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.
RcppEigen provides an interface from R to and from Eigen by using the facilities offered by the Rcpp package for seamless R and C++ integration.
A few examples are over at the Rcpp Gallery. A simple one is
#include <RcppEigen.h>
// [[Rcpp::depends(RcppEigen)]]
using Eigen::Map; // 'maps' rather than copies
using Eigen::MatrixXd; // variable size matrix, double precision
using Eigen::VectorXd; // variable size vector, double precision
using Eigen::SelfAdjointEigenSolver; // one of the eigenvalue solvers
// [[Rcpp::export]]
VectorXd getEigenValues(Map<MatrixXd> M) {
SelfAdjointEigenSolver<MatrixXd> es(M);
return es.eigenvalues();
}which can be turned into a function callable from R via a simple
sourceCpp("eigenExample.cpp")
due to the two Rcpp directives to use headers from the RcppEigen package, and to export
the getEigenValues() function -- but read the full
post for details.
The package is mature and under active development, following the Eigen release cycle.
The package contains a pdf vignette which is a pre-print of the paper by Bates and Eddelbuettel in JSS (2013, v52i05).
Douglas Bates, Dirk Eddelbuettel, Romain Francois, and Yixuan Qiu
GPL (>= 2)
