Eddelbuettel - Seamless R and C++ Integration with Rcpp

1.3.4 Comparison1.4 Summary; 2 Tools and Setup; 2.1 Overall Setup; 2.2 Compilers; 2.2.1 General Setup; 2.2.2 Platform-Specific Notes; 2.3 The R Application Programming Interface; 2.4 A First Compilation with Rcpp; 2.5 The Inline Package; 2.5.1 Overview; 2.5.2 Using Includes; 2.5.3 Using Plugins; 2.5.4 Creating Plugins; 2.6 Rcpp Attributes; 2.7 Exception Handling; Part II Core Data Types; 3 Data Structures: Part One; 3.1 The RObject Class; 3.2 The IntegerVector Class; 3.2.1 A First Example: Returning Perfect Numbers; 3.2.2 A Second Example: Using Inputs.

3.2.3 A Third Example: Using Wrong Inputs3.3 The NumericVector Class; 3.3.1 A First Example: Using Two Inputs; 3.3.2 A Second Example: Introducing clone; 3.3.3 A Third Example: Matrices; 3.4 Other Vector Classes; 3.4.1 LogicalVector; 3.4.2 CharacterVector; 3.4.3 RawVector; 4 Data Structures: Part Two; 4.1 The Named Class; 4.2 The List aka GenericVector Class; 4.2.1 List to Retrieve Parameters from R; 4.2.2 List to Return Parameters to R; 4.3 The DataFrame Class; 4.4 The Function Class; 4.4.1 A First Example: Using a Supplied Function; 4.4.2 A Second Example: Accessing an R Function.

4.5 The Environment Class4.6 The S4 Class; 4.7 ReferenceClasses; 4.8 The R Mathematics Library Functions; Part III Advanced Topics; 5 Using Rcpp in Your Package; 5.1 Introduction; 5.2 Using Rcpp.package.skeleton; 5.2.1 Overview; 5.2.2 R Code; 5.2.3 C++ Code; 5.2.4 DESCRIPTION; 5.2.5 Makevars and Makevars.win; 5.2.6 NAMESPACE; 5.2.7 Help Files; 5.2.7.1 mypackage-package. Rd; 5.2.7.2 rcpp_hello_world. Rd; 5.3 Case Study: The wordcloud Package; 5.4 Further Examples; 6 Extending Rcpp; 6.1 Introduction; 6.2 Extending Rcpp::wrap; 6.2.1 Intrusive Extension; 6.2.2 Nonintrusive Extension.

6.2.3 Templates and Partial Specialization6.3 Extending Rcpp::as; 6.3.1 Intrusive Extension; 6.3.2 Nonintrusive Extension; 6.3.3 Templates and Partial Specialization; 6.4 Case Study: The RcppBDT Package; 6.5 Further Examples; 7 Modules; 7.1 Motivation; 7.1.1 Exposing Functions Using Rcpp; 7.1.2 Exposing Classes Using Rcpp; 7.2 Rcpp Modules; 7.2.1 Exposing C++ Functions Using Rcpp Modules; 7.2.1.1 Documentation for Exposed Functions Using Rcpp Modules; 7.2.1.2 Formal Arguments Specification; 7.2.2 Exposing C++ Classes Using Rcpp Modules; 7.2.2.1 Initial Example.

7.2.2.2 Exposing Constructors Using Rcpp Modules.

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 statisticians 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 pa.