## Rcpp [![Build Status](https://travis-ci.org/RcppCore/Rcpp.svg)](https://travis-ci.org/RcppCore/Rcpp) [![License](http://img.shields.io/badge/license-GPL%20%28%3E=%202%29-brightgreen.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html) [![CRAN](http://www.r-pkg.org/badges/version/Rcpp)](http://cran.rstudio.com/package=Rcpp) [![Downloads](http://cranlogs.r-pkg.org/badges/Rcpp?color=brightgreen)](http://www.r-pkg.org/pkg/Rcpp)

### Seamless R and C++ Integration

The [Rcpp package](http://cran.r-project.org/package=Rcpp) 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](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-introduction.pdf)
vignette (also published as a [JSS paper](http://www.jstatsoft.org/v40/i08/)) provides a good
entry point to Rcpp as do the [Rcpp website](http://www.rcpp.org), the
[Rcpp page](http://dirk.eddelbuettel.com/code/rcpp.html) and the 
[Rcpp Gallery](http://gallery.rcpp.org). Full documentation
is provided by the [Rcpp book](http://www.rcpp.org/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](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-extending.pdf) vignette.

Rcpp also provides Rcpp modules, a framework that allows exposing 
C++ functions and classes to the R level. The [Rcpp-modules](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-modules.pdf) 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](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-sugar.pdf)
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](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-attributes.pdf) vignettes for more details. 

### Documentation 

The package ships with nine pdf vignettes. 

Additional documentation is available via the 
[JSS paper](http://www.jstatsoft.org/v40/i08/) by Eddelbuettel and
Francois (2011, JSS) paper (corresponding to the 'intro' vignette) 
and the [book](http://www.rcpp.org/book) by Eddelbuettel (2013, Springer); 
see 'citation("Rcpp")' for details.

### Examples

The [Rcpp Gallery](http://gallery.rcpp.org) showcases over 90 fully documented 
and working examples. 

A number of examples are included as are over 920 unit tests in over 470 unit
test functions provide additional usage examples.

The [CRAN](http://cran.r-project.org) network contains 
(as of May 2015) over 380 packages which also provide usage
examples, with another 50+ as part of [BioConductor](http://www.bioconductor.org).

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 July 2015, there
are about 423 CRAN packages using Rcpp, a further 57 BioConductor packages as
well as an unknown number of GitHub, Bitbucket, R-Forge, ... repositories using
Rcpp.

### Installation

Released and tested versions of Rcpp are available via the
[CRAN](http://cran.r-project.org) network, and can be installed from within R via 

```R
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](http://cran.r-project.org/web/packages/Rcpp/vignettes/Rcpp-FAQ.pdf).

### Authors

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

### License

GPL (>= 2)