Seamless R and C++ Integration

The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at < https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details.

Boost C++ Header Files

Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.84.0-0, the following Boost libraries are included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast' 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision' 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process' 'propery_tree' 'qvm' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'url' 'utility' 'uuid'.

A C++11 Interface for R's C Interface

Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.

An Interruptible Progress Bar with OpenMP Support for C++ in R Packages

Allows to display a progress bar in the R console for long running computations taking place in c++ code, and support for interrupting those computations even in multithreaded code, typically using OpenMP.

Test Coverage for Packages

Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' < https://about.codecov.io> or 'Coveralls' < https://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.

Functions to Inline C, C++, Fortran Function Calls from R

Functionality to dynamically define R functions and S4 methods with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling conventions.

R-Friendly Threading in C++

Provides a C++11-style thread class and thread pool that can safely be interrupted from R. See Nagler (2021) .

Snowball Stemmers Based on the C 'libstemmer' UTF-8 Library

An R interface to the C 'libstemmer' library that implements Porter's word stemming algorithm for collapsing words to a common root to aid comparison of vocabulary. Currently supported languages are Arabic, Basque, Catalan, Danish, Dutch, English, Finnish, French, German, Greek, Hindi, Hungarian, Indonesian, Irish, Italian, Lithuanian, Nepali, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil and Turkish.

Parse XML

Work with XML files using a simple, consistent interface. Built on top of the 'libxml2' C library.

Linear Predictive Models Based on the LIBLINEAR C/C++ Library

A wrapper around the LIBLINEAR C/C++ library for machine learning (available at < https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.