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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Becker, R.A., Chambers, J.M. & Wilks, A.R. The New S Language 1988   book  
    Abstract: This book is often called the ``Blue Book'', and introduced what is now known as S version 3, or S3.
    BibTeX:
    @book{R:Becker+Chambers+Wilks:1988,
      author = {Richard A. Becker and John M. Chambers and Allan R. Wilks},
      title = {The New S Language},
      publisher = {Chapman & Hall},
      year = {1988}
    }
    
    Chambers, J.M. & Hastie, T.J. Statistical Models in S 1992   book  
    Abstract: This is also called the ``White Book''. It described software for statistical modeling in S and introduced the S3 version of classes and methods.
    BibTeX:
    @book{R:Chambers+Hastie:1992,
      author = {John M. Chambers and Trevor J. Hastie},
      title = {Statistical Models in S},
      publisher = {Chapman & Hall},
      year = {1992},
      note = {ISBN 9780412830402}
    }
    
    Chambers, J.M. Programming with Data 1998   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Chambers:1998,
      author = {John M. Chambers},
      title = {Programming with Data},
      publisher = {Springer},
      year = {1998},
      note = {ISBN 0-387-98503-4},
      url = {http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/}
    }
    
    Venables, W.N. & Ripley, B.D. Modern Applied Statistics with S. Fourth Edition 2002   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Venables+Ripley:2002,
      author = {William N. Venables and Brian D. Ripley},
      title = {Modern Applied Statistics with S. Fourth Edition},
      publisher = {Springer},
      year = {2002},
      note = {ISBN 0-387-95457-0},
      url = {http://www.stats.ox.ac.uk/pub/MASS4/}
    }
    
    Venables, W.N. & Ripley, B.D. S Programming 2000   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Venables+Ripley:2000,
      author = {William N. Venables and Brian D. Ripley},
      title = {S Programming},
      publisher = {Springer},
      year = {2000},
      note = {ISBN 0-387-98966-8},
      url = {http://www.stats.ox.ac.uk/pub/MASS3/Sprog/}
    }
    
    Nolan, D. & Speed, T. Stat Labs: Mathematical Statistics Through Applications 2000   book URL 
    Abstract: Integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.
    BibTeX:
    @book{R:Nolan+Speed:2000,
      author = {Deborah Nolan and Terry Speed},
      title = {Stat Labs: Mathematical Statistics Through Applications},
      publisher = {Springer},
      year = {2000},
      note = {ISBN 0-387-98974-9},
      url = {http://www.stat.Berkeley.EDU/users/statlabs/}
    }
    
    Pinheiro, J.C. & Bates, D.M. Mixed-Effects Models in S and S-Plus 2000   book  
    Abstract: A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.
    BibTeX:
    @book{R:Pinheiro+Bates:2000,
      author = {Jose C. Pinheiro and Douglas M. Bates},
      title = {Mixed-Effects Models in S and S-Plus},
      publisher = {Springer},
      year = {2000},
      note = {ISBN 0-387-98957-0}
    }
    
    Harrell, F.E. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression 2001   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Harrell:2001,
      author = {Frank E. Harrell},
      title = {Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression},
      publisher = {Springer},
      year = {2001},
      note = {ISBN 0-387-95232-2},
      url = {http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RmS}
    }
    
    Limas, M.C., Meré, J.O., de Cos Juez, F.J. & de Pisón Ascacibar, F.J.M. Control de Calidad. Metodologia para el analisis previo a la modelización de datos en procesos industriales. Fundamentos teóricos y aplicaciones con R. 2001   book  
    Abstract: 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.
    BibTeX:
    @book{R:Limas+Mere+Juez:2001,
      author = {Manuel Castejón Limas and Joaquin Ordieres Meré and Fco. Javier de Cos Juez and Fco. Javier Martinez de Pisón Ascacibar},
      title = {Control de Calidad. Metodologia para el analisis previo a la modelización de datos en procesos industriales. Fundamentos teóricos y aplicaciones con R.},
      publisher = {Servicio de Publicaciones de la Universidad de La Rioja},
      year = {2001},
      note = {ISBN 84-95301-48-2}
    }
    
    Fox, J. An R and S-Plus Companion to Applied Regression 2002   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Fox:2002,
      author = {John Fox},
      title = {An R and S-Plus Companion to Applied Regression},
      publisher = {Sage Publications},
      year = {2002},
      note = {ISBN 0-761-92279-2},
      url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html}
    }
    
    Dalgaard, P. Introductory Statistics with R 2002 , pp. 288  book URL 
    BibTeX:
    @book{R:Dalgaard:2002,
      author = {Peter Dalgaard},
      title = {Introductory Statistics with R},
      publisher = {Springer},
      year = {2002},
      pages = {288},
      note = {ISBN 0-387-95475-9},
      url = {http://www.biostat.ku.dk/~pd/ISwR.html}
    }
    
    Iacus, S. & Masarotto, G. Laboratorio di statistica con R 2003 , pp. 384  book  
    BibTeX:
    @book{R:Iacus+Masarotto:2003,
      author = {Stefano Iacus and Guido Masarotto},
      title = {Laboratorio di statistica con R},
      publisher = {McGraw-Hill},
      year = {2003},
      pages = {384},
      note = {ISBN 88-386-6084-0}
    }
    
    Parmigiani, G., Garrett, E.S., Irizarry, R.A. & Zeger, S.L. The Analysis of Gene Expression Data 2003   book  
    BibTeX:
    @book{R:Parmigiani+Garrett+Irizarry+Zeger:2003,
      author = {Giovanni Parmigiani and Elizabeth S. Garrett and Rafael A. Irizarry and Scott L. Zeger},
      title = {The Analysis of Gene Expression Data},
      publisher = {Springer},
      year = {2003},
      note = {ISBN 0-387-95577-1}
    }
    
    Huet, S., Bouvier, A., Gruet, M.-A. & Jolivet, E. Statistical Tools for Nonlinear Regression 2003   book  
    BibTeX:
    @book{R:Huet+Bouvier+Gruet+Jolivet:2003,
      author = {Sylvie Huet and Annie Bouvier and Marie-Anne Gruet and Emmanuel Jolivet},
      title = {Statistical Tools for Nonlinear Regression},
      publisher = {Springer},
      year = {2003},
      note = {ISBN 0-387-40081-8}
    }
    
    Mase, S., Kamakura, T., Jimbo, M. & Kanefuji, K. Introduction to Data Science for engineers--- Data analysis using free statistical software R (in Japanese) 2004 , pp. 254  book  
    BibTeX:
    @book{R:Mase+Kamakura+Jimbo:2004,
      author = {S. Mase and T. Kamakura and M. Jimbo and K. Kanefuji},
      title = {Introduction to Data Science for engineers--- Data analysis using free statistical software R (in Japanese)},
      publisher = {Suuri-Kogaku-sha, Tokyo},
      year = {2004},
      pages = {254},
      note = {ISBN 4901683128}
    }
    
    Faraway, J.J. Linear Models with R 2004   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Faraway:2004,
      author = {Julian J. Faraway},
      title = {Linear Models with R},
      publisher = {Chapman & Hall/CRC},
      year = {2004},
      note = {ISBN 1-584-88425-8},
      url = {http://www.maths.bath.ac.uk/~jjf23/LMR/}
    }
    
    Heiberger, R.M. & Holland, B. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS 2004   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Heiberger+Holland:2004,
      author = {Richard M. Heiberger and Burt Holland},
      title = {Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS},
      publisher = {Springer},
      year = {2004},
      note = {ISBN 0-387-40270-5},
      url = {http://astro.temple.edu/~rmh/HH}
    }
    
    Verzani, J. Using R for Introductory Statistics 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Verzani:2005,
      author = {John Verzani},
      title = {Using R for Introductory Statistics},
      publisher = {Chapman & Hall/CRC},
      year = {2005},
      note = {ISBN 1-584-88450-9},
      url = {http://wiener.math.csi.cuny.edu/UsingR/}
    }
    
    Ligges, U. Programmieren mit R 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Ligges:2005,
      author = {Uwe Ligges},
      title = {Programmieren mit R},
      publisher = {Springer-Verlag},
      year = {2005},
      note = {ISBN 3-540-20727-9, in German},
      url = {http://www.statistik.uni-dortmund.de/~ligges/PmitR/}
    }
    
    Murtagh, F. Correspondence Analysis and Data Coding with JAVA and R 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Murtagh:2005,
      author = {Fionn Murtagh},
      title = {Correspondence Analysis and Data Coding with JAVA and R},
      publisher = {Chapman & Hall/CRC},
      year = {2005},
      note = {ISBN 1-584-88528-9},
      url = {http://www.cs.rhul.ac.uk/home/fionn/}
    }
    
    Murrell, P. R Graphics 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Murrell:2005,
      author = {Paul Murrell},
      title = {R Graphics},
      publisher = {Chapman & Hall/CRC},
      year = {2005},
      note = {ISBN 1-584-88486-X},
      url = {http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html}
    }
    
    Crawley, M.J. Statistics: An Introduction using R 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Crawley:2005,
      author = {Michael J. Crawley},
      title = {Statistics: An Introduction using R},
      publisher = {Wiley},
      year = {2005},
      note = {ISBN 0-470-02297-3},
      url = {http://www.bio.ic.ac.uk/research/crawley/statistics/}
    }
    
    Everitt, B.S. An R and S-Plus Companion to Multivariate Analysis 2005   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Everitt:2005,
      author = {Brian S. Everitt},
      title = {An R and S-Plus Companion to Multivariate Analysis},
      publisher = {Springer},
      year = {2005},
      note = {ISBN 1-85233-882-2},
      url = {http://biostatistics.iop.kcl.ac.uk/publications/everitt/}
    }
    
    Deonier, R.C., Tavaré, S. & Waterman, M.S. Computational Genome Analysis: An Introduction 2005   book  
    Abstract: 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.
    BibTeX:
    @book{R:Deonier+Tavare+Waterman:2005,
      author = {Richard C. Deonier and Simon Tavaré and Michael S. Waterman},
      title = {Computational Genome Analysis: An Introduction},
      publisher = {Springer},
      year = {2005},
      note = {ISBN: 0-387-98785-1}
    }
    
    Bioinformatics and Computational Biology Solutions Using R and Bioconductor 2005   book  
    Abstract: 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.
    BibTeX:
    @book{R:Gentleman+Carey+Huber:2005,,
      title = {Bioinformatics and Computational Biology Solutions Using R and Bioconductor},
      publisher = {Springer},
      year = {2005},
      note = {ISBN: 0-387-25146-4}
    }
    
    Therneau, T.M. & Grambsch, P.M. Modeling Survival Data: Extending the Cox Model 2000   book  
    Abstract: 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.
    BibTeX:
    @book{R:Therneau+Grambsch:2000,
      author = {Terry M. Therneau and Patricia M. Grambsch},
      title = {Modeling Survival Data: Extending the Cox Model},
      publisher = {Springer},
      year = {2000},
      note = {ISBN: 0-387-98784-3}
    }
    
    Everitt, B. & Hothorn, T. A Handbook of Statistical Analyses Using R 2006   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Everitt+Hothorn:2006,
      author = {Brian Everitt and Torsten Hothorn},
      title = {A Handbook of Statistical Analyses Using R},
      publisher = {Chapman & Hall/CRC},
      year = {2006},
      note = {ISBN 1-584-88539-4},
      url = {http://cran.r-project.org/src/contrib/Descriptions/HSAUR.html}
    }
    
    Faraway, J.J. Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models 2006   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Faraway:2006,
      author = {Julian J. Faraway},
      title = {Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models},
      publisher = {Chapman & Hall/CRC},
      year = {2006},
      note = {ISBN 1-584-88424-X},
      url = {http://www.maths.bath.ac.uk/~jjf23/ELM/}
    }
    
    Jureckova, J. & Picek, J. Robust Statistical Methods with R 2006   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Jureckova+Picek:2006,
      author = {Jana Jureckova and Jan Picek},
      title = {Robust Statistical Methods with R},
      publisher = {Chapman & Hall/CRC},
      year = {2006},
      note = {ISBN 1-584-88454-1},
      url = {http://www.fp.vslib.cz/kap/picek/robust/}
    }
    
    Wood, S.N. Generalized Additive Models: An Introduction with R 2006   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Wood:2006,
      author = {Simon N. Wood},
      title = {Generalized Additive Models: An Introduction with R},
      publisher = {Chapman & Hall/CRC},
      year = {2006},
      note = {ISBN 1-584-88474-6},
      url = {http://cran.r-project.org/src/contrib/Descriptions/gamair.html}
    }
    
    Pfaff, B. Analysis of Integrated and Cointegrated Time Series with R 2006   book  
    Abstract: The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and inference in cointegrated vector autoregressive models.
    BibTeX:
    @book{R:Pfaff:2006,
      author = {Bernhard Pfaff},
      title = {Analysis of Integrated and Cointegrated Time Series with R},
      publisher = {Springer},
      year = {2006},
      note = {ISBN 0-387-98784-3}
    }
    
    Le, N.D. & Zidek, J.V. Statistical Analysis of Environmental Space-Time Processes 2006   book  
    Abstract: 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.
    BibTeX:
    @book{R:Le+Zidek:2006,
      author = {Nhu D. Le and James V. Zidek},
      title = {Statistical Analysis of Environmental Space-Time Processes},
      publisher = {Springer},
      year = {2006},
      note = {ISBN 0-387-26209-1}
    }
    
    Diggle, P.J. & Ribeiro, P.J. Model-based Geostatistics 2006   book  
    Abstract: 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.
    BibTeX:
    @book{R:Diggle+Ribeiro:2006,
      author = {Peter J. Diggle and Paulo Justiniano Ribeiro},
      title = {Model-based Geostatistics},
      publisher = {Springer},
      year = {2006},
      note = {ISBN 0-387-32907-2}
    }
    
    Paradis, E. Analysis of Phylogenetics and Evolution with R 2006   book  
    Abstract: 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.
    BibTeX:
    @book{R:Paradis:2006,
      author = {Emmanuel Paradis},
      title = {Analysis of Phylogenetics and Evolution with R},
      publisher = {Springer},
      year = {2006},
      note = {ISBN 0-387-32914-5}
    }
    
    Dudoit, S. & van der Laan, M.J. Multiple Testing Procedures and Applications to Genomics 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Dudoit+Laan:2007,
      author = {Sandrine Dudoit and Mark J. van der Laan},
      title = {Multiple Testing Procedures and Applications to Genomics},
      publisher = {Springer},
      year = {2007},
      note = {ISBN: 978-0-387-49316-9}
    }
    
    Ligges, U. Programmieren mit R 2007   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Ligges:2007,
      author = {Uwe Ligges},
      title = {Programmieren mit R},
      publisher = {Springer-Verlag},
      year = {2007},
      edition = {2nd},
      note = {ISBN 3-540-36332-7, in German},
      url = {http://www.statistik.uni-dortmund.de/~ligges/PmitR/}
    }
    
    Maindonald, J. & Braun, J. Data Analysis and Graphics Using R 2007 , pp. 502  book URL 
    Abstract: Following a brief introduction to R, this has extensive examples that illustrate practical data analysis using R. There is extensive advice on practical data analysis. Topics covered include exploratory data analysis, tests and confidence intervals, regression, genralized linear models, survival analysis, time series, multi-level models, trees and random forests, classification, and ordination.
    BibTeX:
    @book{R:Maindonald+Braun:2007,
      author = {John Maindonald and John Braun},
      title = {Data Analysis and Graphics Using R},
      publisher = {Cambridge University Press},
      year = {2007},
      pages = {502},
      edition = {2nd},
      note = {ISBN 978-0-521-86116-8},
      url = {http://wwwmaths.anu.edu.au/~johnm/r-book.html}
    }
    
    Dolic, D. Statistik mit R. Einführung für Wirtschafts- und Sozialwissenschaftler 2004   book  
    BibTeX:
    @book{R:Dolic:2004,
      author = {Dubravko Dolic},
      title = {Statistik mit R. Einführung für Wirtschafts- und Sozialwissenschaftler},
      publisher = {R. Oldenbourg},
      year = {2004},
      note = {ISBN 3-486-27537-2, in German}
    }
    
    Behr, A. Einführung in die Statistik mit R 2005   book  
    BibTeX:
    @book{R:Behr:2005,
      author = {Andreas Behr},
      title = {Einführung in die Statistik mit R},
      publisher = {Vahlen},
      year = {2005},
      note = {ISBN 3-8006-3219-5, in German}
    }
    
    Lynch, S.M. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Lynch:2007,
      author = {Scott M. Lynch},
      title = {Introduction to Applied Bayesian Statistics and Estimation for Social Scientists},
      publisher = {Springer},
      year = {2007},
      note = {ISBN 978-0-387-71264-2}
    }
    
    Albert, J. Bayesian Computation with R 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Albert:2007,
      author = {Jim Albert},
      title = {Bayesian Computation with R},
      publisher = {Springer},
      year = {2007},
      note = {ISBN 978-0-387-71384-7}
    }
    
    Marin, J.-M. & Robert, C.P. Bayesian Core: A Practical Approach to Computational Bayesian Statistics 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Marin+Robert:2007,
      author = {Jean-Michel Marin and Christian P. Robert},
      title = {Bayesian Core: A Practical Approach to Computational Bayesian Statistics},
      publisher = {Springer},
      year = {2007},
      note = {ISBN 978-0-387-38979-0}
    }
    
    Cook, D. & Swayne, D.F. Interactive and Dynamic Graphics for Data Analysis 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Cook+Swayne:2007,
      author = {Dianne Cook and Deborah F. Swayne},
      title = {Interactive and Dynamic Graphics for Data Analysis},
      publisher = {Springer},
      year = {2007},
      note = {ISBN 978-0-387-71761-6}
    }
    
    Siegmund, D. & Yakir, B. The Statistics of Gene Mapping 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Siegmund+Yakir:2007,
      author = {David Siegmund and Benjamin Yakir},
      title = {The Statistics of Gene Mapping},
      publisher = {Springer},
      year = {2007},
      note = {ISBN 978-0-387-49684-9}
    }
    
    Sachs, L. & Hedderich, Jü. Angewandte Statistik. Methodensammlung mit R 2006   book  
    Abstract: 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.
    BibTeX:
    @book{R:Sachs+Hedderich:2006,
      author = {Lothar Sachs and Jürgen Hedderich},
      title = {Angewandte Statistik. Methodensammlung mit R},
      publisher = {Springer},
      year = {2006},
      edition = {12th (completely revised)},
      note = {ISBN 978-3-540-32160-6}
    }
    
    Iacus, S.M. Simulation and Inference for Stochastic Differential Equations: With R Examples 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Iacus:2007,
      author = {Stefano M. Iacus},
      title = {Simulation and Inference for Stochastic Differential Equations: With R Examples},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-75838-1}
    }
    
    Chambers, J.M. Software for Data Analysis: Programming with R 2008   book URL 
    Abstract: The R version of S4 and other R techniques. This book guides the reader in programming with R, from interactive use and writing simple functions to the design of R packages and intersystem interfaces.
    BibTeX:
    @book{R:Chambers:2008,
      author = {John M. Chambers},
      title = {Software for Data Analysis: Programming with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-75935-7},
      url = {http://stat.stanford.edu/~jmc4/Rbook/}
    }
    
    Rizzo, M.L. Statistical Computing with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Rizzo:2008,
      author = {Maria L. Rizzo},
      title = {Statistical Computing with R},
      publisher = {Chapman & Hall/CRC},
      year = {2008},
      note = {ISBN 1-584-88545-9}
    }
    
    Greenacre, M. Correspondence Analysis in Practice, Second Edition 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Greenacre:2007,
      author = {Michael Greenacre},
      title = {Correspondence Analysis in Practice, Second Edition},
      publisher = {Chapman & Hall/CRC},
      year = {2007},
      note = {ISBN 1-584-88616-1}
    }
    
    Gentleman, R. Bioinformatics with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Gentleman:2008a,
      author = {Robert Gentleman},
      title = {Bioinformatics with R},
      publisher = {Chapman & Hall/CRC},
      year = {2008},
      note = {ISBN 1-420-06367-7}
    }
    
    Boland, P.J. Statistical and Probabilistic Methods in Actuarial Science 2007   book  
    Abstract: 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.
    BibTeX:
    @book{R:Boland:2007,
      author = {Philip J. Boland},
      title = {Statistical and Probabilistic Methods in Actuarial Science},
      publisher = {Chapman & Hall/CRC},
      year = {2007},
      note = {ISBN 1-584-88695-1}
    }
    
    Sarkar, D. Lattice: Multivariate Data Visualization with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Sarkar:2008,
      author = {Sarkar, Deepayan},
      title = {Lattice: Multivariate Data Visualization with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-75968-5}
    }
    
    Braun, W.J. & Murdoch, D.J. A First Course in Statistical Programming with R 2007 , pp. 362  book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Braun+Murdoch:2007,
      author = {W. John Braun and Duncan J. Murdoch},
      title = {A First Course in Statistical Programming with R},
      publisher = {Cambridge University Press},
      year = {2007},
      pages = {362},
      note = {ISBN 978-0521872652},
      url = {http://www.stats.uwo.ca/faculty/braun/statprog/}
    }
    
    Keele, L. Semiparametric Regression for the Social Sciences 2008   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Keele:2008,
      author = {Keele, Luke},
      title = {Semiparametric Regression for the Social Sciences},
      publisher = {Wiley},
      year = {2008},
      note = {ISBN 978-0470319918},
      url = {http://www.polisci.ohio-state.edu/faculty/lkeele/keele.html}
    }
    
    Claude, J. Morphometrics with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Claude:2008,
      author = {Claude, Julien},
      title = {Morphometrics with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-77789-4}
    }
    
    Pfaff, B. Analysis of Integrated and Cointegrated Time Series with R, Second Edition 2008   book  
    Abstract: 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.''
    BibTeX:
    @book{R:Pfaff:2008,
      author = {Pfaff, Bernhard},
      title = {Analysis of Integrated and Cointegrated Time Series with R, Second Edition},
      publisher = {Springer},
      year = {2008},
      edition = {2nd},
      note = {ISBN 978-0-387-75966-1}
    }
    
    Spector, P. Data Manipulation with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Spector:2008,
      author = {Phil Spector},
      title = {Data Manipulation with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-74730-9}
    }
    
    Cryer, J.D. & Chan, K.-S. Time Series Analysis With Applications in R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Cryer+Chan:2008,
      author = {Jonathan D. Cryer and Kung-Sik Chan},
      title = {Time Series Analysis With Applications in R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-75958-6}
    }
    
    Shumway, R.H. & Stoffer, D.S. Time Series Analysis and Its Applications With R Examples 2006   book  
    Abstract: 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.
    BibTeX:
    @book{R:Shumway+Stoffer:2006,
      author = {Robert H. Shumway and David S. Stoffer},
      title = {Time Series Analysis and Its Applications With R Examples},
      publisher = {Springer},
      year = {2006},
      note = {ISBN 978-0-387-29317-2}
    }
    
    Peng, R.D. & Dominici, F. Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Peng+Dominici:2008,
      author = {Roger D. Peng and Francesca Dominici},
      title = { Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health },
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-78166-2}
    }
    
    Bivand, R.S., Pebesma, E.J. & Gómez-Rubio, V. Applied Spatial Data Analysis with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Bivand+Pebesma+Gomez-Rubio:2008,
      author = {Roger S. Bivand and Edzer J. Pebesma and Virgilio Gómez-Rubio},
      title = {Applied Spatial Data Analysis with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-78170-}
    }
    
    Nason, G.P. Wavelet Methods in Statistics with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Nason:2008,
      author = {G. P. Nason},
      title = {Wavelet Methods in Statistics with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-75960-9}
    }
    
    Kleiber, C. & Zeileis, A. Applied Econometrics with R 2008   book  
    Abstract: 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.
    BibTeX:
    @book{R:Kleiber+Zeileis:2008,
      author = {Christian Kleiber and Achim Zeileis},
      title = {Applied Econometrics with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-77316-2}
    }
    
    Reimann, C., Filzmoser, P., Garrett, R. & Dutter, R. Statistical Data Analysis Explained: Applied Environmental Statistics with R 2008   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Reimann+Filzmoser+Garrett:2008,
      author = {Clemens Reimann and Peter Filzmoser and Robert Garrett and Rudolf Dutter},
      title = {Statistical Data Analysis Explained: Applied Environmental Statistics with R},
      publisher = {Wiley},
      year = {2008},
      note = {ISBN: 978-0-470-98581-6},
      url = {http://www.statistik.tuwien.ac.at/StatDA}
    }
    
    Sheather, S. A Modern Approach to Regression with R 2008   book  
    Abstract: A Modern Approach to Regression with R focuses on tools and techniques for building regression models using real-world data and assessing their validity. When weaknesses in the model are identified, the next step is to address each of these weaknesses. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. The regression output and plots that appear throughout the book have been generated using R. On the book website you will find the R code used in each example in the text. You will also find SAS code and STATA code to produce the equivalent output on the book website. Primers containing expanded explanations of R, SAS and STATA and their use in this book are also available on the book website. The book contains a number of new real data sets from applications ranging from rating restaurants, rating wines, predicting newspaper circulation and magazine revenue, comparing the performance of NFL kickers, and comparing finalists in the Miss America pageant across states. One of the aspects of the book that sets it apart from many other regression books is that complete details are provided for each example. The book is aimed at first year graduate students in statistics and could also be used for a senior undergraduate class.
    BibTeX:
    @book{R:Sheather:2008,
      author = {Simon Sheather},
      title = {A Modern Approach to Regression with R},
      publisher = {Springer},
      year = {2008},
      note = {ISBN 978-0-387-09607-0}
    }
    
    Gentleman, R. R Programming for Bioinformatics 2008   book URL 
    Abstract: Thanks to its data handling and modeling capabilities and its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics builds the programming skills needed to use R for solving bioinformatics and computational biology problems. Drawing on the author's experiences as an R expert, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.
    BibTeX:
    @book{R:Gentleman:2008b,
      author = {Robert Gentleman},
      title = {R Programming for Bioinformatics},
      publisher = {Chapman & Hall/CRC},
      year = {2008},
      note = {ISBN 978-1-420-06367-7},
      url = {http://www.bioconductor.org/pub/RBioinf/}
    }
    
    Ritz, C. & Streibig, J.C. Nonlinear Regression with R 2009   book  
    Abstract: R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. The book starts out giving a basic introduction to fitting nonlinear regression models in R. Subsequent chapters explain the salient features of the main fitting function nls(), the use of model diagnostics, how to deal with various model departures, and carry out hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered.
    BibTeX:
    @book{R:Ritz+Streibig:2009,
      author = {Christian Ritz and Jens C. Streibig},
      title = {Nonlinear Regression with R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN 978-0-387-09615-5}
    }
    
    Zuur, A., Ieno, E.N., Walker, N., Saveiliev, A.A. & Smith, G.M. Mixed Effects Models and Extensions in Ecology with R 2009   book  
    Abstract: Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data. Data and R code from all chapters are available from http://www.highstat.com.
    BibTeX:
    @book{R:Zuur+Ieno+Walker:2009,
      author = {Alain Zuur and Elena N. Ieno and Neil Walker and Anatoly A. Saveiliev and Graham M. Smith},
      title = {Mixed Effects Models and Extensions in Ecology with R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN 978-0-387-87457-9}
    }
    
    Dalgaard, P. Introductory Statistics with R 2008 , pp. 380  book URL 
    Abstract: This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.
    BibTeX:
    @book{R:Dalgaard:2008,
      author = {Peter Dalgaard},
      title = {Introductory Statistics with R},
      publisher = {Springer},
      year = {2008},
      pages = {380},
      edition = {2nd},
      note = {ISBN 978-0-387-79053-4},
      url = {http://www.biostat.ku.dk/~pd/ISwR.html}
    }
    
    Ligges, U. Programmieren mit R 2009   book URL 
    Abstract: 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.
    BibTeX:
    @book{R:Ligges:2009,
      author = {Uwe Ligges},
      title = {Programmieren mit R},
      publisher = {Springer-Verlag},
      year = {2009},
      edition = {3rd},
      note = {ISBN 978-3-540-79997-9, in German},
      url = {http://www.statistik.tu-dortmund.de/~ligges/PmitR/}
    }
    
    Muenchen, R.A. R for SAS and SPSS Users 2009   book  
    Abstract: This book demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download.
    BibTeX:
    @book{R:Muenchen:2009,
      author = {Robert A. Muenchen},
      title = {R for SAS and SPSS Users},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-09417-5}
    }
    
    Bolker, B.M. Ecological Models and Data in R 2008 , pp. 408  book URL 
    Abstract: This book is a truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. The book shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.
    BibTeX:
    @book{R:Bolker:2008,
      author = {Benjamin M. Bolker},
      title = {Ecological Models and Data in R},
      publisher = {Princeton University Press},
      year = {2008},
      pages = {408},
      note = {ISBN 978-0-691-12522-0},
      url = {http://www.zoology.ufl.edu/bolker/emdbook/}
    }
    
    Foulkes, A.S. Applied Statistical Genetics with R: For Population-Based Association Studies 2009   book  
    Abstract: In this introductory graduate level text, Dr.~Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations. Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field.
    BibTeX:
    @book{R:Foulkes:2009,
      author = {Andrea S. Foulkes},
      title = {Applied Statistical Genetics with R: For Population-Based Association Studies},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-89553-6}
    }
    
    Petris, G., Petrone, S. & Campagnoli, P. Dynamic Linear Models with R 2009   book  
    Abstract: After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
    BibTeX:
    @book{R:Petris+Petrone+Campagnoli:2009,
      author = {Giovanni Petris and Sonia Petrone and Patriza Campagnoli},
      title = {Dynamic Linear Models with R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-77237-0}
    }
    
    Albert, J. Bayesian Computation with R 2009   book  
    Abstract: Bayesian Computing Using R introduces Bayesian modeling by the use of computation using the R language. T he 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. The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner's g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.
    BibTeX:
    @book{R:Albert:2009,
      author = {Jim Albert},
      title = {Bayesian Computation with R},
      publisher = {Springer},
      year = {2009},
      edition = {2nd},
      note = {ISBN: 978-0-387-92297-3}
    }
    
    Cowpertwait, P.S.P. & Metcalfe, A. Introductory Time Series with R 2009   book  
    Abstract: This book gives you a step-by-step introduction to analysing time series using the open source software R. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence confirms understanding of both the model and the R routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://www.massey.ac.nz/~pscowper/ts. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyze time series as part of their taught program or their research.
    BibTeX:
    @book{R:Cowpertwait+Metcalfe:2009,
      author = {Paul S. P. Cowpertwait and Andrew Metcalfe},
      title = {Introductory Time Series with R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-88697-8}
    }
    
    Velten, K. Mathematical Modeling and Simulation: Introduction for Scientists and Engineers 2009   book URL 
    Abstract: This introduction into mathematical modeling and simulation is exclusively based on open source software, and it includes many examples from such diverse fields as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical, and process engineering. Requiring only little mathematical prerequisite in calculus and linear algebra, it is accessible to scientists, engineers, and students at the undergraduate level. The reader is introduced into CAELinux, Calc, Code-Saturne, Maxima, R, and Salome-Meca, and the entire book software --- including 3D CFD and structural mechanics simulation software --- can be used based on a free CAELinux-Live-DVD that is available in the Internet (works on most machines and operating systems).
    BibTeX:
    @book{R:Velten:2009,
      author = {Kai Velten},
      title = {Mathematical Modeling and Simulation: Introduction for Scientists and Engineers},
      publisher = {Wiley-VCH},
      year = {2009},
      note = {ISBN: 978-3-527-40758-3},
      url = {http://www.fbg.fh-wiesbaden.de/velten}
    }
    
    Hoff, P.D. A First Course in Bayesian Statistical Methods 2009   book  
    Abstract: This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers with only a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. R code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.
    BibTeX:
    @book{R:Hoff:2009,
      author = {Peter D. Hoff},
      title = {A First Course in Bayesian Statistical Methods},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-92299-7}
    }
    
    Reymann, D. Wettbewerbsanalysen für kleine und mittlere Unternehmen (KMUs) --- Theoretische Grundlagen und praktische Anwendung am Beispiel gartenbaulicher Betriebe 2009   book URL 
    Abstract: In diesem Buch werden die Grundlagen wesentlicher Komponenten von unternehmens- und konkurrentenbezogenen Wettbewerbsanalysen dargestellt. Dabei stehen folgende Teilanalysen im Mittelpunkt: Die Analyse des Einzugsgebietes; die Ermittlung des Marktpotentials und des Marktanteiles; die Ermittlung der Stärken und Schwächen im Verhältnis zur Konkurrenz; die Analyse der Kundenstruktur (Kundentypologisierung). Zu jeder der Teilanalysen werden nach der Darstellung der theoretischen Grundlagen Hinweise und Anleitungen zur praktischen Umsetzung und Durchführung gegeben und jeweils eine vertiefende Betrachtung angeschlossen. Das Buch zielt insbesondere auf kleine und mittlere Unternehmen (KMUs) ab, in denen keine großen spezialisierten Marketingabteilungen existieren. Verwendet werden Verfahren, bei denen sich zum einen der zeitliche Aufwand für die Durchführung in vertretbaren Grenzen hält, zum anderen Analysen, die mit Hilfe von frei verfügbarer Software oder frei verfügbaren Daten durchzuführen sind. Für den Statistikteil werden R-Skripte verwendet, die alle frei von der Webseite des Autors heruntergeladen werden können. Es handelt sich dabei um Skripte zur Berechnung des breaking-points nach Converse, zur Berechnung der Einkaufswahrscheinlichkeit nach Huff und zur Erstellung von Profildiagrammen im Rahmen von SWOT-Analysen sowie von Imageprofilen. Im Kapitel zur Kundentypologisierung wird die Durchführung von Cluster- und Faktoranlysen zur Typologisierung erläutert und der Anhang gibt Hinweise zur Installation und zum Einsatz von R für die beschriebenen Analysen.
    BibTeX:
    @book{R:Reymann:2009,
      author = {Detlev Reymann},
      title = {Wettbewerbsanalysen für kleine und mittlere Unternehmen (KMUs) --- Theoretische Grundlagen und praktische Anwendung am Beispiel gartenbaulicher Betriebe},
      publisher = {Verlag Detlev Reymann},
      year = {2009},
      note = {ISBN: 978-3-00-027013-0},
      url = {http://www.reymann.eu}
    }
    
    Pekar, S. & Brabec, M. Moderni analyza biologickych dat. 1. Zobecnene linearni modely v prostredi R [Modern Analysis of Biological Data. 1. Generalised Linear Models in R] 2009   book  
    Abstract: Kniha je zamerena na regresni modely, konkretne jednorozmerne zobecnene linearni modely (GLM). Je urcena predevsim studentum a kolegum z biologickych oboru a vyzaduje pouze zakladni statisticke vzdelani, jakym je napr. jednosemestrovy kurz biostatistiky. Text knihy obsahuje nezbytne minimum statisticke teorie, predevsim vsak reseni 18 realnych prikladu z oblasti biologie. Kazdy priklad je rozpracovan od popisu a stanoveni cile pres vyvoj statistickeho modelu az po zaver. K analyze dat je pouzit popularni a volne dostupny statisticky software R. Priklady byly zamerne vybrany tak, aby upozornily na lecktere problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout. Zaroven maji ctenare motivovat k tomu, jak o statistickych modelech premyslet a jak je pouzivat. Reseni prikladu si muse ctenar vyzkouset sam na datech, jez jsou dodavana spolu s knihou.
    BibTeX:
    @book{R:Pekar+Brabec:2009,
      author = {Stano Pekar and Marek Brabec},
      title = {Moderni analyza biologickych dat. 1. Zobecnene linearni modely v prostredi R [Modern Analysis of Biological Data. 1. Generalised Linear Models in R]},
      publisher = {Scientia},
      year = {2009},
      note = {ISBN: 978-80-86960-44-9, in Czech}
    }
    
    Zuur, A.F., Ieno, E.N. & Meesters, E. A Beginner's Guide to R 2009   book  
    Abstract: Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R.
    BibTeX:
    @book{R:Zuur+Ieno+Meesters:2009,
      author = {Alain F. Zuur and Elena N. Ieno and Erik Meesters},
      title = {A Beginner's Guide to R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-93836-3}
    }
    
    Varmuza, K. & Filzmoser, P. Introduction to Multivariate Statistical Analysis in Chemometrics 2009   book URL 
    Abstract: Using formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects both the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts. In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts. It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data.
    BibTeX:
    @book{R:Varmuza+Filzmoser:2009,
      author = {Kurt Varmuza and Peter Filzmoser},
      title = {Introduction to Multivariate Statistical Analysis in Chemometrics},
      publisher = {CRC Press},
      year = {2009},
      note = {ISBN: 9781420059472},
      url = {http://www.statistik.tuwien.ac.at/public/filz/}
    }
    
    Ramsay, J.O., Hooker, G. & Graves, S. Functional Data Analysis with R and Matlab 2009   book  
    Abstract: This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase the functional data analysis. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language.
    BibTeX:
    @book{R:Ramsay+Hooker+Graves:2009,
      author = {J. O. Ramsay and Giles Hooker and Spencer Graves},
      title = {Functional Data Analysis with R and Matlab},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-98184-0}
    }
    
    Stevens, M.H.H. A Primer of Ecology with R 2009   book  
    Abstract: This book combines an introduction to the major theoretical concepts in general ecology with the programming language R, a cutting edge Open Source tool. Starting with geometric growth and proceeding through stability of multispecies interactions and species-abundance distributions, this book demystifies and explains fundamental ideas in population and community ecology. Graduate students in ecology, along with upper division undergraduates and faculty, will all find this to be a useful overview of important topics.
    BibTeX:
    @book{R:Stevens:2009,
      author = {M. Henry H. Stevens},
      title = {A Primer of Ecology with R},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-89881-0}
    }
    
    Wickham, H. ggplot: Elegant Graphics for Data Analysis 2009   book  
    Abstract: This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e., you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, to and you'll find it easy to get graphics out of your head and on to the screen or page.
    BibTeX:
    @book{R:Wickham:2009,
      author = {Hadley Wickham},
      title = {ggplot: Elegant Graphics for Data Analysis},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-98140-6}
    }
    
    Heiberger, R.M. & Neuwirth, E. R Through Excel 2009   book  
    Abstract: The primary focus of the book is on the use of menu systems from the Excel menu bar into the capabilities provided by R. The presentation is designed as a computational supplement to introductory statistics texts. The authors provide RExcel examples for most topics in the introductory course. Data can be transferred from Excel to R and back. The clickable RExcel menu supplements the powerful R command language. Results from the analyses in R can be returned to the spreadsheet. Ordinary formulas in spreadsheet cells can use functions written in R. Discussions of the development, implementation, and applications of this technology are available at http://rcom.univie.ac.at/.
    BibTeX:
    @book{R:Heiberger+Neuwirth:2009,
      author = {Richard M. Heiberger and Erich Neuwirth},
      title = {R Through Excel},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-1-4419-0051-7}
    }
    
    Broman, K.W. & Sen, S. A Guide to QTL Mapping with R/qtl 2009   book  
    Abstract: This book is a comprehensive guide to the practice of QTL mapping and the use of R/qtl, including study design, data import and simulation, data diagnostics, interval mapping and generalizations, two-dimensional genome scans, and the consideration of complex multiple-QTL models. Two moderately challenging case studies illustrate QTL analysis in its entirety. The book alternates between QTL mapping theory and examples illustrating the use of R/qtl. Novice readers will find detailed explanations of the important statistical concepts and, through the extensive software illustrations, will be able to apply these concepts in their own research. Experienced readers will find details on the underlying algorithms and the implementation of extensions to R/qtl.
    BibTeX:
    @book{R:Broman+Sen:2009,
      author = {Karl W. Broman and Saunak Sen},
      title = {A Guide to QTL Mapping with R/qtl},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-92124-2}
    }
    
    Millot, G. Comprendre et réaliser les tests statistiques à l'aide de R 2009 , pp. 704  book URL 
    Abstract: Ce livre s'adresse aux étudiants, médecins et chercheurs désirant réaliser des tests alors qu'ils débutent en statistique. Son originalité est de proposer non seulement une explication très détaillée sur l'utilisation des tests les plus classiques, mais aussi la possibilité de réaliser ces tests à l'aide de R. Illustré par de nombreuses figures et accompagné d'exercices avec correction, l'ouvrage traite en profondeur de notions essentielles comme la check-list à effectuer avant de réaliser un test, la gestion des individus extrêmes, l'origine de la p value, la puissance ou la conclusion d'un test. Il explique comment choisir un test à partir de ses propres données. Il décrit 35 tests statistiques sous forme de fiches, dont 24 non paramétriques, ce qui couvre la plupart des tests à une ou deux variables observées. Il traite de toutes les subtilités des tests, comme les corrections de continuité, les corrections de Welch pour le test t et l'anova, ou les corrections de p value lors des comparaisons multiples. Il propose un exemple d'application de chaque test à l'aide de R, en incluant toutes les étapes du test, et notamment l'analyse graphique des données. En résumé, cet ouvrage devrait contenter à la fois ceux qui recherchent un manuel de statistique expliquant le fonctionnement des tests et ceux qui recherchent un manuel d'utilisation de R.
    BibTeX:
    @book{R:Millot:2009,
      author = {Gael Millot},
      title = {Comprendre et réaliser les tests statistiques à l'aide de R},
      publisher = {de boeck université},
      year = {2009},
      pages = {704},
      edition = {1st},
      note = {ISBN: 2804101797},
      url = {http://perso.curie.fr/Gael.Millot/Publications_livre.htm}
    }
    
    Vinod, H.D. Hands-on Intermediate Econometrics Using R: Templates for Extending Dozens of Practical Examples 2008   book  
    Abstract: This book explains how to use R software to teach econometrics by providing interesting examples, using actual data applied to important policy issues. It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression. The R program snippets are included on a CD accompanying the book. These are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification. The book has received endorsements from top econometricians.
    BibTeX:
    @book{R:Vinod:2008,
      author = {Hrishikesh D. Vinod},
      title = {Hands-on Intermediate Econometrics Using R: Templates for Extending Dozens of Practical Examples},
      publisher = {World Scientific},
      year = {2008},
      note = {ISBN: 10-981-281-885-5}
    }
    
    Wright, D.B. & London, K. Modern Regression Techniques Using R: A Practical Guide 2009   book  
    Abstract: Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter, and datasets are available from the book's web page at http://www.uk.sagepub.com/booksProdSampleMaterials.nav?prodId=Book233198. The authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.
    BibTeX:
    @book{R:Wright:2009,
      author = {Daniel B. Wright and Kamala London},
      title = {Modern Regression Techniques Using R: A Practical Guide},
      publisher = {SAGE},
      year = {2009},
      note = {ISBN: 9781847879035}
    }
    
    Steyerberg, E.W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating 2009   book  
    Abstract: This book provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or done simplistically, and updating of previously developed models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical prediction models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formats. The steps are illustrated with many small case-studies and R code, with data sets made available in the public domain. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to updating of a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for clinical epidemiologists and biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision making.
    BibTeX:
    @book{R:Steyerberg:2009,
      author = {Ewout W. Steyerberg},
      title = {Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating},
      publisher = {Springer},
      year = {2009},
      note = {ISBN: 978-0-387-77243-1}
    }
    
    Kabacoff, R. R in Action 2010   book URL 
    Abstract: R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. As you work through various examples illustrating R's features, you'll also get a crash course in practical statistics, including basic and advanced models for normal and non- normal data, longitudinal and survival data, and a wide variety of multivariate methods. Both data mining methodologies and approaches to messy and incomplete data are included.
    BibTeX:
    @book{R:Kabacoff:2010,
      author = {Rob Kabacoff},
      title = {R in Action},
      publisher = {Manning},
      year = {2010},
      url = {http://www.manning.com/kabacoff}
    }
    
    Advances in Social Science Research Using R 2010   book  
    Abstract: This book covers recent advances for quantitative researchers with practical examples from social sciences. The following twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software. McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime. Koenker: Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition. Gelman: R graphics in the context of voter participation in US elections. Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment. Markus and Gu: New R tools for exploratory data analysis including bubble plots. Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts, relevant to anyone with an investment portfolio. Foster and Kecojevic: Extends the usual analysis of covariance (ANCOVA) illustrated by growth charts for Saudi children. Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment. Haupt, Schnurbus, and Tschernig: consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation. Rindskopf: R methods to fit a multinomial based multivariate analysis of variance (ANOVA) with examples from psychology, sociology, political science, and medicine. Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site. Numatsi and Rengifo: explain persistent discrete jumps in financial series subject to misspecification.
    BibTeX:
    @book{R:Vinod:2010,,
      title = {Advances in Social Science Research Using R},
      publisher = {Springer},
      year = {2010},
      note = {ISBN:978-1-4419-1763-8}
    }
    
    Robert, C. & Casella, G. Introducing Monte Carlo Methods with R 2010   book  
    Abstract: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis-Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader.
    BibTeX:
    @book{R:Robert+Casella:2010,
      author = {Christian Robert and George Casella},
      title = {Introducing Monte Carlo Methods with R},
      publisher = {Springer},
      year = {2010},
      note = {ISBN: 978-1-4419-1575-7}
    }
    
    Ihaka, R. & Gentleman, R. R: A Language for Data Analysis and Graphics 1996 Journal of Computational and Graphical Statistics
    Vol. 5(3), pp. 299-314 
    article URL 
    BibTeX:
    @article{R:Ihaka+Gentleman:1996,
      author = {Ross Ihaka and Robert Gentleman},
      title = {R: A Language for Data Analysis and Graphics},
      journal = {Journal of Computational and Graphical Statistics},
      year = {1996},
      volume = {5},
      number = {3},
      pages = {299--314},
      url = {http://www.amstat.org/publications/jcgs/}
    }
    
    Cribari-Neto, F. & Zarkos, S.G. R: Yet another econometric programming environment 1999 Journal of Applied Econometrics
    Vol. 14, pp. 319-329 
    article URL 
    BibTeX:
    @article{R:Cribari-Neto+Zarkos:1999,
      author = {Francisco Cribari-Neto and Spyros G. Zarkos},
      title = {R: Yet another econometric programming environment},
      journal = {Journal of Applied Econometrics},
      year = {1999},
      volume = {14},
      pages = {319-329},
      url = {http://www.interscience.wiley.com/jpages/0883-7252/}
    }
    
    Gentleman, R. & Ihaka, R. Lexical Scope and Statistical Computing 2000 Journal of Computational and Graphical Statistics
    Vol. 9, pp. 491-508 
    article URL 
    BibTeX:
    @article{R:Gentleman+Ihaka:2000,
      author = {Robert Gentleman and Ross Ihaka},
      title = {Lexical Scope and Statistical Computing},
      journal = {Journal of Computational and Graphical Statistics},
      year = {2000},
      volume = {9},
      pages = {491--508},
      url = {http://www.amstat.org/publications/jcgs/}
    }
    
    Murrell, P. & Ihaka, R. An Approach to Providing Mathematical Annotation in Plots 2000 Journal of Computational and Graphical Statistics
    Vol. 9, pp. 582-599 
    article URL 
    BibTeX:
    @article{R:Murrell+Ihaka:2000,
      author = {Paul Murrell and Ross Ihaka},
      title = {An Approach to Providing Mathematical Annotation in Plots},
      journal = {Journal of Computational and Graphical Statistics},
      year = {2000},
      volume = {9},
      pages = {582--599},
      url = {http://www.amstat.org/publications/jcgs/}
    }
    
    Ellner, S.P. Review of R, Version 1.1.1 2001 Bulletin of the Ecological Society of America
    Vol. 82(2), pp. 127-128 
    article  
    BibTeX:
    @article{R:Ellner:2001,
      author = {Stephen P. Ellner},
      title = {Review of R, Version 1.1.1},
      journal = {Bulletin of the Ecological Society of America},
      year = {2001},
      volume = {82},
      number = {2},
      pages = {127--128}
    }
    
    Ripley, B.D. The R Project in Statistical Computing 2001 MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network.
    Vol. 1(1), pp. 23-25 
    article URL 
    BibTeX:
    @article{R:Ripley:2001,
      author = {Brian D. Ripley},
      title = {The R Project in Statistical Computing},
      journal = {MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network.},
      year = {2001},
      volume = {1},
      number = {1},
      pages = {23--25},
      url = {http://ltsn.mathstore.ac.uk/newsletter/feb2001/pdf/rproject.pdf}
    }
    
    Proceedings of the 2nd International Workshop on Distributed Statistical Computing (DSC 2001) 2001   proceedings URL 
    BibTeX:
    @proceedings{R:Hornik+Leisch:2001,,
      title = {Proceedings of the 2nd International Workshop on Distributed Statistical Computing (DSC 2001)},
      year = {2001},
      note = {ISSN 1609-395X},
      url = {http://www.ci.tuwien.ac.at/Conferences/DSC.html}
    }
    
    Ribeiro, Jr., P.J. & Brown, P.E. Some words on the R project 2001 The ISBA Bulletin
    Vol. 8(1), pp. 12-16 
    article URL 
    BibTeX:
    @article{R:Ribeiro+Brown:2001,
      author = {Ribeiro, Jr., Paulo J. and Patrick E. Brown},
      title = {Some words on the R project},
      journal = {The ISBA Bulletin},
      year = {2001},
      volume = {8},
      number = {1},
      pages = {12--16},
      url = {http://www.iami.mi.cnr.it/isba/index.html}
    }
    
    Kuonen, D. Introduction au data mining avec R : vers la reconquête du `knowledge discovery in databases' par les statisticiens 2001 Bulletin of the Swiss Statistical Society
    Vol. 40, pp. 3-7 
    article URL 
    BibTeX:
    @article{R:Kuonen:2001,
      author = {Diego Kuonen},
      title = {Introduction au data mining avec R : vers la reconquête du `knowledge discovery in databases' par les statisticiens},
      journal = {Bulletin of the Swiss Statistical Society},
      year = {2001},
      volume = {40},
      pages = {3-7},
      url = {http://www.statoo.com/en/publications/2001.R.SSS.40/}
    }
    
    Kuonen, D. & Furrer, R. Data mining avec R dans un monde libre 2001 Flash Informatique Spécial Été, pp. 45-50  article URL 
    BibTeX:
    @article{R:Kuonen+Furrer:2001,
      author = {Diego Kuonen and Reinhard Furrer},
      title = {Data mining avec R dans un monde libre},
      journal = {Flash Informatique Spécial Été},
      year = {2001},
      pages = {45-50},
      url = {http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page45.html}
    }
    
    Furrer, R. & Kuonen, D. GRASS GIS et R: main dans la main dans un monde libre 2001 Flash Informatique Spécial Été, pp. 51-56  article URL 
    BibTeX:
    @article{R:Furrer+Kuonen:2001,
      author = {Reinhard Furrer and Diego Kuonen},
      title = {GRASS GIS et R: main dans la main dans un monde libre},
      journal = {Flash Informatique Spécial Été},
      year = {2001},
      pages = {51-56},
      url = {http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-sp-1/sp-1-page51.html}
    }
    
    Kuonen, D. & Chavez, V. R - un exemple du succès des modèles libres 2001 Flash Informatique
    Vol. 2, pp. 3-7 
    article URL 
    BibTeX:
    @article{R:Kuonen+Chavez:2001,
      author = {Diego Kuonen and Valerie Chavez},
      title = {R - un exemple du succès des modèles libres},
      journal = {Flash Informatique},
      year = {2001},
      volume = {2},
      pages = {3-7},
      url = {http://sawww.epfl.ch/SIC/SA/publications/FI01/fi-2-1/2-1-page3.html}
    }
    
    Ricci, V. R : un ambiente opensource per l'analisi statistica dei dati 2004 Economia e Commercio
    Vol. 1, pp. 69-82 
    article URL 
    Abstract: 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.
    BibTeX:
    @article{R:Ricci:2004,
      author = {Vito Ricci},
      title = {R : un ambiente opensource per l'analisi statistica dei dati},
      journal = {Economia e Commercio},
      year = {2004},
      volume = {1},
      pages = {69--82},
      url = {http://www.dsa.unipr.it/soliani/allegato.pdf}
    }
    
    Ricci, V. Rappresentazione analitica delle distribuzioni statistiche con R (Prima parte) 2005 Economia e Commercio
    Vol. 1/2, pp. 47-60 
    article URL 
    Abstract: This paper deals with distribution fitting using R environment for statistical computing. It treats briefly some theoretical issues and it points out especially practical ones proposing some examples of R statements for data graphical exploration and presentation, parameters' estimates of patterns and tests for goodness of fit.
    BibTeX:
    @article{R:Ricci:2005,
      author = {Vito Ricci},
      title = {Rappresentazione analitica delle distribuzioni statistiche con R (Prima parte)},
      journal = {Economia e Commercio},
      year = {2005},
      volume = {1/2},
      pages = {47--60},
      url = {http://cran.r-project.org/doc/contrib/Ricci-distributions-it.pdf}
    }
    

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