Statistical Computing in C++ and R (Chapman & Hall/CRC Computer Science & Data Analysis)

Focusing on coding-based statistical analysis methodologies, this volume on statistical computing provides detailed instruction on crafting applications using the R and C++ programming languages. Beginning with an overview of object-oriented programming fundamentals, the work cover topics such as numerical liner algebra, abstract data structures, data structures in C++, and parallel computing in C++ and R. Chapters include practice exercises and code examples and access to additional online resources is provided. Eubank is a professor of mathematics at Arizona State University and Kupresanin is a statistician at the Lawrence Livermore National Laboratory. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

Parallel processing can be ideally suited for the solving of more complex problems in statistical computing. This book discusses code development in C++ and R, before going beyond to look at the valuable use of these two languages in unison. It covers linear equation solution with regression and linear models motivation, optimization with maximum likelihood and nonlinear least squares motivation, and random number generation. While the text does require a working knowledge of both the basic concepts in statistics and experience in programming, it does not require knowledge specific to C++ or R.