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: 2.1.2
Depends: Matrix
Imports: stats, methods
LinkingTo: Matrix
Published: 2016-09-21
Author: Jim Baglama [aut, cph], Lothar Reichel [aut, cph], B. W. Lewis [aut, cre, cph]
Maintainer: B. W. Lewis <blewis at illposed.net>
BugReports: https://github.com/bwlewis/irlba/issues
License: GPL-3
NeedsCompilation: yes
Materials: README
In views: NumericalMathematics
CRAN checks: irlba results

Downloads:

Reference manual: irlba.pdf
Vignettes: irlba Manual
Package source: irlba_2.1.2.tar.gz
Windows binaries: r-devel: irlba_2.1.2.zip, r-release: irlba_2.1.2.zip, r-oldrel: irlba_2.1.1.zip
OS X Mavericks binaries: r-release: irlba_2.1.2.tgz, r-oldrel: irlba_2.1.2.tgz
Old sources: irlba archive

Reverse dependencies:

Reverse depends: clustrd, DDRTree, s4vd, semisupKernelPCA
Reverse imports: bigpca, denoiseR, gyriq, igraph, MFPCA, OmicKriging, recommenderlab
Reverse suggests: KernelKnn, steadyICA

Linking:

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