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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: rsvd results


Reference manual: rsvd.pdf
Package source: rsvd_0.6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.