RSpectra: Solvers for Large Scale Eigenvalue and SVD Problems

R interface to the 'Spectra' library <> for large scale eigenvalue and SVD 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: 0.12-0
Depends: R (≥ 3.0.2)
Imports: Matrix (≥ 1.1-0), Rcpp (≥ 0.11.5)
LinkingTo: Rcpp, RcppEigen (≥
Suggests: knitr
Published: 2016-06-12
Author: Yixuan Qiu [aut, cre], Jiali Mei [aut] (Function interface of matrix operation), Gael Guennebaud [ctb] (Eigenvalue solvers from the 'Eigen' library), Jitse Niesen [ctb] (Eigenvalue solvers from the 'Eigen' library)
Maintainer: Yixuan Qiu <yixuan.qiu at>
License: MPL (≥ 2)
NeedsCompilation: yes
Materials: README NEWS
In views: NumericalMathematics
CRAN checks: RSpectra results


Reference manual: RSpectra.pdf
Vignettes: Large Scale Eigenvalue Decomposition and SVD with RSpectra
Package source: RSpectra_0.12-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: RSpectra_0.12-0.tgz
OS X Mavericks binaries: r-oldrel: RSpectra_0.12-0.tgz
Old sources: RSpectra archive

Reverse dependencies:

Reverse depends: onlinePCA, SmartSVA
Reverse imports: dtwclust, Gmedian, kpcalg, MatrixCorrelation, miRNAss, quanteda, rARPACK, SISIR, textmineR
Reverse linking to: Gmedian
Reverse suggests: dimRed, MFKnockoffs


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