rsvd: Randomized Singular Value Decomposition

Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.

Version: 1.0.0
Depends: R (≥ 3.2.2)
Imports: Matrix
Suggests: ggplot2, testthat
Published: 2018-11-06
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <erichson at uw.edu>
BugReports: https://github.com/erichson/rSVD/issues
License: GPL (≥ 3)
URL: https://github.com/erichson/rSVD
NeedsCompilation: no
Citation: rsvd citation info
CRAN checks: rsvd results

Downloads:

Reference manual: rsvd.pdf
Package source: rsvd_1.0.0.tar.gz
Windows binaries: r-devel: rsvd_1.0.0.zip, r-release: rsvd_1.0.0.zip, r-oldrel: rsvd_1.0.0.zip
OS X binaries: r-release: rsvd_1.0.0.tgz, r-oldrel: rsvd_1.0.0.tgz
Old sources: rsvd archive

Reverse dependencies:

Reverse imports: sparsepca
Reverse suggests: stm

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rsvd to link to this page.