Publications related to R

This page gives a partially annotated list of books and other publications that are related to S or R and may be useful to the R user community.

[1] Richard A. Becker, John M. Chambers, and Allan R. Wilks. The New S Language. Chapman & Hall, London, 1988.
[ bib ]

This book is often called the ``Blue Book'', and introduced what is now known as S version 2.

[2] John M. Chambers and Trevor J. Hastie. Statistical Models in S. Chapman & Hall, London, 1992.
[ bib | Publisher Info ]

This is also called the ``White Book'', and introduced S version 3, which added structures to facilitate statistical modeling in S.

[3] John M. Chambers. Programming with Data. Springer, New York, 1998. ISBN 0-387-98503-4.
[ bib | Publisher Info | http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/ ]

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

[4] William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, 2002. ISBN 0-387-95457-0.
[ bib | Publisher Info | http://www.stats.ox.ac.uk/pub/MASS4/ ]

A highly recommended book on how to do statistical data analysis using R or S-Plus. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization. The `on-line complements' available at the books homepage provide updates of the book, as well as further details of technical material.

[5] William N. Venables and Brian D. Ripley. S Programming. Springer, 2000. ISBN 0-387-98966-8.
[ bib | Publisher Info | http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ ]

This 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.

[6] Deborah Nolan and Terry Speed. Stat Labs: Mathematical Statistics Through Applications. Springer Texts in Statistics. Springer, 2000. ISBN 0-387-98974-9.
[ bib | Publisher Info | http://www.stat.Berkeley.EDU/users/statlabs/ ]

Integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.

[7] Jose C. Pinheiro and Douglas M. Bates. Mixed-Effects Models in S and S-Plus. Springer, 2000. ISBN 0-387-98957-0.
[ bib | Publisher Info ]

A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.

[8] Manuel Castejón Limas, Joaquín Ordieres Meré, Fco. Javier de Cos Juez, and Fco. Javier Martínez de Pisón Ascacibar. Control de Calidad. Metodologia para el analisis previo a la modelización de datos en procesos industriales. Fundamentos teóricos y aplicaciones con R. Servicio de Publicaciones de la Universidad de La Rioja, 2001. ISBN 84-95301-48-2.
[ bib ]

This book, written in Spanish, is oriented to researchers interested in applying multivariate analysis techniques to real processes. It combines the theoretical basis with applied examples coded in R.

[9] John Fox. An R and S-Plus Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, USA, 2002. ISBN 0761922792.
[ bib | http://www.socsci.mcmaster.ca/jfox/Books/Companion/ ]

A companion book to a text or course on applied regression (such as ``Applied Regression, Linear Models, and Related Methods'' by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.

[10] Peter Dalgaard. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9.
[ bib | Publisher Info | http://www.biostat.ku.dk/~pd/ISwR.html ]

[11] Stefano Iacus and Guido Masarotto. Laboratorio di statistica con R. McGraw-Hill, Milano, 2003. ISBN 88-386-6084-0.
[ bib | Publisher Info ]

[12] John Maindonald and John Braun. Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, 2003. ISBN 0-521-81336-0.
[ bib | Publisher Info | http://wwwmaths.anu.edu.au/~johnm/r-book.html ]

[13] Giovanni Parmigiani, Elizabeth S. Garrett, Rafael A. Irizarry, and Scott L. Zeger. The Analysis of Gene Expression Data. Springer, New York, 2003. ISBN 0-387-95577-1.
[ bib | Publisher Info ]

[14] Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, and Emmanuel Jolivet. Statistical Tools for Nonlinear Regression. Springer, New York, 2003. ISBN 0-387-40081-8.
[ bib | Publisher Info ]

[15] S. Mase, T. Kamakura, M. Jimbo, and K. Kanefuji. Introduction to Data Science for engineers- Data analysis using free statistical software R (in Japanese). Suuri-Kogaku-sha, Tokyo, April 2004. ISBN 4901683128.
[ bib ]

[16] Julian J. Faraway. Linear Models with R. Chapman & Hall/CRC, Boca Raton, FL, 2004. ISBN 1-584-88425-8.
[ bib | Publisher Info | http://www.stat.lsa.umich.edu/~faraway/LMR/ ]

The first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.

[17] Richard M. Heiberger and Burt Holland. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer, 2004. ISBN 0-387-40270-5.
[ bib | Publisher Info | http://astro.temple.edu/~rmh/HH ]

A contemporary presentation of statistical methods featuring 200 graphical displays for exploring data and displaying analyses. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S-Plus, R, and SAS executable functions are provided and discussed. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.

[18] John Verzani. Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005. ISBN 1-584-88450-9.
[ bib | Publisher Info | http://wiener.math.csi.cuny.edu/UsingR/ ]

There are few books covering introductory statistics using R, and this book fills a gap as a true ``beginner'' book. With emphasis on data analysis and practical examples, `Using R for Introductory Statistics' encourages understanding rather than focusing on learning the underlying theory. It includes a large collection of exercises and numerous practical examples from a broad range of scientific disciplines. It comes complete with an online resource containing datasets, R functions, selected solutions to exercises, and updates to the latest features. A full solutions manual is available from Chapman & Hall/CRC.

[19] Ross Ihaka and Robert Gentleman. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299-314, 1996.
[ bib | http://www.amstat.org/publications/jcgs/ ]

[20] Francisco Cribari-Neto and Spyros G. Zarkos. R: Yet another econometric programming environment. Journal of Applied Econometrics, 14:319-329, 1999.
[ bib | http://www.interscience.wiley.com/jpages/0883-7252/ | http://www.R-project.org/nosvn/papers/Cribari-Neto+Zarkos:1999.pdf ]

[21] Robert Gentleman and Ross Ihaka. Lexical scope and statistical computing. Journal of Computational and Graphical Statistics, 9:491-508, 2000.
[ bib | http://www.amstat.org/publications/jcgs/ ]

[22] Paul Murrell and Ross Ihaka. An approach to providing mathematical annotation in plots. Journal of Computational and Graphical Statistics, 9:582-599, 2000.
[ bib | http://www.amstat.org/publications/jcgs/ ]

[23] Stephen P. Ellner. Review of R, version 1.1.1. Bulletin of the Ecological Society of America, 82(2):127-128, April 2001.
[ bib ]

[24] Brian D. Ripley. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network., 1(1):23-25, February 2001.
[ bib | http://ltsn.mathstore.ac.uk/newsletter/feb2001/pdf/rproject.pdf ]

[25] Kurt Hornik and Friedrich Leisch, editors. Proceedings of the 2nd International Workshop on Distributed Statistical Computing (DSC 2001), Technische Universität Wien, Vienna, Austria, 2001. ISSN 1609-395X.
[ bib | http://www.ci.tuwien.ac.at/Conferences/DSC.html ]

[26] Paulo J. Ribeiro, Jr. and Patrick E. Brown. Some words on the r project. The ISBA Bulletin, 8(1):12-16, March 2001.
[ bib | http://www.iami.mi.cnr.it/isba/index.html ]

[27] Diego Kuonen. Introduction au data mining avec R : vers la reconquête du `knowledge discovery in databases' par les statisticiens. Bulletin of the Swiss Statistical Society, 40:3-7, 2001.
[ bib | http://www.statoo.com/en/publications/2001.R.SSS.40/ ]

[28] Diego Kuonen and Reinhard Furrer. Data mining avec R dans un monde libre. Flash Informatique Spécial Été, pages 45-50, sep 2001.
[ bib | http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page45.html ]

[29] Reinhard Furrer and Diego Kuonen. GRASS GIS et R: main dans la main dans un monde libre. Flash Informatique Spécial Été, pages 51-56, sep 2001.
[ bib | http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page51.html ]

[30] Diego Kuonen and Valerie Chavez. R - un exemple du succès des modèles libres. Flash Informatique, 2:3-7, 2001.
[ bib | http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-2-1/2-1-page3.html ]

[31] Vito Ricci. R : un ambiente opensource per l'analisi statistica dei dati. Economia e Commercio, 1:69-82, 2004.
[ bib ]

This paper would be a short introduction and overview about the language and environment for statistical analysis R, without entering in specific details too much computational. I give a look about this opensource software pointing out its main features, its functionalities, its pros and cons describing some libraries and the kind of analysis they support. I supply a summary, with a short description, about many resources concerning R that can be found in the Web: the most are in English language, but there are also some in the Italian language. The aim of this work is to contribute in increasing of the use of the R environment in Italy among statistical researchers trying to ``advertise'' this software and its opensource philosophy.

This file has been generated by bibtex2html 1.71

[1]	Richard A. Becker, John M. Chambers, and Allan R. Wilks. The New S Language. Chapman & Hall, London, 1988. [ bib ] This book is often called the ``Blue Book'', and introduced what is now known as S version 2.
[2]	John M. Chambers and Trevor J. Hastie. Statistical Models in S. Chapman & Hall, London, 1992. [ bib \| Publisher Info ] This is also called the ``White Book'', and introduced S version 3, which added structures to facilitate statistical modeling in S.
[3]	John M. Chambers. Programming with Data. Springer, New York, 1998. ISBN 0-387-98503-4. [ bib \| Publisher Info \| http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/ ] This ``Green Book'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.
[4]	William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, 2002. ISBN 0-387-95457-0. [ bib \| Publisher Info \| http://www.stats.ox.ac.uk/pub/MASS4/ ] A highly recommended book on how to do statistical data analysis using R or S-Plus. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization. The `on-line complements' available at the books homepage provide updates of the book, as well as further details of technical material.
[5]	William N. Venables and Brian D. Ripley. S Programming. Springer, 2000. ISBN 0-387-98966-8. [ bib \| Publisher Info \| http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ ] This 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.
[6]	Deborah Nolan and Terry Speed. Stat Labs: Mathematical Statistics Through Applications. Springer Texts in Statistics. Springer, 2000. ISBN 0-387-98974-9. [ bib \| Publisher Info \| http://www.stat.Berkeley.EDU/users/statlabs/ ] Integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.
[7]	Jose C. Pinheiro and Douglas M. Bates. Mixed-Effects Models in S and S-Plus. Springer, 2000. ISBN 0-387-98957-0. [ bib \| Publisher Info ] A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.
[8]	Manuel Castejón Limas, Joaquín Ordieres Meré, Fco. Javier de Cos Juez, and Fco. Javier Martínez de Pisón Ascacibar. Control de Calidad. Metodologia para el analisis previo a la modelización de datos en procesos industriales. Fundamentos teóricos y aplicaciones con R. Servicio de Publicaciones de la Universidad de La Rioja, 2001. ISBN 84-95301-48-2. [ bib ] This book, written in Spanish, is oriented to researchers interested in applying multivariate analysis techniques to real processes. It combines the theoretical basis with applied examples coded in R.
[9]	John Fox. An R and S-Plus Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, USA, 2002. ISBN 0761922792. [ bib \| http://www.socsci.mcmaster.ca/jfox/Books/Companion/ ] A companion book to a text or course on applied regression (such as ``Applied Regression, Linear Models, and Related Methods'' by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.
[10]	Peter Dalgaard. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9. [ bib \| Publisher Info \| http://www.biostat.ku.dk/~pd/ISwR.html ]
[11]	Stefano Iacus and Guido Masarotto. Laboratorio di statistica con R. McGraw-Hill, Milano, 2003. ISBN 88-386-6084-0. [ bib \| Publisher Info ]
[12]	John Maindonald and John Braun. Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, 2003. ISBN 0-521-81336-0. [ bib \| Publisher Info \| http://wwwmaths.anu.edu.au/~johnm/r-book.html ]
[13]	Giovanni Parmigiani, Elizabeth S. Garrett, Rafael A. Irizarry, and Scott L. Zeger. The Analysis of Gene Expression Data. Springer, New York, 2003. ISBN 0-387-95577-1. [ bib \| Publisher Info ]
[14]	Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, and Emmanuel Jolivet. Statistical Tools for Nonlinear Regression. Springer, New York, 2003. ISBN 0-387-40081-8. [ bib \| Publisher Info ]
[15]	S. Mase, T. Kamakura, M. Jimbo, and K. Kanefuji. Introduction to Data Science for engineers- Data analysis using free statistical software R (in Japanese). Suuri-Kogaku-sha, Tokyo, April 2004. ISBN 4901683128. [ bib ]
[16]	Julian J. Faraway. Linear Models with R. Chapman & Hall/CRC, Boca Raton, FL, 2004. ISBN 1-584-88425-8. [ bib \| Publisher Info \| http://www.stat.lsa.umich.edu/~faraway/LMR/ ] The first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.
[17]	Richard M. Heiberger and Burt Holland. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer, 2004. ISBN 0-387-40270-5. [ bib \| Publisher Info \| http://astro.temple.edu/~rmh/HH ] A contemporary presentation of statistical methods featuring 200 graphical displays for exploring data and displaying analyses. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S-Plus, R, and SAS executable functions are provided and discussed. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.
[18]	John Verzani. Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005. ISBN 1-584-88450-9. [ bib \| Publisher Info \| http://wiener.math.csi.cuny.edu/UsingR/ ] There are few books covering introductory statistics using R, and this book fills a gap as a true ``beginner'' book. With emphasis on data analysis and practical examples, `Using R for Introductory Statistics' encourages understanding rather than focusing on learning the underlying theory. It includes a large collection of exercises and numerous practical examples from a broad range of scientific disciplines. It comes complete with an online resource containing datasets, R functions, selected solutions to exercises, and updates to the latest features. A full solutions manual is available from Chapman & Hall/CRC.
[19]	Ross Ihaka and Robert Gentleman. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299-314, 1996. [ bib \| http://www.amstat.org/publications/jcgs/ ]
[20]	Francisco Cribari-Neto and Spyros G. Zarkos. R: Yet another econometric programming environment. Journal of Applied Econometrics, 14:319-329, 1999. [ bib \| http://www.interscience.wiley.com/jpages/0883-7252/ \| http://www.R-project.org/nosvn/papers/Cribari-Neto+Zarkos:1999.pdf ]
[21]	Robert Gentleman and Ross Ihaka. Lexical scope and statistical computing. Journal of Computational and Graphical Statistics, 9:491-508, 2000. [ bib \| http://www.amstat.org/publications/jcgs/ ]
[22]	Paul Murrell and Ross Ihaka. An approach to providing mathematical annotation in plots. Journal of Computational and Graphical Statistics, 9:582-599, 2000. [ bib \| http://www.amstat.org/publications/jcgs/ ]
[23]	Stephen P. Ellner. Review of R, version 1.1.1. Bulletin of the Ecological Society of America, 82(2):127-128, April 2001. [ bib ]
[24]	Brian D. Ripley. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network., 1(1):23-25, February 2001. [ bib \| http://ltsn.mathstore.ac.uk/newsletter/feb2001/pdf/rproject.pdf ]
[25]	Kurt Hornik and Friedrich Leisch, editors. Proceedings of the 2nd International Workshop on Distributed Statistical Computing (DSC 2001), Technische Universität Wien, Vienna, Austria, 2001. ISSN 1609-395X. [ bib \| http://www.ci.tuwien.ac.at/Conferences/DSC.html ]
[26]	Paulo J. Ribeiro, Jr. and Patrick E. Brown. Some words on the r project. The ISBA Bulletin, 8(1):12-16, March 2001. [ bib \| http://www.iami.mi.cnr.it/isba/index.html ]
[27]	Diego Kuonen. Introduction au data mining avec R : vers la reconquête du `knowledge discovery in databases' par les statisticiens. Bulletin of the Swiss Statistical Society, 40:3-7, 2001. [ bib \| http://www.statoo.com/en/publications/2001.R.SSS.40/ ]
[28]	Diego Kuonen and Reinhard Furrer. Data mining avec R dans un monde libre. Flash Informatique Spécial Été, pages 45-50, sep 2001. [ bib \| http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page45.html ]
[29]	Reinhard Furrer and Diego Kuonen. GRASS GIS et R: main dans la main dans un monde libre. Flash Informatique Spécial Été, pages 51-56, sep 2001. [ bib \| http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page51.html ]
[30]	Diego Kuonen and Valerie Chavez. R - un exemple du succès des modèles libres. Flash Informatique, 2:3-7, 2001. [ bib \| http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-2-1/2-1-page3.html ]
[31]	Vito Ricci. R : un ambiente opensource per l'analisi statistica dei dati. Economia e Commercio, 1:69-82, 2004. [ bib ] This paper would be a short introduction and overview about the language and environment for statistical analysis R, without entering in specific details too much computational. I give a look about this opensource software pointing out its main features, its functionalities, its pros and cons describing some libraries and the kind of analysis they support. I supply a summary, with a short description, about many resources concerning R that can be found in the Web: the most are in English language, but there are also some in the Italian language. The aim of this work is to contribute in increasing of the use of the R environment in Italy among statistical researchers trying to ``advertise'' this software and its opensource philosophy.