rsvd: Randomized Singular Value Decomposition

Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.

Version: 0.6
Depends: R (≥ 3.2.2)
Suggests: ggplot2, plyr, scales, grid, testthat, knitr, rmarkdown
Published: 2016-07-29
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <nbe at st-andrews.ac.uk>
BugReports: https://github.com/Benli11/rSVD
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/Benli11/rSVD
NeedsCompilation: no
CRAN checks: rsvd results

Downloads:

Reference manual: rsvd.pdf
Package source: rsvd_0.6.tar.gz
Windows binaries: r-devel: rsvd_0.6.zip, r-release: rsvd_0.6.zip, r-oldrel: rsvd_0.6.zip
OS X El Capitan binaries: r-release: rsvd_0.6.tgz
OS X Mavericks binaries: r-oldrel: rsvd_0.6.tgz
Old sources: rsvd archive

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