rARPACK: Solvers for Large Scale Eigenvalue and SVD Problems

An R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/> to solve large scale eigenvalue/vector problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function which does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. Matrices can be given in either dense or sparse form.

Version: 0.9-0
Depends: R (≥ 3.0.2)
Imports: Matrix (≥ 1.1-0), Rcpp (≥ 0.11.5)
LinkingTo: Rcpp, RcppEigen (≥
Published: 2015-11-18
Author: Yixuan Qiu, Jiali Mei and authors of the ARPACK library. See file AUTHORS for details.
rARPACK author details
Maintainer: Yixuan Qiu <yixuan.qiu at cos.name>
BugReports: https://github.com/yixuan/rARPACK/issues
License: BSD_3_clause + file LICENSE
Copyright: see file COPYRIGHTS
URL: https://github.com/yixuan/rARPACK
NeedsCompilation: yes
Materials: README NEWS
In views: NumericalMathematics
CRAN checks: rARPACK results


Reference manual: rARPACK.pdf
Package source: rARPACK_0.9-0.tar.gz
Windows binaries: r-devel: rARPACK_0.9-0.zip, r-release: rARPACK_0.9-0.zip, r-oldrel: rARPACK_0.9-0.zip
OS X Snow Leopard binaries: r-release: rARPACK_0.7-0.tgz, r-oldrel: rARPACK_0.7-0.tgz
OS X Mavericks binaries: r-release: rARPACK_0.8-1.tgz
Old sources: rARPACK archive

Reverse dependencies:

Reverse depends: FastKM, onlinePCA
Reverse imports: lfda, stocc