@comment{{-----------------end-of-books------------------------------------}}
@book{van2018flexible,
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}
}
@book{kelley_oceanographic_2018,
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}
}
@book{Mas2018AnalisisEspacialR,
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}
}
@book{R:Rahlf:2017,
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
programming.},
language = {en}
}
@book{SteveMurray:2017,
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.}
}
@book{R:Leemis:2016,
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.}
}
@book{R:Dayal:2015,
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
useful.}
}
@book{R:Sun:2015,
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
}
@book{R:Kohl:2015de,
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}
}
@book{R:Kohl:2015en,
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}
}
@book{R:Blangiardo+Cameletti:2015,
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}
}
@book{R:Daroczi:2015,
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.}
}
@book{R:Bloomfield:2014,
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
optimization
* Describes how to find eigenvalues and eigenfunctions
* Discusses interpolation and curve fitting
* Considers the analysis of time serie
Using R for Numerical Analysis in Science and
Engineering provides a solid introduction to the most
useful numerical methods for scientific and
engineering data analysis using R.}
}
@book{R:Hothorn+Everitt:2014,
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}
}
@book{R:Rahlf:2014,
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}
}
@book{R:Stowell:2014,
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
programming
* How to work with data files, prepare and manipulate
data, and combine and restructure datasets
* How to summarize continuous and categorical variables
* What is a probability distribution
* How to create and customize plots
* How to do hypothesis testing
* How to build and use regression and linear models
Who this book is for: No prior knowledge of R or of
programming is assumed, making this book ideal if you
are more accustomed to using point-and-click style
statistical packages. You should have some prior
experience with statistics, however.}
}
@book{R:Tsay:2014,
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}
}
@book{R:Nash:2014,
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.}
}
@book{R:Crawley:2014,
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.}
}
@book{R:Bellanger+Tomassone:2014,
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}
}
@book{R:Noel:2013,
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.}
}
@book{R:Xie:2013,
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.}
}
@book{R:Murray:2013,
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.}
}
@book{R:Tsay:2013,
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}
}
@book{R:Kohl:2013,
title = {Analyse von Genexpressionsdaten --- mit {R} und
{Bioconductor}},
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}
}
@book{R:Knell:2013,
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
results.}
}
@book{R:Hilbe:2013,
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.}
}
@book{R:Daroczi+Puhle+Berlinger:2013,
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.}
}
@book{R:Gandrud:2013,
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.}
}
@book{R:Eddelbuettel:2013,
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}
}
@book{R:Chen:2013,
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.}
}
@book{R:Pekar+Brabec:2012,
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
{R}]},
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}
}
@book{R:Soetaert+Cash+Mazzia:2012,
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
analysis.}
}
@book{R:Stowell:2012,
author = {Sarah Stowell},
title = {Instant {R}: An Introduction to {R} for Statistical
Analysis},
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.}
}
@book{R:Pfaff:2012,
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
self-study.}
}
@book{R:Lunn:2012,
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
series},
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.}
}
@book{R:Lawrence:2012,
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.}
}
@book{R:Brostroem:2012,
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
analysis.}
}
@book{R:Rizopoulos:2012,
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
outcomes.}
}
@book{R:Dennis:2012,
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
graphing.}
}
@book{R:Cornillon:2012,
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.}
}
@book{R:Shipunov+Baldin+Volkova:2012,
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}
}
@book{R:Aragon:2011,
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.}
}
@book{R:Cornillon+Matzner-Lober:2011,
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.}
}
@book{R:Peternelli+Mello:2011,
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.}
}
@book{R:Teetor:2011a,
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.}
}
@book{R:Teetor:2011b,
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.}
}
@book{R:Murrell:2011,
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.}
}
@book{R:Chihara+Hesterberg:2011,
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
}
@book{R:Fox+Weisberg:2011,
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
methodology.}
}
@book{R:Mittal:2011,
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.}
}
@book{R:Williams:2011,
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}
}
@book{R:Gilli+Maringer+Schumann:2011,
title = {Numerical Methods and Optimization in Finance},
publisher = {Academic Press},
year = 2011,
author = {Gilli, Manfred and Maringer, Dietmar and Schumann,
Enrico},
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}
}
@book{R:Falissard:2011,
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.}
}
@book{R:Eubank:2011,
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
environment.}
}
@book{R:Ekstrom:2011,
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.}
}
@book{R:Curran:2011,
author = {Curran, James Michael},
title = {Introduction to Data Analysis with R for Forensic
Scientists},
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}
}
@book{R:Robert+Casella:2011,
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
encore.}
}
@book{R:Jahans:2011,
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.}
}
@book{R:Berridge:2011,
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.}
}
@book{R:Vasishth+Broe:2010,
author = {Shravan Vasishth and Michael Broe},
title = {{The Foundations of Statistics: A Simulation-based
Approach}},
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}
}
@book{R:Muenchen+Hilbe:2010,
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}
}
@book{R:Kabacoff:2010,
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
included.}
}
@book{R:Cornillon+Guyader+Husson:2010,
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
Rouviere},
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.}
}
@book{R:Lafaye:2010,
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
appliqu{\'e}es},
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
utilis{\'e}es.}
}
@book{R:Adler:2010,
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).}
}
@book{R:Quick:2010,
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.}
}
@book{R:Husson+Le+Pages:2010,
author = {Francois Husson and S\'ebastien L\^e and J\'er\^ome
Pag\`es},
title = {Exploratory Multivariate Analysis by Example Using
{R}},
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}
}
@book{R:Ruppert:2010,
author = {David Ruppert},
title = {Statistics and Data Analysis for Financial
Engineering},
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
helpful.},
orderinfo = {springer.txt}
}
@book{R:Robert+Casella:2010,
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}
}
@book{R:Chen:2010,
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.}
}
@book{R:Gaetan+Guyon:2010,
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}
}
@book{R:Robinson+Hamann:2010,
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}
}
@book{R:Vinod:2010,
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
misspecification.},
orderinfo = {springer.txt}
}
@book{R:Bloomfield:2009,
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 }
}
@book{R:Ligges:2009,
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}
}
@book{R:Pekar+Brabec:2009,
author = {Stano Pekar and Marek Brabec},
title = {Moderni analyza biologickych dat. 1. Zobecnene
linearni modely v prostredi {R} [Modern Analysis of
Biological Data. 1. Generalised Linear Models in {R}]},
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}
}
@book{R:Muenchen:2009,
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
download.},
orderinfo = {springer.txt}
}
@book{R:Heiberger+Neuwirth:2009,
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}
}
@book{R:Hoff:2009,
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}
}
@book{R:Cowpertwait+Metcalfe:2009,
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
research.},
orderinfo = {springer.txt}
}
@book{R:Jones+Maillardet+Robinson:2009,
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.}
}
@book{R:Stevens:2009,
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}
}
@book{R:Varmuza+Filzmoser:2009,
author = {Kurt Varmuza and Peter Filzmoser},
title = {Introduction to Multivariate Statistical Analysis in
Chemometrics},
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
data.}
}
@book{R:Broman+Sen:2009,
author = {Karl W. Broman and Saunak Sen},
title = {A Guide to QTL Mapping with R/qtl},
publisher = {Springer},
year = 2009,
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}
}
@book{R:Velten:2009,
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).}
}
@book{R:Albert:2009,
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}
}
@book{R:Ramsay+Hooker+Graves:2009,
author = {J. O. Ramsay and Giles Hooker and Spencer Graves},
title = {Functional Data Analysis with {R} and {Matlab}},
publisher = {Springer},
year = 2009,
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}
}
@book{R:Wickham:2009,
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
page.},
orderinfo = {springer.txt}
}
@book{R:Sawitzki:2009,
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
R.}
}
@book{R:Petris+Petrone+Campagnoli:2009,
author = {Giovanni Petris and Sonia Petrone and Patriza
Campagnoli},
title = {Dynamic Linear Models with {R}},
publisher = {Springer},
year = 2009,
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}
}
@book{R:Millot:2009,
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.}
}
@book{R:Husson+Le+Pages:2009,
author = {Francois Husson and S\'ebastien L\^e and J\'er\^ome
Pag\`es},
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.}
}
@book{R:Steyerberg:2009,
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
making.},
orderinfo = {springer.txt}
}
@book{R:Reymann:2009,
author = {Detlev Reymann},
title = {{Wettbewerbsanalysen f\"ur kleine und mittlere
Unternehmen (KMUs) --- Theoretische Grundlagen und
praktische Anwendung am Beispiel gartenbaulicher
Betriebe}},
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
}
@book{R:Wright:2009,
author = {Daniel B. Wright and Kamala London},
title = {Modern Regression Techniques Using {R}: A Practical
Guide},
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
\url{https://uk.sagepub.com/en-gb/eur/modern-regression-techniques-using-r/book233198}.
The authors are donating all royalties from the book
to the American Partnership for Eosinophilic
Disorders.}
}
@book{R:Ritz+Streibig:2009,
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}
}
@book{R:Foulkes:2009,
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}
}
@book{R:Zuur+Ieno+Walker:2009,
author = {Alain Zuur and Elena N. Ieno and Neil Walker and
Anatoly A. Saveiliev and Graham M. Smith},
title = {Mixed Effects Models and Extensions in Ecology with
{R}},
publisher = {Springer},
year = 2009,
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
\url{http://www.highstat.com}.},
orderinfo = {springer.txt}
}
@book{R:Zuur+Ieno+Meesters:2009,
author = {Alain F. Zuur and Elena N. Ieno and Erik Meesters},
title = {A Beginner's Guide to {R}},
publisher = {Springer},
year = 2009,
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}
}
@book{R:Iacus:2007,
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
book.},
orderinfo = {springer.txt}
}
@book{R:Sheather:2008,
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}
}
@book{R:Sarkar:2008,
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}
}
@book{R:Bivand+Pebesma+Gomez-Rubio:2008,
author = {Roger S. Bivand and Edzer J. Pebesma and Virgilio
G{\'o}mez-Rubio},
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:
\url{https://asdar-book.org/}.},
orderinfo = {springer.txt}
}
@book{R:Peng+Dominici:2008,
author = {Roger D. Peng and Francesca Dominici},
title = { Statistical Methods for Environmental Epidemiology
with {R}: A Case Study in Air Pollution and Health },
publisher = {Springer},
year = 2008,
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}
}
@book{R:Gentleman:2008a,
author = {Robert Gentleman},
title = {Bioinformatics with {R}},
publisher = {Chapman \& Hall/CRC},
year = 2008,
address = {Boca Raton, FL},
isbn = {1-420-06367-7}
}
@book{R:Gentleman:2008b,
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}
}
@book{R:Spector:2008,
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}
}
@book{R:Pfaff:2008,
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}
}
@book{R:Dalgaard:2008,
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.}
}
@book{R:Rizzo:2008,
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}
}
@book{R:Keele:2008,
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.}
}
@book{R:Cryer+Chan:2008,
author = {Jonathan D. Cryer and Kung-Sik Chan},
title = {Time Series Analysis With Applications in {R}},
publisher = {Springer},
year = 2008,
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}
}
@book{R:Chambers:2008,
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}
}
@book{R:Vinod:2008,
author = {Hrishikesh D. Vinod},
title = {Hands-on Intermediate Econometrics Using {R}:
Templates for Extending Dozens of Practical Examples},
publisher = {World Scientific},
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
econometricians.}
}
@book{R:Nason:2008,
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
purposes.},
orderinfo = {springer.txt}
}
@book{R:Reimann+Filzmoser+Garrett:2008,
author = {Clemens Reimann and Peter Filzmoser and Robert Garrett
and Rudolf Dutter},
title = {Statistical Data Analysis Explained: Applied
Environmental Statistics with {R}},
publisher = {Wiley},
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
book.}
}
@book{R:Claude:2008,
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}
}
@book{R:Kleiber+Zeileis:2008,
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}
}
@book{R:Bolker:2008,
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.}
}
@book{R:Braun+Murdoch:2007,
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.}
}
@book{R:Lynch:2007,
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}
}
@book{R:Dudoit+Laan:2007,
author = {Sandrine Dudoit and Mark J. {van der Laan}},
title = {Multiple Testing Procedures and Applications to
Genomics},
publisher = {Springer},
year = 2007,
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}
}
@book{R:Boland:2007,
author = {Philip J. Boland},
title = {Statistical and Probabilistic Methods in Actuarial
Science},
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}
}
@book{R:Greenacre:2007,
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.}
}
@book{R:Maindonald+Braun:2007,
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
ordination.}
}
@book{R:Marin+Robert:2007,
author = {Jean-Michel Marin and Christian P. Robert},
title = {Bayesian Core: A Practical Approach to Computational
Bayesian Statistics},
publisher = {Springer},
year = 2007,
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}
}
@book{R:Cook+Swayne:2007,
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}
}
@book{R:Siegmund+Yakir:2007,
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}
}
@book{R:Wood:2006,
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.}
}
@book{R:Shumway+Stoffer:2006,
author = {Robert H. Shumway and David S. Stoffer},
title = {Time Series Analysis and Its Applications With {R}
Examples},
publisher = {Springer},
year = 2006,
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}
}
@book{R:Diggle+Ribeiro:2006,
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}
}
@book{R:Le+Zidek:2006,
author = {Nhu D. Le and James V. Zidek},
title = {Statistical Analysis of Environmental Space-Time
Processes},
publisher = {Springer},
year = 2006,
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}
}
@book{R:Sachs+Hedderich:2006,
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}
}
@book{R:Faraway:2006,
author = {Julian J. Faraway},
title = {Extending Linear Models with {R}: Generalized Linear,
Mixed Effects and Nonparametric Regression Models},
publisher = {Chapman \& Hall/CRC},
year = 2006,
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.}
}
@book{R:Jureckova+Picek:2006,
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}
}
@book{R:Paradis:2006,
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}
}
@book{R:Everitt+Hothorn:2006,
author = {Brian Everitt and Torsten Hothorn},
title = {A Handbook of Statistical Analyses Using {R}},
publisher = {Chapman \& Hall/CRC},
year = 2006,
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}
}
@book{R:Deonier+Tavare+Waterman:2005,
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
R.},
orderinfo = {springer.txt}
}
@book{R:Murrell:2005,
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}
}
@book{R:Crawley:2005,
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.}
}
@book{R:Verzani:2005,
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.}
}
@book{R:Murtagh:2005,
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}
}
@book{R:Everitt:2005,
author = {Brian S. Everitt},
title = {An {R} and {S-Plus} Companion to Multivariate
Analysis},
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}
}
@book{R:Behr:2005,
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}
}
@book{R:Gentleman+Carey+Huber:2005,
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}
}
@book{R:Mase+Kamakura+Jimbo:2004,
author = {S. Mase and T. Kamakura and M. Jimbo and K. Kanefuji},
title = {Introduction to Data Science for engineers--- Data
analysis using free statistical software {R} (in
Japanese)},
publisher = {Suuri-Kogaku-sha, Tokyo},
year = 2004,
month = {April},
isbn = {4901683128},
pages = 254
}
@book{R:Heiberger+Holland:2004,
author = {Richard M. Heiberger and Burt Holland},
title = {Statistical Analysis and Data Display: An Intermediate
Course with Examples in {S-Plus}, {R}, and {SAS}},
publisher = {Springer},
year = 2004,
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}
}
@book{R:Faraway:2004,
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.}
}
@book{R:Dolic:2004,
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}
}
@book{R:Huet+Bouvier+Gruet+Jolivet:2003,
author = {Sylvie Huet and Annie Bouvier and Marie-Anne Gruet and
Emmanuel Jolivet},
title = {Statistical Tools for Nonlinear Regression},
publisher = {Springer},
year = 2003,
address = {New York},
isbn = {978-0-387-21574-7},
publisherurl = {https://www.springer.com/978-0-387-21574-7},
orderinfo = {springer.txt}
}
@book{R:Iacus+Masarotto:2003,
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
}
@book{R:Parmigiani+Garrett+Irizarry+Zeger:2003,
author = {Giovanni Parmigiani and Elizabeth S. Garrett and
Rafael A. Irizarry and Scott L. Zeger},
title = {The Analysis of Gene Expression Data},
publisher = {Springer},
year = 2003,
address = {New York},
isbn = {978-0-387-21679-9},
publisherurl = {https://www.springer.com/978-0-387-21679-9},
orderinfo = {springer.txt}
}
@book{R:Venables+Ripley:2002,
author = {William N. Venables and Brian D. Ripley},
title = {Modern Applied Statistics with {S}. Fourth Edition},
publisher = {Springer},
year = 2002,
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}
}
@book{R:Fox:2002,
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.}
}
@book{R:Limas+Mere+Juez:2001,
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
Rioja},
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
R.}
}
@book{R:Harrell:2001,
author = {Frank E. Harrell},
title = {Regression Modeling Strategies, with Applications to
Linear Models, Survival Analysis and Logistic
Regression},
publisher = {Springer},
year = 2001,
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}
}
@book{R:Venables+Ripley:2000,
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}
}
@book{R:Therneau+Grambsch:2000,
author = {Terry M. Therneau and Patricia M. Grambsch},
title = {Modeling Survival Data: Extending the {Cox} Model},
publisher = {Springer},
year = 2000,
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}
}
@book{R:Pinheiro+Bates:2000,
author = {Jose C. Pinheiro and Douglas M. Bates},
title = {Mixed-Effects Models in {S} and {S-Plus}},
publisher = {Springer},
year = 2000,
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}
}
@book{R:Nolan+Speed:2000,
author = {Deborah Nolan and Terry Speed},
title = {Stat Labs: Mathematical Statistics Through
Applications},
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}
}
@book{R:Chambers:1998,
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}
}
@book{R:Chambers+Hastie:1992,
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}
}
@book{R:Becker+Chambers+Wilks:1988,
author = {Richard A. Becker and John M. Chambers and Allan
R. Wilks},
title = {The New {S} Language},
publisher = {Chapman \& Hall},
year = 1988,
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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