Seamless R and C++ Integration with Rcpp
By: by Dirk Eddelbuettel
Contributors: SpringerLink (Online service)
Series: Use R! -- 64
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 220
ISBN-13: 9781461468684
Subjects: Mathematical statistics; Statistics; Computer science; Probability and Statistics in Computer Science; Statistics and Computing/Statistics Programs; Statistical Theory and Methods
Description: Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++.  With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical computing is finally accessible to most R users.  Rcpp should be part of every statistician's toolbox.  — Michael Braun, MIT Sloan School of Management Seamless R and C++ Integration with Rcpp is simply a wonderful book.  For anyone who uses C/C++ and R, it is an indispensable resource.  The writing is outstanding.  A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++.^

I love this book. — Robert McCulloch, University of Chicago Booth School of Business Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. — Sanjog Misra, UCLA Anderson School of Management The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain.^

Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! — Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark 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++.^

Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages.  He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software.  He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.
Table of contents: title: Preface; title: Introduction; title: A Gentle Introduction to Rcpp; title: Tools and Setup; title: Core Data Types; title: Data Structures: Part One; title: Data Structures: Part Two; title: Advanced Topics; title: Using Rcpp in your package; title: Extending Rcpp; title: Modules; title: Sugar; title: Applications; title: RInside; title: RcppArmadillo; title: RcppGSL; title: RcppEigen Appendix; title: C++ for R programmers; title: Indices; title: References .