Seamless R and C++ integration with Rcpp Dirk Eddelbuettel

Format:

Book

Author/Creator:

Eddelbuettel, Dirk

Series:

Use R! 64

Language:

English

Subjects (All):

Application program interfaces (Computer software).

R (Computer program language).

C++ (Computer program language).

Statistics--Computer programs.

Statistics.

Statistics and Computing/Statistics Programs.

Statistical Theory and Methods.

Probability and Statistics in Computer Science.

APIs (interfaces).

Local Subjects:

Statistics.

Statistics and Computing/Statistics Programs.

Statistical Theory and Methods.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource

Place of Publication:

New York, NY Springer ©2013

Language Note:

English

System Details:

PDF

text file

Summary:

This book 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++. -- Edited summary from book

Contents:

Introduction. A Gentle Introduction to Rcpp Tools and Setup Core Data Types. Data Structures: Part One Data Structures: Part Two Advanced Topics. Using Rcpp in Your Package Extending Rcpp Modules Sugar Applications. RInside RcppArmadillo RcppGSL RcppEigen

Appendix. C++ for R Programmers

Machine generated contents note: pt. I Introduction

1. Gentle Introduction to Rcpp

1.1. Background: From R to C++

1.2. First Example

1.2.1. Problem Setting

1.2.2. First R Solution

1.2.3. First C++ Solution

1.2.4. Using Inline

1.2.5. Using Rcpp Attributes

1.2.6. Second R Solution

1.2.7. Second C++ Solution

1.2.8. Third R Solution

1.2.9. Third C++ Solution

1.3. Second Example

1.3.1. Problem Setting

1.3.2. R Solution

1.3.3. C++ Solution

1.3.4. Comparison

1.4. 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. R Application Programming Interface

2.4. First Compilation with Rcpp

2.5. 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

pt. II Core Data Types

3. Data Structures: Part One

3.1. RObject Class

3.2. Integer Vector Class

3.2.1. First Example: Returning Perfect Numbers

3.2.2. Second Example: Using Inputs

3.2.3. Third Example: Using Wrong Inputs

3.3. Numeric Vector Class

3.3.1. First Example: Using Two Inputs

3.3.2. Second Example: Introducing clone

3.3.3. Third Example: Matrices

3.4. Other Vector Classes

3.4.1. Logical Vector

3.4.2. Character Vector

3.4.3. Raw Vector

4. Data Structures: Part Two

4.1. Named Class

4.2. List aka Generic Vector Class

4.2.1. List to Retrieve Parameters from R

4.2.2. List to Return Parameters to R

4.3. DataFrame Class

4.4. Function Class

4.4.1. First Example: Using a Supplied Function

4.4.2. Second Example: Accessing an R Function

4.5. Environment Class

4.6. S4 Class

4.7. Reference Classes

4.8. R Mathematics Library Functions

pt. 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

5.3. 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 Specialization

6.3. 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

7.2.2. Exposing C++ Classes Using Rcpp Modules

7.3. Using Modules in Other Packages

7.3.1. Namespace Import/Export

7.3.2. Support for Modules in Skeleton Generator

7.3.3. Module Documentation

7.4. Case Study: The RcppCNPy Package

7.5. Further Examples

8. Sugar

8.1. Motivation

8.2. Operators

8.2.1. Binary Arithmetic Operators

8.2.2. Binary Logical Operators

8.2.3. Unary Operators

8.3. Functions

8.3.1. Functions Producing a Single Logical Result

8.3.2. Functions Producing Sugar Expressions

8.3.3. Mathematical Functions

8.3.4. d/q/p/q Statistical Functions

8.4. Performance

8.5. Implementation

8.5.1. Curiously Recurring Template Pattern

8.5.2. VectorBase Class

8.5.3. Example: sapply

8.6. Case Study: Computing π Using Rcpp sugar

pt. IV Applications

9. RInside

9.1. Motivation

9.2. First Example: Hello, World!

9.3. Second Example: Data Transfer

9.4. Third Example: Evaluating R Expressions

9.5. Fourth Example: Plotting from C++ via R

9.6. Fifth Example: Using RInside Inside MPI

9.7. Other Examples

10. RcppArmadillo

10.1. Overview

10.2. Motivation: FastLm

10.2.1. Implementation

10.2.2. Performance Comparison

10.2.3. Caveat

10.3. Case Study: Kalman Filter Using RcppArmadillo

10.4. RcppArmadillo and Armadillo Differences

11. RcppGSL

11.1. Introduction

11.2. Motivation: FastLm

11.3. Vectors

11.3.1. GSL Vectors

11.3.2. RcppGSL::vector

11.3.3. Mapping

11.3.4. Vector Views

11.4. Matrices

11.4.1. Creating Matrices

11.4.2. Implicit Conversion

11.4.3. Indexing

11.4.4. Methods

11.4.5. Matrix Views

11.5. Using RcppGSL in Your Package

11.5.1. configure Script

11.5.2. src Directory

11.5.3. R Directory

11.6. Using RcppGSL with inline

11.7. Case Study: GSL-Based B-Spline Fit Using RcppGSL

12. RcppEigen

12.1. Introduction

12.2. Eigen classes

12.2.1. Fixed-Size Vectors and Matrices

12.2.2. Dynamic-Size Vectors and Matrices

12.2.3. Arrays for Per-Component Operations

12.2.4. Mapped Vectors and Matrices and Special Matrices

12.3. Case Study: Kalman filter using RcppEigen

12.4. Linear Algebra and Matrix Decompositions

12.4.1. Basic Solvers

12.4.2. Eigenvalues and Eigenvectors

12.4.3. Least-Squares Solvers

12.4.4. Rank-Revealing Decompositions

12.5. Case Study: C++ Factory for Linear Models in RcppEigen

pt. V Appendix

A. C++ for R Programmers

A.1. Compiled Not Interpreted

A.2. Statically Typed

A.3. Better C

A.4. Object-Oriented (But Not Like S3 or S4)

A.5. Generic Programming and the STL

A.6. Template Programming

A.7. Further Reading on C++

Notes:

Includes bibliographical references and indexes

Print version record

Other Format:

Printed edition:

ISBN:

9781461468684

146146868X

OCLC:

848789357

Access Restriction:

Restricted for use by site license