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R - Books

Books related to R

This page gives a partially annotated list of books that are relatedto S or R and may be useful to the R user community. See also the listof other publications related to R. Analternative searchable listing of both sets together isavailable here.

[1]Thomas Rahlf. Data Visualisation with R. Springer International Publishing, New York, 2017. ISBN 978-3-319-49750-1.[bib| Publisher Info|]

This book introduces readers to the fundamentals of creating presentation graphics using R, based on 100 detailed and complete scripts. It shows how bar and column charts, population pyramids, Lorenz curves, box plots, scatter plots, time series, radial polygons, Gantt charts, heat maps, bump charts, mosaic and balloon charts, and a series of different thematic map types can be created using Rs Base Graphics System. Every example uses real data and includes step-by-step explanations of the figures and their programming.

[2]Steven Murray. Apprendre R en un Jour. SJ Murray, 2017. Ebook.[bib|]

'Apprendre R en un Jour' donne au lecteur les comptences cls au travers d'une approche axe sur des exemples et est idal pour les universitaires, scientifiques, mathmaticiens et ingnieurs. Le livre ne suppose aucune connaissance pralable en programmation et couvre progressivement toutes les tapes essentielles pour prendre de l'assurance et devenir comptent en R en une journe. Les sujets couverts incluent: comment importer, manipuler, formater, itrer (en boucle), questionner, effectuer des statistiques lmentaires sur, et tracer des graphiques partir de donnes, l'aide d'une explication tape par tape de la technique et de dmonstrations que le lecteur est encourag de reproduire sur son ordinateur, en utilisant des ensembles de donnes dj en mmoire dans R. Chaque fin de chapitre inclut aussi des exercices (avec solutions la fin du livre) pour s'entraner, mettre en pratique les comptences cls et habiliter le lecteur construire sur les bases acquises au cours de ce livre d'introduction.

[3]Lawrence Leemis. Learning Base R. Lightning Source, 2016. ISBN 978-0-9829174-8-0.[bib|]

Learning Base R provides an introduction to the R language for those with and without prior programming experience. It introduces the key topics to begin analyzing data and programming in R. The focus is on the R language rather than a particular application. The book can be used for self-study or an introductory class on R. Nearly 200 exercises make this book appropriate for a classroom setting. The chapter titles are Introducing R; R as a Calculator; Simple Objects; Vectors; Matrices; Arrays; Built-In Functions; User-Written Functions; Utilities; Complex Numbers; Character Strings; Logical Elements; Relational Operators; Coercion; Lists; Data Frames; Built-In Data Sets; Input/Output; Probability; High-Level Graphics; Custom Graphics; Conditional Execution; Iteration; Recursion; Simulation; Statistics; Linear Algebra; Packages.

[4]Vikram Dayal. An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing. Springer, 2015. ISBN 978-81-322-2340-5.[bib|]

This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.

[5]C.Sun. Empirical Research in Economics: Growing up with R. Pine Square, Starkville, Mississippi, USA, 1st edition, 2015. ISBN 978-0-9965854-0-8. Supplementary materials are available at[bib|]

Empirical Research in Economics: Growing up with R presents a systematic approach to conducting empirical research in economics with the flexible and free software of R. At present, there is a lack of integration among course work, research methodology, and software usage in statistical analysis of economic data. The objective of this book is to help young professionals conduct an empirical study in economics over a reasonable period, with the expectation of four months in general.

[6]Matthias Kohl. Einfhrung in die statistische Datenanalyse mit R., London, 2015. ISBN 978-87-403-1156-3. In German.[bib| Publisher Info]

Das Buch bietet eine Einfhrung in die statistische Datenanalyse unter Verwendung der freien Statistiksoftware R, der derzeit wohl mchtigsten Statistiksoftware. Die Analysen werden anhand realer Daten durchgefhrt und besprochen. Nach einer kurzen Beschreibung der Statistiksoftware R werden wichtige Kenngren und Diagramme der deskriptiven Statistik vorgestellt. Anschlieend werden Empfehlungen fr die Erstellung von Diagrammen gegeben, wobei ein spezielles Augenmerk auf die Auswahl geeigneter Farben gelegt wird. Die zweite Hlfte des Buches behandelt die Grundlagen der schlieenden Statistik. Zunchst wird eine Reihe von Wahrscheinlichkeitsverteilungen eingefhrt und deren Anwendungen anhand von Beispielen illustriert. Es folgt eine Beschreibung, wie die in der Praxis unbekannten Parameter der Verteilungen auf Basis vorliegender Daten geschtzt werden knnen. Im abschlieenden Kapitel werden statistische Hypothesentests eingefhrt und die fr die Praxis wichtigsten Tests besprochen.

[7]Matthias Kohl. Introduction to statistical data analysis with R., London, 2015. ISBN 978-87-403-1123-5.[bib| Publisher Info]

The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data. After a brief description of the statistical software R, important parameters and diagrams of descriptive statistics are introduced. Subsequently, recommendations for generating diagrams are provided, where special attention is given to the selection of appropriate colors. The second half of the book addresses the basics of inferential statistics. First, a number of probability distributions are introduced and their applicability is illustrated by examples. Next, the book describes how the parameters of these distributions, which are unknown in practice, may be estimated from given data. The final chapter introduces statistical tests and reviews the most important tests for practical applications.

[8]Marta Blangiardo and Michela Cameletti. Spatial and Spatio-temporal Bayesian Models with R-INLA. Wiley, Chichester, West Sussex, United Kingdom, 1st edition, 2015. ISBN 978-1-118-32655-8.[bib|]

[9]Gergely Darczi. Mastering Data Analysis with R. Packt Publishing, 9 2015. ISBN 9781783982028.[bib|]

An intermediate and practical book on various fields of data analysis with R: from loading data from text files, databases or APIs; munging; transformations; modeling with traditional statistical methods and machine learning to visualization of tabular, network, time-series and spatial data with hands-on examples.

[10]VictorA. Bloomfield. Using R for Numerical Analysis in Science and Engineering. Chapman & Hall/CRC, 2014. ISBN 978-1439884485.[bib|]

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R's powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also:

* Explains how to statistically analyze and fit data to linear and nonlinear models

* Explores numerical differentiation, integration, and optimization

* Describes how to find eigenvalues and eigenfunctions

* Discusses interpolation and curve fitting

* Considers the analysis of time serie

Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

[11]Torsten Hothorn and BrianS. Everitt. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC Press, Boca Raton, Florida, USA, 3rd edition, 2014. ISBN 978-1-4822-0458-2.[bib|]

[12]Thomas Rahlf. Datendesign mit R. 100 Visualisierungsbeispiele. Open Source Press, Mnchen, 2014. ISBN 978-3-95539-094-5. In German.[bib| Publisher Info|]

Die Visualisierung von Daten hat in den vergangenen Jahren stark an Beachtung gewonnen. Zu den traditionellen Anwendungsbereichen in der Wissenschaft oder dem Marketing treten neue Gebiete wie Big-Data-Analysen oder der Datenjournalismus. Mit der Open Source Software R, die sich zunehmend als Standard im Bereich der Statistiksoftware etabliert, steht ein mchtiges Werkzeug zur Verfgung, das hinsichtlich der Visualisierungsmglichkeiten praktisch keine Wnsche offen lsst. Dieses Buch fhrt in die Grundlagen der Gestaltung von Prsentationsgrafiken mit R ein und zeigt anhand von 100 vollstndigen Skript-Beispielen, wie Sie Balken- und Sulendiagramme, Bevlkerungspyramiden, Lorenzkurven, Streudiagramme, Zeitreihendarstellungen, Radialpolygone, Gantt-Diagramme, Profildiagramme, Heatmaps, Bumpcharts, Mosaik- und Ballonplots sowie eine Reihe verschiedener thematischer Kartentypen mit dem Base Graphics System von R erstellen. Fr jedes Beispiel werden reale Daten verwendet sowie die Abbildung und deren Programmierung Schritt fr Schritt erlutert. Die gedruckte Ausgabe enthlt einen persnlichen Zugangs-Code, der Ihnen kostenlos Zugriff auf die Online-Ausgabe dieses Buches gewhrt.

[13]Sarah Stowell. Using R for Statistics. Apress, 2014. ISBN 978-1484201404.[bib|]

R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter.

Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background.

After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics.

What you'll learn:

* How to apply statistical concepts using R and some R programming

* How to work with data files, prepare and manipulate data, and combine and restructure datasets

* How to summarize continuous and categorical variables

* What is a probability distribution

* How to create and customize plots

* How to do hypothesis testing

* How to build and use regression and linear models

Who this book is for: No prior knowledge of R or of programming is assumed, making this book ideal if you are more accustomed to using point-and-click style statistical packages. You should have some prior experience with statistics, however.

[14]RueyS. Tsay. Multivariate Time Series Analysis With R and Financial Applications. John Wiley, New Jersey, 2014. ISBN 978-1-118-61790-8.[bib| Publisher Info|]

This book is based on my experience in teaching and research on multivariate time series analysis over the past 30 years. It summarizes the basic concepts and ideas of analyzing multivariate dependent data, provides econometric and statistical models useful for describing the dynamic dependence between variables, discusses the identifiability problem when the models become too flexible, introduces ways to search for simplifying structure hidden in high-dimensional time series, addresses the applicabilities and limitations of multivariate time series methods, and, equally important, develops the R MTS package for readers to apply the methods and models discussed in the book. The vector autoregressive models and multivariate volatility models are discussed and demonstrated.

[15]J.C. Nash. Nonlinear Parameter Optimization Using R Tools. Wiley, 2014. ISBN 9781118883969.[bib]

A systematic and comprehensive treatment of optimization software using R. In recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under nonlinear, multivariable conditions, more quickly than ever before. Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.

[16]MichaelJ. Crawley. Statistics: An Introduction using R. Wiley, 2nd edition, 2014. ISBN 978-1-118-94109-6.[bib| Publisher Info|]

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.

[17]Lise Bellanger and Richard Tomassone. Exploration de donnes et mthodes statistiques avec le logiciel R. Rfrences sciences. Ellipses, 1st edition, 2014. ISBN 978-2-7298-8486-4.[bib| Publisher Info|]

La Statistique envahit pratiquement tous les domaines d'application, aucun n'en est exclus; elle permet d'explorer et d'analyser des corpus de donnes de plus en plus volumineux : l're des big data et du data mining s'ouvre nous ! Cette omniprsence s'accompagne bien souvent de l'absence de regard critique tant sur l'origine des donnes que sur la manire de les traiter. La facilit d'utilisation des logiciels de traitement statistique permet de fournir quasi instantanment des graphiques et des rsultats numriques. Le risque est donc grand d'une acceptation aveugle des conclusions qui dcoulent de son emploi, comme simple citoyen ou comme homme politique. Les auteurs insistent sur les concepts sans ngliger la rigueur, ils dcrivent les outils de dcryptage des donnes. L'ouvrage couvre un large spectre de mthodes allant du pr-traitement des donnes aux mthodes de prvision, en passant par celles permettant leur visualisation et leur synthse. De nombreux exemples issus de champs d'application varis sont traits l'aide du logiciel libre R, dont les commandes sont commentes. L'ouvrage est destin aux tudiants de masters scientifiques ou d'coles d'ingnieurs ainsi qu'aux professionnels voulant utiliser la Statistique de manire rflchie : des sciences de la vie l'archologie, de la sociologie l'analyse financire.

[18]Yvonnick Noel. Psychologie statistique avec R. Pratique R. Springer, Paris, 2013. ISBN 978-2-8178-0424-8.[bib|]

This book provides a detailed presentation of all basics of statistical inference for psychologists, both in a fisherian and a bayesian approach. Although many authors have recently advocated for the use of bayesian statistics in psychology (Wagenmaker et al., 2010, 2011; Kruschke, 2010; Rouder et al., 2009) statistical manuals for psychologists barely mention them. This manual provides a full bayesian toolbox for commonly encountered problems in psychology and social sciences, for comparing proportions, variances and means, and discusses the advantages. But all foundations of the frequentist approach are also provided, from data description to probability and density, through combinatorics and set algebra. A special emphasis has been put on the analysis of categorical data and contingency tables. Binomial and multinomial models with beta and Dirichlet priors are presented, and their use for making (between rows or between cells) contrasts in contingency tables is detailed on real data. An automatic search of the best model for all problem types is implemented in the AtelieR package, available on CRAN. ANOVA is also presented in a Bayesian flavor (using BIC), and illustrated on real data with the help of the AtelieR and R2STATS packages (a GUI for GLM and GLMM in R). In addition to classical and Bayesian inference on means, direct and Bayesian inference on effect size and standardized effects are presented, in agreement with recent APA recommendations.

[19]Yihui Xie. Dynamic Documents with R and knitr. Chapman & Hall/CRC, 2013. ISBN 978-1482203530.[bib| Publisher Info|]

Suitable for both beginners and advanced users, this book shows you how to write reports in simple languages such as Markdown. The reports range from homework, projects, exams, books, blogs, and web pages to any documents related to statistical graphics, computing, and data analysis. While familiarity with LaTeX and HTML is helpful, the book requires no prior experience with advanced programs or languages. For beginners, the text provides enough features to get started on basic applications. For power users, the last several chapters enable an understanding of the extensibility of the knitr package.

[20]Steven Murray. Learn R in a Day. SJ Murray, 2013. Ebook.[bib|]

`Learn R in a Day' provides the reader with key programming skills through an examples-oriented approach and is ideally suited for academics, scientists, mathematicians and engineers. The book assumes no prior knowledge of computer programming and progressively covers all the essential steps needed to become confident and proficient in using R within a day. Topics include how to input, manipulate, format, iterate (loop), query, perform basic statistics on, and plot data, via a step-by-step technique and demonstrations using in-built datasets which the reader is encouraged to replicate on their computer. Each chapter also includes exercises (with solutions) to practice key skills and empower the reader to build on the essentials gained during this introductory course.

[21]RueyS. Tsay. An Introduction to Analysis of Financial Data with R. John Wiley, New Jersey, 2013. ISBN 978-0-470-89081-3.[bib| Publisher Info|]

This book provides a concise introduction to econometric and statistical analysis of financial data. It focuses on scalar financial time series with applications. High-frequency data and volatility models are discussed. The book also uses case studies to illustrate the application of modeling financial data.

[22]Matthias Kohl. Analyse von Genexpressionsdaten --- mit R und Bioconductor. Ventus Publishing ApS, London, 2013. ISBN 978-87-403-0349-0. In German.[bib| Publisher Info]

Das Buch bietet eine Einfhrung in die Verwendung von R und Bioconductor fr die Analyse von Mikroarray-Daten. Es werden die Arraytechnologien von Affymetrix und Illumina ausfhrlich behandelt. Darber hinaus wird auch auf andere Arraytechnologien eingegangen. Alle notwendigen Schritte beginnend mit dem Einlesen der Daten und der Qualittskontrolle ber die Vorverarbeitung der Daten bis hin zur statistischen Analyse sowie der Enrichment Analyse werden besprochen. Jeder der Schritte wird anhand einfacher Beispiele praktisch vorgefhrt, wobei der im Buch verwendete R-Code separat zum Download bereitsteht.

[23]RobertJ Knell. Introductory R: A Beginner's Guide to Data Visualisation and Analysis using R. (See web site), March 2013. ISBN 978-0-9575971-0-5.[bib|]

R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a bit about statistics but who hasn't yet got to grips with the ways of R. This book: walks you through the basics of R's command line interface; gives a set of simple rules to follow to make sure you import your data properly; introduces the script editor and gives advice on workflow; contains a detailed introduction to drawing graphs in R and gives advice on how to deal with some of the most common errors that you might encounter. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

[24]Joseph Hilbe. Methods of Statistical Model Estimation. Chapman & Hall/CRC Press, Boca Raton, FL, 2013. ISBN 978-1-4398-5802-8.[bib| Discount Info|]

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them.

[25]Gergely Darczi, Michael Puhle, Edina Berlinger, Pter Cska, Daniel Havran, Mrton Michaletzky, Zsolt Tulassay, Kata Vradi, and Agnes Vidovics-Dancs. Introduction to R for Quantitative Finance. Packt Publishing, November 2013. ISBN 9781783280933.[bib|]

The book focuses on how to solve real-world quantitative finance problems using the statistical computing language R. Introduction to R for Quantitative Finance covers diverse topics ranging from time series analysis to financial networks. Each chapter briefly presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.

[26]Christopher Gandrud. Reproducible Research with R and RStudio. Chapman & Hall/CRC The R series. Chapman & Hall/CRC Press, Boca Raton, FL, 2013. ISBN 978-1-4665-7284-3.[bib|]

Bringing together computational research tools in one accessible source, Reproducible Research with R and RStudio guides you in creating dynamic and highly reproducible research. Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results. The book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation knitr for combining statistical analysis and results into one document LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available Unix-like shell programs for compiling large projects and converting documents from one markup language to another RStudio to tightly integrate reproducible research tools in one place.

[27]Dirk Eddelbuettel. Seamless R and C++ Integration with Rcpp. Use R! Springer, New York, 2013. ISBN 978-1-4614-6867-7.[bib| Discount Info| Publisher Info]

Seamless R and C ++ Integration with Rcpp provides the first comprehensive introduction to Rcpp, which has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++.

[28]Din Chen. Applied Meta-Analysis with R. Chapman & Hall/CRC Biostatistics series. Chapman & Hall/CRC Press, Boca Raton, FL, 2013. ISBN 978-1-4665-0599-5.[bib| Discount Info|]

In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R. Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data. Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R) in public health, medical research, governmental agencies, and the pharmaceutical industry.

[29]Stef van Buuren. Flexible Imputation of Missing Data. Chapman & Hall/CRC Interdisciplinary series. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-6824-9.[bib| Discount Info|]

Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science---multiple imputation---fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise. Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author's package MICE is included throughout the book. This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.

[30]Stano Pekar and Marek Brabec. Moderni analyza biologickych dat. 2. Linearni modely s korelacemi v prostredi R [Modern Analysis of Biological Data. 2. Linear Models with Correlations in R]. Masaryk University Press, Brno, 2012. ISBN 978-80-21058-12-5. In Czech.[bib| Publisher Info]

Publikace navazuje na prvni dil Moderni analyzy biologickych dat a predstavuje vybrane modely a metody statisticke analyzy korelovanych dat. Tedy linearni metody, ktere jsou vhodnym nastrojem analyzy dat s casovymi, prostorovymi a fylogenetickymi zavislostmi v datech. Text knihy je praktickou priruckou analyzy dat v prostredi jednoho z nejrozsahlejsich statistickych nastroju na svete, volne dostupneho softwaru R. Je sestaven z 19 vzorove vyresenych a okomentovanych prikladu, ktere byly vybrany tak, aby ukazaly spravnou konstrukci modelu a upozornily na problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout. Text je psan jednoduchym jazykem srozumitelnym pro ctenare bez specialniho matematickeho vzdelani. Kniha je predevsim urcena studentum i vedeckym pracovnikum biologickych, zemedelskych, veterinarnich, lekarskych a farmaceutickych oboru, kteri potrebuji korektne analyzovat vysledky svych pozorovani ci experimentu s komplikovanejsi strukturou danou zavislostmi mezi opakovanymi merenimi stejneho subjektu.

[31]K.Soetaert, J.Cash, and F.Mazzia. Solving Differential Equations in R. Use R! Springer, 2012. ISBN 978-3-642-28070-2.[bib| Publisher Info]

Mathematics plays an important role in many scientific and engineering disciplines. This book deals with the numerical solution of differential equations, a very important branch of mathematics. Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial differential equations and delay differential equations. The solution of differential equations using R is the main focus of this book. It is therefore intended for the practitioner, the student and the scientist, who wants to know how to use R for solving differential equations. However, it has been our goal that non-mathematicians should at least understand the basics of the methods, while obtaining entrance into the relevant literature that provides more mathematical background. Therefore, each chapter that deals with R examples is preceded by a chapter where the theory behind the numerical methods being used is introduced. In the sections that deal with the use of R for solving differential equations, we have taken examples from a variety of disciplines, including biology, chemistry, physics, pharmacokinetics. Many examples are well-known test examples, used frequently in the field of numerical analysis.

[32]Sarah Stowell. Instant R: An Introduction to R for Statistical Analysis. Jotunheim Publishing, 2012. ISBN 978-0-957-46490-2.[bib|]

This book gives an introduction to using R, with a focus on performing popular statistical methods. It is suitable for anyone that is familiar with basic statistics and wants to begin using R to analyse data and create statistical plots. No prior knowledge of R or of programming is assumed, making this book ideal if you are more accustomed to using point-and-click style statistical packages.

[33]Bernhard Pfaff. Financial Risk Modelling and Portfolio Optimisation with R. Wiley, Chichester, UK, 2012. ISBN 978-0-470-97870-2.[bib| Publisher Info|]

Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimisation will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

[34]David Lunn. The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman & Hall/CRC Texts in Statistical Science series. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-5848-8849-9.[bib| Discount Info|]

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions---all those aspects of the art of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of tricks that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book's website.

[35]Michael Lawrence. Programming Graphical User Interfaces in R. Chapman & Hall/CRC the R series. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-5682-6.[bib| Discount Info|]

Programming Graphical User Interfaces with R introduces each of the major R packages for GUI programming: RGtk2, qtbase, Tcl/Tk, and gWidgets. With examples woven through the text as well as stand-alone demonstrations of simple yet reasonably complete applications, the book features topics especially relevant to statisticians who aim to provide a practical interface to functionality implemented in R. The accompanying package, ProgGUIinR, includes the complete code for all examples as well as functions for browsing the examples from the respective chapters. Accessible to seasoned, novice, and occasional R users, this book shows that for many purposes, adding a graphical interface to one's work is not terribly sophisticated or time consuming.

[36]Gran Brostrm. Event History Analysis with R. Chapman & Hall/CRC the R series. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-3164-9.[bib| Discount Info|]

With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times. A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.

[37]Dimitris Rizopoulos. Joint Models for Longitudinal and Time-to-Event Data, with Applications in R. Chapman & Hall/CRC, Boca Raton, 2012. ISBN 978-1-4398-7286-4.[bib| Publisher Info|]

The last 20 years have seen an increasing interest in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout. Aimed at applied researchers and graduate students, this text provides a comprehensive overview of the framework of random effects joint models. Emphasis is given on applications such that readers will obtain a clear view on the type of research questions that are best answered using a joint modeling approach, the basic features of these models, and how they can be extended in practice. Special mention is given in checking the assumptions using residual plots, and on dynamic predictions for the survival and longitudinal outcomes.

[38]Brian Dennis. The R Student Companion. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-7540-7.[bib| Discount Info|]

R is the amazing, free, open-access software package for scientific graphs and calculations used by scientists worldwide. The R Student Companion is a student-oriented manual describing how to use R in high school and college science and mathematics courses. Written for beginners in scientific computation, the book assumes the reader has just some high school algebra and has no computer programming background. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R in an appendix. In addition, each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups. Several of the chapters explore algebra concepts that are highly useful in scientific applications, such as quadratic equations, systems of linear equations, trigonometric functions, and exponential functions. Each chapter provides an instructional review of the algebra concept, followed by a hands-on guide to performing calculations and graphing in R. R is intuitive, even fun. Fantastic, publication-quality graphs of data, equations, or both can be produced with little effort. By integrating mathematical computation and scientific illustration early in a student's development, R use can enhance one's understanding of even the most difficult scientific concepts. While R has gained a strong reputation as a package for statistical analysis, The R Student Companion approaches R more completely as a comprehensive tool for scientific computing and graphing.

[39]Pierre-Andre Cornillon. R for Statistics. Chapman & Hall/CRC Press, Boca Raton, FL, 2012. ISBN 978-1-4398-8145-3.[bib| Discount Info|]

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

[40]A.B. Shipunov, E.M. Baldin, P.A. Volkova, A.I. Korobejnikov, S.A. Nazarova, S.V. Petrov, and V.G. Sufijanov. Nagljadnaja statistika. Ispoljzuem R! / Vusial statistics. Use R! DMK Press, Moscow, 2012. ISBN 978-5-94074-828-1.[bib]

This is the first big book about R in Russian. It is intended to help people who begin to learn statistical methods. All explanations are based on R. The book may also serve as an introduction reference to R.

[41]Yves Aragon. Sries temporelles avec R. Mthodes et cas. Springer, Collection Pratique R, 1st edition, 2011. ISBN 978-2-8178-0207-7.[bib| Publisher Info]

Ce livre tudie sous un angle original le concept de srie temporelle, dont la complexit thorique et l'utilisation sont souvent sources de difficults. La thorie distingue par exemple les notions de sries stationnaire et non stationnaire, mais il n'est pas rare de pouvoir modliser une srie par deux modles incompatibles. De plus, un peu d'intimit avec les sries montre qu'on peut s'appuyer sur des graphiques varis pour en comprendre assez rapidement la structure, avant toute modlisation. Ainsi, au lieu d'tudier des mthodes de modlisation, puis de les illustrer, l'auteur prend ici le parti de s'intresser un nombre limit de sries afin de trouver ce qu'on peut dire de chacune. Avant d'aborder ces tudes de cas, il procde quelques rappels et commence par prsenter les graphiques pour sries temporelles offerts par R. Il revient ensuite sur des notions fondamentales de statistique mathmatique, puis rvise les concepts et les modles classiques de sries. Il prsente les structures de sries temporelles dans R et leur importation. Il revisite le lissage exponentiel la lumire des travaux les plus rcents. Un chapitre est consacr la simulation. Six sries sont ensuite tudies par le menu en confrontant plusieurs approches.

[42]PierreAndr Cornillon and Eric Matzner-Lober. Rgression avec R. Springer, Collection Pratique R, 1st edition, 2011. ISBN 978-2-8178-0183-4.[bib| Publisher Info]

Cet ouvrage expose en dtail l'une des mthodes statistiques les plus courantes : la rgression. Il concilie thorie et applications, en insistant notamment sur l'analyse de donnes relles avec le logiciel R. Les premiers chapitres sont consacrs la rgression linaire simple et multiple, et expliquent les fondements de la mthode, tant au niveau des choix oprs que des hypothses et de leur utilit. Puis ils dveloppent les outils permettant de vrifier les hypothses de base mises en uvre par la rgression, et prsentent les modles d'analyse de la variance et covariance. Suit l'analyse du choix de modle en rgression multiple. Les derniers chapitres prsentent certaines extensions de la rgression, comme la rgression sous contraintes (ridge, lasso et lars), la rgression sur composantes (PCR et PLS), et, enfin, introduisent la rgression non paramtrique (spline et noyau). La prsentation tmoigne d'un rel souci pdagogique des auteurs qui bnficient d'une exprience d'enseignement auprs de publics trs varis. Les rsultats exposs sont replacs dans la perspective de leur utilit pratique grce l'analyse d'exemples concrets. Les commandes permettant le traitement des exemples sous le logiciel R figurent dans le corps du texte. Chaque chapitre est complt par une suite d'exercices corrigs. Le niveau mathmatique requis rend ce livre accessible aux lves ingnieurs, aux tudiants de niveau Master et aux chercheurs actifs dans divers domaines des sciences appliques.

[43]LuizAlexandre Peternelli and MarcioPupin Mello. Conhecendo o R: uma viso estatstica. Srie Didtica. Editora UFV, Viosa, MG, Brazil, 1 edition, March 2011. ISBN 978-85-7269-400-1.[bib|]

Este material de grande valia para estudantes ou pesquisadores que usam ferramentas estatsticas em trabalhos de pesquisa ou em uma simples anlise de dados, constitui ponto de partida para aqueles que desejam comear a utilizar o R e suas ferramentas estatsticas ou, mesmo, para os que querem ter sempre mo material de referncia fcil, objetivo e abrangente para uso desse software.

[44]Paul Teetor. R Cookbook. O'Reilly, first edition, 2011. ISBN 978-0-596-80915-7.[bib|]

Perform data analysis with R quickly and efficiently with the task-oriented recipes in this cookbook. Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master. R Cookbook will help both beginners and experienced statistical programmers unlock and use the power of R.

[45]Paul Teetor. 25 Recipes for Getting Started with R. O'Reilly, 2011. ISBN 978-1-4493-0322-8.[bib|]

This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to get started with R for basic statistics, graphics, and regression. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R.

[46]Paul Murrell. R Graphics, Second Edition. Chapman & Hall/CRC the R series. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-3176-2.[bib| Discount Info|]

Extensively updated to reflect the evolution of statistics and computing, the second edition of the bestselling R Graphics comes complete with new packages and new examples. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graphics. Organized into five parts, R Graphics covers both traditional and newer, R-specific graphics systems. The book reviews the graphics facilities of the R language and describes R's powerful grid graphics system. It then covers the graphics engine, which represents a common set of fundamental graphics facilities, and provides a series of brief overviews of the major areas of application for R graphics and the major extensions of R graphics.

[47]Laura Chihara and Tim Hesterberg. Mathematical Statistics with Resampling and R. Wiley, 1st edition, 2011. ISBN 978-1-1180-2985-5.[bib| Publisher Info|]

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis, Calculation of sampling distributions, The Central Limit Theorem, Monte Carlo sampling, Maximum likelihood estimation and properties of estimators, Confidence intervals and hypothesis tests, Regression, Bayesian methods. Case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the material. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

[48]John Fox and Sanford Weisberg. An R Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, USA, second edition, 2011. ISBN 978-1-4129-7514-8.[bib|]

A companion book to a text or course on applied regression (such as Applied Regression Analysis and Generalized Linear Models, Second Edition by John Fox or Applied Linear Regression, Third edition by Sanford Weisberg). It introduces R, and concentrates on how to use linear and generalized-linear models in R while assuming familiarity with the statistical methodology.

[49]Hrishi Mittal. R Graphs Cookbook. Packt Publishing, 2011. ISBN 1849513066.[bib|]

The R Graph Cookbook takes a practical approach to teaching how to create effective and useful graphs using R. This practical guide begins by teaching you how to make basic graphs in R and progresses through subsequent dedicated chapters about each graph type in depth. It will demystify a lot of difficult and confusing R functions and parameters and enable you to construct and modify data graphics to suit your analysis, presentation, and publication needs.

[50]Graham Williams. Data Mining with Rattle and R: The art of excavating data for knowledge discovery. Use R! Springer, 2011. ISBN 978-1-4419-9889-7.[bib| Discount Info| Publisher Info|]

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

[51]Manfred Gilli, Dietmar Maringer, and Enrico Schumann. Numerical Methods and Optimization in Finance. Academic Press, 2011. ISBN 978-0-12-375662-6.[bib| Publisher Info|]

The book explains tools for computational finance. It covers fundamental numerical analysis and computational techniques, for example for option pricing, but two topics are given special attention: simulation and optimization. Many chapters are organized as case studies, dealing with problems like portfolio insurance or risk estimation; in particular, several chapters explain optimization heuristics and how to use them for portfolio selection or the calibration of option pricing models. Such practical examples allow readers to learn the required steps for solving specific problems, and to apply these steps to other problems, too. At the same time, the chosen applications are relevant enough to make the book a useful reference on how to handle given problems. Matlab and R sample code is provided in the text and can be downloaded from the book's website; an R package `NMOF' is also available.

[52]Bruno Falissard. Analysis of Questionnaire Data with R. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-1766-7.[bib| Discount Info|]

While theoretical statistics relies primarily on mathematics and hypothetical situations, statistical practice is a translation of a question formulated by a researcher into a series of variables linked by a statistical tool. As with written material, there are almost always differences between the meaning of the original text and translated text. Additionally, many versions can be suggested, each with their advantages and disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations. As indicated in the title, the syntax of these statistical formulations is based on the well-known R language, chosen for its popularity, simplicity, and power of its structure. Although syntax is vital, understanding the semantics is the real challenge of any good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand. Sometimes data sets contain errors, inconsistencies between answers, or missing data. More often, available statistical tools are not formally appropriate for the given situation, making it difficult to assess to what extent this slight inadequacy affects the interpretation of results. Analysis of Questionnaire Data with R tackles these and other common challenges in the practice of statistics.

[53]RandallL. Eubank. Statistical Computing with C++ and R. Chapman & Hall/CRC the R series. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4200-6650-0.[bib| Discount Info|]

With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a boot-camp on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.

[54]ClausThorn Ekstrom. The R Primer. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-6206-3.[bib| Discount Info|]

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. Rather than explore the many options available for every command as well as the ever-increasing number of packages, the book focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point. The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated. While base R is used throughout, other functions or packages are listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R. Additional topics and R code are available from the book's supporting website at

[55]JamesMichael Curran. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, Boca Raton, FL, 2011. ISBN 9781420088267.[bib| Publisher Info]

Keywords: Criminal investigation, Data processing, Forensic sciences, Forensic statistics, R (Computer program language), Statistical methods

[56]ChristianP. Robert and George Casella. Mthodes de Monte-Carlo avec R. Pratique R. Springer, 1st edition, 2011. ISBN 978-2-8178-0180-3. French translation of Introducting Monte Carlo Methods with R.[bib| Publisher Info]

Les techniques informatiques de simulation sont essentielles au statisticien. Afin que celui-ci puisse les utiliser en vue de rsoudre des problmes statistiques, il lui faut au pralable dvelopper son intuition et sa capacit produire lui-mme des modles de simulation. Ce livre adopte donc le point de vue du programmeur pour exposer ces outils fondamentaux de simulation stochastique. Il montre comment les implmenter sous R et donne les cls d'une meilleure comprhension des mthodes exposes en vue de leur comparaison, sans s'attarder trop longuement sur leur justification thorique. Les auteurs prsentent les algorithmes de base pour la gnration de donnes alatoires, les techniques de Monte-Carlo pour l'intgration et l'optimisation, les diagnostics de convergence, les chanes de Markov, les algorithmes adaptatifs, les algorithmes de Metropolis- Hastings et de Gibbs. Tous les chapitres incluent des exercices. Les programmes R sont disponibles dans un package spcifique. Le livre s'adresse toute personne que la simulation statistique intresse et n'exige aucune connaissance pralable du langage R, ni aucune expertise en statistique baysienne, bien que nombre d'exercices relvent de ce champ prcis. Cet ouvrage sera utile aux tudiants et aux professionnels actifs dans les domaines de la statistique, des tlcommunications, de l'conomtrie, de la finance et bien d'autres encore.

[57]ChrisHay Jahans. R Companion to Linear Models. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-7365-6.[bib| Discount Info|]

Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

[58]DamonM. Berridge. Multivariate Generalized Linear Mixed Models Using R. Chapman & Hall/CRC Press, Boca Raton, FL, 2011. ISBN 978-1-4398-1326-3.[bib| Discount Info|]

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.

[59]Shravan Vasishth and Michael Broe. The Foundations of Statistics: A Simulation-based Approach. Springer, 2010. ISBN 978-3-642-16312-8.[bib| Discount Info| Publisher Info]

Statistics and hypothesis testing are routinely used in areas (such as linguistics) that are traditionally not mathematically intensive. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on. As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research --- they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided (the freely available programming language R is used throughout). Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US.

[60]RobertA. Muenchen and JosephM. Hilbe. R for Stata Users. Statistics and Computing. Springer, 2010. ISBN 978-1-4419-1317-3.[bib| Discount Info| Publisher Info]

This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.

[61]Rob Kabacoff. R in Action. Manning, 2010.[bib|]

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.

[62]Pierre-Andr Cornillon, Arnaud Guyader, Franois Husson, Nicolas Jgou, Julie Josse, Maela Kloareg, Eric Matzner-Lober, and Laurent Rouviere. Statistiques avec R. Didact Statistiques. Presses Universitaires de Rennes, 2nd edition, 2010. ISBN 978-2-7535-1087-6.[bib| Publisher Info|]

Aprs seulement dix ans d'existence, le logiciel R est devenu un outil incontournable de statistique et de visualisation de donnes tant dans le monde universitaire que dans celui de l'entreprise. Ce dveloppement exceptionnel s'explique par ses trois principales qualits: il est gratuit, trs complet et en essor permanent. Ce livre s'articule en deux grandes parties : la premire est centre sur le fonctionnement du logiciel R tandis que la seconde met en oeuvre une vingtaine de mthodes statistiques au travers de fiches. Ces fiches sont chacune bases sur un exemple concret et balayent un large spectre de techniques classiques en traitement de donnes. Ce livre s'adresse aux dbutants comme aux utilisateurs rguliers de R. Il leur permettra de raliser rapidement des graphiques et des traitements statistiques simples ou labors. Pour cette deuxime dition, le texte a t rvis et augment. Certaines fiches ont t compltes, d'autres utilisent de nouveaux exemples. Enfin des fiches ont t ajoutes ainsi que quelques nouveaux exercices.

[63]PierreLafaye deMicheaux, Rmy Drouilhet, and Benot Liquet. Le Logiciel R. Matriser le langage, effectuer des analyses statistiques. Springer, Collection Statistiques et Probabilits appliques, 1st edition, 2010. ISBN 9782817801148.[bib|]

Ce livre est consacr un outil dsormais incontournable pour l'analyse de donnes, l'laboration de graphiques et le calcul statistique : le logiciel R. Aprs avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des donnes, importation et exportation, accs la documentation, reprsentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage dtaillent l'ensemble des manipulations permettant la manipulation avec R d'un trs grand nombre de mthodes et de notions statistiques : simulation de variables alatoires, intervalles de confiance, tests d'hypothses, valeur-p, bootstrap, rgression linaire, ANOVA (y compris rptes), et d'autres encore. crit avec un grand souci de pdagogie et clart, et agrment de nombreux exercices et travaux pratiques, ce livre accompagnera idalement tous les utilisateurs de R -- et cela sur les environnements Windows, Macintosh ou Linux -- qu'ils soient dbutants ou d'un niveau avanc : tudiants, enseignants ou chercheurs en statistique, mathmatiques, mdecine, informatique, biologie, psychologie, sciences infirmires, etc. Il leur permettra de matriser en profondeur le fonctionnement de ce logiciel. L'ouvrage sera aussi utile aux utilisateurs plus confirms qui retrouveront expos ici l'ensemble des fonctions R les plus couramment utilises.

[64]Joseph Adler. R in a Nutshell [deutsche Ausgabe]. O'Reilly Verlag, Kln, 1. edition, 2010. ISBN 978-3-89721-649-5. Mit Funktions- und Datensatzreferenz; Begleitpaket nutshellDE mit Beispieldaten und -code (auf der Verlagsseite des Buchs).[bib| Publisher Info]

Das Buch ist ein umfangreiches Handbuch und Nachschlagewerk zu R. Es beschreibt die Installation und Erweiterung der Software und gibt einen breiten berblick ber die Programmiersprache. Anhand unzhliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik behandelt es, wie Daten eingelesen, transformiert und grafisch dargestellt werden. Anhand realer Datenstze werden zahlreiche Methoden und Verfahren der statistischen Datenanalyse mit R demonstriert. Die Funktionsreferenz wurde fr die deutsche Ausgabe vollstndig neu verfasst.

[65]JohnM. Quick. The Statistical Analysis with R Beginners Guide. Packt Publishing, 2010. ISBN 1849512086.[bib|]

The Statistical Analysis with R Beginners Guide will take you on a journey as the strategist for an ancient Chinese kingdom. Along the way, you will learn how to use R to arrive at practical solutions and how to effectively communicate your results. Ultimately, the fate of the kingdom depends on your ability to make informed, data- driven decisions with R.

[66]Francois Husson, Sbastien L, and Jrme Pags. Exploratory Multivariate Analysis by Example Using R. Computer Sciences and Data Analysis. Chapman & Hall/CRC, 2010. ISBN 978-1-4398-3580-7.[bib| Discount Info| Publisher Info| http://factomin