R Development Page
Contributed R Packages
Below is a list of all packages provided by project Rcpp  Seamless R and C++ Integration.
Important note for package
binaries: RForge provides these binaries only for
the most recent version of R, but not for older
versions. In order to successfully install the
packages provided on RForge, you have to switch
to the most recent version of R or, alternatively,
install from the package sources (.tar.gz).
Rcpp  Seamless R and C++ Integration


The Rcpp package provides R functions as well as a C++ library
which facilitate 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 "Rcppintroduction" vignette provides a good
entry point to Rcpp.
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 "Rcppextending" vignette.
Rcpp also provides Rcpp modules, a framework that allows exposing
C++ functions and classes to the R level. The "Rcppmodules" vignette
details the current set of features of Rcppmodules.
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 lowlevel loop code. The "Rcppsugar" vignette
gives an overview of the feature.
Rcpp attributes provide a highlevel 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.
Many examples are included, and around 872 unit tests in 422 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.
Additional documentation is available via the paper by Eddelbuettel and
Francois (2011, JSS) paper and the book by Eddelbuettel (2013, Springer);
see citation("Rcpp") for details. 

Version: 0.10.6.1 
Last change: 20131124 16:34:12+01 
Rev.: 4600 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get Rcpp 0.12.1 from CRAN 

R install command:
install.packages("Rcpp", repos="http://RForge.Rproject.org") 


RcppArmadillo  Rcpp integration for Armadillo templated linear algebra library


R and Armadillo integration using Rcpp
Armadillo is a templated C++ linear algebra library (by Conrad Sanderson)
that aims towards a good balance between speed and ease of use. Integer,
floating point and complex numbers are supported, as well as a subset of
trigonometric and statistics functions. Various matrix decompositions are
provided through optional integration with LAPACK and ATLAS libraries.
A delayed evaluation approach is employed (during compile time) to combine
several operations into one, and to reduce (or eliminate) the need for
temporaries. This is accomplished through recursive templates and template
metaprogramming.
This library is useful if C++ has been decided as the language of choice
(due to speed and/or integration capabilities), rather than another language.
The RcppArmadillo package includes the header files from the templated
Armadillo library (currently version 3.920.1). Thus users do not need to
install Armadillo itself in order to use RcppArmadillo.
This Armadillo integration provides a nice illustration of the
capabilities of the Rcpp package for seamless R and C++ integration.
Armadillo is licensed under the MPL 2.0, while RcppArmadillo (the Rcpp
bindings/bridge to Armadillo) is licensed under the GNU GPL version 2
or later, as is the rest of Rcpp. 

Version: 0.3.920.3 
Last change: 20131123 20:24:15+01 
Rev.: 4598 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppArmadillo 0.6.100.0.0 from CRAN 

R install command:
install.packages("RcppArmadillo", repos="http://RForge.Rproject.org") 


RcppBDT  Rcpp bindings for the Boost Date_Time library


This package provides R with access to Boost Date_Time
functionality by using Rcpp modules.
Currently only Date functionality is covered.
Boost header files are needed to build the package. Linking is optional to
provide supplementary date to/from strings conversion functions. 

Version: 0.2.1.5 
Last change: 20131216 02:03:19+01 
Rev.: 4601 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppBDT 0.2.3 from CRAN 

R install command:
install.packages("RcppBDT", repos="http://RForge.Rproject.org") 


RcppCImg  Bindings for the CImg image manipulation library


The package exposes classes from the CImg C++ library
to the R level via Rcpp modules 

Version: 0.0 
Last change: 20101119 08:35:23+01 
Rev.: 2461 

Download:
(.tar.gz) 
(.zip) 
Build status: Failed to build 

R install command:
install.packages("RcppCImg", repos="http://RForge.Rproject.org") 


RcppCNPy  Rcpp bindings for NumPy files


This package provides R with access to the cnpy library written
by Carl Rogers which provides read and write facilities for files created
with (or for) the NumPy extension for Python. Vectors and matrices of
numeric types can be read or written to and from files as well as compressed
files. Support for integer files is available if the package has been built
with std=c++0x or std=c++11 which is needed for long long int support. 

Version: 0.2.0.2 
Last change: 20130221 03:53:55+01 
Rev.: 4270 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppCNPy 0.2.4 from CRAN 

R install command:
install.packages("RcppCNPy", repos="http://RForge.Rproject.org") 


RcppClassic  Deprecated classic Rcpp API


The RcppClassic package provides a deprecated C++ library which
facilitates the integration of R and C++.
New projects should use the new Rcpp API in the Rcpp package. 

Version: 0.9.4.1 
Last change: 20140123 16:18:09+01 
Rev.: 4607 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppClassic 0.9.6 from CRAN 

R install command:
install.packages("RcppClassic", repos="http://RForge.Rproject.org") 


RcppClassicExamples  Examples using RcppClassic to interface R and C++


The Rcpp package contains a C++ library that facilitates the
integration of R and C++ in various ways via a rich API. This API was
preceded by an earlier version which has been deprecated since 2010 (but is
still supported to provide backwards compatibility in the package
RcppClassic). This package RcppClassicExamples provides usage examples for
the older, deprecated API. There is also a corresponding package
RcppExamples package with examples for the newer, current API which we
strongly recommend as the basis for all new development. 

Version: 0.1.2 
Last change: 20140125 19:33:02+01 
Rev.: 4608 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppClassicExamples 0.1.1 from CRAN 

R install command:
install.packages("RcppClassicExamples", repos="http://RForge.Rproject.org") 


RcppDE  Global optimization by differential evolution in C++


This package provides an efficient C++ based implementation of the
DEoptim function which performs global optimization by differential evolution.
Its creation was motivated by trying to see if the old approximation "easier,
shorter, faster: pick any two" could in fact be extended to achieving all
three goals while moving the code from plain old C to modern C++. The
initial version did in fact do so, but a good part of the gain was due to
an implicit code review which eliminated a few inefficiencies which have
since been eliminated in DEoptim. 

Version: 0.1.1 
Last change: 20120408 22:14:35+02 
Rev.: 3568 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppDE 0.1.4 from CRAN 

R install command:
install.packages("RcppDE", repos="http://RForge.Rproject.org") 


RcppEigen  Rcpp integration for the Eigen templated linear algebra library.


R and Eigen integration using Rcpp.
Eigen is a C++ template library for linear algebra: matrices,
vectors, numerical solvers and related algorithms. It supports dense
and sparse matrices on integer, floating point and complex numbers,
decompositions of such matrices, and solutions of linear systems. Its
performance on many algorithms is comparable with some of the best
implementations based on Lapack and level3 BLAS.
The RcppEigen package includes the header files from the Eigen C++
template library (currently version 3.2.0). Thus users do not need to
install Eigen itself in order to use RcppEigen.
Eigen is licensed under the GNU LGPL version 3 or later, and also
under the GNU GPL version 2 or later. RcppEigen (the Rcpp
bindings/bridge to Eigen) is licensed under the GNU GPL version 2 or
later, as is the rest of Rcpp. 

Version: 0.3.2.0 
Last change: 20131123 20:27:20+01 
Rev.: 4599 

Download:
(.tar.gz) 
(.zip) 
Build status: Failed to build  Stable Release: Get RcppEigen 0.3.2.5.1 from CRAN 

R install command:
install.packages("RcppEigen", repos="http://RForge.Rproject.org") 


RcppExamples  Examples using Rcpp to interface R and C++


Examples for Seamless R and C++ integration
The Rcpp package contains a C++ library that facilitates the integration of
R and C++ in various ways. This package provides some usage examples.
Note that the documentation in this package currently does not cover all the
features in the package. It is not even close. On the other hand, the site
http://gallery.rcpp.org is regrouping a number of examples for Rcpp. 

Version: 0.1.6.1 
Last change: 20131018 14:55:12+02 
Rev.: 4578 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppExamples 0.1.6 from CRAN 

R install command:
install.packages("RcppExamples", repos="http://RForge.Rproject.org") 


RcppGSL  Rcpp integration for GNU GSL vectors and matrices


Rcpp integration for GNU GSL vectors and matrices
The GNU Scientific Library (GSL) is a collection of numerical routines for
scientific 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,
quasirandom sequences, Monte Carlo integration, Ntuples, differential
equations, simulated annealing, numerical differentiation, interpolation,
series acceleration, Chebyshev approximations, rootfinding, 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 easytouse interface between GSL data
structures and R using concepts from Rcpp which is itself a package that
eases the interfaces between R and C++.
This package also serves as a prime example of how to build a package
that uses Rcpp to connect to another thirdparty library. The autoconf
script, inline plugin and example package can all be used as a stanza to
write a similar package against another library. 

Version: 0.2.0.3 
Last change: 20131022 20:38:37+02 
Rev.: 4580 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppGSL 0.3.0 from CRAN 

R install command:
install.packages("RcppGSL", repos="http://RForge.Rproject.org") 


RcppModels  Classes for linear and generalized linear and nonlinear models


Provides classes and methods for linear, generalized
linear and nonlinear models that use linear predictor expressions. 

Version: 0.1.0 
Last change: 20111223 15:15:32+01 
Rev.: 3432 

Download:
(.tar.gz) 
(.zip) 
Build status: Current 

R install command:
install.packages("RcppModels", repos="http://RForge.Rproject.org") 


RcppParDE  Global optimization by differential evolution in C++ using OpenMP parallel computing


This package provides an efficient C++ based implementation of the
DEoptim function which performs global optimization by differential evolution.
It builds on RcppDE package which aims to show that "easier, shorter,
faster: pick any three" is achievable when moving code from plain old C to
modern C++  and attempts to reap further gains by using parallel
execution using the OpenMP framework for multithreaded computing on
multicore systems. At present, this is _highly experimental_ and may set
your hair on fire or worse. 

Version: 0.1.0 
Last change: 20110728 02:34:04+02 
Rev.: 3154 

Download:
(.tar.gz) 
(.zip) 
Build status: Failed to build 

R install command:
install.packages("RcppParDE", repos="http://RForge.Rproject.org") 


RcppSMC  Rcpp bindings for Sequential Monte Carlo


This package provides R with access to the Sequential
Monte Carlo Template Classes by Johansen (Journal of Statistical
Software, 2009, v30, i6).
At present, two additional examples have been added, and the first
example from the JSS paper has been extended. Further integration
and extensions are planned. 

Version: 0.1.1.1 
Last change: 20130211 21:06:36+01 
Rev.: 4252 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppSMC 0.1.4 from CRAN 

R install command:
install.packages("RcppSMC", repos="http://RForge.Rproject.org") 


RcppXts  Interface the xts API via Rcpp


This package provides access to some of the C level functions of
the xts package.
In its current state, the package is mostly a proofofconcept to support
adding useful functions, and does not yet add any of its own. 

Version: 0.0.4.2 
Last change: 20131023 03:21:10+02 
Rev.: 4582 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get RcppXts 0.0.4 from CRAN 

R install command:
install.packages("RcppXts", repos="http://RForge.Rproject.org") 


int64  64 bit integer types


64 bit integer types 

Version: 1.1.2 
Last change: 20111223 15:00:58+01 
Rev.: 3431 

Download:
(.tar.gz) 
(.zip) 
Build status: Current  Stable Release: Get int64 1.1.2 from CRAN 

R install command:
install.packages("int64", repos="http://RForge.Rproject.org") 


patches  Collection of hot patches for R


Collection of hot patches for R. At the moment the
package installs a faster version of base::sequence back into the
base namespace 

Version: 0.0 
Last change: 20101129 12:36:04+01 
Rev.: 2589 

Download:
(.tar.gz) 
(.zip) 
Build status: Current 

R install command:
install.packages("patches", repos="http://RForge.Rproject.org") 


wls  Iteratively ReWeighted Least Squares


A class based on C++ code for leastsquares fitting and
a module exposing the C++ class through the Rcpp package
provides leastsquares fits for varying weights, with a given
response vector and model matrix. 

Version: 0.5 
Last change: 20101121 23:23:57+01 
Rev.: 2480 

Download:
(.tar.gz) 
(.zip) 
Build status: Current 

R install command:
install.packages("wls", repos="http://RForge.Rproject.org") 


Build status codes
0  Current: the package is available for download. The corresponding package passed checks on the Linux and Windows platform without ERRORs.
1  Scheduled for build: the package has been recognized by the build system and provided in the staging area.
2  Building: the package has been sent to the build machines. It will be built and checked using the latest patched version of R. Note that it is included in a batch of several packages. Thus, this process will take some time to finish.
3  Failed to build: the package failed to build or did not pass the checks on the Linux and/or Windows platform. It is not made available since it does not meet the policies.
4  Conflicts: two or more packages of the same name exist. None of them will be built. Maintainers are asked to negotiate further actions.
5  Offline: the package is not available. The build system may be offline or the package maintainer did not trigger a rebuild (done e.g., via committing to the package repository).
If your package is not shown on this page or not building, then check the build system status report.