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RcppArmadillo

R and Armadillo via Rcpp

Overview

Armadillo is a templated C++ linear algebra library written 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. 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.

Status

The package is under active development with releases to CRAN about once a month.

Documentation

The package contains a pdf vignette which is a pre-print of the paper by Eddelbuettel and Sanderson in CSDA (2014).

Authors

Romain Francois, Dirk Eddelbuettel and Doug Bates

License

GPL (>= 2)

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Version

Install

install.packages('RcppArmadillo')

Monthly Downloads

346,983

Version

0.7.200.2.0

License

GPL (>= 2)

Maintainer

Dirk Eddelbuettel

Last Published

July 24th, 2016

Functions in RcppArmadillo (0.7.200.2.0)

armadillo_set_seed

Set the Armadillo Random Number Generator to the given value
RcppArmadillo-package

R and Armadillo Integration
armadillo_set_seed_random

Set the Armadillo Random Number Generator to a random value
RcppArmadillo.package.skeleton

Create a skeleton for a new package that intends to use RcppArmadillo
armadillo_version

Report the version of Armadillo
fastLm

Bare-bones linear model fitting function