**Thinking inside the box**, and kindly contributed to R-bloggers)

The text below went out as a post to the

r-packages list a few days ago, but I thought it would make sense to post it

on the blog too. So with a little html markup…

#### Summary

Version 0.9.0 of the Rcpp package is now on CRAN and its mirrors. This

release marks another step in the development of the package, and a few key

points are highlighted below. More details are in the NEWS and ChangeLog

files included in the package.

#### Overview

Rcpp is an R package and associated C++ library that facilitates integration

of C++ code in R packages.

The package features a complete set of C++ classes (`Rcpp::IntegerVector`

,

`Rcpp:NumericVector`

, `Rcpp::Function`

, `Rcpp::Environment`

, …) that makes it

easier to manipulate R objects of matching types (integer vectors, functions,

environments, etc …).

Rcpp takes advantage of C++ language features such as the explicit

constructor / destructor lifecycle of objects to manage garbage collection

automatically and transparently. We believe this is a major improvement over

use of `PROTECT`

/ `UNPROTECT`

. When an Rcpp object is created, it protects the

underlying SEXP so that the garbage collector does not attempt to reclaim the

memory. This protection is withdrawn when the object goes out of

scope. Moreover, users generally do not need to manage memory directly (via

calls to new / delete or malloc / free) as this is done by the Rcpp classes

or the corresponding STL containers.

A few key points about Rcpp:

- a rich API covering all core R data types including vectors, matrices,

functions, environments, … (with the exeception of factors which are less

useful in C++) - seamless (bi-directional) data interchange between R and C++
- possibility of inline use permitting definition, compilation, linking and

loading of C++ functions directly from R - extensive documentation now covering eight vignettes
- exception handling and error propagation back to R
- extensive test suite using RUnit covering over 700 tests
- extension packages RcppArmadillo and RcppGSL provide easy-to-use

integration with the Armadillo (linear algebra) and GNU GSL librasries - increasing adoption among R users and package developers with now

twenty packages from

CRAN or BioConductor depending on Rcpp - support for the legacy ‘classic’ Rcpp is now provided by the RcppClassic

package which is being released concurrently with Rcpp 0.9.0

Several key features were added during the 0.8.* cycles and are described below.

#### Rcpp sugar

Rcpp now provides syntactic sugar: vectorised expressions at the C++ level

which are motivated by the corresponding R expressions. This covers

operators (binary arithmetic, binary logical, unary), functions (producing

single logical results, mathematical functions and d/p/q/r statistical

functions). Examples comprises anything from ifelse() to pmin()/pmax() or

A really simply example is a function

SEXP foo( SEXP xx, SEXP yy){ NumericVector x(xx), y(yy) ; return ifelse( x < y, x*x, -(y*y) ) ; }

which deploys the sugar ‘ifelse’ function modeled after the corresponding R

function. Another simple example is

double square( double x){ return x*x ; } SEXP foo( SEXP xx ){ NumericVector x(xx) ; return sapply( x, square ) ; }

where use the sugar function ‘sapply’ to sweep a simple C++ function which

operates elementwise across the supplied vector.

The Rcpp-sugar vignette describes sugar in more detail.

#### Rcpp modules

Rcpp modules are inspired by Boost.Python and make exposing C++ functions or

classes to R even easier. A first illustration is provided by this simple

C++ code snippet

const char* hello( const std::string& who ){ std::string result( "hello " ) ; result += who ; return result.c_str() ; } RCPP_MODULE(yada){ using namespace Rcpp ; function( "hello", &hello ) ; }

which (after compiling and loading) we can access in R as

yada <- Module( "yada" ) yada$hello( "world" )

In a similar way, C++ classes can be exposed very easily.

Rcpp modules are also described in more detail in their own vignette.

#### Reference Classes

R release 2.12.0 introduced Reference Classes. These are formal S4 classes

with the corresponding dispatch method, but passed by reference and easy to

use. Reference Classes can also be exposed to R by using Rcpp modules.

#### Extension packackages

The RcppArmadillo

package permits use of the advanced C++ library ‘Armadillo,

a C++ linear algebra library aiming towards a good balance between speed and

ease of use, providing integer, floating point and complex matrices and

vectors with lapack / blas support via R. Armadillo uses templates for a

delayed evaluation approach is employed (during compile time) to combine

several operations into one and reduce (or eliminate) the need for

temporaries. Armadillo is useful if C++ has been decided as the language of

choice, rather than another language like Matlab ® or Octave, and aims to be

as expressive as the former. Via Rcpp and RcppArmadillo, R users now have

easy access to this functionality. Examples are provided in the RcppArmadillo

package.

The RcppGSL

package permits easy use of the GNU Scientific Library (GSL), a

collection of numerical routines for scientifc computing. It is particularly

useful for C and C++ programs as it provides a standard C interface to a wide

range of mathematical routines such as special functions, permutations,

combinations, fast fourier transforms, eigensystems, random numbers,

quadrature, random distributions, quasi-random sequences, Monte Carlo

integration, N-tuples, differential equations, simulated annealing, numerical

differentiation, interpolation, series acceleration, Chebyshev

approximations, root-finding, discrete Hankel transforms physical constants,

basis splines and wavelets. There are over 1000 functions in total with an

extensive test suite. The RcppGSL package provides an easy-to-use interface

between GSL data structures and R using concepts from Rcpp. The RcppGSL

package also contains a vignette with more documentation.

#### Legacy ‘classic’ API

Packages still using code interfacing the initial ‘classic’ Rcpp API are

encouraged to migrate to the new API. Should a code transition not be

possible, backwards compatibility is provided by the RcppClassic package

released alongside Rcpp 0.9.0. By including RcppClassic.h and building

against the RcppClassic package and library, vintage code can remain

operational using the classic API. The short vignette in the RcppClassic

package has more details.

#### Documentation

The package contains a total of eight vignettes the first of which provides a

short and succinct introduction to the Rcpp package along with several

motivating examples.

#### Links

#### Support

Questions about Rcpp should be directed to the Rcpp-devel mailing list

https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel

Dirk Eddelbuettel, Romain Francois, Doug Bates and John Chambers

December 2010

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