SCM

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: R-Forge 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 R-Forge, you have to switch to the most recent version of R or, alternatively, install from the package sources (.tar.gz).

Packages

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 "Rcpp-introduction" 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 "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" vignette 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. 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: 2013-11-24 16:34:12+01 | Rev.: 4600
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get Rcpp 0.11.3 from CRAN
  R install command: install.packages("Rcpp", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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 meta-programming. 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: 2013-11-23 20:24:15+01 | Rev.: 4598
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppArmadillo 0.4.550.1.0 from CRAN
  R install command: install.packages("RcppArmadillo", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2013-12-16 02:03:19+01 | Rev.: 4601
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppBDT 0.2.3 from CRAN
  R install command: install.packages("RcppBDT", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2010-11-19 08:35:23+01 | Rev.: 2461
  Download: linux(.tar.gz) | windows(.zip) | Build status: Failed to build
  R install command: install.packages("RcppCImg", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2013-02-21 03:53:55+01 | Rev.: 4270
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppCNPy 0.2.3 from CRAN
  R install command: install.packages("RcppCNPy", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2014-01-23 16:18:09+01 | Rev.: 4607
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppClassic 0.9.5 from CRAN
  R install command: install.packages("RcppClassic", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2014-01-25 19:33:02+01 | Rev.: 4608
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppClassicExamples 0.1.1 from CRAN
  R install command: install.packages("RcppClassicExamples", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2012-04-08 22:14:35+02 | Rev.: 3568
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppDE 0.1.2 from CRAN
  R install command: install.packages("RcppDE", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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 level-3 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: 2013-11-23 20:27:20+01 | Rev.: 4599
  Download: linux(.tar.gz) | windows(.zip) | Build status: Failed to build | Stable Release: Get RcppEigen 0.3.2.2.0 from CRAN
  R install command: install.packages("RcppEigen", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2013-10-18 14:55:12+02 | Rev.: 4578
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppExamples 0.1.6 from CRAN
  R install command: install.packages("RcppExamples", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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, 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 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 third-party 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: 2013-10-22 20:38:37+02 | Rev.: 4580
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppGSL 0.2.2 from CRAN
  R install command: install.packages("RcppGSL", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2011-12-23 15:15:32+01 | Rev.: 3432
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("RcppModels", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2011-07-28 02:34:04+02 | Rev.: 3154
  Download: linux(.tar.gz) | windows(.zip) | Build status: Failed to build
  R install command: install.packages("RcppParDE", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2013-02-11 21:06:36+01 | Rev.: 4252
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppSMC 0.1.4 from CRAN
  R install command: install.packages("RcppSMC", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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 proof-of-concept to support adding useful functions, and does not yet add any of its own.
  Version: 0.0.4.2 | Last change: 2013-10-23 03:21:10+02 | Rev.: 4582
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get RcppXts 0.0.4 from CRAN
  R install command: install.packages("RcppXts", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


int64

64 bit integer types

  64 bit integer types
  Version: 1.1.2 | Last change: 2011-12-23 15:00:58+01 | Rev.: 3431
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get int64 1.1.2 from CRAN
  R install command: install.packages("int64", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


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: 2010-11-29 12:36:04+01 | Rev.: 2589
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("patches", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


wls

Iteratively Re-Weighted Least Squares

  A class based on C++ code for least-squares fitting and a module exposing the C++ class through the Rcpp package provides least-squares fits for varying weights, with a given response vector and model matrix.
  Version: 0.5 | Last change: 2010-11-21 23:23:57+01 | Rev.: 2480
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current
  R install command: install.packages("wls", repos="http://R-Forge.R-project.org")
 
Logs:  
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)

 

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.

Thanks to:
Vienna University of Economics and Business University of Wisconsin - Madison Powered By FusionForge