title = {Flexible Imputation of Missing Data},
  author = {van Buuren, S.},
  isbn = {9781138588318},
  lccn = {2018017122},
  series = {Chapman \& Hall/CRC Interdisciplinary Statistics},
  url = {https://www.routledge.com/Flexible-Imputation-of-Missing-Data-Second-Edition/Buuren/p/book/9781138588318},
  year = {2018},
  publisher = {CRC Press LLC}
  address = {New York},
  title = {Oceanographic {Analysis} with {R}},
  isbn = {978-1-4939-8842-6},
  url = {https://www.springer.com/us/book/9781493988426},
  abstract = {This book presents the R software environment as a key tool for oceanographic computations and provides a rationale for using R over the more widely-used tools of the field such as MATLAB. Kelley provides a general introduction to R before introducing the ‘oce’ package. This package greatly simplifies oceanographic analysis by handling the details of discipline-specific file formats, calculations, and plots. Designed for real-world application and developed with open-source protocols, oce supports a broad range of practical work. Generic functions take care of general operations such as subsetting and plotting data, while specialized functions address more specific tasks such as tidal decomposition, hydrographic analysis, and ADCP coordinate transformation. In addition, the package makes it easy to document work, because its functions automatically update processing logs stored within its data objects. Kelley teaches key R functions using classic examples from the history of oceanography, specifically the work of Alfred Redfield, Gordon Riley, J. Tuzo Wilson, and Walter Munk. Acknowledging the pervasive popularity of MATLAB, the book provides advice to users who would like to switch to R. Including a suite of real-life applications and over 100 exercises and solutions, the treatment is ideal for oceanographers, technicians, and students who want to add R to their list of tools for oceanographic analysis.},
  language = {en},
  urldate = {2018-07-22},
  publisher = {Springer-Verlag},
  author = {Kelley, Dan E.},
  month = oct,
  year = {2018}
  author = {Jean-Francois Mas},
  title = {Análisis espacial con R: Usa R como un Sistema de Información Geográfica},
  year = {2018},
  language = {Spanish},
  publisher = {European Scientific Institute},
  location = {Republic of Macedonia},
  isbn = {978-608-4642-66-4},
  pages = {114},
  url = {http://eujournal.org/files/journals/1/books/JeanFrancoisMas.pdf}
  title = {Data Visualisation with R},
  author = {Thomas Rahlf},
  year = 2017,
  publisher = {Springer International Publishing},
  address = {New York},
  publisherurl = {https://www.springer.com/us/book/9783319497501},
  isbn = {978-3-319-49750-1},
  url = {http://www.datavisualisation-r.com},
  abstract = {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 R’s Base Graphics
                  System. Every example uses real data and includes
                  step-by-step explanations of the figures and their
  language = {en}
  author = {Steven Murray},
  title = {Apprendre R en un Jour},
  publisher = {SJ Murray},
  url = {https://www.amazon.com/dp/B071W6ZJCV/ref=sr_1_1?s=digital-text&ie=UTF8&qid=1496261881&sr=1-1},
  year = 2017,
  asin = {B071W6ZJCV},
  note = {Ebook},
  abstract = {'Apprendre R en un Jour' donne au lecteur les
                  compétences clés au travers d'une approche axée sur
                  des exemples et est idéal pour les universitaires,
                  scientifiques, mathématiciens et ingénieurs. Le
                  livre ne suppose aucune connaissance préalable en
                  programmation et couvre progressivement toutes les
                  étapes essentielles pour prendre de l'assurance et
                  devenir compétent en R en une journée. Les sujets
                  couverts incluent: comment importer, manipuler,
                  formater, itérer (en boucle), questionner, effectuer
                  des statistiques élémentaires sur, et tracer des
                  graphiques à partir de données, à l'aide d'une
                  explication étape par étape de la technique et de
                  démonstrations que le lecteur est encouragé de
                  reproduire sur son ordinateur, en utilisant des
                  ensembles de données déjà en mémoire dans R. Chaque
                  fin de chapitre inclut aussi des exercices (avec
                  solutions à la fin du livre) pour s'entraîner,
                  mettre en pratique les compétences clés et habiliter
                  le lecteur à construire sur les bases acquises au
                  cours de ce livre d'introduction.}
  author = {Lawrence Leemis},
  title = {Learning Base R},
  publisher = {Lightning Source},
  url = {https://www.amazon.com/Learning-Base-Lawrence-Mark-Leemis/dp/0982917481},
  year = 2016,
  isbn = {978-0-9829174-8-0},
  abstract = {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.}
  author = {Vikram Dayal},
  title = {An Introduction to R for Quantitative Economics:
                  Graphing, Simulating and Computing},
  publisher = {Springer},
  url = {https://www.springer.com/978-81-322-2340-5},
  year = 2015,
  isbn = {978-81-322-2340-5},
  abstract = {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
  author = {Sun, C.},
  title = {Empirical Research in Economics: Growing up with {R}},
  publisher = {Pine Square},
  address = {Starkville, Mississippi, USA},
  edition = {1st},
  abstract = {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.},
  pages = 579,
  isbn = {978-0-9965854-0-8},
  lccn = 2015911715,
  url = {https://www.amazon.com/Empirical-Research-Economics-Changyou-Sun/dp/0996585400/ref=aag_m_pw_dp?ie=UTF8&m=A1TZL30UWYSSR8},
  year = 2015
  title = {{Einführung in die statistische Datenanalyse mit R}},
  author = {Matthias Kohl},
  year = 2015,
  publisher = {bookboon.com},
  address = {London},
  publisherurl = {https://bookboon.com/de/einfuhrung-in-die-statistische-datenanalyse-mit-r-ebook},
  isbn = {978-87-403-1156-3},
  note = {In German},
  abstract = {Das Buch bietet eine Einführung in die statistische
                  Datenanalyse unter Verwendung der freien
                  Statistiksoftware R, der derzeit wohl mächtigsten
                  Statistiksoftware. Die Analysen werden anhand realer
                  Daten durchgeführt und besprochen. Nach einer kurzen
                  Beschreibung der Statistiksoftware R werden wichtige
                  Kenngrößen und Diagramme der deskriptiven Statistik
                  vorgestellt. Anschließend werden Empfehlungen für die
                  Erstellung von Diagrammen gegeben, wobei ein
                  spezielles Augenmerk auf die Auswahl geeigneter Farben
                  gelegt wird. Die zweite Hälfte des Buches behandelt
                  die Grundlagen der schließenden Statistik. Zunächst
                  wird eine Reihe von Wahrscheinlichkeitsverteilungen
                  eingeführt und deren Anwendungen anhand von Beispielen
                  illustriert. Es folgt eine Beschreibung, wie die in
                  der Praxis unbekannten Parameter der Verteilungen auf
                  Basis vorliegender Daten geschätzt werden können. Im
                  abschließenden Kapitel werden statistische
                  Hypothesentests eingeführt und die für die Praxis
                  wichtigsten Tests besprochen.},
  language = {de}
  title = {Introduction to statistical data analysis with {R}},
  author = {Matthias Kohl},
  year = 2015,
  publisher = {bookboon.com},
  address = {London},
  publisherurl = {https://bookboon.com/en/introduction-to-statistical-data-analysis-with-r-ebook},
  isbn = {978-87-403-1123-5},
  abstract = {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.},
  language = {en}
  author = {Marta Blangiardo and Michela Cameletti},
  title = {Spatial and Spatio-temporal Bayesian Models with {R-INLA}},
  year = 2015,
  publisher = {Wiley},
  address = {Chichester, West Sussex, United Kingdom},
  isbn = {978-1-118-32655-8},
  edition = {1st},
  pages = 320,
  url = {https://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118326555.html}
  title = {Mastering Data Analysis with R},
  author = {Gergely Dar{\'o}czi},
  publisher = {Packt Publishing},
  year = 2015,
  month = 9,
  isbn = 9781783982028,
  url = {https://www.packtpub.com/product/mastering-data-analysis-with-r/9781783982028},
  totalpages = 396,
  abstract = {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.}
  author = {Victor A. Bloomfield},
  title = {Using {R} for Numerical Analysis in Science and Engineering},
  publisher = {Chapman & Hall/CRC},
  url = {http://www.crcpress.com/product/isbn/9781439884485},
  year = 2014,
  isbn = {978-1439884485},
  abstract = {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

                  * 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.}
  author = {Torsten Hothorn and Brian S. Everitt},
  title = {A Handbook of Statistical Analyses Using {R}},
  year = 2014,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, Florida, USA},
  isbn = {978-1-4822-0458-2},
  edition = {3rd},
  url = {http://www.crcpress.com/product/isbn/9781482204582}
  title = {Datendesign mit {R}. 100 Visualisierungsbeispiele},
  author = {Thomas Rahlf},
  year = 2014,
  publisher = {Open Source Press},
  address = {M{\"u}nchen},
  publisherurl = {https://www.springer.com/us/book/9783319497501},
  isbn = {978-3-95539-094-5},
  url = {http:///www.datenvisualisierung-r.de},
  note = {In German},
  abstract = {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 m{\"a}chtiges Werkzeug zur Verf{\"u}gung,
                  das hinsichtlich der Visualisierungsm{\"o}glichkeiten
                  praktisch keine W{\"u}nsche offen l{\"a}sst. Dieses
                  Buch f{\"u}hrt in die Grundlagen der Gestaltung von
                  Pr{\"a}sentationsgrafiken mit R ein und zeigt anhand
                  von 100 vollst{\"a}ndigen Skript-Beispielen, wie Sie
                  Balken- und S{\"a}ulendiagramme,
                  Bev{\"o}lkerungspyramiden, 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. F{\"u}r
                  jedes Beispiel werden reale Daten verwendet sowie die
                  Abbildung und deren Programmierung Schritt f{\"u}r
                  Schritt erl{\"a}utert. Die gedruckte Ausgabe
                  enth{\"a}lt einen pers{\"o}nlichen Zugangs-Code, der
                  Ihnen kostenlos Zugriff auf die Online-Ausgabe dieses
                  Buches gew{\"a}hrt.},
  language = {de}
  author = {Sarah Stowell},
  title = {Using R for Statistics},
  publisher = {Apress},
  url = {https://www.apress.com/9781484201404},
  year = 2014,
  isbn = {978-1484201404},
  abstract = {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

                  * 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.}
  title = {Multivariate Time Series Analysis With R and Financial Applications},
  author = {Ruey S. Tsay},
  year = 2014,
  publisher = {John Wiley},
  address = {New Jersey},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-1118617908.html},
  isbn = {978-1-118-61790-8},
  url = {https://faculty.chicagobooth.edu/ruey-s-tsay/research/multivariate-time-series-analysis-with-r-and-financial-applications},
  abstract = {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.},
  language = {en}
  title = {Nonlinear Parameter Optimization Using R Tools},
  author = {Nash, J.C.},
  isbn = {9781118883969},
  lccn = {2014000044},
  year = {2014},
  publisher = {Wiley},
  abstract = {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
		  non–linear, 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.}
  author = {Michael J. Crawley},
  title = {Statistics: An Introduction using {R}},
  edition = {2nd},
  publisher = {Wiley},
  year = 2014,
  isbn = {978-1-118-94109-6},
  url = {http://www.bio.ic.ac.uk/research/crawley/statistics/},
  publisherurl = {https://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118941098.html},
  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.}
  title = {Exploration de donn{\'e}es et m{\'e}thodes statistiques avec le logiciel {R}},
  author = {Lise Bellanger and Richard Tomassone},
  year = 2014,
  pages = 480,
  edition = {1st},
  publisher = {Ellipses},
  series = {R{\'e}f{\'e}rences sciences},
  isbn = {978-2-7298-8486-4},
  url = {http://www.math.sciences.univ-nantes.fr/~bellanger/ouvrage.html},
  publisherurl = {https://www.editions-ellipses.fr/accueil/1263-exploration-de-donnees-et-methodes-statistiques-data-analysis-data-mining-avec-le-logiciel-r-9782729884864.html},
  abstract = {La Statistique envahit pratiquement tous les
                  domaines d'application, aucun n'en est exclus; elle
                  permet d'explorer et d'analyser des corpus de
                  donn{\'e}es de plus en plus volumineux : l'{\`e}re
                  des big data et du data mining s'ouvre {\`a} nous !
                  Cette omnipr{\'e}sence s'accompagne bien souvent de
                  l'absence de regard critique tant sur l'origine des
                  donn{\'e}es que sur la mani{\`e}re de les traiter.
                  La facilit{\'e} d'utilisation des logiciels de
                  traitement statistique permet de fournir quasi
                  instantan{\'e}ment des graphiques et des
                  r{\'e}sultats num{\'e}riques. Le risque est donc
                  grand d'une acceptation aveugle des conclusions qui
                  d{\'e}coulent de son emploi, comme simple citoyen ou
                  comme homme politique. Les auteurs insistent sur les
                  concepts sans n{\'e}gliger la rigueur, ils
                  d{\'e}crivent les outils de d{\'e}cryptage des
                  donn{\'e}es. L'ouvrage couvre un large spectre de
                  m{\'e}thodes allant du pr{\'e}-traitement des
                  donn{\'e}es aux m{\'e}thodes de pr{\'e}vision, en
                  passant par celles permettant leur visualisation et
                  leur synth{\`e}se. De nombreux exemples issus de
                  champs d'application vari{\'e}s sont trait{\'e}s
                  {\`a} l'aide du logiciel libre R, dont les commandes
                  sont comment{\'e}es.  L'ouvrage est destin{\'e} aux
                  {\'e}tudiants de masters scientifiques ou
                  d'{\'e}coles d'ing{\'e}nieurs ainsi qu'aux
                  professionnels voulant utiliser la Statistique de
                  mani{\`e}re r{\'e}fl{\'e}chie : des sciences de la
                  vie {\`a} l'arch{\'e}ologie, de la sociologie {\`a}
                  l'analyse financi{\`e}re.},
  language = {fr}
  title = {Psychologie statistique avec {R}},
  author = {Yvonnick Noel},
  series = {Pratique R},
  year = 2013,
  publisher = {Springer},
  address = {Paris},
  isbn = {978-2-8178-0424-8},
  abstract = {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.}
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman \& Hall/CRC},
  year = 2013,
  isbn = {978-1482203530},
  url = {https://github.com/yihui/knitr-book/},
  publisherurl = {https://www.taylorfrancis.com/books/dynamic-documents-knitr-yihui-xie/10.1201/b15166},
  abstract = {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.}
  author = {Steven Murray},
  title = {Learn {R} in a Day},
  publisher = {SJ Murray},
  url = {https://www.amazon.com/Learn-R-Day-Steven-Murray-ebook/dp/B00GC2LKOK/ref=cm_cr_pr_pb_t},
  year = 2013,
  asin = {B00GC2LKOK},
  note = {Ebook},
  abstract = {`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.}
  title = {An Introduction to Analysis of Financial Data with R},
  author = {Ruey S. Tsay},
  year = 2013,
  publisher = {John Wiley},
  address = {New Jersey},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-0470890819.html},
  isbn = {978-0-470-89081-3},
  url = {https://faculty.chicagobooth.edu/ruey-s-tsay/research/an-introduction-to-analysis-of-financial-data-with-r},
  abstract = {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.},
  language = {en}
  title = {Analyse von Genexpressionsdaten --- mit {R} und
  author = {Matthias Kohl},
  year = 2013,
  publisher = {Ventus Publishing ApS},
  address = {London},
  publisherurl = {https://bookboon.com/de/analyse-von-genexpressionsdaten-ebook},
  isbn = {978-87-403-0349-0},
  note = {In German},
  abstract = {Das Buch bietet eine Einf{\"u}hrung in die Verwendung
                  von R und Bioconductor f{\"u}r die Analyse von
                  Mikroarray-Daten.  Es werden die Arraytechnologien von
                  Affymetrix und Illumina ausf{\"u}hrlich behandelt.
                  Dar{\"u}ber hinaus wird auch auf andere
                  Arraytechnologien eingegangen.  Alle notwendigen
                  Schritte beginnend mit dem Einlesen der Daten und der
                  Qualit{\"a}tskontrolle {\"u}ber die Vorverarbeitung
                  der Daten bis hin zur statistischen Analyse sowie der
                  Enrichment Analyse werden besprochen.  Jeder der
                  Schritte wird anhand einfacher Beispiele praktisch
                  vorgef{\"u}hrt, wobei der im Buch verwendete R-Code
                  separat zum Download bereitsteht.},
  language = {de}
  title = {Introductory {R}: A Beginner's Guide to Data
                  Visualisation and Analysis using {R}},
  author = {Knell, Robert J},
  year = 2013,
  isbn = {978-0-9575971-0-5},
  publisher = {(See web site)},
  month = {March},
  url = {http://www.introductoryr.co.uk},
  abstract = {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
  title = {Methods of Statistical Model Estimation},
  author = {Hilbe, Joseph},
  isbn = {978-1-4398-5802-8},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439858028},
  year = 2013,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  title = {Introduction to {R} for Quantitative Finance},
  author = {Gergely Dar{\'o}czi and Michael Puhle and Edina
                  Berlinger and P{\'e}ter Cs{\'o}ka and Daniel Havran
                  and M{\'a}rton Michaletzky and Zsolt Tulassay and Kata
                  V{\'a}radi and Agnes Vidovics-Dancs},
  publisher = {Packt Publishing},
  year = 2013,
  month = {November},
  isbn = 9781783280933,
  url = {https://www.packtpub.com/product/introduction-to-r-for-quantitative-finance/9781783280933},
  totalpages = 164,
  abstract = {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.}
  title = {Reproducible Research with {R} and {RStudio}},
  author = {Gandrud, Christopher},
  isbn = {978-1-4665-7284-3},
  series = {Chapman \& Hall/CRC The R series},
  url = {https://www.taylorfrancis.com/books/reproducible-research-studio-christopher-gandrud/10.1201/b15100},
  year = 2013,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  author = {Dirk Eddelbuettel},
  title = {Seamless R and C++ Integration with Rcpp},
  publisher = {Springer},
  series = {Use R!},
  year = 2013,
  address = {New York},
  isbn = {978-1-4614-6867-7},
  publisherurl = {https://www.springer.com/978-1-4614-6867-7},
  abstract = {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++. },
  orderinfo = {springer.txt}
  title = {Applied Meta-Analysis with {R}},
  author = {Chen, Din},
  isbn = {978-1-4665-0599-5},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC Biostatistics series},
  url = {http://www.crcpress.com/product/isbn/9781466505995},
  year = 2013,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = { 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.}
  author = {Stano Pekar and Marek Brabec},
  title = {Moderni analyza biologickych dat. 2.  Linearni modely
                  s korelacemi v prostredi {R} [Modern Analysis of
                  Biological Data. 2. Linear Models with Correlations in
  year = 2012,
  publisher = {Masaryk University Press},
  address = {Brno},
  publisherurl = {https://www.press.muni.cz/en/editorial-series-of-munipress/moderni-analyza-biologickych-dat},
  isbn = {978-80-21058-12-5},
  note = {In Czech},
  abstract = {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.},
  language = {cz}
  title = {Solving Differential Equations in R},
  author = {Soetaert, K. and Cash, J. and Mazzia, F.},
  isbn = {978-3-642-28070-2},
  series = {Use R!},
  year = 2012,
  publisher = {Springer},
  publisherurl = {https://www.springer.com/978-3-642-28070-2},
  abstract = {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
  author = {Sarah Stowell},
  title = {Instant {R}: An Introduction to {R} for Statistical
  publisher = {Jotunheim Publishing},
  year = 2012,
  isbn = {978-0-957-46490-2},
  url = {http://www.instantr.com/wp-content/uploads/2012/11/},
  abstract = {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.}
  author = {Pfaff, Bernhard},
  title = {Financial Risk Modelling and Portfolio Optimisation
                  with {R}},
  publisher = {Wiley},
  address = {Chichester, UK},
  year = 2012,
  isbn = {978-0-470-97870-2},
  url = {https://www.pfaffikus.de/books/wiley/},
  publisherurl = {https://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470978708.html},
  abstract = {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
  title = {The {BUGS} Book: A Practical Introduction to
                  {B}ayesian Analysis},
  author = {Lunn, David},
  isbn = {978-1-5848-8849-9},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC Texts in Statistical Science
  url = {http://www.crcpress.com/product/isbn/9781584888499},
  year = 2012,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  title = {Programming Graphical User Interfaces in {R}},
  author = {Lawrence, Michael},
  isbn = {978-1-4398-5682-6},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC the R series},
  url = {http://www.crcpress.com/product/isbn/9781439856826},
  year = 2012,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  title = {Event History Analysis with R},
  author = {G{\"o}ran Brostr{\"o}m},
  isbn = {978-1-4398-3164-9},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC the R series},
  url = {http://www.crcpress.com/product/isbn/9781439831649},
  year = 2012,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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
  author = {Dimitris Rizopoulos},
  title = {Joint Models for Longitudinal and Time-to-Event Data,
                  with Applications in {R}},
  publisher = {Chapman \& Hall/CRC},
  address = {Boca Raton},
  year = 2012,
  isbn = {978-1-4398-7286-4},
  url = {http://jmr.R-Forge.R-project.org/},
  publisherurl = {http://www.crcpress.com/product/isbn/9781439872864},
  abstract = {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
  title = {The {R} Student Companion},
  author = {Dennis, Brian},
  isbn = {978-1-4398-7540-7},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439875407},
  year = 2012,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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
  title = {R for Statistics},
  author = {Cornillon, Pierre-Andre},
  isbn = {978-1-4398-8145-3},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439881453},
  year = 2012,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  author = {A. B. Shipunov and E. M. Baldin and P. A. Volkova and
                  A. I. Korobejnikov and S. A. Nazarova and S. V. Petrov
                  and V. G. Sufijanov},
  title = {{Nagljadnaja statistika. Ispoljzuem R! / Vusial
                  statistics. Use R!}},
  pages = 298,
  address = {Moscow},
  year = 2012,
  isbn = {978-5-94074-828-1},
  abstract = {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.},
  publisher = {DMK Press}
  author = {Yves Aragon},
  title = {S{\'e}ries temporelles avec {R}. M{\'e}thodes et cas},
  publisher = {Springer, Collection Pratique R},
  year = 2011,
  pages = 265,
  edition = {1st},
  isbn = {978-2-8178-0207-7},
  abstract = {Ce livre {\'e}tudie sous un angle original le concept
                  de s{\'e}rie temporelle, dont la complexit{\'e}
                  th{\'e}orique et l'utilisation sont souvent sources de
                  difficult{\'e}s. La th{\'e}orie distingue par exemple
                  les notions de s{\'e}ries stationnaire et non
                  stationnaire, mais il n'est pas rare de pouvoir
                  mod{\'e}liser une s{\'e}rie par deux mod{\`e}les
                  incompatibles. De plus, un peu d'intimit{\'e} avec les
                  s{\'e}ries montre qu'on peut s'appuyer sur des
                  graphiques vari{\'e}s pour en comprendre assez
                  rapidement la structure, avant toute
                  mod{\'e}lisation. Ainsi, au lieu d'{\'e}tudier des
                  m{\'e}thodes de mod{\'e}lisation, puis de les
                  illustrer, l'auteur prend ici le parti de
                  s'int{\'e}resser {\`a} un nombre limit{\'e} de
                  s{\'e}ries afin de trouver ce qu'on peut dire de
                  chacune. Avant d'aborder ces {\'e}tudes de cas, il
                  proc{\'e}de {\`a} quelques rappels et commence par
                  pr{\'e}senter les graphiques pour s{\'e}ries
                  temporelles offerts par R. Il revient ensuite sur des
                  notions fondamentales de statistique math{\'e}matique,
                  puis r{\'e}vise les concepts et les mod{\`e}les
                  classiques de s{\'e}ries. Il pr{\'e}sente les
                  structures de s{\'e}ries temporelles dans R et leur
                  importation. Il revisite le lissage exponentiel {\`a}
                  la lumi{\`e}re des travaux les plus r{\'e}cents. Un
                  chapitre est consacr{\'e} {\`a} la simulation. Six
                  s{\'e}ries sont ensuite {\'e}tudi{\'e}es par le menu
                  en confrontant plusieurs approches.}
  author = {Pierre Andr{\'e} Cornillon and Eric Matzner-Lober},
  title = {R{\'e}gression avec {R}},
  publisher = {Springer, Collection Pratique R},
  year = 2011,
  pages = 242,
  edition = {1st},
  isbn = {978-2-8178-0183-4},
  abstract = {Cet ouvrage expose en d{\'e}tail l'une des
                  m{\'e}thodes statistiques les plus courantes : la
                  r{\'e}gression. Il concilie th{\'e}orie et
                  applications, en insistant notamment sur l'analyse de
                  donn{\'e}es r{\'e}elles avec le logiciel R. Les
                  premiers chapitres sont consacr{\'e}s {\`a} la
                  r{\'e}gression lin{\'e}aire simple et multiple, et
                  expliquent les fondements de la m{\'e}thode, tant au
                  niveau des choix op{\'e}r{\'e}s que des hypoth{\`e}ses
                  et de leur utilit{\'e}. Puis ils d{\'e}veloppent les
                  outils permettant de v{\'e}rifier les hypoth{\`e}ses
                  de base mises en {\oe}uvre par la r{\'e}gression, et
                  pr{\'e}sentent les mod{\`e}les d'analyse de la
                  variance et covariance. Suit l'analyse du choix de
                  mod{\`e}le en r{\'e}gression multiple. Les derniers
                  chapitres pr{\'e}sentent certaines extensions de la
                  r{\'e}gression, comme la r{\'e}gression sous
                  contraintes (ridge, lasso et lars), la r{\'e}gression
                  sur composantes (PCR et PLS), et, enfin, introduisent
                  {\`a} la r{\'e}gression non param{\'e}trique (spline
                  et noyau). La pr{\'e}sentation t{\'e}moigne d'un
                  r{\'e}el souci p{\'e}dagogique des auteurs qui
                  b{\'e}n{\'e}ficient d'une exp{\'e}rience
                  d'enseignement aupr{\`e}s de publics tr{\`e}s
                  vari{\'e}s. Les r{\'e}sultats expos{\'e}s sont
                  replac{\'e}s dans la perspective de leur utilit{\'e}
                  pratique gr{\^a}ce {\`a} 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 compl{\'e}t{\'e} par une
                  suite d'exercices corrig{\'e}s. Le niveau
                  math{\'e}matique requis rend ce livre accessible aux
                  {\'e}l{\`e}ves ing{\'e}nieurs, aux {\'e}tudiants de
                  niveau Master et aux chercheurs actifs dans divers
                  domaines des sciences appliqu{\'e}es.}
  address = {Vi\c{c}osa, MG, Brazil},
  author = {Peternelli, Luiz Alexandre and Mello, Marcio Pupin},
  edition = 1,
  isbn = {978-85-7269-400-1},
  month = {March},
  pages = 185,
  publisher = {Editora UFV},
  series = {S\'{e}rie Did\'{a}tica},
  title = {{Conhecendo o R: uma vis\~{a}o estat\'{\i}stica}},
  url = {https://www.editoraufv.com.br/produto/conhecendo-o-r-uma-visao-mais-que-estatistica/1109294},
  year = 2011,
  abstract = {Este material \'e de grande valia para estudantes ou
                  pesquisadores que usam ferramentas estat\'isticas em
                  trabalhos de pesquisa ou em uma simples an\'alise de
                  dados, constitui ponto de partida para aqueles que
                  desejam come\c{c}ar a utilizar o R e suas ferramentas
                  estat\'isticas ou, mesmo, para os que querem ter
                  sempre \`a m\~ao material de refer\^encia f\'acil,
                  objetivo e abrangente para uso desse software.}
  author = {Paul Teetor},
  title = {R Cookbook},
  publisher = {O'Reilly},
  year = 2011,
  isbn = {978-0-596-80915-7},
  edition = {first},
  abstract = {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.}
  author = {Paul Teetor},
  title = {25 Recipes for Getting Started with {R}},
  publisher = {O'Reilly},
  url = {http://oreilly.com/catalog/9781449303228},
  year = 2011,
  isbn = {978-1-4493-0322-8},
  abstract = {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.}
  title = {R Graphics, Second Edition},
  author = {Murrell, Paul},
  isbn = {978-1-4398-3176-2},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC the R series},
  url = {https://www.stat.auckland.ac.nz/~paul/RG2e/},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = { 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.}
  author = {Laura Chihara and Tim Hesterberg},
  title = {Mathematical Statistics with Resampling and R},
  publisher = {Wiley},
  url = {https://sites.google.com/site/chiharahesterberg/home},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-1118029852.html},
  year = 2011,
  isbn = {978-1-1180-2985-5},
  abstract = {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.},
  edition = {1st},
  pages = 440
  author = {John Fox and Sanford Weisberg},
  title = {An {R} Companion to Applied Regression},
  edition = {second},
  publisher = {Sage Publications},
  year = 2011,
  address = {Thousand Oaks, CA, USA},
  isbn = {978-1-4129-7514-8},
  url = {https://socialsciences.mcmaster.ca/jfox/Books/Companion/index.html},
  abstract = {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
  author = {Hrishi Mittal},
  title = {R Graphs Cookbook},
  publisher = {Packt Publishing},
  year = 2011,
  isbn = {1849513066},
  abstract = {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.}
  author = {Graham Williams},
  title = {Data Mining with {Rattle} and {R}: The art of
                  excavating data for knowledge discovery},
  publisher = {Springer},
  year = 2011,
  series = {Use R!},
  isbn = {978-1-4419-9889-7},
  publisherurl = {https://www.springer.com/978-1-4419-9889-7},
  url = {https://rattle.togaware.com/},
  abstract = {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.},
  orderinfo = {springer.txt}
  title = {Numerical Methods and Optimization in Finance},
  publisher = {Academic Press},
  year = 2011,
  author = {Gilli, Manfred and Maringer, Dietmar and Schumann,
  isbn = {978-0-12-375662-6},
  abstract = {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.},
  publisherurl = {https://www.elsevier.com/books/numerical-methods-and-optimization-in-finance/gilli/978-0-12-815065-8},
  url = {http://nmof.net}
  title = {Analysis of Questionnaire Data with {R}},
  author = {Falissard, Bruno},
  isbn = {978-1-4398-1766-7},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439817667},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  title = {Statistical Computing with {C++} and {R}},
  author = {Eubank, Randall L.},
  isbn = {978-1-4200-6650-0},
  orderinfo = {crcpress.txt},
  series = {Chapman \& Hall/CRC the R series},
  url = {http://www.crcpress.com/product/isbn/9781420066500},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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
  title = {The {R} Primer},
  author = {Ekstrom, Claus Thorn},
  isbn = {978-1-4398-6206-3},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439862063},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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 www.statistics.life.ku.dk/primer.}
  author = {Curran, James Michael},
  title = {Introduction to Data Analysis with R for Forensic
  year = 2011,
  publisher = {{CRC} Press},
  address = {Boca Raton, {FL}},
  isbn = 9781420088267,
  lccn = {{HV8073} {.C79} 2011},
  keywords = {Criminal investigation, Data processing, Forensic
                  sciences, Forensic statistics, R {(Computer} program
                  language), Statistical methods},
  publisherurl = {http://www.crcpress.com/product/isbn/9781420088267}
  author = {Christian P. Robert and George Casella},
  title = {M{\'e}thodes de {Monte-Carlo} avec {R}},
  pages = 256,
  edition = {1st},
  publisher = {Springer},
  year = 2011,
  series = {Pratique R},
  isbn = {978-2-8178-0180-3},
  note = {French translation of Introducting Monte Carlo Methods
                  with R},
  abstract = {Les techniques informatiques de simulation sont
                  essentielles au statisticien. Afin que celui-ci puisse
                  les utiliser en vue de r{\'e}soudre des probl{\`e}mes
                  statistiques, il lui faut au pr{\'e}alable
                  d{\'e}velopper son intuition et sa capacit{\'e} {\`a}
                  produire lui-m{\^e}me des mod{\`e}les de
                  simulation. Ce livre adopte donc le point de vue du
                  programmeur pour exposer ces outils fondamentaux de
                  simulation stochastique. Il montre comment les
                  impl{\'e}menter sous R et donne les cl{\'e}s d'une
                  meilleure compr{\'e}hension des m{\'e}thodes
                  expos{\'e}es en vue de leur comparaison, sans
                  s'attarder trop longuement sur leur justification
                  th{\'e}orique. Les auteurs pr{\'e}sentent les
                  algorithmes de base pour la g{\'e}n{\'e}ration de
                  donn{\'e}es al{\'e}atoires, les techniques de
                  Monte-Carlo pour l'int{\'e}gration et l'optimisation,
                  les diagnostics de convergence, les cha{\^i}nes 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 sp{\'e}cifique. Le livre
                  s'adresse {\`a} toute personne que la simulation
                  statistique int{\'e}resse et n'exige aucune
                  connaissance pr{\'e}alable du langage R, ni aucune
                  expertise en statistique bay{\'e}sienne, bien que
                  nombre d'exercices rel{\`e}vent de ce champ
                  pr{\'e}cis. Cet ouvrage sera utile aux {\'e}tudiants
                  et aux professionnels actifs dans les domaines de la
                  statistique, des t{\'e}l{\'e}communications, de
                  l'{\'e}conom{\'e}trie, de la finance et bien d'autres
  title = {R Companion to Linear Models},
  author = {Chris Hay Jahans},
  isbn = {978-1-4398-7365-6},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439873656},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  title = {Multivariate Generalized Linear Mixed Models Using R},
  author = {Berridge, Damon M.},
  isbn = {978-1-4398-1326-3},
  orderinfo = {crcpress.txt},
  url = {http://www.crcpress.com/product/isbn/9781439813263},
  year = 2011,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {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.}
  author = {Shravan Vasishth and Michael Broe},
  title = {{The Foundations of Statistics: A Simulation-based
  publisher = {Springer},
  year = 2010,
  isbn = {978-3-642-16312-8},
  publisherurl = {https://www.springer.com/gp/book/9783642163128},
  abstract = {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.},
  orderinfo = {springer.txt}
  author = {Robert A. Muenchen and Joseph M. Hilbe},
  title = {{R} for {Stata} Users},
  publisher = {Springer},
  year = 2010,
  series = {Statistics and Computing},
  isbn = {978-1-4419-1317-3},
  publisherurl = {https://www.springer.com/gp/book/9781441913173},
  abstract = {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.},
  orderinfo = {springer.txt}
  author = {Rob Kabacoff},
  title = {{R} in Action},
  publisher = {Manning},
  url = {https://www.manning.com/books/r-in-action},
  year = 2010,
  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
  author = {Pierre-Andr\'e Cornillon and Arnaud Guyader and Fran\c
                  cois Husson and Nicolas J\'egou and Julie Josse and
                  Maela Kloareg and Eric Matzner-Lober and Laurent
  title = {Statistiques avec {R}},
  publisher = {Presses Universitaires de Rennes},
  year = 2010,
  series = {Didact Statistiques},
  isbn = {978-2-7535-1087-6},
  edition = {2nd},
  url = {http://www.pur-editions.fr/detail.php?idOuv=1836},
  abstract = {Apr\`es seulement dix ans d'existence, le logiciel R
                  est devenu un outil incontournable de statistique et
                  de visualisation de donn\'ees tant dans le monde
                  universitaire que dans celui de l'entreprise. Ce
                  d\'eveloppement exceptionnel s'explique par ses trois
                  principales qualit\'es: il est gratuit, tr\`es complet
                  et en essor permanent. Ce livre s'articule en deux
                  grandes parties : la premi\`ere est centr\'ee sur le
                  fonctionnement du logiciel R tandis que la seconde met
                  en oeuvre une vingtaine de m\'ethodes statistiques au
                  travers de fiches. Ces fiches sont chacune bas\'ees
                  sur un exemple concret et balayent un large spectre de
                  techniques classiques en traitement de donn\'ees. Ce
                  livre s'adresse aux d\'ebutants comme aux utilisateurs
                  r\'eguliers de R. Il leur permettra de r\'ealiser
                  rapidement des graphiques et des traitements
                  statistiques simples ou \'elabor\'es. Pour cette
                  deuxi\`eme \'edition, le texte a \'et\'e r\'evis\'e et
                  augment\'e.  Certaines fiches ont \'et\'e
                  compl\'et\'ees, d'autres utilisent de nouveaux
                  exemples. Enfin des fiches ont \'et\'e ajout\'ees
                  ainsi que quelques nouveaux exercices.}
  author = {Pierre Lafaye de Micheaux and R{\'e}my Drouilhet and
                  Beno{\^i}t Liquet},
  title = {Le Logiciel R. Ma{\^i}triser le langage, effectuer des
                  analyses statistiques},
  publisher = {Springer, Collection Statistiques et Probabilit{\'e}s
  year = 2010,
  pages = 490,
  edition = {1st},
  isbn = {9782817801148},
  url = {http://www.biostatisticien.eu/springeR/},
  abstract = {Ce livre est consacr{\'e} {\`a} un outil d{\'e}sormais
                  incontournable pour l'analyse de donn{\'e}es,
                  l'{\'e}laboration de graphiques et le calcul
                  statistique : le logiciel R. Apr{\`e}s avoir introduit
                  les principaux concepts permettant une utilisation
                  sereine de cet environnement informatique
                  (organisation des donn{\'e}es, importation et
                  exportation, acc{\`e}s {\`a} la documentation,
                  repr{\'e}sentations graphiques, programmation,
                  maintenance, etc.), les auteurs de cet ouvrage
                  d{\'e}taillent l'ensemble des manipulations permettant
                  la manipulation avec R d'un tr{\`e}s grand nombre de
                  m{\'e}thodes et de notions statistiques : simulation
                  de variables al{\'e}atoires, intervalles de confiance,
                  tests d'hypoth{\`e}ses, valeur-p, bootstrap,
                  r{\'e}gression lin{\'e}aire, ANOVA (y compris
                  r{\'e}p{\'e}t{\'e}es), et d'autres encore. {\'E}crit
                  avec un grand souci de p{\'e}dagogie et clart{\'e}, et
                  agr{\'e}ment{\'e} de nombreux exercices et travaux
                  pratiques, ce livre accompagnera id{\'e}alement tous
                  les utilisateurs de R -- et cela sur les
                  environnements Windows, Macintosh ou Linux -- qu'ils
                  soient d{\'e}butants ou d'un niveau avanc{\'e} :
                  {\'e}tudiants, enseignants ou chercheurs en
                  statistique, math{\'e}matiques, m{\'e}decine,
                  informatique, biologie, psychologie, sciences
                  infirmi{\`e}res, etc. Il leur permettra de
                  ma{\^i}triser en profondeur le fonctionnement de ce
                  logiciel. L'ouvrage sera aussi utile aux utilisateurs
                  plus confirm{\'e}s qui retrouveront expos{\'e} ici
                  l'ensemble des fonctions R les plus couramment
  author = {Joseph Adler},
  title = {{R} in a Nutshell [deutsche Ausgabe]},
  edition = {1.},
  year = 2010,
  pages = 768,
  publisher = {O'Reilly Verlag},
  address = {K\"oln},
  isbn = {978-3-89721-649-5},
  publisherurl = {https://oreilly.de/produkt/r-in-a-nutshell/},
  language = {de},
  abstract = {Das Buch ist ein umfangreiches Handbuch und
                  Nachschlagewerk zu R.  Es beschreibt die Installation
                  und Erweiterung der Software und gibt einen breiten
                  \"Uberblick \"uber die Programmiersprache.  Anhand
                  unz\"ahliger Beispiele aus Medizin, Wirtschaft, Sport
                  und Bioinformatik behandelt es, wie Daten eingelesen,
                  transformiert und grafisch dargestellt werden.  Anhand
                  realer Datens\"atze werden zahlreiche Methoden und
                  Verfahren der statistischen Datenanalyse mit R
                  demonstriert.  Die Funktionsreferenz wurde f\"ur die
                  deutsche Ausgabe vollst\"andig neu verfasst.},
  note = {Mit Funktions- und Datensatzreferenz; Begleitpaket
                  nutshellDE mit Beispieldaten und -code (auf der
                  Verlagsseite des Buchs).}
  author = {John M. Quick},
  title = {The Statistical Analysis with {R} Beginners Guide},
  publisher = {Packt Publishing},
  year = 2010,
  isbn = {1849512086},
  abstract = {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.}
  author = {Francois Husson and S\'ebastien L\^e and J\'er\^ome
  title = {Exploratory Multivariate Analysis by Example Using
  publisher = {Chapman \& Hall/CRC},
  year = 2010,
  url = {http://factominer.free.fr/book/},
  series = {Computer Sciences and Data Analysis},
  isbn = {978-1-4398-3580-7},
  abstract = {Full of real-world case studies and practical advice,
                  Exploratory Multivariate Analysis by Example Using R
                  focuses on four fundamental methods of multivariate
                  exploratory data analysis that are most suitable for
                  applications. It covers principal component analysis
                  (PCA) when variables are quantitative, correspondence
                  analysis (CA) and multiple correspondence analysis
                  (MCA) when variables are categorical, and hierarchical
                  cluster analysis.  The authors take a geometric point
                  of view that provides a unified vision for exploring
                  multivariate data tables. Within this framework, they
                  present the principles, indicators, and ways of
                  representing and visualizing objects that are common
                  to the exploratory methods. The authors show how to
                  use categorical variables in a PCA context in which
                  variables are quantitative, how to handle more than
                  two categorical variables in a CA context in which
                  there are originally two variables, and how to add
                  quantitative variables in an MCA context in which
                  variables are categorical. They also illustrate the
                  methods and the ways they can be exploited using
                  examples from various fields.  Throughout the text,
                  each result correlates with an R command accessible in
                  the FactoMineR package developed by the authors. All
                  of the data sets and code are available at
                  \url{http://factominer.free.fr/book/}. By using the
                  theory, examples, and software presented in this book,
                  readers will be fully equipped to tackle real-life
                  multivariate data.},
  orderinfo = {crcpress.txt}
  author = {David Ruppert},
  title = {Statistics and Data Analysis for Financial
  publisher = {Springer},
  year = 2010,
  series = {Use R!},
  isbn = {978-1-4419-7786-1},
  publisherurl = {https://www.springer.com/978-1-4419-7786-1},
  abstract = {Financial engineers have access to enormous quantities
                  of data but need powerful methods for extracting
                  quantitative information, particularly about
                  volatility and risks. Key features of this textbook
                  are: illustration of concepts with financial markets
                  and economic data, R Labs with real-data exercises,
                  and integration of graphical and analytic methods for
                  modeling and diagnosing modeling errors. Despite some
                  overlap with the author's undergraduate textbook
                  Statistics and Finance: An Introduction, this book
                  differs from that earlier volume in several important
                  aspects: it is graduate-level; computations and
                  graphics are done in R; and many advanced topics are
                  covered, for example, multivariate distributions,
                  copulas, Bayesian computations, VaR and expected
                  shortfall, and cointegration.  The prerequisites are
                  basic statistics and probability, matrices and linear
                  algebra, and calculus.  Some exposure to finance is
  orderinfo = {springer.txt}
  author = {Christian Robert and George Casella},
  title = {Introducing {Monte Carlo} Methods with {R}},
  publisher = {Springer},
  year = 2010,
  series = {Use R},
  isbn = {978-1-4419-1575-7},
  publisherurl = {https://www.springer.com/978-1-4419-1575-7},
  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.},
  orderinfo = {springer.txt}
  title = {Clinical Trial Data Analysis with {R}},
  author = {Chen, Din},
  isbn = {978-1-4398-4020-7},
  series = {Chapman \& Hall/CRC Biostatistics series},
  url = {https://www.taylorfrancis.com/books/clinical-trial-data-analysis-using-ding-geng-din-chen-karl-peace/10.1201/b10478},
  year = 2010,
  publisher = {Chapman \& Hall/CRC Press},
  address = {Boca Raton, FL},
  abstract = {Too often in biostatistical research and clinical
                  trials, a knowledge gap exists between developed
                  statistical methods and the applications of these
                  methods. Filling this gap, Clinical Trial Data
                  Analysis Using R provides a thorough presentation of
                  biostatistical analyses of clinical trial data and
                  shows step by step how to implement the statistical
                  methods using R. The book's practical, detailed
                  approach draws on the authors' 30 years of real-world
                  experience in biostatistical research and clinical
                  development. Each chapter presents examples of
                  clinical trials based on the authors' actual
                  experiences in clinical drug development. Various
                  biostatistical methods for analyzing the data are then
                  identified. The authors develop analysis code step by
                  step using appropriate R packages and functions. This
                  approach enables readers to gain an understanding of
                  the analysis methods and R implementation so that they
                  can use R to analyze their own clinical trial
                  data. With step-by-step illustrations of R
                  implementations, this book shows how to easily use R
                  to simulate and analyze data from a clinical trial. It
                  describes numerous up-to-date statistical methods and
                  offers sound guidance on the processes involved in
                  clinical trials.}
  author = {Carlo Gaetan and Xavier Guyon},
  title = {Spatial Statistics and Modeling},
  publisher = {Springer},
  year = 2010,
  series = {Springer Series in Statistics},
  isbn = {978-0-387-92256-0},
  publisherurl = {https://www.springer.com/978-0-387-92256-0},
  abstract = { Spatial statistics are useful in subjects as diverse
                  as climatology, ecology, economics, environmental and
                  earth sciences, epidemiology, image analysis and
                  more. This book covers the best-known spatial models
                  for three types of spatial data: geostatistical data
                  (stationarity, intrinsic models, variograms, spatial
                  regression and space-time models), areal data
                  (Gibbs-Markov fields and spatial auto-regression) and
                  point pattern data (Poisson, Cox, Gibbs and Markov
                  point processes). The level is relatively advanced,
                  and the presentation concise but complete. The most
                  important statistical methods and their asymptotic
                  properties are described, including estimation in
                  geostatistics, autocorrelation and second-order
                  statistics, maximum likelihood methods, approximate
                  inference using the pseudo-likelihood or Monte-Carlo
                  simulations, statistics for point processes and
                  Bayesian hierarchical models. A chapter is devoted to
                  Markov Chain Monte Carlo simulation (Gibbs sampler,
                  Metropolis-Hastings algorithms and exact simulation).
                  A large number of real examples are studied with R,
                  and each chapter ends with a set of theoretical and
                  applied exercises. While a foundation in probability
                  and mathematical statistics is assumed, three
                  appendices introduce some necessary background. The
                  book is accessible to senior undergraduate students
                  with a solid math background and Ph.D. students in
                  statistics. Furthermore, experienced statisticians and
                  researchers in the above-mentioned fields will find
                  the book valuable as a mathematically sound
                  reference. This book is the English translation of
                  Mod{\'e}lisation et Statistique Spatiales published by
                  Springer in the series Math{\'e}matiques & Applications, a
                  series established by Soci{\'e}t{\'e} de Math{\'e}matiques
                  Appliqu{\'e}es et Industrielles (SMAI).},
  orderinfo = {springer.txt}
  author = {Andrew P. Robinson and Jeff D. Hamann},
  title = {Forest Analytics with {R}},
  publisher = {Springer},
  year = 2010,
  series = {Use R!},
  isbn = {978-1-4419-7761-8},
  publisherurl = {https://www.springer.com/978-1-4419-7761-8},
  abstract = {Forest Analytics with R combines practical,
                  down-to-earth forestry data analysis and solutions to
                  real forest management challenges with
                  state-of-the-art statistical and data-handling
                  functionality. The authors adopt a problem-driven
                  approach, in which statistical and mathematical tools
                  are introduced in the context of the forestry problem
                  that they can help to resolve. All the tools are
                  introduced in the context of real forestry datasets,
                  which provide compelling examples of practical
                  applications.  The modeling challenges covered within
                  the book include imputation and interpolation for
                  spatial data, fitting probability density functions to
                  tree measurement data using maximum likelihood,
                  fitting allometric functions using both linear and
                  non-linear least-squares regression, and fitting
                  growth models using both linear and non-linear
                  mixed-effects modeling. The coverage also includes
                  deploying and using forest growth models written in
                  compiled languages, analysis of natural resources and
                  forestry inventory data, and forest estate planning
                  and optimization using linear programming.  The book
                  would be ideal for a one-semester class in forest
                  biometrics or applied statistics for natural resources
                  management. The text assumes no programming
                  background, some introductory statistics, and very
                  basic applied mathematics.},
  orderinfo = {springer.txt}
  editor = {Hrishikesh D. Vinod},
  title = {Advances in Social Science Research Using {R}},
  publisher = {Springer},
  year = 2010,
  series = {Lecture Notes in Statistics},
  isbn = {978-1-4419-1763-8},
  publisherurl = {https://www.springer.com/978-1-4419-1763-8},
  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
  orderinfo = {springer.txt}
  author = {Victor Bloomfield},
  title = {Computer Simulation and Data Analysis in Molecular
                  Biology and Biophysics: An Introduction Using {R}},
  publisher = {Springer},
  publisherurl = {https://www.springer.com/978-1-4419-0083-8},
  year = 2009,
  isbn = {978-1-4419-0083-8},
  abstract = {This book provides an introduction, suitable for
                  advanced undergraduates and beginning graduate
                  students, to two important aspects of molecular
                  biology and biophysics: computer simulation and data
                  analysis. It introduces tools to enable readers to
                  learn and use fundamental methods for constructing
                  quantitative models of biological mechanisms, both
                  deterministic and with some elements of randomness,
                  including complex reaction equilibria and kinetics,
                  population models, and regulation of metabolism and
                  development; to understand how concepts of probability
                  can help in explaining important features of DNA
                  sequences; and to apply a useful set of statistical
                  methods to analysis of experimental data from
                  spectroscopic, genomic, and proteomic sources.  These
                  quantitative tools are implemented using the free,
                  open source software program R. R provides an
                  excellent environment for general numerical and
                  statistical computing and graphics, with capabilities
                  similar to Matlab.  Since R is increasingly used in
                  bioinformatics applications such as the BioConductor
                  project, it can serve students as their basic
                  quantitative, statistical, and graphics tool as they
                  develop their careers }
  author = {Uwe Ligges},
  title = {Programmieren mit {R}},
  year = 2009,
  publisher = {Springer-Verlag},
  address = {Heidelberg},
  note = {In German},
  isbn = {978-3-540-79997-9},
  edition = {3rd},
  url = {http://www.statistik.tu-dortmund.de/~ligges/PmitR/},
  publisherurl = {https://www.springer.com/978-3-540-79997-9},
  abstract = {R ist eine objekt-orientierte und interpretierte
                  Sprache und Programmierumgebung f\"ur Datenanalyse und
                  Grafik --- frei erh\"altlich unter der GPL.  Das Buch
                  f\"uhrt in die Grundlagen der Sprache R ein und
                  vermittelt ein umfassendes Verst\"andnis der
                  Sprachstruktur.  Die enormen Grafikf\"ahigkeiten von R
                  werden detailliert beschrieben.  Der Leser kann leicht
                  eigene Methoden umsetzen, Objektklassen definieren und
                  ganze Pakete aus Funktionen und zugeh\"origer
                  Dokumentation zusammenstellen.  Ob Diplomarbeit,
                  Forschungsprojekte oder Wirtschaftsdaten, das Buch
                  unterst\"utzt alle, die R als flexibles Werkzeug zur
                  Datenanalyse und -visualisierung einsetzen m\"ochten.},
  language = {de}
  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}]},
  year = 2009,
  publisher = {Scientia},
  address = {Praha},
  series = {Biologie dnes},
  publisherurl = {http://www.scientia.cz/biologie/4373-moderni-analyza-biologickych-dat-zobecnene-linearni-modely-v-prostredi-r.html},
  isbn = {978-80-86960-44-9},
  note = {In Czech},
  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.},
  language = {cz}
  author = {Robert A. Muenchen},
  title = {{R} for {SAS} and {SPSS} Users},
  publisher = {Springer},
  year = 2009,
  series = {Springer Series in Statistics and Computing},
  isbn = {978-1-4614-0685-3},
  publisherurl = {https://www.springer.com/978-1-4614-0685-3},
  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
  orderinfo = {springer.txt}
  author = {Richard M. Heiberger and Erich Neuwirth},
  title = {R Through {Excel}},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-1-4419-0051-7},
  publisherurl = {https://www.springer.com/978-1-4419-0051-7},
  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.},
  orderinfo = {springer.txt}
  author = {Peter D. Hoff},
  title = {A First Course in Bayesian Statistical Methods},
  publisher = {Springer},
  year = 2009,
  series = {Springer Series in Statistics for Social and
                  Behavioral Sciences},
  isbn = {978-0-387-92299-7},
  publisherurl = {https://www.springer.com/978-0-387-92299-7},
  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.  },
  orderinfo = {springer.txt}
  author = {Paul S. P. Cowpertwait and Andrew Metcalfe},
  title = {Introductory Time Series with {R}},
  publisher = {Springer},
  year = 2009,
  series = {Springer Series in Statistics},
  isbn = {978-0-387-88697-8},
  publisherurl = {https://www.springer.com/978-0-387-88697-8},
  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 \url{http://www.maths.adelaide.edu.au/emac2009/}.
                  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
  orderinfo = {springer.txt}
  author = {Owen Jones and Robert Maillardet and Andrew Robinson},
  title = {Introduction to Scientific Programming and Simulation
                  Using {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2009,
  address = {Boca Raton, FL},
  isbn = {978-1-4200-6872-6},
  publisherurl = {https://www.taylorfrancis.com/books/introduction-scientific-programming-simulation-using-owen-jones-robert-maillardet-andrew-robinson/10.1201/9781420068740},
  abstract = {This book teaches the skills needed to perform
                  scientific programming while also introducing
                  stochastic modelling. Stochastic modelling in
                  particular, and mathematical modelling in general, are
                  intimately linked to scientific programming because
                  the numerical techniques of scientific programming
                  enable the practical application of mathematical
                  models to real-world problems.}
  author = {M. Henry H. Stevens},
  title = {A Primer of Ecology with {R}},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-387-89881-0},
  publisherurl = {https://www.springer.com/978-0-387-89881-0},
  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.},
  orderinfo = {springer.txt}
  author = {Kurt Varmuza and Peter Filzmoser},
  title = {Introduction to Multivariate Statistical Analysis in
  publisher = {CRC Press},
  address = {Boca Raton, FL},
  year = 2009,
  isbn = {9781420059472},
  url = {http://cstat.tuwien.ac.at/filz/},
  publisherurl = {https://www.routledge.com/Introduction-to-Multivariate-Statistical-Analysis-in-Chemometrics/Varmuza-Filzmoser/p/book/9781420059472},
  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
  author = {Karl W. Broman and Saunak Sen},
  title = {A Guide to QTL Mapping with R/qtl},
  publisher = {Springer},
  year = 2009,
  series = {SBH/Statistics for Biology and Health},
  isbn = {978-0-387-92124-2},
  publisherurl = {https://www.springer.com/978-0-387-92124-2},
  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.  },
  orderinfo = {springer.txt}
  author = {Kai Velten},
  title = {Mathematical Modeling and Simulation: Introduction for
                  Scientists and Engineers},
  publisher = {Wiley-VCH},
  year = 2009,
  isbn = {978-3-527-40758-3},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-3527407588.html},
  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).}
  author = {Jim Albert},
  title = {Bayesian Computation with {R}},
  edition = {2nd},
  publisher = {Springer},
  year = 2009,
  series = {Springer Series in Statistics},
  isbn = {978-0-387-92298-0},
  publisherurl = {https://www.springer.com/978-0-387-92298-0},
  abstract = {Bayesian Computing Using 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.  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.},
  orderinfo = {springer.txt}
  author = {J. O. Ramsay and Giles Hooker and Spencer Graves},
  title = {Functional Data Analysis with {R} and {Matlab}},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-387-98184-0},
  publisherurl = {https://www.springer.com/978-0-387-98184-0},
  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
                  \url{http://www.functionaldata.org} for applying
                  existing functions and developing new ones in either
                  language.  },
  orderinfo = {springer.txt}
  author = {Hadley Wickham},
  title = {ggplot: Elegant Graphics for Data Analysis},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-98140-6},
  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
  orderinfo = {springer.txt}
  author = {G{\"u}nther Sawitzki},
  title = {Computational Statistics},
  subtitle = {An Introduction to {R}},
  address = {Boca Raton, FL},
  publisher = {Chapman \& Hall/CRC Press},
  year = 2009,
  pages = {XIV + 251},
  isbn = {978-1-4200-8678-2},
  note = {Includes bibliographical references and index},
  language = {eng},
  publisherurl = {http://www.crcpress.com/product/isbn/9781420086782},
  abstract = {Suitable for a compact course or self-study,
                  Computational Statistics:  An Introduction to R
                  illustrates how to use the freely available R software
                  package for data analysis, statistical programming,
                  and graphics.  Integrating R code and examples
                  throughout, the text only requires basic knowledge of
                  statistics and computing.  This introduction covers
                  one-sample analysis and distribution diagnostics,
                  regression, two-sample problems and comparison of
                  distributions, and multivariate analysis.  It uses a
                  range of examples to demonstrate how R can be employed
                  to tackle statistical problems.  In addition, the
                  handy appendix includes a collection of R language
                  elements and functions, serving as a quick reference
                  and starting point to access the rich information that
                  comes bundled with R.  Accessible to a broad audience,
                  this book explores key topics in data analysis,
                  regression, statistical distributions, and
                  multivariate statistics.  Full of examples and with a
                  color insert, it helps readers become familiar with
  author = {Giovanni Petris and Sonia Petrone and Patriza
  title = {Dynamic Linear Models with {R}},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-387-77238-7},
  publisherurl = {https://www.springer.com/978-0-387-77238-7},
  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.},
  orderinfo = {springer.txt}
  author = {Gael Millot},
  title = {Comprendre et r\'{e}aliser les tests statistiques
                  \`{a} l'aide de {R}},
  year = 2009,
  publisher = {de boeck universit\'{e}},
  address = {Louvain-la-Neuve, Belgique},
  isbn = {2804101797},
  pages = 704,
  edition = {1st},
  url = {http://perso.curie.fr/Gael.Millot/Publications_livre.htm},
  language = {fr},
  abstract = {Ce livre s'adresse aux \'{e}tudiants, m\'{e}decins et
                  chercheurs d\'{e}sirant r\'{e}aliser des tests alors
                  qu'ils d\'{e}butent en statistique. Son
                  originalit\'{e} est de proposer non seulement une
                  explication tr\`{e}s d\'{e}taill\'{e}e sur
                  l'utilisation des tests les plus classiques, mais
                  aussi la possibilit\'{e} de r\'{e}aliser ces tests
                  \`{a} l'aide de R. Illustr\'{e} par de nombreuses
                  figures et accompagn\'{e} d'exercices avec correction,
                  l'ouvrage traite en profondeur de notions essentielles
                  comme la check-list \`{a} effectuer avant de
                  r\'{e}aliser un test, la gestion des individus
                  extr\^{e}mes, l'origine de la p value, la puissance ou
                  la conclusion d'un test. Il explique comment choisir
                  un test \`{a} partir de ses propres donn\'{e}es. Il
                  d\'{e}crit 35 tests statistiques sous forme de fiches,
                  dont 24 non param\'{e}triques, ce qui couvre la
                  plupart des tests \`{a} une ou deux variables
                  observ\'{e}es. Il traite de toutes les subtilit\'{e}s
                  des tests, comme les corrections de continuit\'{e},
                  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 \`{a} l'aide de R, en incluant toutes les
                  \'{e}tapes du test, et notamment l'analyse graphique
                  des donn\'{e}es. En r\'{e}sum\'{e}, cet ouvrage
                  devrait contenter \`{a} la fois ceux qui recherchent
                  un manuel de statistique expliquant le fonctionnement
                  des tests et ceux qui recherchent un manuel
                  d'utilisation de R.}
  author = {Francois Husson and S\'ebastien L\^e and J\'er\^ome
  title = {Analyse de donn{\'e}es avec {R}},
  publisher = {Presses Universitaires de Rennes},
  year = 2009,
  url = {http://factominer.free.fr/book/},
  series = {Didact Statistiques},
  isbn = {978-2-7535-0938-2},
  publisherurl = {http://www.pur-editions.fr/detail.php?idOuv=2166},
  abstract = {Ce livre est focalis\'e sur les quatre m\'ethodes
                  fondamentales de l'analyse des donn\'ees, celles qui
                  ont le plus vaste potentiel d'application : analyse en
                  composantes principales, analyse factorielle des
                  correspondances, analyse des correspondances multiples
                  et classification ascendante hi\'erarchique. La plus
                  grande place accord\'ee aux m\'ethodes factorielles
                  tient d'une part aux concepts plus nombreux et plus
                  complexes n\'ecessaires \`a leur bonne utilisation et
                  d'autre part au fait que c'est \`a travers elles que
                  sont abord\'ees les sp\'ecificit\'es des diff\'erents
                  types de donn\'ees. Pour chaque m\'ethode, la
                  d\'emarche adopt\'ee est la m\^eme. Un exemple permet
                  d'introduire la probl\'ematique et concr\'etise
                  presque pas \`a pas les \'el\'ements th\'eoriques. Cet
                  expos\'e est suivi de plusieurs exemples trait\'es de
                  fa\c con d\'etaill\'ee pour illustrer l'apport de la
                  m\'ethode dans les applications. Tout le long du
                  texte, chaque r\'esultat est accompagn\'e de la
                  commande R qui permet de l'obtenir.  Toutes ces
                  commandes sont accessibles \`a partir de FactoMineR,
                  package R d\'evelopp\'e par les auteurs. Ainsi, avec
                  cet ouvrage, le lecteur dispose d'un \'equipement
                  complet (bases th\'eoriques, exemples, logiciels) pour
                  analyser des donn\'ees multidimensionnelles.}
  author = {Ewout W. Steyerberg},
  title = {Clinical Prediction Models:  A Practical Approach to
                  Development, Validation, and Updating},
  publisher = {Springer},
  year = 2009,
  series = {SBH/Statistics for Biology and Health},
  isbn = {978-0-387-77243-1},
  publisherurl = {https://www.springer.com/978-0-387-77243-1},
  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
  orderinfo = {springer.txt}
  author = {Detlev Reymann},
  title = {{Wettbewerbsanalysen f\"ur kleine und mittlere
                  Unternehmen (KMUs) --- Theoretische Grundlagen und
                  praktische Anwendung am Beispiel gartenbaulicher
  publisher = {Verlag Detlev Reymann},
  address = {Geisenheim},
  isbn = {978-3-00-027013-0},
  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 St\"arken und Schw\"achen im
                  Verh\"altnis 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 Durchf\"uhrung gegeben und
                  jeweils eine vertiefende Betrachtung angeschlossen.
                  Das Buch zielt insbesondere auf kleine und mittlere
                  Unternehmen (KMUs) ab, in denen keine gro\ss{}en
                  spezialisierten Marketingabteilungen existieren.
                  Verwendet werden Verfahren, bei denen sich zum einen
                  der zeitliche Aufwand f\"ur die Durchf\"uhrung in
                  vertretbaren Grenzen h\"alt, zum anderen Analysen, die
                  mit Hilfe von frei verf\"ugbarer Software oder frei
                  verf\"ugbaren Daten durchzuf\"uhren sind. F\"ur den
                  Statistikteil werden R-Skripte verwendet, die alle
                  frei von der Webseite des Autors heruntergeladen
                  werden k\"onnen.  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 Durchf\"uhrung von
                  Cluster- und Faktoranlysen zur Typologisierung
                  erl\"autert und der Anhang gibt Hinweise zur
                  Installation und zum Einsatz von R f\"ur die
                  beschriebenen Analysen.},
  language = {de},
  publisherurl = {http://www.reymann.eu/index.php?option=com_content&task=view&id=10&Itemid=13},
  url = {http://www.reymann.org/},
  year = 2009
  author = {Daniel B. Wright and Kamala London},
  title = {Modern Regression Techniques Using {R}: A Practical
  publisher = {SAGE},
  year = 2009,
  address = {London, UK},
  isbn = {9781847879035},
  publisherurl = {https://uk.sagepub.com/en-gb/eur/modern-regression-techniques-using-r/book233198},
  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
                  The authors are donating all royalties from the book
                  to the American Partnership for Eosinophilic
  author = {Christian Ritz and Jens C. Streibig},
  title = {Nonlinear Regression with R},
  publisher = {Springer},
  year = 2009,
  address = {New York},
  isbn = {978-0-387-09615-5},
  publisherurl = {https://www.springer.com/978-0-387-09615-5},
  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.},
  orderinfo = {springer.txt}
  author = {Andrea S. Foulkes},
  title = {Applied Statistical Genetics with {R}:  For
                  Population-Based Association Studies},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-387-89554-3},
  publisherurl = {https://www.springer.com/978-0-387-89554-3},
  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.},
  orderinfo = {springer.txt}
  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
  publisher = {Springer},
  year = 2009,
  address = {New York},
  isbn = {978-0-387-87457-9},
  publisherurl = {https://www.springer.com/978-0-387-87457-9},
  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
  orderinfo = {springer.txt}
  author = {Alain F. Zuur and Elena N. Ieno and Erik Meesters},
  title = {A Beginner's Guide to {R}},
  publisher = {Springer},
  year = 2009,
  series = {Use R},
  isbn = {978-0-387-93836-3},
  publisherurl = {https://www.springer.com/978-0-387-93836-3},
  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.  },
  orderinfo = {springer.txt}
  author = {Stefano M. Iacus},
  title = {Simulation and Inference for Stochastic Differential
                  Equations: With {R} Examples},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75838-1},
  publisherurl = {https://www.springer.com/978-0-387-75838-1},
  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
  orderinfo = {springer.txt}
  author = {Simon Sheather},
  title = {A Modern Approach to Regression with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-09607-0},
  publisherurl = {https://www.springer.com/978-0-387-09607-0},
  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.},
  orderinfo = {springer.txt}
  author = {Sarkar, Deepayan},
  title = {Lattice: {M}ultivariate Data Visualization with {R}},
  publisher = {Springer},
  url = {http://lmdvr.r-forge.r-project.org},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75968-5},
  publisherurl = {https://www.springer.com/978-0-387-75968-5},
  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.},
  orderinfo = {springer.txt}
  author = {Roger S. Bivand and Edzer J. Pebesma and Virgilio
  title = {Applied Spatial Data Analysis with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-78170-9},
  publisherurl = {https://www.springer.com/978-0-387-78170-9},
  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:
  orderinfo = {springer.txt}
  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,
  address = {New York},
  isbn = {978-0-387-78166-2},
  publisherurl = {https://www.springer.com/978-0-387-78166-2},
  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.},
  orderinfo = {springer.txt}
  author = {Robert Gentleman},
  title = {Bioinformatics with {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2008,
  address = {Boca Raton, FL},
  isbn = {1-420-06367-7}
  author = {Robert Gentleman},
  title = {{R} Programming for Bioinformatics},
  publisher = {Chapman \& Hall/CRC},
  year = 2008,
  series = {Computer Science \& Data Analysis},
  address = {Boca Raton, FL},
  isbn = {9781420063677},
  url = {http://master.bioconductor.org/help/publications/books/r-programming-for-bioinformatics/},
  publisherurl = {http://www.crcpress.com/product/isbn/9781420063677},
  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.},
  orderinfo = {crcpress.txt}
  author = {Phil Spector},
  title = {Data Manipulation with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-74730-9},
  publisherurl = {https://www.springer.com/978-0-387-74730-9},
  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.},
  orderinfo = {springer.txt}
  author = {Pfaff, Bernhard},
  title = {Analysis of Integrated and Cointegrated Time Series
                  with {R}, Second Edition},
  edition = {2nd},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75966-1},
  publisherurl = {https://www.springer.com/978-0-387-75966-1},
  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 G{\"o}ttingen, 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.''},
  orderinfo = {springer.txt}
  author = {Peter Dalgaard},
  title = {Introductory Statistics with {R}},
  edition = {2nd},
  year = 2008,
  publisher = {Springer},
  isbn = {978-0-387-79053-4},
  pages = 380,
  publisherurl = {https://www.springer.com/gp/book/9780387790534},
  orderinfo = {springer.txt},
  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.}
  author = {Maria L. Rizzo},
  title = {Statistical Computing with {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2008,
  address = {Boca Raton, FL},
  isbn = {9781584885450},
  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.},
  orderinfo = {crcpress.txt}
  author = {Keele, Luke},
  title = {Semiparametric Regression for the Social Sciences},
  publisher = {Wiley},
  address = {Chichester, UK},
  year = 2008,
  isbn = {978-0470319918},
  url = {http://lukekeele.com/},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-0470319917.html},
  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.}
  author = {Jonathan D. Cryer and Kung-Sik Chan},
  title = {Time Series Analysis With Applications in {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75958-6},
  publisherurl = {https://www.springer.com/978-0-387-75958-6},
  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.},
  orderinfo = {springer.txt}
  author = {John M. Chambers},
  title = {Software for Data Analysis: Programming with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75935-7},
  url = {http://statweb.stanford.edu/~jmc4/Rbook/},
  publisherurl = {https://www.springer.com/gp/book/9780387759357},
  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.},
  orderinfo = {springer.txt}
  author = {Hrishikesh D. Vinod},
  title = {Hands-on Intermediate Econometrics Using {R}:
                  Templates for Extending Dozens of Practical Examples},
  publisher = {World Scientific},
  address = {Hackensack, NJ},
  year = 2008,
  isbn = {10-981-281-885-5},
  doi = {10.1142/6895},
  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
  author = {G. P. Nason},
  title = {Wavelet Methods in Statistics with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-75960-9},
  publisherurl = {https://www.springer.com/978-0-387-75960-9},
  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
  orderinfo = {springer.txt}
  author = {Clemens Reimann and Peter Filzmoser and Robert Garrett
                  and Rudolf Dutter},
  title = {Statistical Data Analysis Explained: Applied
                  Environmental Statistics with {R}},
  publisher = {Wiley},
  address = {Chichester, UK},
  year = 2008,
  isbn = {978-0-470-98581-6},
  url = {http://file.statistik.tuwien.ac.at/StatDA/},
  publisherurl = {https://www.wiley.com/WileyCDA/WileyTitle/productCd-047098581X.html},
  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
  author = {Claude, Julien},
  title = {Morphometrics with {R}},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-77789-4},
  publisherurl = {https://www.springer.com/978-0-387-77789-4},
  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.},
  orderinfo = {springer.txt}
  author = {Christian Kleiber and Achim Zeileis},
  title = {Applied Econometrics with R},
  publisher = {Springer},
  year = 2008,
  address = {New York},
  isbn = {978-0-387-77316-2},
  publisherurl = {https://www.springer.com/978-0-387-77316-2},
  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
                  \url{https://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.  },
  orderinfo = {springer.txt}
  author = {Benjamin M. Bolker},
  title = {Ecological Models and Data in {R}},
  year = 2008,
  publisher = {Princeton University Press},
  isbn = {978-0-691-12522-0},
  pages = 408,
  url = {http://ms.mcmaster.ca/~bolker/emdbook/},
  publisherurl = {http://press.princeton.edu/titles/8709.html},
  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.}
  author = {W. John Braun and Duncan J. Murdoch},
  title = {A First Course in Statistical Programming with {R}},
  year = 2007,
  publisher = {Cambridge University Press},
  address = {Cambridge},
  isbn = {978-0521872652},
  pages = 362,
  url = {http://rtricks4kids.ok.ubc.ca/wjbraun/other.php},
  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.}
  author = {Scott M. Lynch},
  title = {Introduction to Applied Bayesian Statistics and
                  Estimation for Social Scientists},
  publisher = {Springer},
  year = 2007,
  address = {New York},
  isbn = {978-0-387-71265-9},
  publisherurl = {https://www.springer.com/978-0-387-71265-9},
  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. },
  orderinfo = {springer.txt}
  author = {Sandrine Dudoit and Mark J. {van der Laan}},
  title = {Multiple Testing Procedures and Applications to
  publisher = {Springer},
  year = 2007,
  series = {Springer Series in Statistics},
  isbn = {978-0-387-49317-6},
  publisherurl = {https://www.springer.com/978-0-387-49317-6},
  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.},
  orderinfo = {springer.txt}
  author = {Philip J. Boland},
  title = {Statistical and Probabilistic Methods in Actuarial
  publisher = {Chapman \& Hall/CRC},
  year = 2007,
  address = {Boca Raton, FL},
  isbn = {9781584886952},
  publisherurl = {http://www.crcpress.com/product/isbn/9781584886952},
  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.},
  orderinfo = {crcpress.txt}
  author = {Michael Greenacre},
  title = {Correspondence Analysis in Practice, Second Edition},
  publisher = {Chapman \& Hall/CRC},
  year = 2007,
  address = {Boca Raton, FL},
  isbn = {9781584886167},
  publisherurl = {https://www.taylorfrancis.com/books/correspondence-analysis-practice-michael-greenacre/10.1201/9781420011234},
  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.}
  author = {John Maindonald and John Braun},
  title = {Data Analysis and Graphics Using {R}},
  edition = {2nd},
  year = 2007,
  publisher = {Cambridge University Press},
  address = {Cambridge},
  isbn = {978-0-521-86116-8},
  pages = 502,
  url = {https://maths-people.anu.edu.au/~johnm/r-book/daagur3.html},
  publisherurl = {https://www.cambridge.org/at/academic/subjects/statistics-probability/computational-statistics-machine-learning-and-information-sc/data-analysis-and-graphics-using-r-example-based-approach-3rd-edition?format=HB&isbn=9780521762939},
  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
  author = {Jean-Michel Marin and Christian P. Robert},
  title = {Bayesian Core: A Practical Approach to Computational
                  Bayesian Statistics},
  publisher = {Springer},
  year = 2007,
  address = {New York},
  isbn = {978-0-387-38979-0},
  publisherurl = {https://www.springer.com/978-0-387-38979-0},
  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. },
  orderinfo = {springer.txt}
  author = {Dianne Cook and Deborah F. Swayne},
  title = {Interactive and Dynamic Graphics for Data Analysis},
  publisher = {Springer},
  year = 2007,
  address = {New York},
  isbn = {978-0-387-71761-6},
  publisherurl = {https://www.springer.com/978-0-387-71761-6},
  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.},
  orderinfo = {springer.txt}
  author = {David Siegmund and Benjamin Yakir},
  title = {The Statistics of Gene Mapping},
  publisher = {Springer},
  year = 2007,
  address = {New York},
  isbn = {978-0-387-49684-9},
  publisherurl = {https://www.springer.com/978-0-387-49684-9},
  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.},
  orderinfo = {springer.txt}
  author = {Simon N. Wood},
  title = {Generalized Additive Models: An Introduction with {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2006,
  address = {Boca Raton, FL},
  isbn = {9781584884743},
  url = {https://CRAN.R-project.org/package=gamair},
  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.}
  author = {Robert H. Shumway and David S. Stoffer},
  title = {Time Series Analysis and Its Applications With {R}
  publisher = {Springer},
  year = 2006,
  address = {New York},
  isbn = {978-0-387-29317-2},
  publisherurl = {https://www.springer.com/978-0-387-29317-2},
  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.},
  orderinfo = {springer.txt}
  author = {Peter J. Diggle and Paulo Justiniano Ribeiro},
  title = {Model-based Geostatistics},
  publisher = {Springer},
  year = 2006,
  isbn = {978-0-387-48536-2},
  publisherurl = {https://www.springer.com/978-0-387-48536-2},
  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.},
  orderinfo = {springer.txt}
  author = {Nhu D. Le and James V. Zidek},
  title = {Statistical Analysis of Environmental Space-Time
  publisher = {Springer},
  year = 2006,
  isbn = {978-0-387-35429-3},
  publisherurl = {https://www.springer.com/978-0-387-35429-3},
  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.},
  orderinfo = {springer.txt}
  author = {Lothar Sachs and J{\"u}rgen Hedderich},
  title = {{Angewandte Statistik. Methodensammlung mit R}},
  year = 2006,
  edition = {12th (completely revised)},
  publisher = {Springer},
  address = {Berlin, Heidelberg},
  isbn = {978-3-540-32160-6},
  publisherurl = {https://www.springer.com/978-3-540-32160-6},
  abstract = {Die Anwendung statistischer Methoden wird heute in der
                  Regel durch den Einsatz von Computern unterst{\"u}tzt.
                  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., vollst{\"a}ndig neu
                  bearbeitete Auflage veranschaulicht Anwendung und
                  Nutzen des Programms anhand zahlreicher mit R
                  durchgerechneter Beispiele.  Sie erl{\"a}utert
                  statistische Ans{\"a}tze 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 erm{\"o}glichen
                  viele Beispiele, Querverweise und ein
                  ausf{\"u}hrliches Sachverzeichnis einen gezielten
                  Zugang zur Statistik, insbesondere f{\"u}r Mediziner,
                  Ingenieure und Naturwissenschaftler.},
  language = {de}
  author = {Julian J. Faraway},
  title = {Extending Linear Models with {R}: Generalized Linear,
                  Mixed Effects and Nonparametric Regression Models},
  publisher = {Chapman \& Hall/CRC},
  year = 2006,
  address = {Boca Raton, FL},
  isbn = {9781584884248},
  url = {http://www.maths.bath.ac.uk/~jjf23/ELM/},
  publisherurl = {https://www.taylorfrancis.com/books/extending-linear-model-julian-faraway/10.1201/b15416},
  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.}
  author = {Jana Jureckova and Jan Picek},
  title = {Robust Statistical Methods with {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2006,
  address = {Boca Raton, FL},
  isbn = {9781584884545},
  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.},
  orderinfo = {crcpress.txt}
  author = {Emmanuel Paradis},
  title = {Analysis of Phylogenetics and Evolution with {R}},
  publisher = {Springer},
  year = 2006,
  series = {Use R},
  address = {New York},
  isbn = {978-1-4614-1743-9},
  publisherurl = {https://www.springer.com/978-1-4614-1743-9},
  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.},
  orderinfo = {springer.txt}
  author = {Brian Everitt and Torsten Hothorn},
  title = {A Handbook of Statistical Analyses Using {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2006,
  address = {Boca Raton, FL},
  isbn = {1-584-88539-4},
  url = {https://CRAN.R-project.org/package=HSAUR},
  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.},
  orderinfo = {crcpress.txt}
  author = {Richard C. Deonier and Simon Tavar{\'e} and Michael
                  S. Waterman},
  title = {Computational Genome Analysis: An Introduction},
  publisher = {Springer},
  year = 2005,
  isbn = {978-0-387-28807-9},
  publisherurl = {https://www.springer.com/978-0-387-28807-9},
  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
  orderinfo = {springer.txt}
  author = {Paul Murrell},
  title = {R Graphics},
  publisher = {Chapman \& Hall/CRC},
  year = 2005,
  address = {Boca Raton, FL},
  isbn = {9781584884866},
  publisherurl = {https://www.taylorfrancis.com/books/graphics-paul-murrell/10.1201/9781420035025},
  url = {http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html},
  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.},
  orderinfo = {crcpress.txt}
  author = {Michael J. Crawley},
  title = {Statistics: An Introduction using {R}},
  publisher = {Wiley},
  year = 2005,
  isbn = {0-470-02297-3},
  url = {http://www.bio.ic.ac.uk/research/crawley/statistics/},
  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.}
  author = {John Verzani},
  title = {Using {R} for Introductory Statistics},
  publisher = {Chapman \& Hall/CRC},
  year = 2005,
  address = {Boca Raton, FL},
  isbn = {9781584884507},
  publisherurl = {https://www.taylorfrancis.com/books/using-introductory-statistics-john-verzani/10.4324/9780203499894},
  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.}
  author = {Fionn Murtagh},
  title = {Correspondence Analysis and Data Coding with {JAVA}
                  and {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2005,
  address = {Boca Raton, FL},
  isbn = {9781420034943},
  url = {http://www.cs.rhul.ac.uk/home/fionn/},
  publisherurl = {http://www.crcpress.com/product/isbn/9781420034943},
  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.},
  orderinfo = {crcpress.txt}
  author = {Brian S. Everitt},
  title = {An {R} and {S-Plus} Companion to Multivariate
  publisher = {Springer},
  year = 2005,
  isbn = {978-1-84628-124-2},
  url = {https://www.springer.com/978-1-84628-124-2},
  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.},
  orderinfo = {springer.txt}
  author = {Andreas Behr},
  title = {Einf\"uhrung in die Statistik mit {R}},
  series = {WiSo Kurzlehrb\"ucher},
  year = 2005,
  publisher = {Vahlen},
  address = {M\"unchen},
  note = {In German},
  isbn = {3-8006-3219-5},
  language = {de}
  editor = {Robert Gentleman and Vince Carey and Wolfgang Huber
                  and Rafael Irizarry and Sandrine Dudoit},
  title = {Bioinformatics and Computational Biology Solutions
                  Using {R} and {Bioconductor}},
  publisher = {Springer},
  year = 2005,
  series = {Statistics for Biology and Health},
  isbn = {978-0-387-29362-2},
  publisherurl = {https://www.springer.com/978-0-387-29362-2},
  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.},
  orderinfo = {springer.txt}
  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
  publisher = {Suuri-Kogaku-sha, Tokyo},
  year = 2004,
  month = {April},
  isbn = {4901683128},
  pages = 254
  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,
  series = {Springer Texts in Statistics},
  isbn = {978-1-4757-4284-8},
  url = {http://astro.temple.edu/~rmh/HH},
  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.},
  publisherurl = {https://www.springer.com/978-1-4757-4284-8},
  orderinfo = {springer.txt}
  author = {Julian J. Faraway},
  title = {Linear Models with {R}},
  publisher = {Chapman \& Hall/CRC},
  year = 2004,
  address = {Boca Raton, FL},
  isbn = {9781584884255},
  url = {http://www.maths.bath.ac.uk/~jjf23/LMR/},
  publisherurl = {https://www.taylorfrancis.com/books/linear-models-julian-faraway/10.4324/9780203507278},
  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.}
  author = {Dubravko Dolic},
  title = {Statistik mit {R}.  Einf\"uhrung f\"ur Wirtschafts-
                  und Sozialwissenschaftler},
  year = 2004,
  publisher = {R. Oldenbourg},
  address = {M\"unchen, Wien},
  note = {In German},
  isbn = {3-486-27537-2},
  language = {de}
  author = {Sylvie Huet and Annie Bouvier and Marie-Anne Gruet and
                  Emmanuel Jolivet},
  title = {Statistical Tools for Nonlinear Regression},
  publisher = {Springer},
  year = 2003,
  address = {New York},
  isbn = {978-0-387-21574-7},
  publisherurl = {https://www.springer.com/978-0-387-21574-7},
  orderinfo = {springer.txt}
  author = {Stefano Iacus and Guido Masarotto},
  title = {Laboratorio di statistica con {R}},
  year = 2003,
  publisher = {McGraw-Hill},
  address = {Milano},
  isbn = {88-386-6084-0},
  pages = 384
  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,
  address = {New York},
  isbn = {978-0-387-21679-9},
  publisherurl = {https://www.springer.com/978-0-387-21679-9},
  orderinfo = {springer.txt}
  author = {William N. Venables and Brian D. Ripley},
  title = {Modern Applied Statistics with {S}. Fourth Edition},
  publisher = {Springer},
  year = 2002,
  address = {New York},
  isbn = {978-0-387-21706-2},
  url = {http://www.stats.ox.ac.uk/pub/MASS4/},
  publisherurl = {https://www.springer.com/978-0-387-21706-2},
  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. },
  orderinfo = {springer.txt}
  author = {John Fox},
  title = {An {R} and {S-Plus} Companion to Applied Regression},
  publisher = {Sage Publications},
  year = 2002,
  address = {Thousand Oaks, CA, USA},
  isbn = {0-761-92279-2},
  url = {https://socialsciences.mcmaster.ca/jfox/Books/Companion/index.html},
  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.}
  author = {Manuel Castej{\'o}n Limas and Joaqu{\'\i}n Ordieres
                  Mer{\'e} and Fco. Javier de Cos Juez and Fco. Javier
                  Mart{\'\i}nez de Pis{\'o}n Ascacibar},
  title = {Control de Calidad. Metodologia para el analisis
                  previo a la modelizaci{\'o}n de datos en procesos
                  industriales. Fundamentos te{\'o}ricos y aplicaciones
                  con R.},
  publisher = {Servicio de Publicaciones de la Universidad de La
  year = 2001,
  isbn = {84-95301-48-2},
  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
  author = {Frank E. Harrell},
  title = {Regression Modeling Strategies, with Applications to
                  Linear Models, Survival Analysis and Logistic
  publisher = {Springer},
  year = 2001,
  isbn = {978-3-319-19425-7},
  publisherurl = {https://www.springer.com/978-3-319-19425-7},
  url = {https://hbiostat.org/doc/rms/},
  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.},
  orderinfo = {springer.txt}
  author = {William N. Venables and Brian D. Ripley},
  title = {S Programming},
  publisher = {Springer},
  year = 2000,
  address = {New York},
  isbn = {978-0-387-21856-4},
  url = {http://www.stats.ox.ac.uk/pub/MASS3/Sprog/},
  publisherurl = {https://www.springer.com/978-0-387-21856-4},
  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.},
  orderinfo = {springer.txt}
  author = {Terry M. Therneau and Patricia M. Grambsch},
  title = {Modeling Survival Data: Extending the {Cox} Model},
  publisher = {Springer},
  year = 2000,
  series = {Statistics for Biology and Health},
  isbn = {978-1-4757-3294-8},
  publisherurl = {https://www.springer.com/978-1-4757-3294-8},
  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.},
  orderinfo = {springer.txt}
  author = {Jose C. Pinheiro and Douglas M. Bates},
  title = {Mixed-Effects Models in {S} and {S-Plus}},
  publisher = {Springer},
  year = 2000,
  isbn = {978-0-387-22747-4},
  publisherurl = {https://www.springer.com/978-0-387-22747-4},
  abstract = {A comprehensive guide to the use of the `nlme' package
                  for linear and nonlinear mixed-effects models.},
  orderinfo = {springer.txt}
  author = {Deborah Nolan and Terry Speed},
  title = {Stat Labs: Mathematical Statistics Through
  publisher = {Springer},
  year = 2000,
  series = {Springer Texts in Statistics},
  isbn = {978-0-387-22743-6},
  url = {https://www.stat.berkeley.edu/users/statlabs/},
  publisherurl = {https://www.springer.com/978-0-387-22743-6},
  abstract = {Integrates theory of statistics with the practice of
                  statistics through a collection of case studies
                  (``labs''), and uses R to analyze the data.},
  orderinfo = {springer.txt}
  author = {John M. Chambers},
  title = {Programming with Data},
  publisher = {Springer},
  year = 1998,
  address = {New York},
  isbn = {978-0-387-98503-9},
  publisherurl = {https://www.springer.com/978-0-387-98503-9},
  abstract = {This ``\emph{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.},
  orderinfo = {springer.txt}
  author = {John M. Chambers and Trevor J. Hastie},
  title = {Statistical Models in {S}},
  publisher = {Chapman \& Hall},
  year = 1992,
  address = {London},
  isbn = {9780412830402},
  publisherurl = {http://www.crcpress.com/product/isbn/9780412830402},
  abstract = {This is also called the ``\emph{White Book}''.  It
                  described software for statistical modeling in S and
                  introduced the S3 version of classes and methods.},
  orderinfo = {crcpress.txt}
  author = {Richard A. Becker and John M. Chambers and Allan
                  R. Wilks},
  title = {The New {S} Language},
  publisher = {Chapman \& Hall},
  year = 1988,
  address = {London},
  abstract = {This book is often called the ``\emph{Blue Book}'',
                  and introduced what is now known as S version 3, or S3.}

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