plus documentation for call() basically suggests the following: dorder <- function(n, r, pfun, dfun) { f <- function(x) NULL con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1))) PF <- call(substitute(pfun), as.name("x")) DF <- call(substitute(dfun), as.name("x")) f[[length(f)]] <- call("*", con, call("*", call("^", PF, r - 1), call("*", call("^", call("-", 1, PF), n - r), DF))) f } Rather tricky, isn't it? The code uses the fact that in S, functions are just lists of special mode with the function body as the last argument, and hence does not work in R (one could make the idea work, though). A version which makes heavy use of substitute() and seems to work under both S and R is dorder <- function(n, r, pfun, dfun) { con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1))) eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x), list(PF = substitute(pfun), DF = substitute(dfun), a = r - 1, b = n - r, K = con))) } (the eval() is not needed in S). However, in R there is a much easier solution: dorder <- function(n, r, pfun, dfun) { con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1))) function(x) { con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x) } } This seems to be the “natural” implementation, and it works because the free variables in the returned function can be looked up in the defining environment (this is lexical scope). Note that what you really need is the function closure, i.e., the body along with all variable bindings needed for evaluating it. Since in the above version, the free variables in the value function are not modified, you can actually use it in S as well if you abstract out the closure operation into a function MC() (for “make closure”): dorder <- function(n, r, pfun, dfun) { con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1))) MC(function(x) { con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x) }, list(con = con, pfun = pfun, dfun = dfun, r = r, n = n)) } Given the appropriate definitions of the closure operator, this works in both R and S, and is much “cleaner” than a substitute/eval solution (or one which overrules the default scoping rules by using explicit access to evaluation frames, as is of course possible in both R and S). For R, MC() simply is MC <- function(f, env) f (lexical scope!), a version for S is MC <- function(f, env = NULL) { env <- as.list(env) if (mode(f) != "function") stop(paste("not a function:", f)) if (length(env) > 0 && any(names(env) == "")) stop(paste("not all arguments are named:", env)) fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL fargs <- c(fargs, env) if (any(duplicated(names(fargs)))) stop(paste("duplicated arguments:", paste(names(fargs)), collapse = ", ")) fbody <- f[length(f)] cf <- c(fargs, fbody) mode(cf) <- "function" return(cf) } Similarly, most optimization (or zero-finding) routines need some arguments to be optimized over and have other parameters that depend on the data but are fixed with respect to optimization. With R scoping rules, this is a trivial problem; simply make up the function with the required definitions in the same environment and scoping takes care of it. With S, one solution is to add an extra parameter to the function and to the optimizer to pass in these extras, which however can only work if the optimizer supports this. Nested lexically scoped functions allow using function closures and maintaining local state. A simple example (taken from Abelson and Sussman) is obtained by typing demo("scoping") at the R prompt. Further information is provided in the standard R reference “R: A Language for Data Analysis and Graphics” (see ) 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 ) 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(). If you run R from within Emacs (see ), 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. Models There are some differences in the modeling code, such as Whereas in S, you would use lm(y ~ x^3) to regress y onx^3, in R, you have to insulate powers of numeric vectors (usingI()), i.e., you have to use lm(y ~ I(x^3)). The glm family objects are implemented differently in R and S. The samefunctionality 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 anexpression with attributes, in R it is a formula with attributes. Theattributes have the same names but are mostly stored differently. Finally, in R y ~ x + 0 is an alternative to y ~ x - 1 forspecifying a model with no intercept. Models with no parameters at allcan be specified by y ~ 0. Others Apart from lexical scoping and its implications, R follows the S language definition in the Blue and White Books as much as possible, and hence really is an “implementation” of S. There are some intentional differences where the behavior of S is considered “not clean”. In general, the rationale is that R should help you detect programming errors, while at the same time being as compatible as possible with S. Some known differences are the following. In R, if x is a list, then x[i] <- NULL and x[[i]]<- NULL remove the specified elements from x. The first ofthese is incompatible with S, where it is a no-op. (Note that you canset 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 executedat the very beginning and end of a session, respectively.In R, the startup mechanism is as follows. R first sources the systemstartup file $R_HOME/library/base/R/Rprofile. Then, itsearches for a site-wide startup profile unless the command line option--no-site-file was given. The name of this file is taken fromthe value of the R_PROFILE environment variable. If that variableis unset, the default is $R_HOME/etc/Rprofile.site($R_HOME/etc/Rprofile in versions prior to 1.4.0). Thiscode 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 homedirectory (in that order) and sources it into the user workspace. Itthen loads a saved image of the user workspace from .RData incase there is one (unless --no-restore was specified). Ifneeded, the functions .First() and .Last() should bedefined in the appropriate startup profiles. In R, T and F are just variables being set to TRUEand FALSE, respectively, but are not reserved words as in S andhence can be overwritten by the user. (This helps e.g. when you havefactors with levels "T" or "F".) Hence, when writing codeyou should always use TRUE and FALSE. In R, dyn.load() can only load shared objects, as createdfor 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 Rdata files created with save(), which is analogous to attachingdirectories in S.) Also, you cannot attach at position 1. Categories do not exist in R, and never will as they are deprecated nowin S. Use factors instead. In R, For() loops are not necessary and hence not supported. In R, assign() uses the argument envir= rather thanwhere= as in S. The random number generators are different, and the seeds have differentlength. R passes integer objects to C as int * rather than long *as in S. R has no single precision storage mode. However, as of version 0.65.1,there is a single precision interface to C/FORTRAN subroutines. By default, ls() returns the names of the objects in the current(under R) and global (under S) environment, respectively. For example,given x <- 1; fun <- function() {y <- 1; ls()} then fun() returns "y" in R and "x" (together withthe rest of the global environment) in S. R allows for zero-extent matrices (and arrays, i.e., some elements ofthe dim attribute vector can be 0). This has been determined auseful feature as it helps reducing the need for special-case tests forempty subsets. For example, if x is a matrix, x[, FALSE]is not NULL but a “matrix” with 0 columns. Hence, such objectsneed 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 TRUEin R). Data frames are not considered as matrices in R (i.e., if DF is adata frame, then is.matrix(DF) returns FALSE in R andTRUE in S). R by default uses treatment contrasts in the unordered case, whereas Suses the Helmert ones. This is a deliberate difference reflecting theopinion that treatment contrasts are more natural. In R, the argument of a replacement function which corresponds to theright hand side must be named ‘value’. E.g., f(a) <- b isevaluated as a <- "f<-"(a, value = b). S always takes the lastargument, irrespective of its name. In S, substitute() searches for names for substitution in thegiven expression in three places: the actual and the default argumentsof the matching call, and the local frame (in that order). R looks inthe local frame only, with the special rule to use a “promise” if avariable is not evaluated. Since the local frame is initialized withthe actual arguments or the default expressions, this is usuallyequivalent to S, until assignment takes place. In S, the index variable in a for() loop is local to the insideof 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 aone-dimensional array (which can have named dimnames). In S(-Plus) the C locale is used, whereas in R the currentoperating system locale is used for determining which characters arealphanumeric and how they are sorted. This affects the set of validnames for R objects (for example accented chars may be allowed in R) andordering in sorts and comparisons (such as whether "aA" < "Bb" istrue or false). From version 1.2.0 the locale can be (re-)set in R bythe Sys.setlocale() function. In S, missing(arg) remains TRUE if arg issubsequently 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 acharacter variable. Use as.character(NA) to create a missingcharacter value. R disallows repeated formal arguments in function calls. In S, dump(), dput() and deparse() are essentiallydifferent interfaces to the same code. In R from version 2.0.0, this isonly true if the same control argument is used, but by default itis not. By default dump() tries to write code that will evaluateto reproduce the object, whereas dput() and deparse()default to options for producing deparsed code that is readable. In R, indexing a vector, matrix, array or data frame with [ usinga character vector index looks only for exact matches (whereas [[and $ allow partial matches). In S, [ allows partialmatches. S has a two-argument version of atan and no atan2. A callin S such as atan(x1, x2) is equivalent to R's atan2(x1,x2). However, beware of named arguments since S's atan(x = a, y= b) is equivalent to R's atan2(y = a, x = b) with the meaningsof x and y interchanged. (R used to have undocumentedsupport for a two-argument atan with positional arguments, butthis has been withdrawn to avoid further confusion.) Numeric constants with no fractional and exponent (i.e., only integer)part are taken as integer in S-Plus 6.x or later, but as double in R. There are also differences which are not intentional, and result from missing or incorrect code in R. The developers would appreciate hearing about any deficiencies you may find (in a written report fully documenting the difference as you see it). Of course, it would be useful if you were to implement the change yourself and make sure it works. Is there anything R can do that S-Plus cannot? Since almost anything you can do in R has source code that you could port to S-Plus with little effort there will never be much you can do in R that you couldn't do in S-Plus if you wanted to. (Note that using lexical scoping may simplify matters considerably, though.) R offers several graphics features that S-Plus does not, such as finer handling of line types, more convenient color handling (via palettes), gamma correction for color, and, most importantly, mathematical annotation in plot texts, via input expressions reminiscent of &tex; constructs. See the help page for plotmath, which features an impressive on-line example. More details can be found in Paul Murrell and Ross Ihaka (2000), “An Approach to Providing Mathematical Annotation in Plots”, Journal of Computational and Graphical Statistics, 9, 582–599. What is R-plus? For a very long time, there was no such thing. XLSolutions Corporation is currently beta testing a commercially supported version of R named R+ (read R plus). In addition, REvolution Computing has released RPro, an enterprise-class statistical analysis system based on R, suitable for deployment in professional, commercial and regulated environments. R Web Interfaces Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated Javascript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided. The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/). Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://osvisions.com/r-online/ for more information. Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTML author to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active. Currently, a modified version of Rcgi by Mai Zhou (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from http://www.ms.uky.edu/~statweb/. CGI-based web access to R is also provided at http://hermes.sdu.dk/cgi-bin/go/. There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse. David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David's paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page at http://www.omegahat.org/CGIwithR/. Rpad, developed and actively maintained by Tom Short, provides a sophisticated environment which combines some of the features of the previous approaches with quite a bit of Javascript, allowing for a GUI-like behavior (with sortable tables, clickable graphics, editable output), etc. Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject. Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets). OpenStatServer is being developed by a team lead by Greg Warnes; it aims “to provide clean access to computational modules defined in a variety of computational environments (R, SAS, Matlab, etc) via a single well-defined client interface” and to turn computational services into web services. Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input. webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource. Finally, Rwui is a web application to to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques. R Add-On Packages Which add-on packages exist for R? Add-on packages in R The R distribution comes with the following packages: base Base R functions (and datasets before R 2.0.0). datasets Base R datasets (added in R 2.0.0). grDevices Graphics devices for base and grid graphics (added in R 2.0.0). graphics R functions for base graphics. grid A rewrite of the graphics layout capabilities, plus some support forinteraction. methods Formally defined methods and classes for R objects, plus otherprogramming tools, as described in the Green Book. splines Regression spline functions and classes. stats R statistical functions. stats4 Statistical functions using S4 classes. tcltk Interface and language bindings to Tcl/Tk GUI elements. tools Tools for package development and administration. utils R utility functions. These “base packages” were substantially reorganized in R 1.9.0. The former base was split into the four packages base, graphics, stats, and utils. Packages ctest, eda, modreg, mva, nls, stepfun and ts were merged into stats, package lqs returned to the recommended package MASS, and package mle moved to stats4. Add-on packages from CRAN The following packages are available from the CRAN src/contrib area. (Packages denoted as Recommended are to be included in all binary distributions of R.) ADaCGH Analysis of data from aCGH experiments. AMORE A MORE flexible neural network package, providing the TAO robust neuralnetwork algorithm. ARES Allelic richness estimation, with extrapolation beyond the sample size. AdaptFit Adaptive semiparametic regression. AlgDesign Algorithmic experimental designs. Calculates exact and approximatetheory experimental designs for D, A, and I criteria. Amelia Amelia II: a program for missing data. AnalyzeFMRI Functions for I/O, visualisation and analysis of functional MagneticResonance Imaging (fMRI) datasets stored in the ANALYZE format. ArDec Time series autoregressive decomposition. BACCO Bayesian Analysis of Computer Code Output. Containsapproximator, calibrator, and emulator, forBayesian prediction of complex computer codes, calibration of computermodels, and emulation of computer programs, respectively. BHH2 Functions and data sets reproducing some examples in “Statistics forExperimenters II” by G. E. P. Box, J. S. Hunter, and W. C. Hunter,2005, John Wiley and Sons. BMA Bayesian Model Averaging for linear models, generalizable linear modelsand survival models (Cox regression). BRugs OpenBUGS and its R interface BRugs. BayHaz Functions for Bayesian Hazard rate estimation. BayesTree Bayesian methods for tree based models. BayesValidate Bayesian software validation using posterior quantiles. Bhat Functions for general likelihood exploration (MLE, MCMC, CIs). BiasedUrn Biased urn model distributions. Biodem A number of functions for biodemographycal analysis. Bolstad Functions and data sets for the book “Introduction to BayesianStatistics” by W. M. Bolstad, 2004, John Wiley and Sons. BootCL Bootstrapping test for chromosomal localization. BradleyTerry Specify and fit the Bradley-Terry model and structured versions. Brobdingnag Very large numbers in R. BSDA Data sets for the book “Basic Statistics and Data Analysis” byL. J. Kitchens, 2003, Duxbury. BsMD Bayes screening and model discrimination follow-up designs. CCA Canonical correlation analysis. CDNmoney Components of Canadian monetary aggregates. CGIwithR Facilities for the use of R to write CGI scripts. CORREP Multivariate correlation estimation. CTFS The CTFS large plot forest dynamics analyses. CVThresh Level-dependent Cross-Validation Thresholding. Cairo Graphics device using cairographics library for creating high-qualityPNG, PDF, SVG, PostScript output and interactive display devices such asX11. CircStats Circular Statistics, from “Topics in Circular Statistics” by S. RaoJammalamadaka and A. SenGupta, 2001, World Scientific. CoCo Graphical modeling for contingency tables using CoCo. CompetingRiskFrailty Competing risk model with frailties for right censored survival data. CreditMetrics Functions for calculating the CreditMetrics risk model. DAAG Various data sets used in examples and exercises in “Data Analysis andGraphics Using R” by John H. Maindonald and W. John Brown, 2003. DAAGbio Data sets and functions, for demonstrations with expression arrays. DAAGxtras Data sets and functions additional to DAAG. DBI A common database interface (DBI) class and method definitions. Allclasses in this package are virtual and need to be extended by thevarious DBMS implementations. DCluster A set of functions for the detection of spatial clusters of diseasesusing count data. DDHFm Variance stabilization by Data-Driven Haar-Fisz (for microarrays). DEoptim Differential Evolution Optimization. DICOM Import and manipulate medical imaging data using the Digital Imaging andCommunications in Medicine (DICOM) Standard. DPpackage Semiparametric Bayesian analysis using Dirichlet process priors. Davies Functions for the Davies quantile function and the Generalized Lambdadistribution. DescribeDisplay R interface to the DescribeDisplay GGobi plugin. Design Regression modeling, testing, estimation, validation, graphics,prediction, and typesetting by storing enhanced model design attributesin the fit. Design is a collection of about 180 functions that assistand streamline modeling, especially for biostatistical and epidemiologicapplications. It also contains new functions for binary and ordinallogistic regression models and the Buckley-James multiple regressionmodel for right-censored responses, and implements penalized maximumlikelihood estimation for logistic and ordinary linear models. Designworks with almost any regression model, but it was especially written towork with logistic regression, Cox regression, accelerated failure timemodels, ordinary linear models, and the Buckley-James model. Devore5 Data sets and sample analyses from “Probability and Statistics forEngineering and the Sciences (5th ed)” by Jay L. Devore, 2000, Duxbury. Devore6 Data sets and sample analyses from “Probability and Statistics forEngineering and the Sciences (6th ed)” by Jay L. Devore, 2003, Duxbury. Devore7 Data sets and sample analyses from “Probability and Statistics forEngineering and the Sciences (7th ed)” by Jay L. Devore, 2008, Thomson. EMV Estimation of missing values in a matrix by a k-th nearestneighboors algorithm. EbayesThresh Empirical Bayes thresholding and related methods. Ecdat Data sets from econometrics textbooks. ElemStatLearn Data sets, functions and examples from the book “The Elements ofStatistical Learning: Data Mining, Inference, and Prediction” by TrevorHastie, Robert Tibshirani and Jerome Friedman (2001), Springer. Epi Statistical analysis in epidemiology, with functions for demographic andepidemiological analysis in the Lexis diagram. FLCore Core package of FLR, fisheries modeling in R. FLEDA Exploratory Data Analysis for FLR. FactoMineR Factor analysis and data mining with R. Fahrmeir Data from the book “Multivariate Statistical Modelling Basedon Generalized Linear Models” by Ludwig Fahrmeir and Gerhard Tutz(1994), Springer. Flury Data sets from from “A First Course in Multivariate Statistics” byBernard Flury (1997), Springer. FortranCallsR Simple Fortran/C/R interface example. FracSim Simulation of one- and two-dimensional fractional and multifractionalLevy motions. FunCluster Functional profiling of cDNA microarray expression data. G1DBN Dynamic Bayesian Network inference using 1st order conditionaldependencies. GAMBoost Generalized additive models by likelihood based boosting. GDD Platform and X11 independent device for creating bitmaps (png, gif andjpeg) using the GD graphics library. GenABEL Genome-wide SNP association analysis. GOSim Computation of functional similarities between GO terms and geneproducts. GPArotation Gradient Projection Algorithm rotation for factor analysis. GRASS An interface between the GRASS geographical information system and R,based on starting R from within the GRASS environment and chosenLOCATION_NAME and MAPSET. Wrapper and helper functions are provided fora range of R functions to match the interface metadata structures. GSA Gene set analysis. GammaTest Gamma Test data analysis. GenKern Functions for generating and manipulating generalised binned kerneldensity estimates. GeneCycle Identification of periodically expressed genes. GeneNT Relevance or Dependency network and signaling pathway discovery. GeneNet Modeling and inferring gene networks. GeneTS A package for analysing multiple gene expression time series data.Currently, implements methods for cell cycle analysis and for inferringlarge sparse graphical Gaussian models. Geneland MCMC inference from individual genetic data based on a spatialstatistical model. GeoXp Interactive exploratory spatial data analysis. GroupSeq Computations related to group-seqential boundaries. HH Support software for “Statistical Analysis and Data Display” byRichard M. Heiberger and Burt Holland, Springer, 2005. HI Simulation from distributions supported by nested hyperplanes. HSAUR Functions, data sets, analyses and examples from the book “A Handbookof Statistical Analyses Using R” by Brian S. Everitt and TorstenHothorn (2006), Chapman & Hall/CRC. HTMLapplets Functions inserting dynamic scatterplots and grids in documentsgenerated by R2HTML. HighProbability Estimation of the alternative hypotheses having frequentist or Bayesianprobabilities at least as great as a specified threshold, given a listof p-values. Hmisc Functions useful for data analysis, high-level graphics, utilityoperations, functions for computing sample size and power, importingdatasets, imputing missing values, advanced table making, variableclustering, character string manipulation, conversion of S objects to&latex; code, recoding variables, and bootstrap repeated measuresanalysis. HydroMe Estimation of soil hydraulic parameters from experimental data. HyperbolicDist Basic functions for the hyperbolic distribution: probability densityfunction, distribution function, quantile function, a routine forgenerating observations from the hyperbolic, and a function for fittingthe hyperbolic distribution to data. ICE Iterated Conditional Expectation: kernel estimators forinterval-censored data. ICS ICS/ICA computation based on two scatter matrices. ICSNP Tools for multivariate nonparametrics. IDPmisc Utilities from the Institute of Data Analyses and Process Design,IDP/ZHW. IPSUR Accompanies “Introduction to Probability and Statistics Using R” byG. Andy Chang and G. Jay Kerns(in progress). ISwR Data sets for “Introductory Statistics with R” by Peter Dalgaard,2002, Springer. Icens Functions for computing the NPMLE for censored and truncated data. InfNet Function that simulates an epidemic in a network of contacts. JLLprod Nonparametric estimation of homothetic and generalized homotheticproduction functions. JGR Java Gui for R. JavaGD Java Graphics Device. JointGLM Joint modeling of mean and dispersion through two interlinked GLM's.Defunct in favor of JointModeling. JointModeling Joint modeling of mean and dispersion. KMsurv Data sets and functions for “Survival Analysis, Techniques for Censoredand Truncated Data” by Klein and Moeschberger, 1997, Springer. Kendall Kendall rank correlation and Mann-Kendall trend test. KernSmooth Functions for kernel smoothing (and density estimation) corresponding tothe book “Kernel Smoothing” by M. P. Wand and M. C. Jones, 1995.Recommended. LDheatmap Heat maps of linkage disequilibrium measures. LMGene Date transformation and identification of differentially expressed genesin gene expression arrays. LearnBayes Functions for Learning Bayesian Inference. Lmoments Estimation of L-moments and the parameters of normal and Cauchypolynomial quantile mixtures. LogConcDEAD Maximum likelihood estimation of a log-concave density. LogicReg Routines for Logic Regression. LoopAnalyst A collection of tools to conduct Levins' Loop Analysis. LowRankQP Low Rank Quadratic Programming: QP problems where the hessian isrepresented as the product of two matrices. MASS Functions and datasets from the main package of Venables and Ripley,“Modern Applied Statistics with S”. Contained in the VRbundle. Recommended. MBA Multilevel B-spline Approximation. MBESS Methods for the Behavioral, Educational, and Social Sciences. MCMCpack Markov chain Monte Carlo (MCMC) package: functions for posteriorsimulation for a number of statistical models. MChtest Monte Carlo hypothesis tests. MEMSS Data sets and sample analyses from “Mixed-effects Models in S andS-PLUS” by J. Pinheiro and D. Bates, 2000, Springer. MFDA Model Based Functional Data Analysis. MKLE Maximum kernel likelihood estimation. MLDS Maximum Likelihood Difference Scaling. MNP Fitting Bayesian Multinomial Probit models via Markov chain Monte Carlo.Along with the standard Multinomial Probit model, it can also fit modelswith different choice sets for each observation and complete or partialordering 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 andSons. MSBVAR Bayesian vector autoregression models, impulse responses andforecasting. MarkedPointProcess Non-parametric analysis of the marks of marked point processes. MasterBayes Maximum likelihood and Markov chain Monte Carlo methods for pedigreereconstruction, analysis and simulation. MatchIt Select matched samples of the original treated and control groups withsimilar covariate distributions. Matching Multivariate and propensity score matching with formal tests of balance. Matrix A Matrix package. MiscPsycho Miscellaneous Psychometrics. NADA Methods described in “Nondetects And Data Analysis: Statistics forCensored Environmental Data” by Dennis R. Helsel, 2004, John Wiley andSons. NISTnls A set of test nonlinear least squares examples from NIST, theU.S. National Institute for Standards and Technology. NMMAPSlite U.S. National Morbidity, Mortality, and Air Pollution Study data lite. NORMT3 Evaluates complex erf, erfc and density of sum of Gaussian and Student'st. NRAIA Data sets with sample code from “Nonlinear Regression Analysis and ItsApplications” by Doug Bates and Donald Watts, 1988, Wiley. ORMDR Odds ratio based multivactor-dimensionality reduction method fordetecting gene-gene interactions. Oarray Arrays with arbitrary offsets. PBSmapping Software evolved from fisheries research conducted at the PacificBiological Station (PBS) in Nanaimo, British Columbia, Canada. Drawsmaps and implements other GIS procedures. PBSmodelling Software to facilitate the design, testing, and operation of computermodels. PET Simulation and reconstruction of PET images. PHYLOGR Manipulation and analysis of phylogenetically simulated data sets (asobtained from PDSIMUL in package PDAP) and phylogenetically-basedanalyses using GLS. PK Estimation of pharmacokinetic parameters. PKfit A nonlinear regression (including a genetic algorithm) program designedto deal with curve fitting for pharmacokinetics. Unified computational interfaces for pop PK. POT Generalized Pareto distribution and Peaks Over Threshold. PSAgraphics Propensity Score Analysis Graphics. PTAk A multiway method to decompose a tensor (array) of any order, as ageneralisation of SVD also supporting non-identity metrics andpenalisations. Also includes some other multiway methods. PearsonICA Independent component analysis using score functions from the Pearsonsystem. PerformanceAnalytics Econometric tools for performance and risk analysis. PhySim Phylogenetic tree simulation. PresenceAbsence Presence-absence model evaluation. ProbForecastGOP Probabilistic weather field forecasts using the Geostatistical OutputPerturbation method introduced by Gel, Raftery and Gneiting (2004). ProbeR Reliability for gene expression from Affymetrix chip. QCA Qualitative Comparative Analysis for crisp sets. QCAGUI QCA Graphical User Interface. QRMlib Code to examine Quantitative Risk Management concepts. R.cache Fast and light-weight caching of objects. R.huge Methods for accessing huge amounts of data. R.matlab Read and write of MAT files together with R-to-Matlab connectivity. R.oo R object-oriented programming with or without references. R.rsp R server pages. R.utils Utility classes and methods useful when programming in R and developingR packages. R2HTML Functions for exporting R objects & graphics in an HTML document. R2WinBUGS Running WinBUGS from R: call a BUGS model, summarize inferences andconvergence in a table and graph, and save the simulations in arrays foreasy access in R. RArcInfo Functions to import Arc/Info V7.x coverages and data. RBGL Interface to the boost C++ graph library. RBloomberg Fetch data from a Bloomberg API using COM. RColorBrewer ColorBrewer palettes for drawing nice maps shaded according to avariable. RFA Regional Frequency Analysis. RGtk2 Facilities for programming graphical interfaces using Gtk (the Gimp ToolKit) version 2. RGrace Mouse/menu driven interactive plotting application. RGraphics Data and functions from the book “R Graphics” by Paul Murrell, 2005,Chapman & Hall/CRC. RII Estimation of the relative index of inequality for interval-censoreddata using natural cubic splines. RJDBC Access to databases through the JDBC interface. RJaCGH Reversible Jump MCMC for the analysis of CGH arrays. RLMM A genotype calling algorithm for Affymetrix SNP arrays. RLRsim Exact (Restricted) Likelihood Ratio tests for mixed and additivemodels. RLadyBug Analysis of infectious diseases using stochastic epidemic models. RMySQL An interface between R and the MySQL database system. RNetCDF An interface to Unidata's NetCDF library functions (version 3) andfurthermore access to Unidata's udunits calendar conversions. ROCR Visualizing the performance of scoring classifiers. RODBC An ODBC database interface. ROracle Oracle Database Interface driver for R. Uses the ProC/C++ embedded SQL. RQuantLib Provides access to (some) of the QuantLib functions from within R;currently limited to some Option pricing and analysis functions. TheQuantLib project aims to provide a comprehensive software framework forquantitative finance. RSQLite Database Interface R driver for SQLite. Embeds the SQLite databaseengine in R. RScaLAPACK An interface to ScaLAPACK functions from R. RSVGTipsDevice An R SVG graphics device with dynamic tips and hyperlinks. RSvgDevice A graphics device for R that uses the new w3.org XML standard forScalable Vector Graphics. RTisean R interface to Tisean algorithms. RUnit Functions implementing a standard Unit Testing framework, withadditional code inspection and report generation tools. RWeka An R interface to Weka, a rich collection of machine learning algorithmsfor data mining tasks. RWinEdt A plug in for using WinEdt as an editor for R. RXshrink Maximum Likelihood Shrinkage via Ridge or Least Angle Regression. RadioSonde A collection of programs for reading and plotting SKEW-T,log p diagramsand wind profiles for data collected by radiosondes (the typical weatherballoon-borne instrument). RandVar Implementation of random variables by means of S4 classes and methods. RandomFields Creating random fields using various methods. RaschSampler Sampling binary matrices with fixed margins. Rcapture Loglinear models in capture-recapture experiments. Rcmdr A platform-independent basic-statistics GUI (graphical userinterface) for R, based on the tcltk package. Rcmdr.HH Rcmdr support for the introductory course at Temple University. RcmdrPlugin.TeachingDemos Rcmdr Teaching Demos plug-in. RcppTemplate An illustration of the use of the Rcpp R/C++ interface library. Reliability Functions for estimating parameters in software reliability models. ResistorArray Electrical properties of resistor networks. Rfwdmv Forward Search for Multivariate Data. RiboSort Classification and analysis of microbial community profiles. Rigroup Provides small integer group functions. Rlab Functions and data sets for the NCSU ST370 class. Rlsf Interface to the LSF queuing system. Rmdr R-Multifactor Dimensionality Reduction. Rmpi An interface (wrapper) to MPI (Message-Passing Interface) APIs. It alsoprovides an interactive R slave environment in which distributedstatistical computing can be carried out. Rpad Utility functions for the Rpad workbook-style interface. Rserve A socket server (TCP/IP or local sockets) which allows binary requeststo be sent to R. Runuran Interface to the UNU.RAN library for Universal Non-Uniform RANdomvariate generators. Rwave An environment for the time-frequency analysis of 1-D signals (andespecially for the wavelet and Gabor transforms of noisy signals), basedon the book “Practical Time-Frequency Analysis: Gabor and WaveletTransforms with an Implementation in S” by Rene Carmona, Wen L. Hwangand Bruno Torresani, 1998, Academic Press. Ryacas An R interfaces to the yacas computer algebra system. SASmixed Data sets and sample linear mixed effects analyses corresponding to theexamples 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. SLmisc Miscellaneous Functions for analysis of gene expression data at SIRS-LabGmbH. SMPracticals Data sets and a few functions for use with the practicals outlined inAppendix A of the book “Statistical Models” by Anthony Davison, 2003,Cambridge University Press. SNPassoc SNP-based whole genome association studies. SNPmaxsel Maximally selected statistics for SNP data. SRPM Shared Reproducibility Package Management. SQLiteDF Stores data frames and matrices in SQLite tables. SciViews A bundle of packages to implement a full reusable GUI API forR. Contains svGUI with the main GUI features,svDialogs for the dialog boxes, svIO for dataimport/export, svMisc with miscellaneous supporting functions,and svViews providing views and report features (views areHTML presentations of the content of R objects, combiningtext, tables and graphs in the same document). SemiPar Functions for semiparametric regression analysis, to complement thebook “Semiparametric Regression” by R. Ruppert, M. P. Wand, andR. J. Carroll, 2003, Cambridge University Press. SenSrivastava Collection of datasets from “Regression Analysis, Theory, Methods andApplications” by A. Sen and M. Srivastava, 1990, Springer. SensoMineR Sensory data analysis. SeqKnn Sequential KNN imputation. SharedHT2 Shared Hotelling T^2 test for small sample microarrayexperiments. Snowball Snowball stemmers. SoPhy Soil Physics Tools: simulation of water flux and solute transport insoil. SparseLogReg Sparse logistic regression. SparseM Basic linear algebra for sparse matrices. SpherWave Spherical Wavelets and SW-based spatially adaptive methods. StatDataML Read and write StatDataML. StoppingRules Stopping rules for microarray classifiers. 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 hypergeometricdistributions). SwissAir Air quality data of Switzerland for one year in 30 min resolution. Synth Causal inference using the synthetic control group method. TIMP A problem solving environment for fitting superposition models. TRAMPR Terminal Restriction Fragment Length Polymorphism (TRFLP) Analysis andMatching Package for R. TSP Traveling Salesperson Problem (TSP). TWIX Trees WIth eXtra splits. TeachingDemos A set of demonstration functions that can be used in a classroom todemonstrate statistical concepts, or on your own to better understandthe concepts or the programming. TwoWaySurvival Additive two-way hazards modeling of right censored survival data. UNF Tools for creating universal numeric fingerprints for data. USPS Unsupervised and Supervised methods of Propensity Score adjustment forbias. UsingR Data sets to accompany the textbook “Using R for IntroductoryStatistics” by J. Verzani, 2005, Chapman & Hall/CRC. VDCutil Utilities supporting VDC, an open source digital library system forquantitative data. VGAM Vector Generalized Linear and Additive Models. 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). WWGbook Functions and datasets for the book “Linear Mixed Models: A PracticalGuide Using Statistical Software” by B. West, K. Welch, and A. Galecki,2006, Chapman & Hall/CRC. WeedMap Map of weed intensity. WhatIf Software for evaluating counterfactuals. XML Tools for reading XML documents and DTDs. ZIGP Zero Inflated Generalized Poisson (ZIGP) regression models. Zelig Everyone's statistical software: an easy-to-use program that canestimate, and help interpret the results of, an enormous range ofstatistical models. aaMI Mutual information for protein sequence alignments. abind Combine multi-dimensional arrays. accuracy A suite of tools designed to test and improve the accuracy ofstatistical computation. acepack ACE (Alternating Conditional Expectations) and AVAS (Additivity andVAriance Stabilization for regression) methods for selecting regressiontransformations. actuar Functions related to actuarial science applications. ada Performs boosting algorithms for a binary response. adabag Adaboost.M1 and Bagging. adapt Adaptive quadrature in up to 20 dimensions. ade4 Multivariate data analysis and graphical display. ade4TkGUI Tcl/Tk Graphical User Interface for ade4. adegenet Genetic data handling for multivariate analysis using ade4. adehabitat A collection of tools for the analysis of habitat selection by animals. adimpro Adaptive smoothing of digital images. adlift Adaptive Wavelet transforms for signal denoising. ads Spatial point patterns analysis. agce Analysis of growth curve experiments. agricolae Statistical procedures for agricultural research. agsemisc Miscellaneous plotting and utility functions. akima Linear or cubic spline interpolation for irregularly gridded data. allelic A fast, unbiased and exact allelic exact test. alr3 Methods and data to accompany the textbook “Applied Linear Regression”by S. Weisberg, 2005, Wiley. amap Another Multidimensional Analysis Package. analogue Analogue methods for palaeoecology. anm Analog model for statistical/empirical downscaling. aod Analysis of Overdispersed Data. apTreeshape Analyses of phylogenetic treeshape. ape Analyses of Phylogenetics and Evolution, providing functions for readingand plotting phylogenetic trees in parenthetic format (standard Newickformat), analyses of comparative data in a phylogenetic framework,analyses of diversification and macroevolution, computing distances fromallelic and nucleotide data, reading nucleotide sequences from GenBankvia internet, and several tools such as Mantel's test, computation ofminimum spanning tree, or the population parameter theta based onvarious approaches. aplpack Another PLot PACKage: stem.leaf, bagplot, faces, spin3R, …. arm Data Analysis using Regression and Multilevel/hierarchical models. arrayImpute Missing imputation for microarray data. arrayMissPattern Exploratory analysis of missing patterns for microarray data. ars Adaptive Rejection Sampling. arules Mining association rules and frequent itemsets with R. arulesSequences Mining frequent sequences. ash David Scott's ASH routines for 1D and 2D density estimation. aspace Estimating centrographic statistics and computational geometries fromspatial point patterns. assist A suite of functions implementing smoothing splines. aster Functions and datasets for Aster modeling (forest graph exponentialfamily conditional or unconditional canonical statistic models for lifehistory trait modeling). asypow A set of routines that calculate power and related quantities utilizingasymptotic likelihood ratio methods. asuR Functions and data sets for a lecture in “Advanced Statistics usingR”. aws Functions to perform adaptive weights smoothing. backtest Exploring portfolio-based hypotheses about financial instruments. bayesSurv Bayesian survival regression with flexible error and (later on alsorandom effects) distributions. bayesm Bayes Inference for Marketing/Micro-econometrics. bayesmix Bayesian mixture models of univariate Gaussian distributions using JAGS. bbmle Modifications and extensions of stats4 MLE code. bcp Bayesian Change Point based on the Barry and Hartigan product partitionmodel. benchden 28 benchmark densities from Berlinet/Devroye (1994). betareg Beta regression for modeling rates and proportions. bicreduc Reduction algorithm for the NPMLE for the distribution function ofbivariate interval-censored data. biglm Linear regression for data too large to fit in memory. bim Bayesian interval mapping diagnostics: functions to interpret QTLCartand Bmapqtl samples. binGroup Evaluation and experimental design for binomial group testing. bindata Generation of correlated artificial binary data. binom Binomial confidence intervals for several parameterizations. bio.infer Compute biological inferences. biopara Self-contained parallel system for R. bitops Functions for Bitwise operations on integer vectors. bivpois Bivariate Poisson models using the EM algorithm. blighty Function for drawing the coastline of the United Kingdom. blockrand Randomization for block random clinical trials. boa Bayesian Output Analysis Program for MCMC. boolean Boolean logit and probit: a procedure for testing Boolean hypotheses. boost Boosting methods for real and simulated data, featuring `BagBoost',`LogitBoost', `AdaBoost', and `L2Boost'. boot Functions and datasets for bootstrapping from the book “BootstrapMethods and Their Applications” by A. C. Davison and D. V. Hinkley,1997, Cambridge University Press. Recommended. bootStepAIC Model selection by bootstrapping the stepAIC() procedure. bootstrap Software (bootstrap, cross-validation, jackknife), data and errata forthe book “An Introduction to the Bootstrap” by B. Efron andR. Tibshirani, 1993, Chapman and Hall. bqtl QTL mapping toolkit for inbred crosses and recombinant inbred lines.Includes maximum likelihood and Bayesian tools. brainwaver Basic wavelet analysis of multivariate time series with a vizualisationand parametrization using graph theory. brew Templating framework for report generation. brlr Bias-reduced logistic regression: fits logistic regression models bymaximum penalized likelihood. butler Unit testing, profiling and benchmarking for R. caMassClass Processing and Classification of protein mass spectra (SELDI) data. caTools Miscellaneous utility functions, including reading/writing ENVI binaryfiles, a LogitBoost classifier, and a base64 encoder/decoder. cacheSweave Tools for caching Sweave computations. cairoDevice Loadable CAIRO/GTK device driver. calibrate Calibration of biplot axes. car Companion to Applied Regression, containing functions for appliedregession, linear models, and generalized linear models, with anemphasis on regression diagnostics, particularly graphical diagnosticmethods. cat Analysis of categorical-variable datasets with missing values. catmap Case-control and TDT meta-analysis package. catspec Special models for categorical variables. cba Clustering for Business Analytics, including implementations of Proximusand Rock. cclust Convex clustering methods, including k-means algorithm, on-lineupdate algorithm (Hard Competitive Learning) and Neural Gas algorithm(Soft Competitive Learning) and calculation of several indexes forfinding the number of clusters in a data set. celsius Retrieve Affymetrix microarray measurements and metadata from Celsius. cfa Analysis of configuration frequencies. cgh Analysis of microarray comparative genome hybridisation data using theSmith-Waterman algorithm. chplot Augmented convex hull plots: informative and nice plots for groupedbivariate data. changeLOS Change in length of hospital stay (LOS). chemCal Calibration functions for analytical chemistry. chron A package for working with chronological objects (times and dates). circular Circular statistics, from “Topics in Circular Statistics” by RaoJammalamadaka and A. SenGupta, 2001, World Scientific. clValid Statistical and biological validation of clustering results. clac Clust Along Chromosomes, a method to call gains/losses in CGH arraydata. class Functions for classification (k-nearest neighbor and LVQ).Contained in the VR bundle. Recommended. classInt Choose univariate class intervals for mapping or other graphicspurposes. classPP Projection Pursuit for supervised classification. classifly Explore classification models in high dimensions. clim.pact Climate analysis and downscaling for monthly and daily data. climatol Functions to fill missing data in climatological (monthly) series and totest their homogeneity, plus functions to draw wind-rose andWalter&Lieth diagrams. clines Calculates Contour Lines. clinfun Utilities for clinical study design and data analyses. clue CLUster Ensembles. clustTool GUI for clustering data with spatial information. cluster Functions for cluster analysis. Recommended. clusterGeneration Random cluster generation (with specified degree of separation). clusterRepro Reproducibility of gene expression clusters. clusterSim Searching for optimal clustering procedure for a data set. clusterfly Explore clustering interactively using R and GGobi. clustvarsel Variable selection for model-based clustering. cmprsk Estimation, testing and regression modeling of subdistribution functionsin competing risks. cobs99 Constrained B-splines: outdated 1999 version. cobs Constrained B-splines: qualitatively constrained (regression) smoothingvia linear programming and sparse matrices. cocorresp Co-correspondence analysis ordination methods for community ecology. coda Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)simulations. codetools Code analysis tools. Recommended for R 2.5.0 or later. coin COnditional INference procedures for the general independence problemincluding two-sample, K-sample, correlation, censored, orderedand multivariate problems. colorRamp Builds single and double gradient color maps. colorspace Mapping between assorted color spaces. combinat Combinatorics utilities. compOverlapCorr Comparing overlapping correlation coefficients. compositions Functions for the consistent analysis of compositional data (e.g.,portions of substances) and positive numbers (e.g., concentrations). concor Concordance, providing “SVD by blocks”. concord Measures of concordance and reliability. conf.design A series of simple tools for constructing and manipulating confoundedand fractional factorial designs. connectedness Find disconnected sets for two-way classification. contrast A collection of contrast methods. copula Classes of commonly used copulas (including elliptical and Archimedian),and methods for density, distribution, random number generators, andplotting. corpora Utility functions for the statistical analysis of corpus frequencydata. corpcor Efficient estimation of covariance and (partial) correlation. corrgram Plot a correlogram. covRobust Robust covariance estimation via nearest neighbor cleaning. coxphf Cox regression with Firth's penalized likelihood. coxrobust Robust Estimation in the Cox proportional hazards regression model. cramer Routine for the multivariate nonparametric Cramer test. crossdes Functions for the construction and randomization of balanced carryoverbalanced designs, to check given designs for balance, and for simulationstudies on the validity of two randomization procedures. crq Quantile regression for randomly censored data. cslogistic Likelihood and posterior analysis of conditionally specified logisticregression models. cts Continuous time autoregressive models and the Kalman filter. ctv Server-side and client-side tools for CRAN task views. cwhmisc Miscellaneous functions by Christian W. Hoffmann. cyclones Cyclone identification. date Functions for dealing with dates. The most useful of them accepts avector of input dates in any of the forms ‘8/30/53’,‘30Aug53’, ‘30 August 1953’, …, ‘August 30 53’, orany mixture of these. dblcens Calculates the NPMLE of the survival distribution for doubly censoreddata. ddesolve Solver for Delay Differential Equations. deal Bayesian networks with continuous and/or discrete variables can belearned and compared from data. debug Debugger for R functions, with code display, graceful error recovery,line-numbered conditional breakpoints, access to exit code, flowcontrol, and full keyboard input. deldir Calculates the Delaunay triangulation and the Dirichlet or Voronoitesselation (with respect to the entire plane) of a planar point set. delt Estimation of multivariate densities with adaptive histograms. denpro Visualization of multivariate density functions and estimates with levelset trees and shape trees, and visualization of multivariate data withtail trees. depmix Dependent Mixture Models: fit (multi-group) mixtures of latent Markovmodels on mixed categorical and continuous (time series) data. desirability Desirabiliy function optimization and ranking. dglm Double generalized linear models. diamonds Functions for illustrating aperture-4 diamond partitions in the plane,or on the surface of an octahedron or icosahedron, for use as analysisor sampling grids. dichromat Color schemes for dichromats: collapse red-green distinctions tosimulate the effects of colour-blindness. digest Two functions for the creation of “hash” digests of arbitrary Robjects using the md5 and sha-1 algorithms permitting easy comparison ofR 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 additionalfunctionality. distrDoc Documentation for packages distr, distrEx,distrSim, and distrTEst. distrEx Extensions of package distr. distrSim Simulation classes based on package distr. distrTEst Estimation and Testing classes based on package distr. distributions Probability distributions based on TI-83 Plus. diveMove Dive analysis and calibration. dlm Maximum likelihood and Bayesian analysis of Dynamic Linear Models. doBy Facilities for groupwise computations. dprep Data preprocessing and visualization functions for classification. dr Functions, methods, and datasets for fitting dimension reductionregression, including pHd and inverse regression methods SIR and SAVE. drc Non-linear regression analysis for multiple curves with focus onconcentration-response, dose-response and time-response curves. drm Regression and association models for clustered categorical responses. drfit Dose-response data evaluation. dse Dynamic System Estimation, a multivariate time series package bundle.Contains dse1 (the base system, including multivariate ARMA andstate space models) and dse2 (extensions for evaluatingestimation techniques, forecasting, and for evaluating forecasting model). dtt Discrete Trigonometric Transforms. dyn Time series regression. dynamicGraph Interactive graphical tool for manipulating graphs. dynlm Dynamic linear models and time series regression. e1071 Miscellaneous functions used at the Department of Statistics at TU Wien(E1071), including moments, short-time Fourier transforms, IndependentComponent Analysis, Latent Class Analysis, support vector machines, andfuzzy clustering, shortest path computation, bagged clustering, and somemore. eRm Estimating extended Rasch models. earth Earth: multivariate adaptive regression spline models. eba Fitting and testing probabilistic choice models, especially the BTL,elimination-by-aspects (EBA), and preference tree (Pretree) models. eco Fitting Bayesian models of ecological inference in 2 by 2 tables. ecodist Dissimilarity-based functions for ecological analysis. edci Edge Detection and Clustering in Images. effects Graphical and tabular effect displays, e.g., of interactions, for linearand generalised linear models. eha A package for survival and event history analysis. eiPack Ecological inference and higher-dimension data management. eigenmodel Semiparametric factor and regression models for symmetric relationaldata. elasticnet Elastic net regularization and variable selection. ellipse Package for drawing ellipses and ellipse-like confidence regions. elliptic A suite of elliptic and related functions including Weierstrass andJacobi forms. elrm Exact Logistic Regression via MCMC. emdbook Data sets and auxiliary functions for “Ecological Models and Data” byBen Bolker (work in progress). emme2 Functions to read from and write to an EMME/2 databank. emplik Empirical likelihood ratio for means/quantiles/hazards from possiblyright censored data. energy E-statistics (energy) tests for comparing distributions: multivariatenormality, Poisson test, multivariate k-sample test for equaldistributions, hierarchical clustering by e-distances. ensembleBMA Probabilistic forecasting using Bayesian Model Averaging of ensemblesusing a mixture of normal distributions. epicalc Epidemiological calculator. epitools Basic tools for applied epidemiology. epsi Edge Preserving Smoothing for Images. equivalence Tests and graphics for assessing tests of equivalence. evd Functions for extreme value distributions. Extends simulation,distribution, quantile and density functions to univariate, bivariateand (for simulation) multivariate parametric extreme valuedistributions, and provides fitting functions which calculate maximumlikelihood estimates for univariate and bivariate models. evdbayes Functions for the bayesian analysis of extreme value models, using MCMCmethods. evir Extreme Values in R: Functions for extreme value theory, which may bedivided into the following groups; exploratory data analysis, blockmaxima, peaks over thresholds (univariate and bivariate), pointprocesses, gev/gpd distributions. exactLoglinTest Monte Carlo exact tests for log-linear models. exactRankTests Computes exact p-values and quantiles using an implementation ofthe Streitberg/Roehmel shift algorithm. exactmaxsel Exact methods for maximally selected statistics for binary responsevariables. experiment Designing and analyzing randomized experiments. extRemes Extreme value toolkit. fBasics The Rmetrics module for “Markets, basic statistics, and stylizedfacts”. Rmetrics is an environment and software collection forteaching financial engineering and computational finance(http://www.Rmetrics.org/). fCalendar The Rmetrics module for “Date, Time and Calendars”. fEcofin The Rmetrics module with “Selected economic and financial data sets”. fExtremes The Rmetrics module for “Beyond the Sample, Dealing with ExtremeValues”. fMultivar The Rmetrics module for “Multivariate Data Analysis”. fOptions The Rmetrics module for “The Valuation of Options”. fPortfolio The Rmetrics module for “Pricing and Hedging of Options”. fSeries The Rmetrics module for “The Dynamical Process Behind FinancialMarkets”. fame Interface for FAME time series database. far Modelization for Functional AutoRegressive processes. faraway Functions and datasets for books by Julian Faraway. fastICA Implementation of FastICA algorithm to perform Independent ComponentAnalysis (ICA) and Projection Pursuit. fda Functional Data Analysis: analysis of data where the basic observationis a function of some sort. fdim Functions for calculating fractal dimension. fdrtool Estimation and control of (local) False Discovery Rates. feature Feature significance for multivariate kernel density estimation. femmeR Output analysis of FEMME model results. ffmanova Fifty-fifty MANOVA. fgac Families of Generalized Archimedean Copulas. fields A collection of programs for curve and function fitting with an emphasison spatial data. The major methods implemented include cubic and thinplate splines, universal Kriging and Kriging for large data sets. Themain feature is that any covariance function implemented in R can beused for spatial prediction. filehash Simple file-based hash table. filehashSQLite Simple key-value database using SQLite as the backend. financial Solving financial problems in R. fingerprint Functions to operate on binary fingerprint data. flexclust Flexible cluster algorithms. flexmix Flexible Mixture Modeling: a general framework for finite mixtures ofregression models using the EM algorithm. fmri Functions for the analysis of fMRI experiments. forecasting A bundle with functions and datasets for forecasting. Containsforecast (time series forecasting), fma (data setsfrom the book “Forecasting: Methods and Applications” by Makridakis,Wheelwright & Hyndman, 1998), and Mcomp (data from theM-competitions). foreign Functions for reading and writing data stored by statistical softwarelike Minitab, S, SAS, SPSS, Stata, Systat, etc. Recommended. forensic Statistical methods in forensic genetics. fork Functions for handling multiple processes: simple wrappers around theUnix process management API calls. fortunes R fortunes. forward Forward search approach to robust analysis in linear and generalizedlinear regression models. fpc Fixed point clusters, clusterwise regression and discriminant plots. fracdiff Maximum likelihood estimation of the parameters of a fractionallydifferenced ARIMA(p,d,q) model (Haslett and Raftery, AppliedStatistics, 1989). frailtypack Fit a shared gamma frailty model and Cox proportional hazards modelusing a Penalized Likelihood on the hazard function. ftnonpar Features and strings for nonparametric regression. fuzzyRankTests Fuzzy rank tests and confidence intervals. g.data Create and maintain delayed-data packages (DDP's). gRbase A package for graphical modelling in R. Defines S4 classes forgraphical meta data and graphical models, and illustrates howhierarchical log-linear models may be implemented and combined withdynamicGraph. gRcox Inference in graphical gaussian models with edge and vertex symmetries. gWidgets gWidgets API for building toolkit-independent, interactiveGUIs. gWidgetsRGtk2 Toolkit implementation of gWidgets for RGtk2. gWidgetsrJava Toolkit implementation of gWidgets for rJava. gafit Genetic algorithm for curve fitting. gam Functions for fitting and working with Generalized Additive Models, asdescribed in chapter 7 of the White Book, and in “Generalized AdditiveModels” by T. Hastie and R. Tibshirani (1990). gamair Data sets used in the book “Generalized Additive Models: AnIntroduction with R” by S. Wood (2006). gamlss Functions to fit Generalized Additive Models for Location Scale andShape. gamlss.dist Extra distributions for GAMLSS modeling. gamlss.mx A GAMLSS add on package for fitting mixtute distributions. gamlss.nl A GAMLSS add on package for fitting non linear parametric models. gamlss.tr A GAMLSS add on for generating and fitting truncated (gamlss.family)distributions. gap Genetic analysis package for both population and family data. gbev Gradient Boosted regression trees with Errors-in-Variables. gbm Generalized Boosted Regression Models: implements extensions to Freundand Schapire's AdaBoost algorithm and J. Friedman's gradient boostingmachine. Includes regression methods for least squares, absolute loss,logistic, Poisson, Cox proportional hazards partial likelihood, andAdaBoost exponential loss. gcl Compute a fuzzy rules or tree classifier from data. gclus Clustering Graphics. Orders panels in scatterplot matrices and parallelcoordinate displays by some merit index. gcmrec Parameters estimation of the general semiparametric model for recurrentevent data proposed by Peña and Hollander. gdata Various functions to manipulate data. gee An implementation of the Liang/Zeger generalized estimating equationapproach to GLMs for dependent data. geepack Generalized estimating equations solver for parameters in mean, scale,and correlation structures, through mean link, scale link, andcorrelation link. Can also handle clustered categorical responses. geiger Analysis of evolutionary diversification. genalg R based genetic algorithm for binary and floating point chromosomes. genetics Classes and methods for handling genetic data. Includes classes torepresent genotypes and haplotypes at single markers up to multiplemarkers 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-basedmethods. geoRglm Functions for inference in generalised linear spatial models. geometry Mesh generation and surface tesselation, based on the Qhull library. ggm Functions for defining directed acyclic graphs and undirected graphs,finding induced graphs and fitting Gaussian Markov models. ggplot Grammar of graphics based plots for R. ggplot2 An implementation of the Grammar of Graphics in R. ghyp Univariate and multivariate generalized hyperbolic distributions. giRaph Data structures and algorithms for computations on graphs. 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. glmc Fitting Generalized Linear Models subject to Constraints. glmmAK Generalized Linear Mixed Models. glmmML A Maximum Likelihood approach to generalized linear models with randomintercept. glmpath L1 regularization path for Generalized Linear Models. glpk Interface to the GNU Linear Programming Kit (GLPK). gmodels Various functions to manipulate models. gmp Arithmetic “without limitations” using the GNU MultiplePrecision library. gmt Interface between the GMT 4.0 map-making software and R. gnm Functions to specify and fit generalized nonlinear models, includingmodels with multiplicative interaction terms such as the UNIDIFF modelfrom sociology and the AMMI model from crop science. gpclib General polygon clipping routines for R based on Alan Murta's Clibrary. gpls Classification using generalized partial least squares for two-group andmulti-group (more than 2 group) classification. gplots Various functions to draw plots. grImport Importing vector graphics. graph Handling of graph data structures. grasper Generalized Regression Analysis and Spatial Predictions for R. gregmisc Miscellaneous functions written/maintained by Gregory R. Warnes. gridBase Integration of base and grid graphics. grnnR A Generalized Regression Neural Network. grouped Regression models for grouped and coarse data, under the Coarsened AtRandom assumption. grplasso Fit user specified models with group lasso penalty. gsl Wrapper for special functions of the Gnu Scientific Library (GSL). gss A comprehensive package for structural multivariate function estimationusing smoothing splines. gstat multivariable geostatistical modelling, prediction and simulation.Includes code for variogram modelling; simple, ordinary and universalpoint or block (co)kriging, sequential Gaussian or indicator(co)simulation, and map plotting functions. gsubfn Miscellaneous string utilities. gtkDevice GTK graphics device driver that may be used independently of the R-GNOMEinterface and can be used to create R devices as embedded components ina GUI using a Gtk drawing area widget, e.g., using RGtk. gtools Various functions to help manipulate data. gvlma Global Validation of Linear Models Assumptions. hapassoc Likelihood inference of trait associations with SNP haplotypes and otherattributes using the EM Algorithm. haplo.ccs Estimate haplotype relative risks in case-control data. haplo.stats Statistical analysis of haplotypes with traits and covariates whenlinkage phase is ambiguous. hapsim Haplotype data simulation. hddplot Use known groups in high-dimensional data to derive scores for plots. hdeco Hierarchical DECOmposition of entropy for categorical map comparisons. hdf5 Interface to the NCSA HDF5 library. hdrcde Highest Density Regions and Conditional Density Estimation. heatmap.plus Heatmap with sensible behavior. heplots Visualizing tests in multivariate linear models. hett Functions for the fitting and summarizing of heteroscedastict-regression. hexView Viewing binary files. hier.part Hierarchical Partitioning: variance partition of a multivariate dataset. hierfstat Estimation of hierarchical F-statistics from haploid or diploid geneticdata with any numbers of levels in the hierarchy, and tests for thesignificance of each F and variance components. hmm.discnp Hidden Markov models with discrete non-parametric observationdistributions. hoa A bundle of packages for higher order likelihood-based inference.Contains cond for approximate conditional inference forlogistic and loglinear models, csampling for conditionalsimulation in regression-scale models, marg for approximatemarginal inference for regression-scale models, and nlreg forhigher order inference for nonlinear heteroscedastic models. homals Homogeneity Analysis (HOMALS) package with optional Tcl/Tk interface. homtest Homogeneity tests for regional frequency analysis. hopach Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH). hot Computation on micro-arrays. howmany A lower bound for the number of correct rejections. httpRequest Implements HTTP Request protocols (GET, POST, and multipart POSTrequests). hwde Models and tests for departure from Hardy-Weinberg equilibrium andindependence between loci. hybridHclust Hybrid hierarchical clustering via mutual clusters. ibdreg Regression methods for IBD linkage with covariates. identity Jacquard condensed coefficients of identity. ifa Independent Factor Analysis. ifs Iterated Function Systems distribution function estimator. igraph Routines for simple graphs. iid.test Testing whether data is independent and identically distributed. impute Imputation for microarray data (currently KNN only). ineq Inequality, concentration and poverty measures, and Lorenz curves(empirical and theoretic). inline Inline C/C++ function calls from R. intcox Implementation of the Iterated Convex Minorant Algorithm for the Coxproportional hazard model for interval censored event data. iplots Interactive graphics for R. ipred Improved predictive models by direct and indirect bootstrap aggregationin classification and regression as well as resampling based estimatorsof prediction error. irr Coefficients of Interrater Reliability and Agreement for quantitative,ordinal and nominal data. irtoys Simple interface to the estimation and plotting of IRT models. ismev Functions to support the computations carried out in “An Introductionto Statistical Modeling of Extreme Values;' by S. Coles, 2001, Springer.The functions may be divided into the following groups; maxima/minima,order statistics, peaks over thresholds and point processes. its An S4 class for handling irregular time series. kappalab The “laboratory for capacities”, an S4 tool box for capacity (ornon-additive measure, fuzzy measure) and integral manipulation on afinite setting. kernelPop Spatially explicit population genetic simulations. kernlab Kernel-based machine learning methods including support vector machines. kin.cohort Analysis of kin-cohort studies. kinship Mixed-effects Cox models, sparse matrices, and modeling data from largepedigrees. kknn Weighted k-nearest neighbors classification and regression. klaR Miscellaneous functions for classification and visualization developedat the Department of Statistics, University of Dortmund. klin Linear equations with Kronecker structure. km.ci Confidence intervals for the Kaplan-Meier estimator. knnFinder Fast nearest neighbor search. knnTree Construct or predict with k-nearest-neighbor classifiers, usingcross-validation to select k, choose variables (by forward orbackwards selection), and choose scaling (from among no scaling, scalingeach column by its SD, or scaling each column by its MAD). The finishedclassifier will consist of a classification tree with one suchk-nn classifier in each leaf. knncat Nearest-neighbor classification with categorical variables. knnflex A more flexible k-NN. kohonen Supervised and unsupervised self-organising maps. ks Kernel smoothing: bandwidth matrices for kernel density estimators andkernel discriminant analysis for bivariate data. kza Kolmogorov-Zurbenko Adpative filter for locating change points in a timeseries. kzft Kolmogorov-Zurbenko Fourier Transform and application. labdsv Laboratory for Dynamic Synthetic Vegephenomenology. labstatR Functions for the book “Laboratorio di statistica con R” byS. M. Iacus and G. Masarotto, 2002, McGraw-Hill. Function names anddocumentation in Italian. lancet.iraqmortality Surveys of Iraq mortality published in The Lancet. languageR Data sets and functions for the book “Analyzing Linguistic Data: Apractical introduction to statistics” by R. H. Baayen, 2007, Cambridge:Cambridge University Press. lars Least Angle Regression, Lasso and Forward Stagewise: efficientprocedures for fitting an entire lasso sequence with the cost of asingle least squares fit. laser Likelihood Analysis of Speciation/Extinction Rates from phylogenies. lasso2 Routines and documentation for solving regression problems whileimposing an L1 constraint on the estimates, based on the algorithm ofOsborne et al. (1998). latentnet Latent position and cluster models for statistical networks. lattice Lattice graphics, an implementation of Trellis Graphics functions.Recommended. latticeExtra Generic functions and standard methods for Trellis-based displays. lawstat Statistical tests widely utilized in biostatistics, public policy andlaw. lazy Lazy learning for local regression. ldDesign Design of experiments for detection of linkage disequilibrium, ldbounds Lan-DeMets method for group sequential boundaries. leaps A package which performs an exhaustive search for the best subsets of agiven set of potential regressors, using a branch-and-bound algorithm,and also performs searches using a number of less time-consumingtechniques. lgtdl A set of methods for longitudinal data objects. lhs Latin Hypercube Samples. limma LInear Models for MicroArray data. linprog Solve linear programming/linear optimization problems by using thesimplex algorithm. lme4 Fit linear and generalized linear mixed-effects models. lmeSplines Fit smoothing spline terms in Gaussian linear and nonlinearmixed-effects models. lmm Linear mixed models. lmomco L-moments and L-comoments. lmtest A collection of tests on the assumptions of linear regression modelsfrom the book “The linear regression model under test” by W. Kraemerand H. Sonnberger, 1986, Physica. locfdr Computation of local false discovery rates. locfit Local Regression, likelihood and density estimation. lodplot Assorted plots of location score versus genetic map position. logcondens Estimate a log-concave probability density from i.i.d. observations. logistf Firth's bias reduced logistic regression approach with penalized profilelikelihood based confidence intervals for parameter estimates. logspline Logspline density estimation. lokern Kernel regression smoothing with adaptive local or global plug-inbandwidth selection. longmemo Datasets and Functionality from the textbook “Statistics forLong-Memory Processes” by J. Beran, 1994, Chapman & Hall. lpSolve Functions that solve general linear/integer problems, assignmentproblems, and transportation problems via interfacing Lp_solve. lpridge Local polynomial (ridge) regression. lsa Latent Semantic Analysis. lspls LS-PLS (least squares — partial least squares) models. lss Accelerated failure time model to right censored data based onleast-squares principle. ltm Analysis of multivariate Bernoulli data using latent trait models(including the Rasch model) under the Item Response Theory approach. luca Likelihood Under Covariate Assumptions (LUCA). lvplot Letter-value box plots. mAr Estimation of multivariate AR models through a computationally efficientstepwise least-squares algorithm. mFilter Miscellenous time series filters. maanova Analysis of N-dye Micro Array experiments using mixed model effect.Contains anlysis of variance, permutation and bootstrap, cluster andconsensus tree. magic A variety of methods for creating magic squares of any order greaterthan 2, and various magic hypercubes. mapLD Linkage Disequilibrium mapping. mapdata Supplement to package maps, providing the larger and/orhigher-resolution databases. mapproj Map Projections: converts latitude/longitude into projected coordinates. maps Draw geographical maps. Projection code and larger maps are in separatepackages. maptools Set of tools for manipulating and reading geographic data, in particularESRI shapefiles. maptree Functions with example data for graphing and mapping models fromhierarchical clustering and classification and regression trees. mathgraph Tools for constructing and manipulating objects from a class of directedand undirected graphs. matlab Emulate MATLAB code using R. maxstat Maximally selected rank and Gauss statistics with several p-valueapproximations. mblm Median-based Linear models, using Theil-Sen single or Siegel repeatedmedians. mboost Gradient boosting for fitting generalized linear, additive andinteraction models. mcgibbsit Warnes and Raftery's MCGibbsit MCMC diagnostic. mclust Model-based clustering and normal mixture modeling including Bayesianregularization. mclust02 Model-based cluster analysis: the 2002 version of MCLUST. mcmc Functions for Markov Chain Monte Carlo (MCMC). mda Code for mixture discriminant analysis (MDA), flexible discriminantanalysis (FDA), penalized discriminant analysis (PDA), multivariateadditive regression splines (MARS), adaptive back-fitting splines(BRUTO), and penalized regression. meboot Maximum entropy bootstrap for time series. meifly Interactive model exploration using GGobi. memisc Miscellaneous Tools for data management, simulation, and presentation ofestimates. merror Accuracy and precision of measurements. meta Fixed and random effects meta-analysis, with functions for tests ofbias, forest and funnel plot. mfp Multiple Fractional Polynomials. mgcv Routines for GAMs and other genralized ridge regression problems withmultiple smoothing parameter selection by GCV or UBRE.Recommended. micEcon Tools for microeconomic analysis and microeconomic modelling. mice Multivariate Imputation by Chained Equations. mimR An R interface to MIM for graphical modeling in R. minpack.lm R interface for two functions from the MINPACK least squaresoptimization library, solving the nonlinear least squares problem by amodification of the Levenberg-Marquardt algorithm. misc3d A collection of miscellaneous 3d plots, including rgl-based isosurfaces. mitools Tools to perform analyses and combine results from multiple-imputationdatasets. mix Estimation/multiple imputation programs for mixed categorical andcontinuous data. mixreg Functions to fit mixtures of regressions. mixstock Mixed stock analysis functions. mixtools Tools for mixture models. mlCopulaSelection Copula selection and fitting using maximum likelihood. mlbench A collection of artificial and real-world machine learning benchmarkproblems, including the Boston housing data. mlica Independent Component Analysis using Maximum Likelihood. mlmRev Examples from Multilevel Modelling Software Review. mlmmm Maximum likelihood estimation under multivariate linear mixed modelswith missing values. mmlcr Mixed-mode latent class regression (also known as mixed-mode mixturemodel regression or mixed-mode mixture regression models) which canhandle both longitudinal and one-time responses. mnormt The multivariate normal and t distributions. moc Fits a variety of mixtures models for multivariate observations withuser-difined distributions and curves. modeest Mode estimation and Chernoff distribution. modehunt Multiscale analysis for density functions. modeltools A collection of tools to deal with statistical models. moments Moments, skewness, kurtosis and related tests. monomvn Estimation for multivariate normal data with monotone missingness. monreg Estimation of monotone regression and variance functions innonparametric models. monoProc Strictly monotone smoothing procedure. mprobit Multivariate probit model for binary/ordinal response. mratios Inferences for ratios of coefficients in the general linear model. msm Functions for fitting continuous-time Markov multi-state models tocategorical processes observed at arbitrary times, optionally withmisclassified responses, and covariates on transition ormisclassification rates. muS2RC S-plus to R Compatibility for package muStat. muStat Prentice rank sum test and McNemar test. muUtil Utility functions for package muStat. muhaz Hazard function estimation in survival analysis. multcomp Multiple comparison procedures for the one-way layout. multcompView Visualizations of paired comparisons. multic Quantitative linkage analysis tools using the variance componentsapproach. multilevel Analysis of multilevel data by organizational and social psychologists. multinomRob Overdispersed multinomial regression using robust (LQD and tanh)estimation. multtest Resampling-based multiple hypothesis testing. mvbutils Utilities by Mark V. Bravington for project organization, editing andbackup, sourcing, documentation (formal and informal), packagepreparation, macro functions, and more. mvnmle ML estimation for multivariate normal data with missing values. mvnormtest Generalization of the Shapiro-Wilk test for multivariate variables. mvtnormpcs Multivariate Normal and T Distribution functions of Dunnett(1989). mvoutlier Multivariate outlier detection based on robust estimates of location andcovariance structure. mvpart Multivariate partitioning. mvtnorm Multivariate normal and t distributions. nFDR Nonparametric Estimate of FDR Based on Bernstein polynomials. nFactors Non-graphical solution to the Cattell Scree Test. ncdf Interface to Unidata netCDF data files. ncomplete Functions to perform the regression depth method (RDM) to binaryregression to approximate the minimum number of observations that can beremoved 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 ofdata from a random transposon mutagenesis experiment, through the use ofa Gibbs sampler. network Tools to create and modify network objects, which can represent a rangeof relational data types. neural RBF and MLP neural networks with graphical user interface. nice Get or set UNIX priority (niceness) of running R process. nlme Fit and compare Gaussian linear and nonlinear mixed-effects models.Recommended. nlmeODE Combine the nlme and odesolve packages formixed-effects modelling using differential equations. nltm NonLinear Transformation Models for survival analysis. nnet Software for single hidden layer perceptrons (“feed-forward neuralnetworks”), and for multinomial log-linear models. Contained in theVR bundle. Recommended. nor1mix One-dimensional normal mixture models classes, for, e.g., densityestimation or clustering algorithms research and teaching; providing thewidely 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 binaryregression to approximate the amount of overlap, i.e., the minimalnumber of observations that need to be removed such that the reduceddata set has no longer overlap. np Nonparametric kernel smoothing methods for mixed datatypes. npmc Nonparametric Multiple Comparisons: provides simultaneous rank testprocedures for the one-way layout without presuming a certaindistribution. nsRFA Non-supervised Regional Frequency Analysis. numDeriv Accurate numerical derivatives. nws Functions for NetWorkSpaces and Sleigh. oc Optimal Classification roll call analysis. oce Analysis of oceanographic data. odesolve An interface for the Ordinary Differential Equation (ODE) solver lsoda.ODEs are expressed as R functions. odfWeave Sweave processing of Open Document Format (ODF) files. onion A collection of routines to manipulate and visualize quaternions andoctonions. optmatch Functions to perform optimal matching, particularly full matching. orientlib Representations, conversions and display of orientation SO(3) data. ouch Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses. outliers A collection of some tests commonly used for identifying outliers. oz Functions for plotting Australia's coastline and state boundaries. pARtial (Partial) attributable risk estimates, corresponding variance estimatesand confidence intervals. paleoTS Modeling evolution in paleontological time-series. pamr Pam: Prediction Analysis for Microarrays. pan Multiple imputation for multivariate panel or clustered data. panel Functions and datasets for fitting models to Panel data. papply Parallel apply function using MPI. partitions Additive partitions of integers. partsm Periodic AutoRegressive Time Series Models. party Unbiased recursive partitioning in a conditional inference framework. pastecs Package for Analysis of Space-Time Ecological Series. pbatR Frontend to PBAT to run within R. pcaPP Robust PCA by Projection Pursuit. pcalg Standard and robust estimation of the skeleton (ugraph) of a DirectedAcyclic Graph (DAG) via the PC algorithm. pcse Panel-Corrected Standard Error estimation. pcurve Fits a principal curve to a numeric multivariate dataset in arbitrarydimensions. Produces diagnostic plots. Also calculates Bray-Curtis andother distance matrices and performs multi-dimensional scaling andprincipal component analyses. pear Periodic Autoregression Analysis. permax Functions intended to facilitate certain basic analyses of DNA arraydata, especially with regard to comparing expression levels between twotypes of tissue. permtest Permutation test to compare variability within and distance between twogroups. perturb Perturbation analysis for evaluating collinearity. pgam Poisson-Gamma Additive Models. pgirmess Functions for analysis and display of ecological and spatial data. pheno Some easy-to-use functions for time series analyses of (plant-)phenological data sets. phpSerialize Serialize R to PHP associative array. pinktoe Converts S trees to HTML/Perl files for interactive tree traversal. pixmap Functions for import, export, plotting and other manipulations ofbitmapped images. plm Linear models for panel data. plotAndPlayGTK A GUI for interactive plots using GTK. plotrix Various useful functions for enhancing plots. plugdensity Kernel density estimation with global bandwidth selection via“plug-in”. pls Partial Least Squares Regression (PLSR) and Principal ComponentRegression (PCR). plsgenomics PLS analyses for genomics. pmg Poor Man's GUI. pmml Generate Predictive Modelling Markup Language (PMML) for variousmodels. poLCA POlytomous variable Latent Class Analysis. polspline Routines for the polynomial spline fitting routines hazard regression,hazard estimation with flexible tails, logspline, lspec, polyclass, andpolymars, by C. Kooperberg and co-authors. polyapost Simulating from the Polya posterior. polycor Polychoric and polyserial correlations. polynom A collection of functions to implement a class for univariate polynomialmanipulations. popbio Construction and analysis of matrix population models. popgen Statistical and POPulation GENetics. poplab Population Lab, a tool for constructing a virtual electronic populationevolving over time. portfolio Classes for analyzing and implementing portfolios. portfolioSim Framework for simulating equity portfolio strategies. powell Optimizes a function using Powell's UObyQA algorithm. powerpkg Power analyses for the affected sib pair and the TDT design. ppc Sample classification of protein mass spectra by peak probabiltycontrasts. pps Functions to select samples using PPS (probability proportional to size)sampling, for stratified simple random sampling, and to compute jointinclusion probabilities for Sampford's method of PPS sampling. prabclus Distance based parametric bootstrap tests for clustering, mainly thoughtfor presence-absence data (clustering of species distribution maps).Jaccard and Kulczynski distance measures, clustering of MDS scores, andnearest neighbor based noise detection. prettyR Pretty descriptive stats. prim Patient Rule Induction Method (PRIM). princurve Fits a principal curve to a matrix of points in arbitrary dimension. proj4 A simple interface to the PROJ.4 cartographic projections library. proptest Tests of the proportional hazards assumption in the Cox model. proto An object oriented system using prototype or object-based (rather thanclass-based) object oriented ideas. pscl R in the Political Science Computational Laboratory, StanfordUniversity. pspline Smoothing splines with penalties on order m derivatives. psy Various procedures used in psychometry: Kappa, ICC, Cronbach alpha,screeplot, PCA and related methods. psych Procedures for personality and psychological research. psychometric Applied psychometric theory: functions useful for correlation theory,meta-analysis (validity-generalization), reliability, item analysis,inter-rater reliability, and classical utility. psyphy Functions for analyzing psychophysical data in R. pwr Basic functions for power analysis. pwt The Penn World Table providing purchasing power parity and nationalincome accounts converted to international prices for 168 countries forsome or all of the years 1950–2000. pvclust Hierarchical clustering with p-value. qcc Quality Control Charts. Shewhart quality control charts for continuous,attribute and count data. Cusum and EWMA charts. Operatingcharacteristic curves. Process capability analysis. Pareto chart andcause-and-effect chart. qgen Quantitative Genetics using R. qp q-order partial correlation graph search algorithm. qtl Analysis of experimental crosses to identify QTLs. qtlDesign Tools for the design of QTL experiments. qtlbim QTL Bayesian Interval Mapping. quadprog For solving quadratic programming problems. qualV Qualitative methods for the validation of models. quantchem Quantitative chemical analysis: calibration and evaluation of results. quantreg Quantile regression and related methods. quantregForest Quantile Regression Forests, a tree-based ensemble method for estimationof conditional quantiles. qvalue Q-value estimation for false discovery rate control. qvcalc Functions to compute quasi-variances and associated measures ofapproximation error. rJava Low-level R to Java interface. Allows creation of objects, callingmethods and accessing fields. race Implementation of some racing methods for the empirical selection of thebest. rake Raking survey datasets by re-weighting. randaes Random number generator based on AES cipher. random True random numbers using random.org. randomSurvivalForest Ishwaran and Kogalur's random survival forest. randomForest Breiman's random forest classifier. rankreg Rank regression estimator for the AFT model with right censored data. rateratio.test Exact rate ratio test. rattle A graphical user interface for data mining in R using GTK. rbugs Functions to prepare files needed for running BUGS in batch mode, andrunning BUGS from R. Support for Linux systems with Wine is emphasized. rcdd C Double Description for R, an interface to the CDD computationalgeometry library. rcdk Interface to the CDK libraries, a Java framework for cheminformatics. rcom R COM Client Interface and internal COM Server. rcomgen Completion generator for R. Recommended for R 2.5.0 or later. rcompletion TAB completion for R using Readline. rda Shrunken Centroids Regularized Discriminant Analysis. ref Functions for creating references, reading from and writing roreferences and a memory efficient refdata type that transparentlyencapsulates matrices and data frames. regress Fitting Gaussian linear models where the covariance structure is alinear combination of known matrices by maximising the residual loglikelihood. Can be used for multivariate models and random effectsmodels. relaimpo RELAtive IMPOrtance of regressors in linear models. relations Data structures for k-ary relations with arbitrary domains,predicate functions, and fitters for consensus relations. relax Functions for report writing, presentation, and programming. relaxo Relaxed Lasso. reldist Functions for the comparison of distributions, including nonparametricestimation of the relative distribution PDF and CDF and numericalsummaries as described in “Relative Distribution Methods in the SocialSciences” by Mark S. Handcock and Martina Morris, 1999, Springer. relimp Functions to facilitate inference on the relative importance ofpredictors in a linear or generalized linear model. relsurv Various functions for regression in relative survival. reshape Flexibly reshape data. resper Sampling from restricted permutations. reweight Adjustment of survey respondent weights. rgdal Provides bindings to Frank Warmerdam's Geospatial Data AbstractionLibrary (GDAL). rgenoud R version of GENetic Optimization Using Derivatives. rggm Robustified methods for Gaussian Graphical Models. rggobi Interface between R and GGobi. rgl 3D visualization device system (OpenGL). rgr The GSC (Geological Survey of Canada) applied geochemistry EDA package. rhosp Side effect risks in hospital: simulation and estimation. rimage Functions for image processing, including Sobel filter, rank filters,fft, histogram equalization, and reading JPEG files. riv Robust Instrumental Variables estimators based on high breakpoint pointS-estimators of multivariate location and scatter matrices. rjacobi Jacobi polynomials and Gauss-Jacobi quadrature related operations. rlecuyer R interface to RNG with multiple streams. rmeta Functions for simple fixed and random effects meta-analysis fortwo-sample comparison of binary outcomes. rmetasim An interface between R and the metasim simulation engine. Facilitatesthe use of the metasim engine to build and run individual basedpopulation genetics simulations. roblm Robust regression estimators. robust Insightful robust package. robustbase Basic Robust Statistics. rpanel Simple interactive controls for R using the tcltk package. rpart Recursive PARTitioning and regression trees. Recommended. rpart.permutation Permutation tests of rpart models. rpubchem R interface to the PubChem collection. rpvm R interface to PVM (Parallel Virtual Machine). Provides interface toPVM 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 robustregression with high breakdown point. rrp Random Recursive Partitioning. rsbml R support for SBML (Systems Biology Markup Language), using libsbml. rsprng Provides interface to SPRNG (Scalable Parallel Random Number Generators)APIs, and examples and documentation for its use. rstream Unified object oriented interface for multiple independent streams ofrandom numbers from different sources. rtiff Read TIFF format images and return them as pixmap objects. rv Simulation-based random variable object class. rwt Rice Wavelet Toolbox wrapper, providing a set of functions forperforming digital signal processing. sac Semiparametric empirical likelihood ratio based test of changepoint withone-change or epidemic alternatives with data-based model diagnostic. sampling A set of tools to select and to calibrate samples. sampfling Implements a modified version of the Sampford sampling algorithm. Givena quantity assigned to each unit in the population, samples are drawnwith probability proportional to te product of the quantities of theunits included in the sample. samr Significance Analysis of Microarrays. sandwich Model-robust standard error estimators for time series and longitudinaldata. sbgcop Semiparametric Bayesian Gaussian copula estimation. sca Simple Component Analysis. scaleboot Approximately unbiased p-values via multiscale bootstrap. scape functions to import and plot results from statistical catch-at-agemodels, used in fisheries stock assessments. scapeMCMC Markov-chain Monte Carlo diagnostic plots, accompanying thescape package. scatterplot3d Plots a three dimensional (3D) point cloud perspectively. schoolmath Functions and datasets for math used in school. sciplot Scientific graphing functions for factorial designs. scope Data manipulation using arbitrary row and column criteria. scuba Scuba diving calculations and decompression models. sdcMicro Statistical Disclosure Control methods for the generation of public andscientific use files. sde Simulation and inference for Stochastic Differential Equations. seacarb Calculates parameters of the seawater carbonate system. seas Detailed seasonal plots of temperature and precipitation data. seewave Time wave analysis and graphical representation. segmented Functions to estimate break-points of segmented relationships inregression models (GLMs). sem Functions for fitting general linear Structural Equation Models (withobserved and unobserved variables) by the method of maximum likelihoodusing the RAM approach. sensitivity Sensitivity analysis. 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 procrustesanalysis, displaying shapes and principal components, testing for meanshape difference, thin-plate spline transformation grids and edgesuperimposition methods. sigma2tools Test of hypothesis about sigma2. signal A set of generally Matlab/Octave-compatible signal processingfunctions. signalextraction Real-time signal extraction (Direct Filter Approach). simco Import Structure files and deduce similarity coefficients from them. simecol SIMulation of ECOLogical (and other) dynamic systems. simex SIMEX and MCSIMEX algorithms for measurement error models. simpleboot Simple bootstrap routines. skewt Density, distribution function, quantile function and random generationfor the skewed t distribution of Fernandez and Steel. sm Software linked to the book “Applied Smoothing Techniques for DataAnalysis: The Kernel Approach with S-Plus Illustrations” byA. W. Bowman and A. Azzalini, 1997, Oxford University Press. sma Functions for exploratory (statistical) microarray analysis. smatr (Standardized) Major Axis estimation and Testing Routines. smoothSurv Survival regression with smoothed error distribution. smoothtail Smooth estimation of generalized Pareto distribution shape parameter. sn Functions for manipulating skew-normal probability distributions and forfitting them to data, in the scalar and the multivariate case. sna A range of tools for social network analysis, including node andgraph-level indices, structural distance and covariance methods,structural equivalence detection, p* modeling, and networkvisualization. snow Simple Network of Workstations: support for simple parallel computing inR. snowFT Fault Tolerant Simple Network of Workstations. snp.plotter Plots of p-values using single SNP and/or haplotype data. snpXpert Tools to analyze SNP data. som Self-Organizing Maps (with application in gene clustering). sound A sound interface for R: Basic functions for dealing with .wavfiles and sound samples. sp A package that provides classes and methods for spatial data, includingutility functions for plotting data as maps, spatial selection, amd muchmore. spBayes Fit Gaussian models with potentially complex hierarchical errorstructures by Markov chain Monte Carlo (MCMC). spatclus Arbitrarily shaped multiple spatial cluster detection for case eventdata. spatgraphs Graphs for 2-d point patterns. spatial Functions for kriging and point pattern analysis from “Modern AppliedStatistics with S” by W. Venables and B. Ripley. Contained in theVR bundle. Recommended. spatialCovariance Computation of spatial covariance matrices for data on rectangles usingone dimensional numerical integration and analytic results. spatialkernel Nonparameteric estimation of spatial segregation in a multivariate pointprocess. spatstat Data analysis and modelling of two-dimensional point patterns, includingmultitype points and spatial covariates. spc Statistical Process Control: evaluation of control charts by means ofthe zero-state, steady-state ARL (Average Run Length), setting upcontrol charts for given in-control ARL, and plotting of the relatedfigures. spdep A collection of functions to create spatial weights matrix objects frompolygon contiguities, from point patterns by distance and tesselations,for summarising these objects, and for permitting their use in spatialdata analysis; a collection of tests for spatial autocorrelation,including global Moran's I and Geary's C, local Moran's I, saddlepointapproximations for global and local Moran's I; and functions forestimating spatial simultaneous autoregressive (SAR) models. (Wasformerly the three packages: spweights, sptests, andspsarlm.) spe Stochastic Proximity Embedding. spectralGP Approximate Gaussian processes using the Fourier basis. spectrino Spectra organizer, visualization and data extraction from within R. spgrass6 Interface between the GRASS 6.0 geographical information system and R. spgwr Geographically weighted regression. splancs Spatial and space-time point pattern analysis functions. spsurvey Spatial survey design and analysis. ssanv Sample Size Adjusted for Nonadherence or Variability of inputparameters. sspir State SPace models In R. st Shrinkage t statistic. staRt Inferenza classica con TI-83 Plus. startupmsg Utilities for start-up messages. stashR A Set of Tools for Administering SHared Repositories. statmod Miscellaneous biostatistical modelling functions. stepPlr L2 penalized logistic regression with a stepwise variable selection. stepwise A stepwise approach to identifying recombination breakpoints in asequence alignment. stinepack Stineman interpolation package. stochasticGEM Fitting Stochastic General Epidemic Models. stochmod Learning and inference algorithms for a variety of probabilisticmodels. stream.net Building and analyzing binary stream networks. strucchange Various tests on structural change in linear regression models. subselect A collection of functions which assess the quality of variable subsetsas surrogates for a full data set, and search for subsets which areoptimal under various criteria. sudoku Sudoku puzzle solver. supclust Methodology for supervised grouping of predictor variables. superpc Supervised principal components. survBayes Fits a proportional hazards model to time to event data by a Bayesianapproach. surveillance Outbreak detection algorithms for surveillance data. survey Summary statistics, generalized linear models, and general maximumlikelihood estimation for stratified, cluster-sampled, unequallyweighted survey samples. surveyNG Complex survey samples: database interface, sparse matrices. survival Functions for survival analysis, including penalised likelihood.Recommended. survrec Survival analysis for recurrent event data. svcR A support vector machine technique for clustering. svcm 2d and 3d Space-Varying Coefficient Models. svmpath Computes the entire regularization path for the two-class svm classifierwith essentialy the same cost as a single SVM fit. systemfit Contains functions for fitting simultaneous systems of equations usingOrdinary Least Sqaures (OLS), Two-Stage Least Squares (2SLS), andThree-Stage Least Squares (3SLS). taskPR Task-Parallel R package. tcltk2 A series of widgets and functions to supplement tcltk. tdist Computes the distribution of a linear combination of independentStudent's t variables. tdm A tool for Therapeutic Drug Monitoring. tdthap Transmission/disequilibrium tests for extended marker haplotypes. tensor Tensor product of arrays. tensorA Advanced tensors arithmetic with named indices. tframe Time Frame coding kernel: functions for writing code that is independentof the way time is represented. tgp Bayesian regression and adaptive sampling with Treed Gaussian Processmodels. time Time tracking for developers. timsac TIMe Series Analysis and Control package. titan Titration analysis for mass spectrometry data. titecrm TIme-To-Event Continual Reassessment Method and calibration tools. tkrgl TK widget tools for rgl package. tkrplot Simple mechanism for placing R graphics in a Tk widget. tlnise Two-level normal independent sampling estimation. tm A framework for text mining applications within R. tradeCosts Post-trade analysis of transaction costs. tree Classification and regression trees. treeglia Stem analysis functions for volume increment and carbon uptakeassessment from tree-rings. triangle Standard distribution functions for the triangle distribution. trimcluster Cluster analysis with trimming. trip Spatial analysis of animal track data. tripEstimation Metropolis sampler and supporting functions for estimating animalmovement from archival tags and satellite fixes. tripack A constrained two-dimensional Delaunay triangulation package. truncgof Goodness-of-fit tests allowing for left truncated data. trust Local optimization using two derivatives and trust regions. tsDyn Time series analysis based on dynamical systems theory. tseries Package for time series analysis with emphasis on non-linear modelling. tseriesChaos Routines for the analysis of non-linear time series. tsfa Time Series Factor Analysis. tuneR Collection of tools to analyze music, handle wave files, transcription,etc. tutoR Student-friendly package to mask common functions. twang Toolkit for Weighting and Analysis of Nonequivalent Groups. tweedie Maximum likelihood computations for Tweedie exponential family models. twslm A two-way semilinear model for normalization and analysis of cDNAmicroarray data. udunits Interface to Unidata's routines to convert units. ump Uniformly Most Powerful tests. unbalhaar Function estimation via Unbalanced Haar wavelets. untb Ecological drift under the UNTB (Unified Neutral Theory ofBiodiversity). urca Unit root and cointegration tests for time series data. urn Functions for sampling without replacement (simulated urns). uroot Unit root tests and graphics for seasonal time series. vabayelMix Variational Bayesian mixture model. varSelRF Variable selection using random forests. vardiag Interactive variogram diagnostics. varmixt Mixture model on the variance for the analysis of gene expression data. vars VAR modeling. vbmp Variational Bayesian Multinomial Probit Regression. vcd Functions and data sets based on the book “Visualizing CategoricalData” by Michael Friendly. vegan Various help functions for vegetation scientists and communityecologists. 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 densityplot. vrtest Variance ratio tests for weak-form market efficiency. waved WaveD transform in R. wavelets Functions for computing wavelet filters, wavelet transforms andmultiresolution analyses. waveslim Basic wavelet routines for time series analysis. wavethresh Software to perform 1-d and 2-d wavelet statistics and transforms. wccsom SOM networks for comparing patterns with peak shifts. wikibooks Functions and datasets for the German WikiBook “GNU R”. wle Robust statistical inference via a weighted likelihood approach. wnominate WNOMINATE roll call analysis software. wombsoft Wombling computation. write.snns Function for exporting data to SNNS (Stuttgart Neural Network Simulator)pattern files. xgobi Interface to the XGobi and XGvis programs for graphical data analysis. xlsReadWrite Natively read and write Excel files. xtable Export data to &latex; and HTML tables. yaImpute Performs popular nearest neighbor routines. zicounts Fit classical, zero-inflated and interval censored count data regressionmodels. zipfR Statistical models for word frequency distributions. zoo A class with methods for totally ordered indexed observations such asirregular time series. See CRAN src/contrib/PACKAGES for more information. There used to be a CRAN src/contrib/Devel directory with packages still “under development” or depending on features only present in the current development versions of R. This area is no longer provided, with packages formerly in this area either in the regular package area or the archive src/contrib/Archive. Add-on packages from Omegahat The Omegahat Project for Statistical Computing provides a variety of open-source software for statistical applications, with special emphasis on web-based software, Java, the Java virtual machine, and distributed computing. A CRAN style R package repository is available via http://www.omegahat.org/R/. Currently, there are the following packages. Aspell An interface to facilities in the aspell library. CGIwithR Facilities for the use of R to write CGI scripts. CORBA Dynamic CORBA client/server facilities for R. Connects to otherCORBA-aware applications developed in arbitrary languages, on differentmachines and allows R functionality to be exported in the same way toother applications. Combinations Compute the combinations of choosing r items from nelements. IDocs Infrastructure for interactive documents. OOP OOP style classes and methods for R and S-Plus. Object references andclass-based method definition are supported in the style of languagessuch as Java and C++. RCurl Allows one to compose HTTP requests to fetch URIs, post forms, etc., andprocess the results returned by the Web server. RDCOMClient Provides dynamic client-side access to (D)COM applications from withinR. RDCOMEvents Provides facilities to use R functions and objects as handlers for DCOMevents. RDCOMServer Facilities for exporting S objects and functions as COM objects. REmbeddedPostgres Allows R functions and objects to be used to implement SQL functions —per-record, aggregate and trigger functions. REventLoop An abstract event loop mechanism that is toolkit independent and can beused to to replace the R event loop. Rexif Extract meta-information from JPEG files. RGdkPixbuf S language functions to access the facilities in the GdkPixbuf libraryfor manipulating images. RGnumeric A plugin for the Gnumeric spreadsheet that allows R functions to becalled from cells within the sheet, automatic recalculation, etc. RGtk Facilities in the S language for programming graphical interfaces usingGtk, the Gnome GUI toolkit. RGtkBindingGenerator A meta-package which generates C and R code to provide bindings to aGtk-based library. RGtkExtra A collection of S functions that provide an interface to the widgets inthe 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 interactiveGnome GUI creator. RGtkHTML A collection of S functions that provide an interface to creating andcontrolling an HTML widget which can be used to display HTMLdocuments from files or content generated dynamically in S. RGtkIPrimitives A collection of low-level primitives for interactive use with R graphicsand the gtkDevice using RGtk. RGtkViewers A collection of tools for viewing different S objects, databases, classand widget hierarchies, S source file contents, etc. RJavaDevice A graphics device for R that uses Java components and graphics.APIs. RMatlab A bi-directional interface between R and Matlab. RObjectTables The C and S code allows one to define R objects to be used as elementsof the search path with their own semantics and facilities for readingand writing variables. The objects implement a simple interface via Rfunctions (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, consistentwith the basic material in “Programming with Data” by JohnM. 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 codeto call Python functionality. RXLisp An interface to call XLisp-Stat functions from within R. Rcompression In-memory decompression for GNU zip and bzip2 formats. Rlibstree Suffix Trees in R via the libstree C library. Rstem Interface to Snowball implementation of Porter's word stemmingalgorithm. RwxWidgets Facilities to program GUIs using wxWidgets in R. Ryacas R interface to yacas. SASXML Example for reading XML files in SAS 8.2 manner. SJava An interface from R to Java to create and call Java objects andmethods. SLanguage Functions and C support utilities to support S language programmingthat can work in both R and S-Plus. SNetscape Plugin for Netscape and JavaScript. SSOAP A client interface to SOAP (Simple Object Access Protocol) servers fromwithin S. SWinRegistry Provides access from within R to read and write the Windows registry. SWinTypeLibs Provides ways to extract type information from type libraries and/orDCOM objects that describes the methods, properties, etc., of aninterface. SXalan Process XML documents using XSL functions implemented in R anddynamically substituting output from R. Slcc Parses C source code, allowing one to analyze and automatically generateinterfaces from S to that code, including the table of S-accessiblenative symbols, parameter count and type information, S constructorsfrom C objects, call graphs, etc. Sxslt An extension module for libxslt, the XML-XSL document translator,that allows XSL functions to be implemented via R functions. XML Tools for reading XML documents and DTDs. Add-on packages from Bioconductor The Bioconductor Project produces an open source software framework that will assist biologists and statisticians working in bioinformatics, with primary emphasis on inference using DNA microarrays. A CRAN style R package repository is available via http://www.bioconductor.org/. The following R packages are contained in the current release of Bioconductor, with more packages under development. AnnBuilder Assemble and process genomic annotation data, from databases such asGenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSCHuman Genome Project. Biobase Object-oriented representation and manipulation of genomic data (S4class structure). Biostrings Class definitions and generics for biological sequences along withpattern matching algorithms. Category A collection of tools for performing category analysis. ChromoViz Draw gene expression profile onto chromosome using SVG. CoCiteStats A collection of software tools for dealing with co-citation data. DEDS Differential Expression via Distance Summary for microarray data. DNAcopy Segments DNA copy number data using circular binary segmentation todetect regions with abnormal copy number. DynDoc Functionality to create and interact with dynamic documents, vignettes,and other navigable documents. EBImage R image processing toolkit. EBarrays Empirical Bayes tools for the analysis of replicated microarray dataacross multiple conditions. GEOquery Get data from NCBI Gene Expression Omnibus (GEO). GLAD Gain and Loss Analysis of DNA. GOstats Tools for manipulating GO and microarrays. GeneMeta A collection of meta-analysis tools for analyzing high throughputexperimental data. GeneR Package manipulating nucleotidic sequences (Embl, Fasta, GenBank). GeneSpring Functions and class definitions to be able to read and write GeneSpringspecific 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 inferringlarge sparse graphical Gaussian models. GeneTraffic GeneTraffic R integration functions. GenomeBase Base functions for genome data package manipulation. GlobalAncova Calculates a global test for differential gene expression betweengroups. GraphAT Graph theoretic Association Tests. HEM Heterogeneous Error Model for analysis of microarray data. Heatplus A heat map displaying covariates and coloring clusters. Icens Functions for computing the NPMLE for censored and truncated data. KEGGSOAP Client-side SOAP access KEGG. LMGene Analysis of microarray data using a linear model and glog datatransformation. LPE Significance analysis of microarray data with small number of replicatesusing the Local Pooled Error (LPE) method. MANOR Micro-Array NORmalization. MCRestimate Misclassification error estimation with cross-validation. MLInterfaces Uniform interfaces to machine learning code for the exprSet class fromBioconductor. MVCClass Model-View-Controller (MVC) classes. MantelCorr Compute Mantel Cluster Correlations. MeasurementError.cor Two-stage measurement error model for correlation estimation withsmaller bias than the usual sample correlation. MergeMaid Cross-study comparison of gene expression array data. Mfuzz Soft clustering of time series gene expression data. MiPP Misclassification Penalized Posterior Classification. OCplus Operating characteristics plus sample size and local fdr for microarrayexperiments. OLIN Optimized Local Intensity-dependent Normalisation of two-colormicroarrays. OLINgui Graphical user interface for OLIN. OrderedList Similarities of ordered gene lists. 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. RLMM A genotype calling algorithm for Affymetrix SNP arrays. RMAGEML Functionality to handle MAGEML documents. RMAPPER Interface to mapper.chip.org. ROC Receiver Operating Characteristic (ROC) approach for identifying genesthat are differentially expressed in two types of samples. RSNPper Interface to chip.org::SNPper for SNP-related data. RankProd Rank Product method for identifying differentially expressed genes. RdbiPgSQL Methods for accessing data stored in PostgreSQL tables. Rdbi Generic framework for database access in R. Resourcerer Read annotation data from TIGR Resourcerer or convert the annotationdata into Bioconductor data package. Rgraphviz An interface with Graphviz for plotting graph objects in R. Ruuid Creates Universally Unique ID values (UUIDs) in R. SAGElyzer Locates genes based on SAGE tags. SBMLR Systems Biology Markup Language (SBML) interface and biochemical systemanalysis tools. SNAData Data from the book “Social Network Analysis” by Wasserman & Faust,1999. ScISI In Silico Interactome. aCGH Classes and functions for Array Comparative Genomic Hybridization data. adSplit Annotation-driven clustering. affxparser Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). affy Methods for Affymetrix Oligonucleotide Arrays. affyQCReport QC Report Generation for affyBatch objects. affyPLM For fitting Probe Level Models. affycomp Graphics toolbox for assessment of Affymetrix expression measures. affycoretools Functions useful for those doing repetitive analyses. affydata Affymetrix data for demonstration purposes. affyio Tools for parsing Affymetrix data files. affylmGUI Graphical User Interface for affy analysis using package limma. affypdnn Probe Dependent Nearest Neighbors (PDNN) for the affy package. altcdfenvs Utilities to handle cdfenvs. annaffy Functions for handling data from Bioconductor Affymetrix annotation datapackages. annotate Associate experimental data in real time to biological metadata from webdatabases such as GenBank, LocusLink and PubMed. Process and storequery results. Generate HTML reports of analyses. apComplex Estimate protein complex membership using AP-MS protein data. applera Tools for Applied Biosystems microarrays AB1700 data analysis andquality controls. arrayMagic Utilities for quality control and processing for two-color cDNAmicroarray data. arrayQuality Performing print-run and array level quality assessment. beadarray Quality control and low-level analysis of BeadArrays. bim Bayesian interval mapping diagnostics: functions to interpret QTLCartand Bmapqtl samples. bioDist A collection of software tools for calculating distance measures. biocViews Categorized views of R package repositories. biomaRt Interface to BioMart databases (e.g., Ensembl) bridge Bayesian Robust Inference for Differential Gene Expression. cellHTS Analysis of cell-based screens. cghMCR Find chromosome regions showing common gains/losses. clusterStab Compute cluster stability scores for microarray data. codelink Manipulation of Codelink Bioarrays data. convert Convert Microarray Data Objects. copa Functions to perform cancer outlier profile analysis. ctc Tools to export and import Tree and Cluster to other programs. daMA Functions for the efficient design of factorial two-color microarrayexperiments and for the statistical analysis of factorial microarraydata. diffGeneAnalysis Performs differential Gene expression Analysis. ecolitk Metadata and tools to work with E. coli. edd Expression density diagnostics: graphical methods and patternrecognition algorithms for distribution shape classification. exprExternal Implementation of exprSet using externalVectors. externalVector Basic class definitions and generics for external pointer based vectorobjects for R. factDesign A set of tools for analyzing data from factorial designed microarrayexperiments. The functions can be used to evaluate appropriate tests ofcontrast and perform single outlier detection. fdrame FDR Adjustments of Microarray Experiments (FDR-AME). gcrma Background adjustment using sequence information. genArise A tool for dual color microarray data. geneRecommender A gene recommender algorithm to identify genes coexpressed with a queryset of genes. genefilter Tools for sequentially filtering genes using a wide variety of filteringfunctions. Example of filters include: number of missing value,coefficient of variation of expression measures, ANOVA p-value,Cox model p-values. Sequential application of filteringfunctions to genes. geneplotter Graphical tools for genomic data, for example for plotting expressiondata along a chromosome or producing color images of expression datamatrices. gff3Plotter Plotting data of experiments on the genomic layout. globaltest Testing globally whether a group of genes is significantly related tosome clinical variable of interest. goCluster Analysis of clustering results in conjunction with annotation data. goTools Functions for description/comparison of oligo ID list using the GeneOntology database. gpls Classification using generalized partial least squares for two-group andmulti-group classification. graph Classes and tools for creating and manipulating graphs within R. gtkWidgets Widgets built using RGtk. hexbin Binning functions, in particular hexagonal bins for graphing. hopach Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH). hypergraph Capabilities for representing and manipulating hypergraphs. iSPlot Link views that are based on the same data set. idiogram Plotting genomic data by chromosomal location. impute Imputation for microarray data (currently KNN only). limma Linear models for microarray data. limmaGUI Graphical User Interface for package limma. logicFS Identification of SNP interactions. maCorrPlot Visualize artificial correlation in microarray data. maDB Microarray database and utility functions for microarray analysis. maSigPro Significant gene expression profile differeneces in time coursemicroarray data. maanova Tools for analyzing micro array experiments. macat MicroArray Chromosome Analysis Tool. made4 Multivariate analysis of microarray data using ADE4. makecdfenv Two functions. One reads a Affymetrix chip description file (CDF) andcreates a hash table environment containing the location/probe setmembership mapping. The other creates a package that automatically loadsthat environment. marray Exploratory analysis for two-color spotted microarray data. matchprobes Tools for sequence matching of probes on arrays. metaArray Integration of microarray data for meta-analysis. mmgmos Multi-chip Modified Gamma Model of Oligonucleotide Signal. multtest Multiple testing procedures for controlling the family-wise error rate(FWER) and the false discovery rate (FDR). Tests can be based ont- or F-statistics for one- and two-factor designs, andpermutation procedures are available to estimate adjustedp-values. nnNorm Spatial and intensity based normalization of cDNA microarray data basedon robust neural nets. nudge Normal Uniform Differential Gene Expression detection. ontoTools Graphs and sparse matrices for working with ontologies; formal objectsfor nomenclatures with provenance management. pairseqsim Pairwise sequence alignment and scoring algorithms for global, local andoverlap alignment with affine gap penalty. pamr Pam: Prediction Analysis for Microarrays. panp Presence-Absence calls from Negative strand matching Probesets. pathRender Render molecular pathways. pdmclass CLASSification of microarray samples using Penalized DiscriminantMethods. pgUtils Utility functions for PostgreSQL databases. pickgene Adaptive gene picking for microarray expression data analysis. plgem Power Law Global Error Model. plier Implements the Affymetrix PLIER (Probe Logarithmic Error IntensityEstimate) algorithm. prada Tools for analyzing and navigating data from high-throughput phenotypingexperiments based on cellular assays and fluorescent detection. qtlvalue Q-value estimation for false discovery rate control. rama Robust Analysis of MicroArrays: robust estimation of cDNA microarrayintensities with replicates using a Bayesian hierarchical model. reb Regional Expression Biases. reposTools Tools for dealing with file repositories and allow users to easilyinstall, update, and distribute packages, vignettes, and other files. rfcdmin Data sets for RFlowCyt examples. rflowcyt Statistical tools and data structures for analytic flow cytometry. safe Significance Analysis of Function and Expression. sagenhaft Functions for reading and comparing SAGE (Serial Analysis of GeneExpression) libraries. siggenes Identifying differentially expressed genes and estimating the FalseDiscovery Rate (FDR) with both the Significance Analysis of Microarrays(SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). simpleaffy Very simple high level analysis of Affymetrix data. simulatorAPMS Computationally simulates the AP-MS technology. sizepower Sample size and power calculation in microrarray studies. snapCGH Segmentation, normalization and processing of aCGH data. splicegear A set of tools to work with alternative splicing. spotSegmentation Microarray spot segmentation and gridding for blocks of microarrayspots. sscore S-score algorithm for Affymetrix oligonucleotide microarrays. ssize Estimate microarry sample size. stam STructured Analysis of Microarray data. stepNorm Stepwise normalization functions for cDNA microarrays. tilingArray Analysis of tiling arrays. timecourse Statistical analysis for developmental microarray time course data. tkWidgets Widgets in Tcl/Tk that provide functionality for Bioconductor packages. twilight Estimation of local false discovery rate. vsn Calibration and variance stabilizing transformations for both Affymetrixand cDNA array data. webbioc Integrated web interface for doing microarray analysis using several ofthe Bioconductor packages. widgetInvoke Evaluation widgets for functions. widgetTools Tools for creating Tcl/Tk widgets, i.e., small-scale graphical userinterfaces. xcms LC/MS and GC/MS data analysis: framework for processing andvisualization of chromatographically separated mass spectral data. y2hStat Analysis of Yeast 2-Hybrid data sets. Other add-on packages Jim Lindsey has written a collection of R packages for nonlinear regression and repeated measurements, consisting of event (event history procedures and models), gnlm (generalized nonlinear regression models), growth (multivariate normal and elliptically-contoured repeated measurements models), repeated (non-normal repeated measurements models), rmutil (utilities for nonlinear regression and repeated measurements), and stable (probability functions and generalized regression models for stable distributions). All analyses in the new edition of his book “Models for Repeated Measurements” (1999, Oxford University Press) were carried out using these packages. Jim has also started dna, a package with procedures for the analysis of DNA sequences. Jim's packages can be obtained from http://popgen.unimaas.nl/~jlindsey/rcode.html. More code has been posted to the R-help mailing list, and can be obtained from the mailing list archive. How can add-on packages be installed? (Unix only.) The add-on packages on CRAN come as gzipped tar files named pkg_version.tar.gz, which may in fact be “bundles” containing more than one package. Provided that tar and gzip are available on your system, type $ R CMD INSTALL /path/to/pkg_version.tar.gz at the shell prompt to install to the library tree rooted at the first directory given in R_LIBS (see below) if this is set and non-null, and to the default library (the library subdirectory of R_HOME) otherwise. (Versions of R prior to 1.3.0 installed to the default library by default.) To install to another tree (e.g., your private one), use $ R CMD INSTALL -l lib /path/to/pkg_version.tar.gz where lib gives the path to the library tree to install to. Even more conveniently, you can install and automatically update packages from within R if you have access to repositories such as CRAN. See the help page for available.packages() for more information. You can use several library trees of add-on packages. The easiest way to tell R to use these is via the environment variable R_LIBS which should be a colon-separated list of directories at which R library trees are rooted. You do not have to specify the default tree in R_LIBS. E.g., to use a private tree in $HOME/lib/R and a public site-wide tree in /usr/local/lib/R-contrib, put R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS into your (Bourne) shell profile or even preferably, add the line R_LIBS="~/lib/R:/usr/local/lib/R-contrib" your ~/.Renviron file. (Note that no export statement is needed or allowed in this file; see the on-line help for Startup for more information.) How can add-on packages be used? To find out which additional packages are available on your system, type library() at the R prompt. This produces something like Packages in `/home/me/lib/R': mystuff My own R functions, nicely packaged but not documented Packages in `/usr/local/lib/R/library': KernSmooth Functions for kernel smoothing for Wand & Jones (1995) MASS Main Package of Venables and Ripley's MASS base The R Base package boot Bootstrap R (S-Plus) Functions (Canty) class Functions for Classification cluster Functions for clustering (by Rousseeuw et al.) datasets The R datasets Package foreign Read data stored by Minitab, S, SAS, SPSS, Stata, ... grDevices The R Graphics Devices and Support for Colours and Fonts graphics The R Graphics Package grid The Grid Graphics Package lattice Lattice Graphics methods Formal Methods and Classes mgcv GAMs with GCV smoothness estimation and GAMMs by REML/PQ nlme Linear and nonlinear mixed effects models nnet Feed-forward Neural Networks and Multinomial Log-Linear Models rpart Recursive partitioning spatial Functions for Kriging and Point Pattern Analysis splines Regression Spline Functions and Classes stats The R Stats Package stats4 Statistical functions using S4 classes survival Survival analysis, including penalised likelihood tcltk Tcl/Tk Interface tools Tools for Package Development utils The R Utils Package You can “load” the installed package pkg by library(pkg) You can then find out which functions it provides by typing one of library(help = pkg) help(package = pkg) You can unload the loaded package pkg by detach("package:pkg") How can add-on packages be removed? Use $ R CMD REMOVE pkg_1 … pkg_n to remove the packages pkg_1, …, pkg_n from the library tree rooted at the first directory given in R_LIBS if this is set and non-null, and from the default library otherwise. (Versions of R prior to 1.3.0 removed from the default library by default.) To remove from library lib, do $ R CMD REMOVE -l lib pkg_1 … pkg_n How can I create an R package? A package consists of a subdirectory containing the files DESCRIPTION and INDEX, and the subdirectories R, data, demo, exec, inst, man, src, and tests (some of which can be missing). Optionally the package can also contain script files configure and cleanup which are executed before and after installation. See section “Creating R packages” in Writing R Extensions, for details. This manual is included in the R distribution, see , 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 , for information on uploading a package to CRAN. How can I contribute to R? R is in active development and there is always a risk of bugs creeping in. Also, the developers do not have access to all possible machines capable of running R. So, simply using it and communicating problems is certainly of great value. One place where functionality is still missing is the modeling software as described in “Statistical Models in S” (see ); 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. R and Emacs Is there Emacs support for R? There is an Emacs package called ESS (“Emacs Speaks Statistics”) which provides a standard interface between statistical programs and statistical processes. It is intended to provide assistance for interactive statistical programming and data analysis. Languages supported include: S dialects (R, S 3/4, and S-Plus 3.x/4.x/5.x/6.x/7.x), LispStat dialects (XLispStat, ViSta), SAS, Stata, and BUGS. ESS grew out of the need for bug fixes and extensions to S-mode 4.8 (which was a GNU Emacs interface to S/S-Plus version 3 only). The current set of developers desired support for XEmacs, R, S4, and MS Windows. In addition, with new modes being developed for R, Stata, and SAS, it was felt that a unifying interface and framework for the user interface would benefit both the user and the developer, by helping both groups conform to standard Emacs usage. The end result is an increase in efficiency for statistical programming and data analysis, over the usual tools. R support contains code for editing R source code (syntactic indentation and highlighting of source code, partial evaluations of code, loading and error-checking of code, and source code revision maintenance) and documentation (syntactic indentation and highlighting of source code, sending examples to running ESS process, and previewing), interacting with an inferior R process from within Emacs (command-line editing, searchable command history, command-line completion of R object and file names, quick access to object and search lists, transcript recording, and an interface to the help system), and transcript manipulation (recording and saving transcript files, manipulating and editing saved transcripts, and re-evaluating commands from transcript files). The latest stable version of ESS are available via CRAN or the ESS web page. The HTML version of the documentation can be found at http://stat.ethz.ch/ESS/. ESS comes with detailed installation instructions. For help with ESS, send email to . Please send bug reports and suggestions on ESS to . 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. Should I run R from within Emacs? Yes, definitely. Inferior R mode provides a readline/history mechanism, object name completion, and syntax-based highlighting of the interaction buffer using Font Lock mode, as well as a very convenient interface to the R help system. Of course, it also integrates nicely with the mechanisms for editing R source using Emacs. One can write code in one Emacs buffer and send whole or parts of it for execution to R; this is helpful for both data analysis and programming. One can also seamlessly integrate with a revision control system, in order to maintain a log of changes in your programs and data, as well as to allow for the retrieval of past versions of the code. In addition, it allows you to keep a record of your session, which can also be used for error recovery through the use of the transcript mode. To specify command line arguments for the inferior R process, use C-u M-x R for starting R. Debugging R from within Emacs To debug R “from within Emacs”, there are several possibilities. To use the Emacs GUD (Grand Unified Debugger) library with the recommended debugger GDB, type M-x gdb and give the path to the R binary as argument. At the gdb prompt, set R_HOME and other environment variables as needed (using e.g. set env R_HOME /path/to/R/, but see also below), and start the binary with the desired arguments (e.g., run –quiet). If you have ESS, you can do C-u M-x R RET - d SPC g d b RET to start an inferior R process with arguments -d gdb. A third option is to start an inferior R process via ESS (M-x R) and then start GUD (M-x gdb) giving the R binary (using its full path name) as the program to debug. Use the program ps to find the process number of the currently running R process then use the attach command in gdb to attach it to that process. One advantage of this method is that you have separate *R* and *gud-gdb* windows. Within the *R* window you have all the ESS facilities, such as object-name completion, that we know and love. When using GUD mode for debugging from within Emacs, you may find it most convenient to use the directory with your code in it as the current working directory and then make a symbolic link from that directory to the R binary. That way .gdbinit can stay in the directory with the code and be used to set up the environment and the search paths for the source, e.g. as follows: set env R_HOME /opt/R set env R_PAPERSIZE letter set env R_PRINTCMD lpr dir /opt/R/src/appl dir /opt/R/src/main dir /opt/R/src/nmath dir /opt/R/src/unix R Miscellanea How can I set components of a list to NULL? You can use x[i] <- list(NULL) to set component i of the list x to NULL, similarly for named components. Do not set x[i] or x[[i]] to NULL, because this will remove the corresponding component from the list. For dropping the row names of a matrix x, it may be easier to use rownames(x) <- NULL, similarly for column names. How can I save my workspace? save.image() saves the objects in the user's .GlobalEnv to the file .RData in the R startup directory. (This is also what happens after q("yes").) Using save.image(file) one can save the image under a different name. How can I clean up my workspace? To remove all objects in the currently active environment (typically .GlobalEnv), you can do rm(list = ls(all = TRUE)) (Without all = TRUE, only the objects with names not starting with a ‘.’ are removed.) How can I get eval() and D() to work? Strange things will happen if you use eval(print(x), envir = e) or D(x^2, "x"). The first one will either tell you that "x" is not found, or print the value of the wrong x. The other one will likely return zero if x exists, and an error otherwise. This is because in both cases, the first argument is evaluated in the calling environment first. The result (which should be an object of mode "expression" or "call") is then evaluated or differentiated. What you (most likely) really want is obtained by “quoting” the first argument upon surrounding it with expression(). For example, R> D(expression(x^2), "x") 2 * x Although this behavior may initially seem to be rather strange, is perfectly logical. The “intuitive” behavior could easily be implemented, but problems would arise whenever the expression is contained in a variable, passed as a parameter, or is the result of a function call. Consider for instance the semantics in cases like D2 <- function(e, n) D(D(e, n), n) or g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2))) g(a * b) See the help page for deriv() for more examples. Why do my matrices lose dimensions? When a matrix with a single row or column is created by a subscripting operation, e.g., row <- mat[2, ], it is by default turned into a vector. In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is created by subscripting it will be coerced into a 2 x 3 x 4 array, losing the unnecessary dimension. After much discussion this has been determined to be a feature. To prevent this happening, add the option drop = FALSE to the subscripting. For example, rowmatrix <- mat[2, , drop = FALSE] # creates a row matrix colmatrix <- mat[, 2, drop = FALSE] # creates a column matrix a <- b[1, 1, 1, drop = FALSE] # creates a 1 x 1 x 1 array The drop = FALSE option should be used defensively when programming. For example, the statement somerows <- mat[index, ] will return a vector rather than a matrix if index happens to have length 1, causing errors later in the code. It should probably be rewritten as somerows <- mat[index, , drop = FALSE] How does autoloading work? R has a special environment called .AutoloadEnv. Using autoload(name, pkg), where name and pkg are strings giving the names of an object and the package containing it, stores some information in this environment. When R tries to evaluate name, it loads the corresponding package pkg and reevaluates name in the new package's environment. Using this mechanism makes R behave as if the package was loaded, but does not occupy memory (yet). See the help page for autoload() for a very nice example. How should I set options? The function options() allows setting and examining a variety of global “options” which affect the way in which R computes and displays its results. The variable .Options holds the current values of these options, but should never directly be assigned to unless you want to drive yourself crazy—simply pretend that it is a “read-only” variable. For example, given test1 <- function(x = pi, dig = 3) { oo <- options(digits = dig); on.exit(options(oo)); cat(.Options$digits, x, "\n") } test2 <- function(x = pi, dig = 3) { .Options$digits <- dig cat(.Options$digits, x, "\n") } we obtain: R> test1() 3 3.14 R> test2() 3 3.141593 What is really used is the global value of .Options, and using options(OPT = VAL) correctly updates it. Local copies of .Options, either in .GlobalEnv or in a function environment (frame), are just silently disregarded. How do file names work in Windows? As R uses C-style string handling, ‘\’ is treated as an escape character, so that for example one can enter a newline as ‘\n’. When you really need a ‘\’, you have to escape it with another ‘\’. Thus, in filenames use something like "c:\\data\\money.dat". You can also replace ‘\’ by ‘/’ ("c:/data/money.dat"). Why does plotting give a color allocation error? On an X11 device, plotting sometimes, e.g., when running demo("image"), results in “Error: color allocation error”. This is an X problem, and only indirectly related to R. It occurs when applications started prior to R have used all the available colors. (How many colors are available depends on the X configuration; sometimes only 256 colors can be used.) One application which is notorious for “eating” colors is Netscape. If the problem occurs when Netscape is running, try (re)starting it with either the -no-install (to use the default colormap) or the -install (to install a private colormap) option. You could also set the colortype of X11() to "pseudo.cube" rather than the default "pseudo". See the help page for X11() for more information. How do I convert factors to numeric? It may happen that when reading numeric data into R (usually, when reading in a file), they come in as factors. If f is such a factor object, you can use as.numeric(as.character(f)) to get the numbers back. More efficient, but harder to remember, is as.numeric(levels(f))[as.integer(f)] In any case, do not call as.numeric() or their likes directly for the task at hand (as as.numeric() or unclass() give the internal codes). Are Trellis displays implemented in R? The recommended package lattice (which is based on another recommended package, grid) provides graphical functionality that is compatible with most Trellis commands. You could also look at coplot() and dotchart() which might do at least some of what you want. Note also that the R version of pairs() is fairly general and provides most of the functionality of splom(), and that R's default plot method has an argument asp allowing to specify (and fix against device resizing) the aspect ratio of the plot. (Because the word “Trellis” has been claimed as a trademark we do not use it in R. The name “lattice” has been chosen for the R equivalent.) What are the enclosing and parent environments? Inside a function you may want to access variables in two additional environments: the one that the function was defined in (“enclosing”), and the one it was invoked in (“parent”). If you create a function at the command line or load it in a package its enclosing environment is the global workspace. If you define a function f() inside another function g() its enclosing environment is the environment inside g(). The enclosing environment for a function is fixed when the function is created. You can find out the enclosing environment for a function f() using environment(f). The “parent” environment, on the other hand, is defined when you invoke a function. If you invoke lm() at the command line its parent environment is the global workspace, if you invoke it inside a function f() then its parent environment is the environment inside f(). You can find out the parent environment for an invocation of a function by using parent.frame() or sys.frame(sys.parent()). So for most user-visible functions the enclosing environment will be the global workspace, since that is where most functions are defined. The parent environment will be wherever the function happens to be called from. If a function f() is defined inside another function g() it will probably be used inside g() as well, so its parent environment and enclosing environment will probably be the same. Parent environments are important because things like model formulas need to be evaluated in the environment the function was called from, since that's where all the variables will be available. This relies on the parent environment being potentially different with each invocation. Enclosing environments are important because a function can use variables in the enclosing environment to share information with other functions or with other invocations of itself (see the section on lexical scoping). This relies on the enclosing environment being the same each time the function is invoked. (In C this would be done with static variables.) Scoping is hard. Looking at examples helps. It is particularly instructive to look at examples that work differently in R and S and try to see why they differ. One way to describe the scoping differences between R and S is to say that in S the enclosing environment is always the global workspace, but in R the enclosing environment is wherever the function was created. How can I substitute into a plot label? Often, it is desired to use the value of an R object in a plot label, e.g., a title. This is easily accomplished using paste() if the label is a simple character string, but not always obvious in case the label is an expression (for refined mathematical annotation). In such a case, either use parse() on your pasted character string or use substitute() on an expression. For example, if ahat is an estimator of your parameter a of interest, use title(substitute(hat(a) == ahat, list(ahat = ahat))) (note that it is ‘==’ and not ‘=’). Sometimes bquote() gives a more compact form, e.g., title(bquote(hat(a) = .(ahat))) where subexpressions enclosed in ‘.()’ are replaced by their values. There are more worked examples in the mailing list achives. What are valid names? When creating data frames using data.frame() or read.table(), R by default ensures that the variable names are syntactically valid. (The argument check.names to these functions controls whether variable names are checked and adjusted by make.names() if needed.) To understand what names are “valid”, one needs to take into account that the term “name” is used in several different (but related) ways in the language: A syntactic name is a string the parser interprets as this typeof expression. It consists of letters, numbers, and the dot and (forversion of R at least 1.9.0) underscore characters, and starts witheither a letter or a dot not followed by a number. Reserved words arenot syntactic names. An object name is a string associated with an object that isassigned in an expression either by having the object name on the leftof an assignment operation or as an argument to the assign()function. It is usually a syntactic name as well, but can be anynon-empty string if it is quoted (and it is always quoted in the call toassign()). An argument name is what appears to the left of the equals signwhen 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 ofthe ‘$’ operator, it must be a syntactic name, or quoted.Otherwise, element names can be any strings. (When an object is used asa database, as in a call to eval() or attach(), theelement names become object names.) Finally, a file name is a string identifying a file in theoperating system for reading, writing, etc. It really has nothing muchto do with names in the language, but it is traditional to call thesestrings file “names”. Are GAMs implemented in R? Package gam from CRAN implements all the Generalized Additive Models (GAM) functionality as described in the GAM chapter of the White Book. In particular, it implements backfitting with both local regression and smoothing splines, and is extendable. There is a gam() function for GAMs in package mgcv, but it is not an exact clone of what is described in the White Book (no lo() for example). Package gss can fit spline-based GAMs too. And if you can accept regression splines you can use glm(). For gaussian GAMs you can use bruto() from package mda. Why is the output not printed when I source() a file? Most R commands do not generate any output. The command 1+1 computes the value 2 and returns it; the command summary(glm(y~x+z, family=binomial)) fits a logistic regression model, computes some summary information and returns an object of class "summary.glm" (see ). 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). Why does outer() behave strangely with my function? As the help for outer() indicates, it does not work on arbitrary functions the way the apply() family does. It requires functions that are vectorized to work elementwise on arrays. As you can see by looking at the code, outer(x, y, FUN) creates two large vectors containing every possible combination of elements of x and y and then passes this to FUN all at once. Your function probably cannot handle two large vectors as parameters. If you have a function that cannot handle two vectors but can handle two scalars, then you can still use outer() but you will need to wrap your function up first, to simulate vectorized behavior. Suppose your function is foo <- function(x, y, happy) { stopifnot(length(x) == 1, length(y) == 1) # scalars only! (x + y) * happy } If you define the general function wrapper <- function(x, y, my.fun, ...) { sapply(seq(along = x), FUN = function(i) my.fun(x[i], y[i], ...)) } then you can use outer() by writing, e.g., outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10) Why does the output from anova() depend on the order of factors in the model? In a model such as ~A+B+A:B, R will report the difference in sums of squares between the models ~1, ~A, ~A+B and ~A+B+A:B. If the model were ~B+A+A:B, R would report differences between ~1, ~B, ~A+B, and ~A+B+A:B . In the first case the sum of squares for A is comparing ~1 and ~A, in the second case it is comparing ~B and ~B+A. In a non-orthogonal design (i.e., most unbalanced designs) these comparisons are (conceptually and numerically) different. Some packages report instead the sums of squares based on comparing the full model to the models with each factor removed one at a time (the famous `Type III sums of squares' from SAS, for example). These do not depend on the order of factors in the model. The question of which set of sums of squares is the Right Thing provokes low-level holy wars on R-help from time to time. There is no need to be agitated about the particular sums of squares that R reports. You can compute your favorite sums of squares quite easily. Any two models can be compared with anova(model1, model2), and drop1(model1) will show the sums of squares resulting from dropping single terms. How do I produce PNG graphics in batch mode? Under Unix, the png() device uses the X11 driver, which is a problem in batch mode or for remote operation. If you have Ghostscript you can use bitmap(), which produces a PostScript file then converts it to any bitmap format supported by Ghostscript. On some installations this produces ugly output, on others it is perfectly satisfactory. In theory one could also use Xvfb from X.Org, which is an X11 server that does not require a screen; and the GDD package from CRAN, which produces PNG, JPEG and GIF bitmaps without X11. How can I get command line editing to work? The Unix command-line interface to R can only provide the inbuilt command line editor which allows recall, editing and re-submission of prior commands provided that the GNU readline library is available at the time R is configured for compilation. Note that the `development' version of readline including the appropriate headers is needed: users of Linux binary distributions will need to install packages such as libreadline-dev (Debian) or readline-devel (Red Hat). How can I turn a string into a variable? If you have varname <- c("a", "b", "d") you can do get(varname[1]) + 2 for a + 2 or assign(varname[1], 2 + 2) for a <- 2 + 2 or eval(substitute(lm(y ~ x + variable), list(variable = as.name(varname[1])) for lm(y ~ x + a) At least in the first two cases it is often easier to just use a list, and then you can easily index it by name vars <- list(a = 1:10, b = rnorm(100), d = LETTERS) vars[["a"]] without any of this messing about. Why do lattice/trellis graphics not work? The most likely reason is that you forgot to tell R to display the graph. Lattice functions such as xyplot() create a graph object, but do not display it (the same is true of Trellis graphics in S-Plus). The print() method for the graph object produces the actual display. When you use these functions interactively at the command line, the result is automatically printed, but in source() or inside your own functions you will need an explicit print() statement. How can I sort the rows of a data frame? To sort the rows within a data frame, with respect to the values in one or more of the columns, simply use order(). Why does the help.start() search engine not work? The browser-based search engine in help.start() utilizes a Java applet. In order for this to function properly, a compatible version of Java must installed on your system and linked to your browser, and both Java and JavaScript need to be enabled in your browser. There have been a number of compatibility issues with versions of Java and of browsers. For further details please consult section “Enabling search in HTML help” in R Installation and Administration. This manual is included in the R distribution, see , and its HTML version is linked from the HTML search page. Why did my .Rprofile stop working when I updated R? Did you read the NEWS file? For functions that are not in the base package you need to specify the correct package namespace, since the code will be run before the packages are loaded. E.g., ps.options(horizontal = FALSE) help.start() needs to be grDevices::ps.options(horizontal = FALSE) utils::help.start() (graphics::ps.options(horizontal = FALSE) in R 1.9.x). Where have all the methods gone? Many functions, particularly S3 methods, are now hidden in namespaces. This has the advantage that they cannot be called inadvertently with arguments of the wrong class, but it makes them harder to view. To see the code for an S3 method (e.g., [.terms) use getS3method("[", "terms") To see the code for an unexported function foo() in the namespace of package "bar" use bar:::foo. Don't use these constructions to call unexported functions in your own code—they are probably unexported for a reason and may change without warning. How can I create rotated axis labels? To rotate axis labels (using base graphics), you need to use text(), rather than mtext(), as the latter does not support par("srt"). ## Increase bottom margin to make room for rotated labels par(mar = c(7, 4, 4, 2) + 0.1) ## Create plot with no x axis and no x axis label plot(1 : 8, xaxt = "n", xlab = "") ## Set up x axis with tick marks alone axis(1, labels = FALSE) ## Create some text labels labels <- paste("Label", 1:8, sep = " ") ## Plot x axis labels at default tick marks text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1, labels = labels, xpd = TRUE) ## Plot x axis label at line 6 (of 7) mtext(1, text = "X Axis Label", line = 6) When plotting the x axis labels, we use srt = 45 for text rotation angle, adj = 1 to place the right end of text at the tick marks, and xpd = TRUE to allow for text outside the plot region. You can adjust the value of the 0.25 offset as required to move the axis labels up or down relative to the x axis. See ?par for more information. Also see Figure 1 and associated code in Paul Murrell (2003), “Integrating grid Graphics Output with Base Graphics Output”, R News, 3/2, 7–12. Why is read.table() so inefficient? By default, read.table() needs to read in everything as character data, and then try to figure out which variables to convert to numerics or factors. For a large data set, this takes condiderable amounts of time and memory. Performance can substantially be improved by using the colClasses argument to specify the classes to be assumed for the columns of the table. What is the difference between package and library? A package is a standardized collection of material extending R, e.g. providing code, data, or documentation. A library is a place (directory) where R knows to find packages it can use (i.e., which were installed). R is told to use a package (to “load” it and add it to the search path) via calls to the function library. I.e., library() is employed to load a package from libraries containing packages. See , for more details. See also Uwe Ligges (2003), “R Help Desk: Package Management”, R News, 3/3, 37–39. I installed a package but the functions are not there To actually use the package, it needs to be loaded using library(). See and for more information. Why doesn't R think these numbers are equal? The only numbers that can be represented exactly in R's numeric type are integers and fractions whose denominator is a power of 2. Other numbers have to be rounded to (typically) 53 binary digits accuracy. As a result, two floating point numbers will not reliably be equal unless they have been computed by the same algorithm, and not always even then. For example R> a <- sqrt(2) R> a * a == 2 [1] FALSE R> a * a - 2 [1] 4.440892e-16 The function all.equal() compares two objects using a numeric tolerance of .Machine$double.eps ^ 0.5. If you want much greater accuracy than this you will need to consider error propagation carefully. For more information, see e.g. David Goldberg (1991), “What Every Computer Scientist Should Know About Floating-Point Arithmetic”, ACM Computing Surveys, 23/1, 5–48, also available via http://docs.sun.com/source/806-3568/ncg_goldberg.html. How can I capture or ignore errors in a long simulation? Use try(), which returns an object of class "try-error" instead of an error, or preferably tryCatch(), where the return value can be configured more flexibly. For example beta[i,] <- tryCatch(coef(lm(formula, data)), error = function(e) rep(NaN, 4)) would return the coefficients if the lm() call succeeded and would return c(NaN, NaN, NaN, NaN) if it failed (presumably there are supposed to be 4 coefficients in this example). Why are powers of negative numbers wrong? You are probably seeing something like R> -2^2 [1] -4 and misunderstanding the precedence rules for expressions in R. Write R> (-2)^2 [1] 4 to get the square of -2. The precedence rules are documented in ?Syntax, and to see how R interprets an expression you can look at the parse tree R> as.list(quote(-2^2)) [[1]] `-` [[2]] 2^2 How can I save the result of each iteration in a loop into a separate file? One way is to use paste() (or sprintf()) to concatenate a stem filename and the iteration number while file.path() constructs the path. For example, to save results into files result1.rda, …, result100.rda in the subdirectory Results of the current working directory, one can use for(i in 1:100) { ## Calculations constructing "some_object" ... fp <- file.path("Results", paste("result", i, ".rda", sep = "")) save(list = "some_object", file = fp) } Why are p-values not displayed when using lmer()? Doug Bates has kindly provided an extensive response in a post to the r-help list, which can be reviewed at https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html. Why are there unwanted lines between polygons in PDF output viewed in Adobe Reader? Output from polygon() (and other functions calling polygon()) with the argument border=NA or border="transparent" should suppress border lines between polygons for all graphics devices. PDF output from R can be made in many ways, both directly, and through for example Postscript or Windows Metafiles converted to PDF in external software. In Adobe Reader, the default setting for line art, such as polygons, is to smooth, which produces the impression of thin borders. Adobe Reader does this both for PDF files written by R or through other software. This is irritating, especially when using Adobe Reader for presentation. The unwanted effect can be removed by turning off smoothing for line art: use the `Edit | Preferences | Page Display | Smooth line art' menu in Adobe Reader 7.0. Why does backslash behave strangely inside strings? This question most often comes up in relation to file names (see ) but it also happens that people complain that they cannot seem to put a single ‘\’ character into a text string unless it happens to be followed by certain other characters. To understand this, you have to distinguish between character strings and representations of character strings. Mostly, the representation in R is just the string with a single or double quote at either end, but there are strings that cannot be represented that way, e.g., strings that themselves contains the quote character. So > str <- "This \"text\" is quoted" > str [1] "This \"text\" is quoted" > cat(str, "\n") This "text" is quoted The escape sequences ‘\"’ and ‘\n’ represent a double quote and the newline character respectively. Printing text strings, using print() or by typing the name at the prompt will use the escape sequences too, but the cat() function will display the string as-is. Notice that ‘"\n"’ is a one-character string, not two; the backslash is not actually in the string, it is just generated in the printed representation. > nchar("\n") [1] 1 > substring("\n", 1, 1) [1] "\n" So how do you put a backslash in a string? For this, you have to escape the escape character. I.e., you have to double the backslash. as in > cat("\\n", "\n") \n Some functions, particularly those involving regular expression matching, themselves use metacharacters, which may need to be escaped by the backslash mechanism. In those cases you may need a quadruple backslash to represent a single literal one. In versions of R up to 2.4.1 an unknown escape sequence like ‘\p’ was quietly interpreted as just ‘p’. Current versions of R emit a warning. R Programming How should I write summary methods? Suppose you want to provide a summary method for class "foo". Then summary.foo() should not print anything, but return an object of class "summary.foo", and you should write a method print.summary.foo() which nicely prints the summary information and invisibly returns its object. This approach is preferred over having summary.foo() print summary information and return something useful, as sometimes you need to grab something computed by summary() inside a function or similar. In such cases you don't want anything printed. How can I debug dynamically loaded code? Roughly speaking, you need to start R inside the debugger, load the code, send an interrupt, and then set the required breakpoints. See section “Finding entry points in dynamically loaded code” in Writing R Extensions. This manual is included in the R distribution, see . How can I inspect R objects when debugging? The most convenient way is to call R_PV from the symbolic debugger. See section “Inspecting R objects when debugging” in Writing R Extensions. How can I change compilation flags? Suppose you have C code file for dynloading into R, but you want to use R CMD SHLIB with compilation flags other than the default ones (which were determined when R was built). Starting with R 2.1.0, users can provide personal Makevars configuration files in $HOME/.R to override the default flags. See section “Add-on packages” in R Installation and Administration. For earlier versions of R, you could change the file $R_HOME/etc/Makeconf to reflect your preferences, or (at least for systems using GNU Make) override them by the environment variable MAKEFLAGS. See section “Creating shared objects” in Writing R Extensions. How can I debug S4 methods? Use the trace() function with argument signature= to add calls to the browser or any other code to the method that will be dispatched for the corresponding signature. See ?trace for details. R Bugs What is a bug? If R executes an illegal instruction, or dies with an operating system error message that indicates a problem in the program (as opposed to something like “disk full”), then it is certainly a bug. If you call .C(), .Fortran(), .External() or .Call() (or .Internal()) yourself (or in a function you wrote), you can always crash R by using wrong argument types (modes). This is not a bug. Taking forever to complete a command can be a bug, but you must make certain that it was really R's fault. Some commands simply take a long time. If the input was such that you know it should have been processed quickly, report a bug. If you don't know whether the command should take a long time, find out by looking in the manual or by asking for assistance. If a command you are familiar with causes an R error message in a case where its usual definition ought to be reasonable, it is probably a bug. If a command does the wrong thing, that is a bug. But be sure you know for certain what it ought to have done. If you aren't familiar with the command, or don't know for certain how the command is supposed to work, then it might actually be working right. Rather than jumping to conclusions, show the problem to someone who knows for certain. Finally, a command's intended definition may not be best for statistical analysis. This is a very important sort of problem, but it is also a matter of judgment. Also, it is easy to come to such a conclusion out of ignorance of some of the existing features. It is probably best not to complain about such a problem until you have checked the documentation in the usual ways, feel confident that you understand it, and know for certain that what you want is not available. If you are not sure what the command is supposed to do after a careful reading of the manual this indicates a bug in the manual. The manual's job is to make everything clear. It is just as important to report documentation bugs as program bugs. However, we know that the introductory documentation is seriously inadequate, so you don't need to report this. If the online argument list of a function disagrees with the manual, one of them must be wrong, so report the bug. How to report a bug When you decide that there is a bug, it is important to report it and to report it in a way which is useful. What is most useful is an exact description of what commands you type, starting with the shell command to run R, until the problem happens. Always include the version of R, machine, and operating system that you are using; type version in R to print this. The most important principle in reporting a bug is to report facts, not hypotheses or categorizations. It is always easier to report the facts, but people seem to prefer to strain to posit explanations and report them instead. If the explanations are based on guesses about how R is implemented, they will be useless; others will have to try to figure out what the facts must have been to lead to such speculations. Sometimes this is impossible. But in any case, it is unnecessary work for the ones trying to fix the problem. For example, suppose that on a data set which you know to be quite large the command R> data.frame(x, y, z, monday, tuesday) never returns. Do not report that data.frame() fails for large data sets. Perhaps it fails when a variable name is a day of the week. If this is so then when others got your report they would try out the data.frame() command on a large data set, probably with no day of the week variable name, and not see any problem. There is no way in the world that others could guess that they should try a day of the week variable name. Or perhaps the command fails because the last command you used was a method for "["() that had a bug causing R's internal data structures to be corrupted and making the data.frame() command fail from then on. This is why others need to know what other commands you have typed (or read from your startup file). It is very useful to try and find simple examples that produce apparently the same bug, and somewhat useful to find simple examples that might be expected to produce the bug but actually do not. If you want to debug the problem and find exactly what caused it, that is wonderful. You should still report the facts as well as any explanations or solutions. Please include an example that reproduces the problem, preferably the simplest one you have found. Invoking R with the --vanilla option may help in isolating a bug. This ensures that the site profile and saved data files are not read. Before you actually submit a bug report, you should check whether the bug has already been reported and/or fixed. First, try the “Search Existing Reports” facility in the Bug Tracking page at http://bugs.R-project.org/. Second, consult https://svn.R-project.org/R/trunk/NEWS, which records changes that will appear in the next release of R, including some bug fixes that do not appear in Bug Tracking. (Windows users should additionally consult https://svn.R-project.org/R/trunk/src/gnuwin32/CHANGES.) Third, if possible try the current r-patched or r-devel version of R. If a bug has already been reported or fixed, please do not submit further bug reports on it. Finally, check carefully whether the bug is with R, or a contributed package. Bug reports on contributed packages should be sent first to the package maintainer, and only submitted to the R-bugs repository by package maintainers, mentioning the package in the subject line. On Unix systems a bug report can be generated using the function bug.report(). This automatically includes the version information and sends the bug to the correct address. Alternatively the bug report can be emailed to or submitted to the Web page at http://bugs.R-project.org/. Please try including results of sessionInfo() in your bug report. There is a section of the bug repository for suggestions for enhancements for R labelled ‘wishlist’. Suggestions can be submitted in the same ways as bugs, but please ensure that the subject line makes clear that this is for the wishlist and not a bug report, for example by starting with ‘Wishlist:’. Comments on and suggestions for the Windows port of R should be sent to . Corrections to and comments on message translation should be sent to the last translator (listed at the top of the appropriate ‘.po’ file) or to the translation team as listed at http://developer.R-project.org/TranslationTeams.html. Acknowledgments Of course, many many thanks to Robert and Ross for the R system, and to the package writers and porters for adding to it. Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D. Ripley, Anthony Rossini, and Andreas Weingessel for their comments which helped me improve this FAQ. More to come soon …