Books related to R

[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 | Discount Info | 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 | Discount Info | 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, New York, 2002. ISBN 0-387-95457-0. [ bib | Discount Info | 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, New York, 2000. ISBN 0-387-98966-8. [ bib | Discount Info | 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 | Discount Info | 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 | Discount Info | Publisher Info ] A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.
[8]	Frank E. Harrell. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. Springer, 2001. ISBN 0-387-95232-2. [ bib | Discount Info | Publisher Info | http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RmS ] There are many books that are excellent sources of knowledge about individual statistical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield “... students typically know the technical details of regression for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies.” Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
[9]	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.
[10]	John Fox. An R and S-Plus Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, USA, 2002. ISBN 0-761-92279-2. [ bib | http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html ] 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.
[11]	Peter Dalgaard. Introductory Statistics with R. Springer, 2002. ISBN 0-387-95475-9. [ bib | Discount Info | Publisher Info | http://www.biostat.ku.dk/~pd/ISwR.html ]
[12]	Stefano Iacus and Guido Masarotto. Laboratorio di statistica con R. McGraw-Hill, Milano, 2003. ISBN 88-386-6084-0. [ bib | Publisher Info ]
[13]	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 ]
[14]	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 | Discount Info | Publisher Info ]
[15]	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 | Discount Info | Publisher Info ]
[16]	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 ]
[17]	Julian J. Faraway. Linear Models with R. Chapman & Hall/CRC, Boca Raton, FL, 2004. ISBN 1-584-88425-8. [ bib | Discount Info | Publisher Info | http://www.maths.bath.ac.uk/~jjf23/LMR/ ] The book 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.
[18]	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 | Discount Info | 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.
[19]	John Verzani. Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005. ISBN 1-584-88450-9. [ bib | Discount Info | 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.
[20]	Fionn Murtagh. Correspondence Analysis and Data Coding with JAVA and R. Chapman & Hall/CRC, Boca Raton, FL, 2005. ISBN 1-584-88528-9. [ bib | Discount Info | Publisher Info | http://www.cs.rhul.ac.uk/home/fionn/ ] This book provides an introduction to methods and applications of correspondence analysis, with an emphasis on data coding - the first step in correspondence analysis. It features a practical presentation of the theory with a range of applications from data mining, financial engineering, and the biosciences. Implementation of the methods is presented using JAVA and R software.
[21]	Paul Murrell. R Graphics. Chapman & Hall/CRC, Boca Raton, FL, 2005. ISBN 1-584-88486-X. [ bib | Discount Info | Publisher Info | http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html ] A description of the core graphics features of R including: a brief introduction to R; an introduction to general R graphics features. The “base” graphics system of R: traditional S graphics. The power and flexibility of grid graphics. Building on top of the base or grid graphics: Trellis graphics and developing new graphics functions.
[22]	Michael J. Crawley. Statistics: An Introduction using R. Wiley, 2005. ISBN 0-470-02297-3. [ bib | Publisher Info | http://www.bio.ic.ac.uk/research/crawley/statistics/ ] The book is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.
[23]	Brian S. Everitt. An R and S-Plus Companion to Multivariate Analysis. Springer, 2005. ISBN 1-85233-882-2. [ bib | Discount Info | Publisher Info | http://biostatistics.iop.kcl.ac.uk/publications/everitt/ ] In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R and S-Plus code is given for each analysis in the book, with any differences between the two highlighted.
[24]	Richard C. Deonier, Simon Tavaré, and Michael S. Waterman. Computational Genome Analysis: An Introduction. Springer, 2005. ISBN: 0-387-98785-1. [ bib | Discount Info | Publisher Info ] Computational Genome Analysis: An Introduction presents the foundations of key p roblems in computational molecular biology and bioinformatics. It focuses on com putational and statistical principles applied to genomes, and introduces the mat hematics and statistics that are crucial for understanding these applications. A ll computations are done with R.
[25]	Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, and Sandrine Dudoit, editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, 2005. ISBN: 0-387-25146-4. [ bib | Discount Info | Publisher Info ] This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms.
[26]	Terry M. Therneau and Patricia M. Grambsch. Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer, 2000. ISBN: 0-387-98784-3. [ bib | Discount Info | Publisher Info ] This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model.
[27]	Brian Everitt and Torsten Hothorn. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC, Boca Raton, FL, 2006. ISBN 1-584-88539-4. [ bib | Discount Info | Publisher Info | http://cran.r-project.org/src/contrib/Descriptions/HSAUR.html ] With emphasis on the use of R and the interpretation of results rather than the theory behind the methods, this book addresses particular statistical techniques and demonstrates how they can be applied to one or more data sets using R. The authors provide a concise introduction to R, including a summary of its most important features. They cover a variety of topics, such as simple inference, generalized linear models, multilevel models, longitudinal data, cluster analysis, principal components analysis, and discriminant analysis. With numerous figures and exercises, A Handbook of Statistical Analysis using R provides useful information for students as well as statisticians and data analysts.
[28]	Julian J. Faraway. Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC, Boca Raton, FL, 2006. ISBN 1-584-88424-X. [ bib | Discount Info | Publisher Info | http://www.maths.bath.ac.uk/~jjf23/ELM/ ] This book surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses.
[29]	Jana Jureckova and Jan Picek. Robust Statistical Methods with R. Chapman & Hall/CRC, Boca Raton, FL, 2006. ISBN 1-584-88454-1. [ bib | Discount Info | Publisher Info | http://www.fp.vslib.cz/kap/picek/robust/ ] This book provides a systematic treatment of robust procedures with an emphasis on practical application. The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands- on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It also includes a brief overview of R in an appendix for those with little experience using the software.
[30]	Simon N. Wood. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Boca Raton, FL, 2006. ISBN 1-584-88474-6. [ bib | Discount Info | Publisher Info | http://cran.r-project.org/src/contrib/Descriptions/gamair.html ] This book imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well- grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix.
[31]	Bernhard Pfaff. Analysis of Integrated and Cointegrated Time Series with R. Use R. Springer, 2006. ISBN 0-387-98784-3. [ bib | Discount Info | Publisher Info ] The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and inference in cointegrated vector autoregressive models.
[32]	Nhu D. Le and James V. Zidek. Statistical Analysis of Environmental Space-Time Processes. Springer, 2006. ISBN 0-387-26209-1. [ bib | Discount Info | Publisher Info ] This book provides a broad introduction to the subject of environmental space-time processes, addressing the role of uncertainty. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development.
[33]	Peter J. Diggle and Paulo Justiniano Ribeiro. Model-based Geostatistics. Springer, 2006. ISBN 0-387-32907-2. [ bib | Discount Info | Publisher Info ] Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics.
[34]	Emmanuel Paradis. Analysis of Phylogenetics and Evolution with R. Use R. Springer, New York, 2006. ISBN 0-387-32914-5. [ bib | Discount Info | Publisher Info ] This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language, an open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses sease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.
[35]	Sandrine Dudoit and Mark J. van der Laan. Multiple Testing Procedures and Applications to Genomics. Springer Series in Statistics. Springer, 2007. ISBN: 978-0-387-49316-9. [ bib | Discount Info | Publisher Info ] This book provides a detailed account of the theoretical foundations of proposed multiple testing methods and illustrates their application to a range of testing problems in genomics.
[36]	Uwe Ligges. Programmieren mit R. Springer-Verlag, Heidelberg, 2nd edition, 2007. ISBN 3-540-36332-7, in German. [ bib | Publisher Info | http://www.statistik.uni-dortmund.de/~ligges/PmitR/ ] R ist eine objekt-orientierte und interpretierte Sprache und Programmierumgebung für Datenanalyse und Grafik - frei erhältlich unter der GPL. Das Buch führt in die Grundlagen der Sprache R ein und vermittelt ein umfassendes Verständnis der Sprachstruktur. Die enormen Grafikfähigkeiten von R werden detailliert beschrieben. Der Leser kann leicht eigene Methoden umsetzen, Objektklassen definieren und ganze Pakete aus Funktionen und zugehöriger Dokumentation zusammenstellen. Ob Diplomarbeit, Forschungsprojekte oder Wirtschaftsdaten, das Buch unterstützt alle, die R als flexibles Werkzeug zur Datenanalyse und -visualisierung einsetzen möchten.
[37]	Dubravko Dolic. Statistik mit R. Einführung für Wirtschafts- und Sozialwissenschaftler. R. Oldenbourg, München, Wien, 2004. ISBN 3-486-27537-2, in German. [ bib ]
[38]	Andreas Behr. Einführung in die Statistik mit R. WiSo Kurzlehrbücher. Vahlen, München, 2005. ISBN 3-8006-3219-5, in German. [ bib ]
[39]	Scott M. Lynch. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. Springer, New York, 2007. ISBN 978-0-387-71264-2. [ bib | Discount Info | Publisher Info ] Introduction to Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research-including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models-and it thoroughly develops each real-data example in painstaking detail.
[40]	Jim Albert. Bayesian Computation with R. Springer, New York, 2007. ISBN 978-0-387-71384-7. [ bib | Discount Info | Publisher Info ] Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.
[41]	Jean-Michel Marin and Christian P. Robert. Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New York, 2007. ISBN 978-0-387-38979-0. [ bib | Discount Info | Publisher Info ] This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language.
[42]	Dianne Cook and Deborah F. Swayne. Interactive and Dynamic Graphics for Data Analysis. Springer, New York, 2007. ISBN 978-0-387-71761-6. [ bib | Discount Info | Publisher Info ] This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.
[43]	David Siegmund and Benjamin Yakir. The Statistics of Gene Mapping. Springer, New York, 2007. ISBN 978-0-387-49684-9. [ bib | Discount Info | Publisher Info ] This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.
[44]	Lothar Sachs and Jürgen Hedderich. Angewandte Statistik. Methodensammlung mit R. Springer, Berlin, Heidelberg, 12th (completely revised) edition, 2006. ISBN 978-3-540-32160-6. [ bib | Publisher Info ] Die Anwendung statistischer Methoden wird heute in der Regel durch den Einsatz von Computern unterstützt. Das Programm R ist dabei ein leicht erlernbares und flexibel einzusetzendes Werkzeug, mit dem der Prozess der Datenanalyse nachvollziehbar verstanden und gestaltet werden kann. Diese 12., vollständig neu bearbeitete Auflage veranschaulicht Anwendung und Nutzen des Programms anhand zahlreicher mit R durchgerechneter Beispiele. Sie erläutert statistische Ansätze und gibt leicht fasslich, anschaulich und praxisnah Studenten, Dozenten und Praktikern mit unterschiedlichen Vorkenntnissen die notwendigen Details, um Daten zu gewinnen, zu beschreiben und zu beurteilen. Neben Hinweisen zur Planung und Auswertung von Studien ermöglichen viele Beispiele, Querverweise und ein ausführliches Sachverzeichnis einen gezielten Zugang zur Statistik, insbesondere für Mediziner, Ingenieure und Naturwissenschaftler.
[45]	Stefano M. Iacus. Simulation and Inference for Stochastic Differential Equations: With R Examples. Springer, New York, 2008. ISBN 978-0-387-75838-1. [ bib | Discount Info | Publisher Info ] This book is very different from any other publication in the field and it is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book should be useful to practitioners and students with minimal mathematical background, but because of the many R programs, probably also to many mathematically well educated practitioners. Many of the methods presented in the book have, so far, not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called `sde' provides functionswith easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations. The book is organized in four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other milestones publication known so far. The third one is focused on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader non-expert in R language, will find a concise introduction to this environment focused on the subject of the book which should allow for instant use of the proposed material. To each R functions presented in the book a documentation page is available at the end of the book.
[46]	Maria L. Rizzo. Statistical Computing with R. Chapman & Hall/CRC, Boca Raton, FL, 2008. ISBN 1-584-88545-9. [ bib | Discount Info | Publisher Info ] This book covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts.
[47]	Michael Greenacre. Correspondence Analysis in Practice, Second Edition. Chapman & Hall/CRC, Boca Raton, FL, 2007. ISBN 1-584-88616-1. [ bib | Discount Info | Publisher Info ] This book shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. T his completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. It includes a computational appendix that provides the R commands that correspond to most of the analyses featured in the book.
[48]	Robert Gentleman. Bioinformatics with R. Chapman & Hall/CRC, Boca Raton, FL, 2008. ISBN 1-420-06367-7. [ bib | Discount Info | Publisher Info ] The Bioconductor project was initiated in 2001 to provide a resource of R packages that specifically address bioinformatics problems. Written by the leader of this project and the original developer of the R software, this book provides an overview of techniques to develop R programming skills for bioinformatics. The book presents comprehensive coverage of a broad range of key topics, including R language fundamentals, object-oriented programming in R, foreign language interfaces, building R packages, handling different data technologies, and debugging. It includes a number of detailed illustrative bioinformatics examples as well as exercises to demonstrate techniques.
[49]	Philip J. Boland. Statistical and Probabilistic Methods in Actuarial Science. Chapman & Hall/CRC, Boca Raton, FL, 2007. ISBN 1-584-88695-1. [ bib | Discount Info | Publisher Info ] This book covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. It presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.
[50]	Deepayan Sarkar. Lattice Multivariate Data Visualization with R. Springer, New York, 2007. ISBN 978-0-387-75968-5. [ bib | Discount Info | Publisher Info ] R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.
[51]	John M. Chambers. Software for Data Analysis: Programming with R. Springer, New York, 2007. ISBN 978-0-387-75935-7. [ bib | Discount Info | Publisher Info ] John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S. Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching. This book guides the reader through programming with R, from interactive use through all stages from simple functions to the design of R packages. It includes key modern enhancements such as classes and methods, namespaces and interfaces to spreadsheets and data bases.
[52]	W. John Braun and Duncan J. Murdoch. A First Course in Statistical Programming with R. Cambridge University Press, Cambridge, 2007. ISBN 978-0521872652. [ bib | Publisher Info | http://www.stats.uwo.ca/faculty/braun/statprog/ ] This book introduces students to statistical programming, using R as a basis. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects.
[53]	Luke Keele. Semiparametric Regression for the Social Sciences. Wiley, Chichester, UK, 2008. ISBN 978-0470319918. [ bib | Publisher Info | http://www.polisci.ohio-state.edu/faculty/lkeele/keele.html ] Smoothing methods have been little used within the social sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences. Readers are introduced to the principles of nonparametric smoothing and to a wide variety of smoothing methods. The author also explains how smoothing methods can be incorporated into parametric linear and generalized linear models. The use of smoothers with these standard statistical models allows the estimation of more flexible functional forms whilst retaining the interpretability of parametric models. The full potential of these techniques is highlighted via the use of detailed empirical examples drawn from the social and political sciences. Each chapter features exercises to aid in the understanding of the methods and applications. All examples in the book were estimated in R. The book contains an appendix with R commands to introduce readers to estimating these models in R. All the R code for the examples in the book are available from the author's website and the publishers website.
[54]	Julien Claude. Morphometrics with R. Springer, New York, 2008. ISBN 978-0-387-77789-4. [ bib | Discount Info | Publisher Info ] Quantifying shape and size variation is essential in evolutionary biology and in many other disciplines. Since the “morphometric revolution of the 90s,” an increasing number of publications in applied and theoretical morphometrics emerged in the new discipline of statistical shape analysis. The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs. This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users. Adopting R gives the user and developer several advantages for performing morphometrics: evolvability, adaptability, interactivity, a single and comprehensive platform, possibility of interfacing with other languages and software, custom analyses, and graphs. The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines. The book fills two gaps: the gap between theoreticians and students by providing worked examples from the acquisition of data to analyses and hypothesis testing, and the gap between user and developers by providing and explaining codes for performing all the steps necessary for morphometrics rather than providing a manual for a given software or package. Students and scientists interested in shape analysis can use the book as a reference for performing applied morphometrics, while prospective researchers will learn how to implement algorithms or interfacing R for new methods. In addition, adopting the R philosophy will enhance exchanges within and outside the morphometrics community. Julien Claude is evolutionary biologist and palaeontologist at the University of Montpellier 2 where he got his Ph.D. in 2003. He works on biodiversity and phenotypic evolution of a variety of organisms, especially vertebrates. He teaches evolutionary biology and biostatistics to undergraduate and graduate students and has developed several functions in R for the package APE.
[55]	Bernhard Pfaff. Analysis of Integrated and Cointegrated Time Series with R, Second Edition. Springer, New York, 2008. ISBN 978-0-387-75966-1. [ bib | Discount Info | Publisher Info ] The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other. Bernhard Pfaff studied economics at the universities of Göttingen, Germany; Davis, California; and Freiburg im Breisgau, Germany. He obtained a diploma and a doctorate degree at the economics department of the latter entity where he was employed as a research and teaching assistant. He has worked for many years as economist and quantitative analyst in research departments of financial institutions and he is the author and maintainer of the contributed R packages “urca” and “vars.”
[56]	Phil Spector. Data Manipulation with R. Springer, New York, 2008. ISBN 978-0-387-74730-9. [ bib | Discount Info | Publisher Info ] Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions.
[57]	Jonathan D. Cryer and Kung-Sik Chan. Time Series Analysis With Applications in R. Springer, New York, 2008. ISBN 978-0-387-75958-6. [ bib | Discount Info | Publisher Info ] Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.
[58]	Robert H. Shumway and David S. Stoffer. Time Series Analysis and Its Applications With R Examples. Springer, New York, 2006. ISBN 978-0-387-29317-2. [ bib | Discount Info | Publisher Info ] Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. The book is designed to be useful as a text for graduate level students in the physical, biological and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. Material from the earlier 1988 Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware.
[59]	Roger D. Peng and Francesca Dominici. Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health. Springer, New York, 2008. ISBN 978-0-387-78166-2. [ bib | Discount Info | Publisher Info ] Advances in statistical methodology and computing have played an important role in allowing researchers to more accurately assess the health effects of ambient air pollution. The methods and software developed in this area are applicable to a wide array of problems in environmental epidemiology. This book provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstrate the application of those methods to real data. The book will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data. The authors describe the different existing approaches to statistical modeling and cover basic aspects of analyzing and understanding air pollution and health data. The case studies in each chapter demonstrate how to use R to apply and interpret different statistical models and to explore the effects of potential confounding factors. A working knowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples. Researchers in this area will find the book useful as a “live” reference. Software for all of the analyses in the book is downloadable from the web and is available under a Free Software license. The reader is free to run the examples in the book and modify the code to suit their needs. In addition to providing the software for developing the statistical models, the authors provide the entire database from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) in a convenient R package. With the database, readers can run the examples and experiment with their own methods and ideas.
[60]	Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio. Applied Spatial Data Analysis with R. Springer, New York, 2008. ISBN 978-0-387-78170-. [ bib | Discount Info | Publisher Info ] Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org.
[61]	G. P. Nason. Wavelet Methods in Statistics with R. Springer, New York, 2008. ISBN 978-0-387-75960-9. [ bib | Discount Info | Publisher Info ] Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book fulfils three purposes. First, it is a gentle introduction to wavelets and their uses in statistics. Second, it acts as a quick and broad reference to many recent developments in the area. The book concentrates on describing the essential elements and provides comprehensive source material references. Third, the book intersperses R code that explains and demonstrates both wavelet and statistical methods. The code permits the user to learn the methods, to carry out their own analyses and further develop their own methods. The book is designed to be read in conjunction with WaveThresh4, the freeware R package for wavelets. The book introduces the wavelet transform by starting with the simple Haar wavelet transform and then builds to consider more general wavelets such as the Daubechies compactly supported series. The book then describes the evolution of wavelets in the directions of complex-valued wavelets, non-decimated transforms, multiple wavelets and wavelet packets as well as giving consideration to boundary conditions initialization. Later chapters explain the role of wavelets in nonparametric regression problems via a variety of techniques including thresholding, cross-validation, SURE, false-discovery rate and recent Bayesian methods, and also consider how to deal with correlated and non-Gaussian noise structures. The book also looks at how nondecimated and packet transforms can improve performance. The penultimate chapter considers the role of wavelets in both stationary and non-stationary time series analysis. The final chapter describes recent work concerning the role of wavelets for variance stabilization for non-Gaussian intensity estimation. The book is aimed at final year undergraduate and Masters students in a numerate discipline (such as mathematics, statistics, physics, economics and engineering) and would also suit as a quick reference for postgraduate or research level activity. The book would be ideal for a researcher to learn about wavelets, to learn how to use wavelet software and then to adapt the ideas for their own purposes.
[62]	Christian Kleiber and Achim Zeileis. Applied Econometrics with R. Springer, New York, 2008. ISBN 978-0-387-77316-2. [ bib | Discount Info | Publisher Info ] This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/package=AER. It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care. The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike.
[63]	Clemens Reimann, Peter Filzmoser, Robert Garrett, and Rudolf Dutter. Statistical Data Analysis Explained: Applied Environmental Statistics with R. Wiley, Chichester, UK, 2008. ISBN: 978-0-470-98581-6. [ bib | Publisher Info | http://www.statistik.tuwien.ac.at/StatDA ] Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

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