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C++ Classes to Embed R in C++ Applications
C++ classes to embed R in C++ applications The 'RInside' packages makes it easier to have "R inside" your C++ application by providing a C++ wrapper class providing the R interpreter. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the eight subdirectories of the examples/ directory of the installed package: standard, mpi (for parallel computing) qt (showing how to embed 'RInside' inside a Qt GUI application), wt (showing how to build a "web-application" using the Wt toolkit), armadillo (for 'RInside' use with 'RcppArmadillo') and eigen (for 'RInside' use with 'RcppEigen'). The example use GNUmakefile(s) with GNU extensions, so a GNU make is required (and will use the GNUmakefile automatically). Doxygen-generated documentation of the C++ classes is available at the 'RInside' website as well.
'Rcpp' Integration for 'GNU GSL' Vectors and Matrices
'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or '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. 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.
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 large number of examples for 'Rcpp'.
R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)
Protocol Buffers are a way of encoding structured data in an
efficient yet extensible format. Google uses Protocol Buffers for almost all
of its internal 'RPC' protocols and file formats. Additional documentation
is available in two included vignettes one of which corresponds to our 'JSS'
Rcpp bindings for the Boost Date_Time library
This package provides R with access to Boost Date_Time functionality by using Rcpp modules. Functionality from Boost Date_Time for dates, durations (both for days and datetimes), timezones, and posix time ("ptime") is provided. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of R's 64) to present a ptime object.
Wrappers for Tools in Other Packages for IDE Friendliness
Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.
Base64 Encoder and Decoder
Compatibility wrapper to replace the orphaned package by Romain Francois. New applications should use the 'openssl' or 'base64enc' package instead.
Fast Linear Models for Objects from the 'bigmemory' Package
A reimplementation of the fastLm() functionality of 'RcppEigen' for big.matrix objects for fast out-of-memory linear model fitting.
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.