RcppArmadillo: R and Armadillo via Rcpp
Synopsis
RcppArmadillo provides an interface from R to and from Armadillo by utilising the Rcpp R/C++ interface library.
What is Armadillo?
Armadillo is a high-quality linear algebra library for the C++ language, aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab (TM). See its website more information about Armadillo.
So give me an example!
Glad you asked. Here is a light-weight and fast implementation of linear regression:
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
Rcpp::List fastLm(const arma::mat& X, const arma::colvec& y) {
int n = X.n_rows, k = X.n_cols;
arma::colvec coef = arma::solve(X, y); // fit model y ~ X
arma::colvec res = y - X*coef; // residuals
double s2 = arma::dot(res, res) / (n - k); // std.errors of coefficients
arma::colvec std_err = arma::sqrt(s2 * arma::diagvec(arma::pinv(arma::trans(X)*X)));
return Rcpp::List::create(Rcpp::Named("coefficients") = coef,
Rcpp::Named("stderr") = std_err,
Rcpp::Named("df.residual") = n - k);
}
You can
Rcpp::sourceCpp()
the file above to compile the function. A version is also included in the
package as the fastLm()
function.
Status
The package is mature yet under active development with releases to CRAN about once every other month, and widely-used by other CRAN packages as can be seen from the CRAN package page. As of January 2023, there are 1035 CRAN packages using RcppArmadillo.
Documentation
The package contains a pdf vignette which is a pre-print of the paper by Eddelbuettel and Sanderson in CSDA (2014), as well as an introductory vignette for the sparse matrix conversions.
Installation
RcppArmadillo is a CRAN package, and lives otherwise in its own habitat on GitHub within the RcppCore GitHub organization.
Run
install.packages("RcppArmadillo")
to install from your nearest CRAN mirror.
Authors
Dirk Eddelbuettel, Romain Francois, Doug Bates, Binxiang Ni, and Conrad Sanderson
License
GPL (>= 2)