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Rcpp

Seamless R and C++ Integration

The Rcpp package provides R functions and a C++ library facilitating the integration of R and C++.

R data types (SEXP) are matched to C++ objects in a class hierarchy. All R types are supported (vectors, functions, environment, etc ...) and each type is mapped to a dedicated class. For example, numeric vectors are represented as instances of the Rcpp::NumericVector class, environments are represented as instances of Rcpp::Environment, functions are represented as Rcpp::Function, etc ... The Rcpp-introduction vignette (also published as a JSS paper) provides a good entry point to Rcpp as do the Rcpp website, the Rcpp page and the Rcpp Gallery. Full documentation is provided by the Rcpp book.

Conversion from C++ to R and back is driven by the templates Rcpp::wrap and Rcpp::as which are highly flexible and extensible, as documented in the Rcpp-extending vignette.

Rcpp also provides Rcpp modules, a framework that allows exposing C++ functions and classes to the R level. The Rcpp-modules vignette details the current set of features of Rcpp-modules.

Rcpp includes a concept called Rcpp sugar that brings many R functions into C++. Sugar takes advantage of lazy evaluation and expression templates to achieve great performance while exposing a syntax that is much nicer to use than the equivalent low-level loop code. The Rcpp-sugar gives an overview of the feature.

Rcpp attributes provide a high-level syntax for declaring C++ functions as callable from R and automatically generating the code required to invoke them. Attributes are intended to facilitate both interactive use of C++ within R sessions as well as to support R package development. Attributes are built on top of Rcpp modules and their implementation is based on previous work in the inline package. See the Rcpp-atttributes vignettes for more details.

Documentation

The package ships with nine pdf vignettes.

Additional documentation is available via the JSS paper by Eddelbuettel and Francois (2011, JSS) paper (corresponding to the 'intro' vignette) and the book by Eddelbuettel (2013, Springer); see 'citation("Rcpp")' for details.

Examples

The Rcpp Gallery showcases over one hundred fully documented and working examples.

A number of examples are included as are 1394 unit tests in 606 unit test functions provide additional usage examples.

An earlier version of Rcpp, containing what we now call the 'classic Rcpp API' was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic, and it is still available for code relying on the older interface. New development should always use this Rcpp package instead.

Other usage examples are provided by packages using Rcpp. As of March 2018, there are 1309 CRAN packages using Rcpp, a further 91 BioConductor packages in its current release as well as an unknown number of GitHub, Bitbucket, R-Forge, ... repositories using Rcpp. All these packages provide usage examples for Rcpp.

Installation

Released and tested versions of Rcpp are available via the CRAN network, and can be installed from within R via

install.packages("Rcpp")

To install from source, ensure you have a complete package development environment for R as discussed in the relevant documentation; also see questions 1.2 and 1.3 in the Rcpp-FAQ.

Support

The best place for questions is the Rcpp-devel mailing list hosted at R-forge. Note that in order to keep spam down, you must be a subscriber in order to post. One can also consult the list archives to see if your question has been asked before.

Another option is to use StackOverflow and its 'rcpp' tag. Search functionality (use rcpp in squared brackets as in [rcpp] my question terms to tag the query) is very valuable as many questions have indeed been asked, and answered, before.

The issue tickets at the GitHub repo are the primary bug reporting interface. As with the other web resources, previous issues can be searched as well.

Authors

Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou, Nathan Russell, Doug Bates, and John Chambers

License

GPL (>= 2)

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Version

Install

install.packages('Rcpp')

Monthly Downloads

1,059,568

Version

0.12.18

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Maintainer

Dirk Eddelbuettel

Last Published

July 23rd, 2018

Functions in Rcpp (0.12.18)

exportAttribute

Rcpp::export Attribute
compileAttributes

Compile Rcpp Attributes for a Package
Rcpp-package

R / C++ interface
formals<--methods

Set the formal arguments of a C++ function
loadRcppModules-deprecated

Loads Rcpp modules on package startup
exposeClass

Create an Rcpp Module to Expose a C++ Class in R
interfacesAttribute

Rcpp::interfaces Attribute
loadModule

Load an Rcpp Module into a Package
registerPlugin

Register an inline plugin
pluginsAttribute

Rcpp::plugins Attribute
setRcppClass

Create a Class Extending a C++ Class
sourceCpp

Source C++ Code from a File or String
populate

Populates a namespace or an environment with the content of a module
dependsAttribute

Rcpp::depends Attribute
evalCpp

Evaluate a C++ Expression
.DollarNames-methods

completion
C++Field-class

Class "C++Field"
Module-class

Rcpp modules
C++OverloadedMethods-class

Class "C++OverloadedMethods"
C++Constructor-class

Class "C++Constructor"
C++Object-class

c++ internal objects
C++Class-class

Reflection information for an internal c++ class
compilerCheck

Check for Minimal (g++) Compiler Version
Rcpp-deprecated

Deprecated Functions in the Rcpp Package
RcppUnitTests

Rcpp : unit tests results
C++Function-class

Class "C++Function"
Rcpp-internal

Rcpp internal functions
Module

Retrieves an Rcpp module
demangle

c++ type information
cppFunction

Define an R Function with a C++ Implementation
LdFlags

(Deprecated) Rcpp Linker Flags
Rcpp.package.skeleton

Create a skeleton for a new package depending on Rcpp
Rcpp.plugin.maker

Facilitating making package plugins