R and MATLAB [Hiebeler 2015-06-23].pdf

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The R Series
R and MATLAB®
David E. Hiebeler
R and MATLAB®
Chapman & Hall/CRC
The R Series
Series Editors
John M. Chambers
Department of Statistics
Stanford University
Stanford, California, USA
Duncan Temple Lang
Department of Statistics
University of California, Davis
Davis, California, USA
Torsten Hothorn
Division of Biostatistics
University of Zurich
Switzerland
Hadley Wickham
RStudio
Boston, Massachusetts, USA
Aims and Scope
This book series reflects the recent rapid growth in the development and application
of R, the programming language and software environment for statistical computing
and graphics. R is now widely used in academic research, education, and industry.
It is constantly growing, with new versions of the core software released regularly
and more than 6,000 packages available. It is difficult for the documentation to
keep pace with the expansion of the software, and this vital book series provides a
forum for the publication of books covering many aspects of the development and
application of R.
The scope of the series is wide, covering three main threads:
• Applications of R to specific disciplines such as biology, epidemiology,
genetics, engineering, finance, and the social sciences.
• Using R for the study of topics of statistical methodology, such as linear and
mixed modeling, time series, Bayesian methods, and missing data.
• The development of R, including programming, building packages, and
graphics.
The books will appeal to programmers and developers of R software, as well as
applied statisticians and data analysts in many fields. The books will feature
detailed worked examples and R code fully integrated into the text, ensuring their
usefulness to researchers, practitioners and students.
Published Titles
Stated Preference Methods Using R,
Hideo Aizaki, Tomoaki Nakatani,
and Kazuo Sato
Using R for Numerical Analysis in Science and Engineering,
Victor A. Bloomfield
Event History Analysis with R,
Göran Broström
Computational Actuarial Science with R,
Arthur Charpentier
Statistical Computing in C++ and R,
Randall L. Eubank and Ana Kupresanin
Reproducible Research with R and RStudio, Second Edition,
Christopher Gandrud
R and MATLAB®David
E. Hiebeler
Nonparametric Statistical Methods Using R,
John Kloke and Joseph McKean
Displaying Time Series, Spatial, and Space-Time Data with R,
Oscar Perpiñán Lamigueiro
Programming Graphical User Interfaces with R,
Michael F. Lawrence
and John Verzani
Analyzing Sensory Data with R,
Sébastien Lê and Theirry Worch
Parallel Computing for Data Science: With Examples in R, C++ and CUDA,
Norman Matloff
Analyzing Baseball Data with R,
Max Marchi and Jim Albert
Growth Curve Analysis and Visualization Using R,
Daniel Mirman
R Graphics, Second Edition,
Paul Murrell
Data Science in R: A Case Studies Approach to Computational Reasoning and
Problem Solving,
Deborah Nolan and Duncan Temple Lang
Multiple Factor Analysis by Example Using R,
Jérôme Pagès
Customer and Business Analytics: Applied Data Mining for Business Decision
Making Using R,
Daniel S. Putler and Robert E. Krider
Implementing Reproducible Research,
Victoria Stodden, Friedrich Leisch,
and Roger D. Peng
Graphical Data Analysis with R,
Antony Unwin
Using R for Introductory Statistics, Second Edition,
John Verzani
Advanced R,
Hadley Wickham
Dynamic Documents with R and knitr, Second Edition,
Yihui Xie

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