Statistical Computing in C++ and R

Zusatztext "?the first treatment of parallel programming in R that I have seen in a book. The text is replete with code examples and there are numerous end-of-chapter exercises."-International Statistical Review! 2013 Informationen zum Autor Randall L. Eubank, Ana Kupresanin Klappentext 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. Zusammenfassung 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 requires a working knowledge of both the basic concepts in statistics and experience in programming. Inhaltsverzeichnis Introduction. Computer Representation of Numbers. A Sketch of C++. Generation of Pseudo-Random Numbers. Programming in R. Creating Classes and Methods in R. Numerical Linear Algebra. Numerical Optimization. Abstract Data Structures. Data Structures in C++. Parallel Computing in C++ and R. An Introduction to Unix. An Introduction to R. C++ Library Extensions (TR1). The Matrix and Vector Classes. The ranGen Class. References. Index.

Autorentext
Randall L. Eubank, Ana Kupresanin

Klappentext
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.

Inhalt
Introduction. Computer Representation of Numbers. A Sketch of C++. Generation of Pseudo-Random Numbers. Programming in R. Creating Classes and Methods in R. Numerical Linear Algebra. Numerical Optimization. Abstract Data Structures. Data Structures in C++. Parallel Computing in C++ and R. An Introduction to Unix. An Introduction to R. C++ Library Extensions (TR1). The Matrix and Vector Classes. The ranGen Class. References. Index.

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