irlba: Fast Truncated SVD, PCA and Symmetric Eigendecomposition for Large Dense and Sparse Matrices

Fast and memory efficient methods for truncated singular and eigenvalue decompositions and principal component analysis of large sparse or dense matrices.

Version: 2.0.0
Depends: Matrix
Imports: stats
Published: 2015-10-11
Author: Jim Baglama and Lothar Reichel
Maintainer: Bryan W. Lewis <blewis at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Materials: README
In views: NumericalMathematics
CRAN checks: irlba results


Reference manual: irlba.pdf
Vignettes: irlba Manual
Package source: irlba_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: irlba_2.0.0.tgz, r-oldrel: irlba_1.0.3.tgz
OS X Mavericks binaries: r-release: irlba_2.0.0.tgz
Old sources: irlba archive

Reverse dependencies:

Reverse depends: clustrd, s4vd, semisupKernelPCA
Reverse imports: bigpca, gyriq, igraph, OmicKriging
Reverse suggests: logisticPCA, steadyICA