rsvd: Randomized Singular Value Decomposition

Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm to compute an approximated low-rank singular value decomposition of large data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) is using the approximated singular value decomposition to compute the most significant principal components. In addition several plot functions are provided.

Version: 0.3
Depends: R (≥ 3.2.2)
Suggests: ggplot2, plyr, scales, grid, testthat
Published: 2015-11-13
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <nbe at st-andrews.ac.uk>
BugReports: https://github.com/Benli11/rPCA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/Benli11/rPCA
NeedsCompilation: no
CRAN checks: rsvd results

Downloads:

Reference manual: rsvd.pdf
Package source: rsvd_0.3.tar.gz
Windows binaries: r-devel: rsvd_0.3.zip, r-release: rsvd_0.3.zip, r-oldrel: not available
OS X Snow Leopard binaries: r-release: rsvd_0.3.tgz, r-oldrel: not available
OS X Mavericks binaries: r-release: rsvd_0.3.tgz