1. Gnomes Gtk 3.16.6 Brings Partial Aspect Ratio Support For Machines

Next:, Previous:, Up: 1.1 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.

GTK+ uses a height-for-width (and width-for-height) geometry management system. Height-for-width means that a widget can change how much vertical space it needs, depending on the amount of horizontal space that it is given (and similar for width-for-height). Customizing the GNOME 3 Shell. Customizing the GNOME 3 Shell Finnbarr P. Murphy (fpm@fpmurphy.com) In this post I will share a modicum of what I have learned to date about customizing the new GNOME 3 Shell.

You can also obtain it by writing to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA, U.S.A. Next:, Previous:, Up: 2.1 What is R? R is a system for statistical computation and graphics.

It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S (see ) and Sussman's. 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 and 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. It is distributed under a GNU-style, and an official part of the project (“ GNU S”).

Next:, Previous:, Up: 2.2 What machines does R run on? R is being developed for the Unix, Windows and Mac families of operating systems.

Support for Mac OS Classic ended with R 1.7.1. The current version of R will configure and build under a number of common Unix platforms including cpu-linux-gnu for the i386, alpha, arm, hppa, ia64, m68k, mips/mipsel, powerpc, s390, sparc (e.g., ), and x8664 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. Next:, Previous:, Up: 2.5.2 How can R be installed (Windows) The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, ME, NT4, 2000, and XP (at least) on Intel and clones (but not on other platforms). The Windows version of R was created by Robert Gentleman and Guido Masarotto, and is now being developed and maintained.

For most installations the Windows installer program will be the easiest tool to use. See the for more details. Previous:, Up: 2.5.3 How can R be installed (Macintosh) The bin/macosx directory of a CRAN site contains a standard Apple installer package inside a disk image named R.dmg. Once downloaded and executed, the installer will install the current non-developer release of R. RAqua is a native Mac OS X Darwin version of R with a R.app Mac OS X GUI.

Inside bin/macosx/powerpc/contrib/ x. Y there are prebuilt binary packages (for powerpc version of Mac OS X) to be used with RAqua corresponding to the “ x. Y” release of R.

The installation of these packages is available through the “Package” menu of the R.app GUI. This port of R for Mac OS X is maintained. The 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. Next:, Previous:, Up: 2.6 Are there Unix binaries for R? The bin/linux directory of a CRAN site contains the following packages. CPU Versions Provider Debian i386 stable/oldstable Johannes Ranke amd64 stable/oldstable Johannes Ranke Red Hat i386 FC3/FC4/FC5/FC6 Martyn Plummer x8664 FC3 Brian Ripley x8664 FC4/FC5/FC6 Martyn Plummer i386 Enterprise Linux Matthew P.

Cox x8664 Enterprise Linux Matthew P. Cox SuSE i386 7.3/8.0/8.1/8.2 Detlef Steuer i586 9.0/9.1/9.2/9.3/10.0/10.1 Detlef Steuer x8664 9.2/9.3/10.0/10.1 Detlef Steuer Ubuntu i386 dapper/edgy/feisty Vincent Goulet VineLinux i386 3.2 Susumu 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. 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 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.

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 contains more details as well download information. Next:, Previous:, Up: 2.7 What documentation exists for R?

Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help( name ) (or? Name) at the R prompt, where name is the name of the topic help is sought for. (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.) This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see. An up-to-date HTML version is always available for web browsing at. Printed copies of the R reference manual for some version(s) are available from Network Theory Ltd, at. 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, statistical modeling and graphics. This document is based on the “Notes on S-Plus” by Bill Venables and David Smith. “Writing R Extensions” ( R-exts) currently describes the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API. “R Data Import/Export” ( R-data) is a guide to importing and exporting data to and from R. “The R Language Definition” ( R-lang), a first version of the “Kernighan & Ritchie of R”, explains evaluation, parsing, object oriented programming, computing on the language, and so forth.

“R Installation and Administration” ( R-admin). “R Internals” ( R-ints) is a guide to R's internal structures. (Added in R 2.4.0.) Books on R include P. Dalgaard (2002), “Introductory Statistics with R”, Springer: New York, ISBN 0-387-95475-9,. 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),.

Maindonald and J. Braun (2003), “Data Analysis and Graphics Using R: An Example-Based Approach”, Cambridge University Press, ISBN 0-521-81336-0,. Masarotto (2002), “Laboratorio di statistica con R”, McGraw-Hill, ISBN 88-386-6084-0 (in Italian),. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN: 1-584-88486-X,. Venables and B. Ripley (2002), “Modern Applied Statistics with S.

Fourth Edition”. Springer, ISBN 0-387-95457-0 has a home page at providing additional material. Its companion is W. Venables and B. 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 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. Krause and M.

Olsen (2005), “The Basics of S-Plus” (Fourth Edition). Springer, ISBN 0-387-26109-5. Pinheiro and D. 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. 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. 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”, 5, 299–314. An annotated bibliography (BibTeX format) of R-related publications which includes most of the above references can be found at. Next:, Previous:, Up: 2.9 What mailing lists exist for R?

Thanks to, there are four mailing lists devoted to R. R-announce A moderated list for major announcements about the development of R and the availability of new code. R-packages A moderated list for announcements on the availability of new or enhanced contributed packages. R-help The `main' R mailing list, for discussion about problems and solutions using R, announcements (not covered by `R-announce' and `R-packages') about the development of R and the availability of new code.

R-devel This list is for questions and discussion about code development in R. Please read the 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. 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.

Gnomes Gtk 3.16.6 Brings Partial Aspect Ratio Support For Machines

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 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, from which they can be read with a web browser, using an NNTP news reader, or via RSS feeds. See for the available mailing lists, and for details on RSS feeds.

Next:, Previous:, Up: 2.11 Can I use R for commercial purposes? R is released under the. 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: 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.

Previous:, Up: 2.13 What is the R Foundation? The R Foundation is a not for profit organization working in the public interest. It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing, provide a reference point for individuals, institutions or commercial enterprises that want to support or interact with the R development community, and to hold and administer the copyright of R software and documentation. See for more information. Next:, Previous:, Up: 3.1 What is S? S is a very high level language and an environment for data analysis and graphics.

In 1998, the Association for Computing Machinery ( ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for the S system, which has forever altered the way people analyze, visualize, and manipulate data. S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers. The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S. Becker and John M.

Chambers (1984), “S. An Interactive Environment for Data Analysis and Graphics,” Monterey: Wadsworth and Brooks/Cole.

This is also referred to as the “ Brown Book”, and of historical interest only. Becker, John M. Chambers and Allan R. Wilks (1988), “The New S Language,” London: Chapman & Hall.

This book is often called the “ Blue Book”, and introduced what is now known as S version 2. Chambers and Trevor J. Hastie (1992), “Statistical Models in S,” London: Chapman & Hall. This is also called the “ White Book”, and introduced S version 3, which added structures to facilitate statistical modeling in S.

Chambers (1998), “Programming with Data,” New York: Springer, ISBN 0-387-98503-4. This “ Green Book” describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process. See for further information on “Stages in the Evolution of S”.

There is a huge amount of user-contributed code for S, available at the at CMU. Next:, Previous:, Up: 3.3 What are the differences between R and S? We can regard S as a language with three current implementations or “engines”, the “old S engine” (S version 3; S-Plus 3.x and 4.x), the “new S engine” (S version 4; S-Plus 5.x and above), and R.

Given this understanding, asking for “the differences between R and S” really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines. For the remainder of this section, “S” refers to the S engines and not the S language. Next:, Previous:, Up: 3.3.2 Models There are some differences in the modeling code, such as. Whereas in S, you would use lm(y x^3) to regress y on x^3, in R, you have to insulate powers of numeric vectors (using I), i.e., you have to use lm(y I(x^3)). The glm family objects are implemented differently in R and S. The same functionality is available but the components have different names. Option na.action is set to 'na.omit' by default in R, but not set in S.

Terms objects are stored differently. In S a terms object is an expression with attributes, in R it is a formula with attributes. The attributes have the same names but are mostly stored differently. Finally, in R y x + 0 is an alternative to y x - 1 for specifying a model with no intercept. Models with no parameters at all can be specified by y 0.

Next:, Previous:, Up: 3.4 Is there anything R can do that S-Plus cannot? Since almost anything you can do in R has source code that you could port to S-Plus with little effort there will never be much you can do in R that you couldn't do in S-Plus if you wanted to. (Note that using lexical scoping may simplify matters considerably, though.) R offers several graphics features that S-Plus does not, such as finer handling of line types, more convenient color handling (via palettes), gamma correction for color, and, most importantly, mathematical annotation in plot texts, via input expressions reminiscent of TeX constructs. See the help page for plotmath, which features an impressive on-line example. More details can be found in Paul Murrell and Ross Ihaka (2000), “An Approach to Providing Mathematical Annotation in Plots”, 9, 582–599. Previous:, Up: 3.5 What is R-plus?

For a very long time, there was no such thing. Is currently beta testing a commercially supported version of R named R+ (read R plus). In addition, has released, an enterprise-class statistical analysis system based on R, suitable for deployment in professional, commercial and regulated environments. Offers, an enterprise-strength statistical computing environment which combines R with enterprise-level validation, documentation, software support, and consulting services, as well as related R-based products. Next:, Previous:, Up: 4 R Web Interfaces Rweb is developed and maintained.

The 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. 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 for more information.

Rcgi is a CGI WWW interface to R. 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 (actually, two versions: one with (bitmap) graphics and one without) as well as the original code are available from.

CGI-based web access to R is also provided at. 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. 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. The package is now maintained by and has a web page at., 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. 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).

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.

By Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. 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.

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, 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. Next:, Previous:, Up: 5.1.1 Add-on packages in R The R distribution comes with the following packages: base Base R functions (and datasets before R 2.0.0). Datasets Base R datasets (added in R 2.0.0). GrDevices Graphics devices for base and grid graphics (added in R 2.0.0).

Graphics R functions for base graphics. Grid A rewrite of the graphics layout capabilities, plus some support for interaction. Methods Formally defined methods and classes for R objects, plus other programming tools, as described in the Green Book. Splines Regression spline functions and classes. Stats R statistical functions.

Stats4 Statistical functions using S4 classes. Tcltk Interface and language bindings to Tcl/Tk GUI elements. Tools Tools for package development and administration.

Utils R utility functions. These “base packages” were substantially reorganized in R 1.9.0. The former base was split into the four packages base, graphics, stats, and utils. Packages ctest, eda, modreg, mva, nls, stepfun and ts were merged into stats, package lqs returned to the recommended package MASS, and package mle moved to stats4.

Next:, Previous:, Up: 5.1.2 Add-on packages from CRAN The following packages are available from the CRAN src/contrib area. (Packages denoted as Recommended are to be included in all binary distributions of R.) ADaCGH Analysis of data from aCGH experiments. AIS Tools to look at the data (“Ad Inidicia Spectata”). AMORE A MORE flexible neural network package, providing the TAO robust neural network algorithm. ARES Allelic richness estimation, with extrapolation beyond the sample size. AcceptanceSampling Creation and evaluation of acceptance sampling plans, AdaptFit Adaptive semiparametic regression. AlgDesign Algorithmic experimental designs.

Calculates exact and approximate theory experimental designs for D, A, and I criteria. Amelia Amelia II: a program for missing data.

AnalyzeFMRI Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE format. ArDec Time series autoregressive decomposition. BACCO Bayesian Analysis of Computer Code Output. Contains approximator, calibrator, and emulator, for Bayesian prediction of complex computer codes, calibration of computer models, and emulation of computer programs, respectively. BARD Better Automated ReDistricting.

BHH2 Functions and data sets reproducing some examples in “Statistics for Experimenters II” by G. Hunter, and W. Hunter, 2005, John Wiley and Sons. BMA Bayesian Model Averaging for linear models, generalizable linear models and survival models (Cox regression). BRugs OpenBUGS and its R interface BRugs. 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. BiodiversityR GUI for biodiversity and community ecology analysis. Bolstad Functions and data sets for the book “Introduction to Bayesian Statistics” by W. 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” by L. 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. CPGchron Create radiocarbon-dated depth chronologies. CTFS The CTFS large plot forest dynamics analyses.

CVThresh Level-dependent Cross-Validation Thresholding. Cairo Graphics device using cairographics library for creating high-quality PNG, PDF, SVG, PostScript output and interactive display devices such as X11. CarbonEL Carbon Event Loop.

CircStats Circular Statistics, from “Topics in Circular Statistics” by S. Rao Jammalamadaka and A. SenGupta, 2001, World Scientific. CoCo Graphical modeling for contingency tables using CoCo. ComPairWise Compare phylogenetic or population genetic data alignments.

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 and Graphics Using R” by John H. Maindonald and W. John Brown, 2003. DAAGbio Data sets and functions, for demonstrations with expression arrays. DAAGxtras Data sets and functions additional to DAAG.

DBI A common database interface (DBI) class and method definitions. All classes in this package are virtual and need to be extended by the various DBMS implementations. DCluster A set of functions for the detection of spatial clusters of diseases using count data. DDHFm Variance stabilization by Data-Driven Haar-Fisz (for microarrays). DEoptim Differential Evolution Optimization. DICOM Import and manipulate medical imaging data using the Digital Imaging and Communications in Medicine (DICOM) Standard. DPpackage Semiparametric Bayesian analysis using Dirichlet process priors.

Davies Functions for the Davies quantile function and the Generalized Lambda distribution. Defaults Create global function defaults. DescribeDisplay R interface to the DescribeDisplay GGobi plugin. Design Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications.

It also contains new functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model.

Devore5 Data sets and sample analyses from “Probability and Statistics for Engineering and the Sciences (5th ed)” by Jay L. Devore, 2000, Duxbury.

Devore6 Data sets and sample analyses from “Probability and Statistics for Engineering and the Sciences (6th ed)” by Jay L. Devore, 2003, Duxbury. Devore7 Data sets and sample analyses from “Probability and Statistics for Engineering and the Sciences (7th ed)” by Jay L. Devore, 2008, Thomson.

EMV Estimation of missing values in a matrix by a k-th nearest neighboors algorithm. EbayesThresh Empirical Bayes thresholding and related methods. Ecdat Data sets from econometrics textbooks. ElemStatLearn Data sets, functions and examples from the book “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001), Springer.

Epi Statistical analysis in epidemiology, with functions for demographic and epidemiological analysis in the Lexis diagram. FKBL Fuzzy Knowledge Base Learning. 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 Based on Generalized Linear Models” by Ludwig Fahrmeir and Gerhard Tutz (1994), Springer.

FieldSim Random fields simulations. Flury Data sets from from “A First Course in Multivariate Statistics” by Bernard Flury (1997), Springer. FortranCallsR Simple Fortran/C/R interface example. FracSim Simulation of one- and two-dimensional fractional and multifractional Levy motions. FunCluster Functional profiling of cDNA microarray expression data. G1DBN Dynamic Bayesian Network inference using 1st order conditional dependencies. GAMBoost Generalized additive models by likelihood based boosting.

GDD Platform and X11 independent device for creating bitmaps (png, gif and jpeg) using the GD graphics library. GLDEX Fit RS and FMKL generalised lambda distributions using discretized and maximum likelihood methods. GOSim Computation of functional similarities between GO terms and gene products.

GPArotation Gradient Projection Algorithm rotation for factor analysis. GRASS An interface between the GRASS geographical information system and R, based on starting R from within the GRASS environment and chosen LOCATIONNAME and MAPSET. Wrapper and helper functions are provided for a range of R functions to match the interface metadata structures. GSA Gene set analysis. GSM Gamma Shape Mixture. GammaTest Gamma Test data analysis. GenABEL Genome-wide SNP association analysis.

GenKern Functions for generating and manipulating generalised binned kernel density estimates. GeneCycle Identification of periodically expressed genes. GeneF Generalized F-statistics. GeneNT Relevance or Dependency network and signaling pathway discovery. GeneNet Modeling and inferring gene networks. GeneTS A package for analysing multiple gene expression time series data. Currently, implements methods for cell cycle analysis and for inferring large sparse graphical Gaussian models.

Geneland MCMC inference from individual genetic data based on a spatial statistical model. GeoXp Interactive exploratory spatial data analysis.

GillespieSSA Gillespie's Stochastic Simulation Algorithm (SSA). GroupSeq Computations related to group-seqential boundaries. HH Support software for “Statistical Analysis and Data Display” by Richard M. Heiberger and Burt Holland, Springer, 2005. HI Simulation from distributions supported by nested hyperplanes. HSAUR Functions, data sets, analyses and examples from the book “A Handbook of Statistical Analyses Using R” by Brian S. Everitt and Torsten Hothorn (2006), Chapman & Hall/CRC.

HTMLapplets Functions inserting dynamic scatterplots and grids in documents generated by R2HTML. HFWutils Utilities by H. Felix Wittmann: Excel connections, string matching, and passing by reference.

HardyWeinberg Graphical tests for Hardy-Weinberg equilibrium. HighProbability Estimation of the alternative hypotheses having frequentist or Bayesian probabilities at least as great as a specified threshold, given a list of p-values. Hmisc Functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, recoding variables, and bootstrap repeated measures analysis. HydroMe Estimation of soil hydraulic parameters from experimental data.

HyperbolicDist Basic functions for the hyperbolic distribution: probability density function, distribution function, quantile function, a routine for generating observations from the hyperbolic, and a function for fitting the hyperbolic distribution to data. ICE Iterated Conditional Expectation: kernel estimators for interval-censored data. ICEinfer Incremental Cost-Effectiveness (ICE) statistical inference (from two unbiased samples). ICS ICS/ICA computation based on two scatter matrices.

ICSNP Tools for multivariate nonparametrics. IDPmisc Utilities from the Institute of Data Analyses and Process Design, IDP/ZHW. IPSUR Accompanies “Introduction to Probability and Statistics Using R” by G. 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 homothetic production 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.

JudgeIt Calculates bias, responsiveness, and other characteristics of two-party electoral systems, with district-level electoral and other data. KMsurv Data sets and functions for “Survival Analysis, Techniques for Censored and Truncated Data” by Klein and Moeschberger, 1997, Springer.

Kendall Kendall rank correlation and Mann-Kendall trend test. KernSmooth Functions for kernel smoothing (and density estimation) corresponding to the book “Kernel Smoothing” by M. LDheatmap Heat maps of linkage disequilibrium measures.

LLAhclust Hierarchical clustering of variables or objects based on the likelihood linkage analysis method. LLN Learning with latent networks. LMGene Date transformation and identification of differentially expressed genes in gene expression arrays.

LearnBayes Functions for Learning Bayesian Inference. Lmoments Estimation of L-moments and the parameters of normal and Cauchy polynomial quantile mixtures. LogConcDEAD Maximum likelihood estimation of a log-concave density. LogicReg Routines for Logic Regression. LoopAnalyst A collection of tools to conduct Levins' Loop Analysis.

LowRankQP Low Rank Quadratic Programming: QP problems where the hessian is represented as the product of two matrices. MASS Functions and datasets from the main package of Venables and Ripley, “Modern Applied Statistics with S”. Contained in the VR bundle. MBA Multilevel B-spline Approximation. MBESS Methods for the Behavioral, Educational, and Social Sciences. MCMCpack Markov chain Monte Carlo (MCMC) package: functions for posterior simulation for a number of statistical models. MChtest Monte Carlo hypothesis tests.

MEMSS Data sets and sample analyses from “Mixed-effects Models in S and S-PLUS” by J. Pinheiro and D. Bates, 2000, Springer. MFDA Model Based Functional Data Analysis. MKLE Maximum kernel likelihood estimation. MLDS Maximum Likelihood Difference Scaling.

MLEcens Computation of the MLE for bivariate (interval) censored data. MNP Fitting Bayesian Multinomial Probit models via Markov chain Monte Carlo. Along with the standard Multinomial Probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives. MPV Data sets from the book “Introduction to Linear Regression Analysis” by D.

Montgomery, E. Vining, 2001, John Wiley and Sons. MSBVAR Bayesian vector autoregression models, impulse responses and forecasting. MarkedPointProcess Non-parametric analysis of the marks of marked point processes.

MasterBayes Maximum likelihood and Markov chain Monte Carlo methods for pedigree reconstruction, analysis and simulation. MatchIt Select matched samples of the original treated and control groups with similar covariate distributions. Matching Multivariate and propensity score matching with formal tests of balance. Matrix A Matrix package. MiscPsycho Miscellaneous Psychometrics.

NADA Methods described in “Nondetects And Data Analysis: Statistics for Censored Environmental Data” by Dennis R. Helsel, 2004, John Wiley and Sons. NISTnls A set of test nonlinear least squares examples from NIST, the U.S. National Institute for Standards and Technology. NMMAPSlite U.S. National Morbidity, Mortality, and Air Pollution Study data lite. NORMT3 Evaluates complex erf, erfc and density of sum of Gaussian and Student's t.

NRAIA Data sets with sample code from “Nonlinear Regression Analysis and Its Applications” by Doug Bates and Donald Watts, 1988, Wiley. NestedCohort Survival analysis for cohorts with missing covariate information. ORMDR Odds ratio based multivactor-dimensionality reduction method for detecting gene-gene interactions.

Oarray Arrays with arbitrary offsets. PBSmapping Software evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo, British Columbia, Canada. Draws maps and implements other GIS procedures.

PBSmodelling Software to facilitate the design, testing, and operation of computer models. PET Simulation and reconstruction of PET images. PHYLOGR Manipulation and analysis of phylogenetically simulated data sets (as obtained from PDSIMUL in package PDAP) and phylogenetically-based analyses using GLS. PK Estimation of pharmacokinetic parameters. PKfit A nonlinear regression (including a genetic algorithm) program designed to deal with curve fitting for pharmacokinetics.

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 a generalisation of SVD also supporting non-identity metrics and penalisations. Also includes some other multiway methods. PearsonICA Independent component analysis using score functions from the Pearson system. 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 Output Perturbation 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 developing R packages.

R2HTML Functions for exporting R objects & graphics in an HTML document. R2WinBUGS Running WinBUGS from R: call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R. RArcInfo Functions to import Arc/Info V7.x coverages and data. RBGL Interface to the boost C graph library. RBloomberg Fetch data from a Bloomberg API using COM. RColorBrewer ColorBrewer palettes for drawing nice maps shaded according to a variable. RDieHarder R interface to the dieharder random number generator test suite.

RFA Regional Frequency Analysis. RFOC Graphics for spherical distributions and earthquake focal mechanisms. RGtk2 Facilities for programming graphical interfaces using Gtk (the Gimp Tool Kit) version 2. RGrace Mouse/menu driven interactive plotting application. RGraphics Data and functions from the book “R Graphics” by Paul Murrell, 2005, Chapman & Hall/CRC.

RII Estimation of the relative index of inequality for interval-censored data 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 additive models.

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) and furthermore access to Unidata's udunits calendar conversions. ROCR Visualizing the performance of scoring classifiers. RODBC An ODBC database interface.

ROptEst Optimally robust estimation. ROptEstTS Optimally robust estimation for regression-type models. ROracle Oracle Database Interface driver for R.

Uses the ProC/C embedded SQL. RPMG Poor Man's Gui: create interactive R analysis sessions. RPyGeo ArcGIS Geoprocessing in R via Python. RQuantLib Provides access to (some) of the QuantLib functions from within R; currently limited to some Option pricing and analysis functions.

The QuantLib project aims to provide a comprehensive software framework for quantitative finance. RSQLite Database Interface R driver for SQLite. Embeds the SQLite database engine in R.

RScaLAPACK An interface to ScaLAPACK functions from R. 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 for Scalable Vector Graphics. RTisean R interface to Tisean algorithms. RUnit Functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools.

RWeka An R interface to Weka, a rich collection of machine learning algorithms for data mining tasks. RWinEdt A plug in for using WinEdt as an editor for R. RXshrink Maximum Likelihood Shrinkage via Ridge or Least Angle Regression. RadioSonde A collection of programs for reading and plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument). RandVar Implementation of random variables by means of S4 classes and methods.

RandomFields Creating random fields using various methods. RaschSampler Sampling binary matrices with fixed margins.

Rcapture Loglinear models in capture-recapture experiments. Rcmdr A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package. RcmdrPlugin.HH Rcmdr support for the introductory course at Temple University. RcmdrPlugin.TeachingDemos Rcmdr Teaching Demos plug-in. 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. Rmetrics Financial engineering and computational finance. Rmpi An interface (wrapper) to MPI (Message-Passing Interface) APIs.

It also provides an interactive R slave environment in which distributed statistical computing can be carried out. RobLox Optimally robust influence curves for location and scale. RobRex Optimally robust influence curves for regression and scale. Rpad Utility functions for the Rpad workbook-style interface.

Rsac Seismic tools for R. Rserve A socket server (TCP/IP or local sockets) which allows binary requests to be sent to R. Runuran Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators. Rvelslant Downhole seismic analysis.

Rwave An environment for the time-frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals), based on the book “Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S” by Rene Carmona, Wen L. Hwang and Bruno Torresani, 1998, Academic Press. Ryacas An R interfaces to the yacas computer algebra system.

SASmixed Data sets and sample linear mixed effects analyses corresponding to the examples in “SAS System for Mixed Models” by R. Stroup and R. Wolfinger, 1996, SAS Institute. SASxport Read and write SAS XPORT files. SIN A SINful approach to selection of Gaussian Graphical Markov Models.

SLmisc Misce.