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>
License: GPL-3
NeedsCompilation: yes
Materials: README
In views: NumericalMathematics
CRAN checks: irlba results


Reference manual: irlba.pdf
Vignettes: irlba Manual
Package source: irlba_2.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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, text2vec
Reverse suggests: KernelKnn, steadyICA


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