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.

Documentation

Manual: rsvd.pdf
Vignette: None available.

Maintainer: N. Benjamin Erichson <nbe at st-andrews.ac.uk>

Author(s): N. Benjamin Erichson*

Install package and any missing dependencies by running this line in your R console:

install.packages("rsvd")

Depends R (>= 3.2.2)
Imports
Suggests ggplot2, plyr, scales, grid, testthat, knitr, rmarkdown
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stm
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Package rsvd
Materials
URL https://github.com/Benli11/rSVD
Task Views
Version 0.6
Published 2016-07-29
License GPL (>= 2)
BugReports https://github.com/Benli11/rSVD
SystemRequirements
NeedsCompilation no
Citation
CRAN checks rsvd check results
Package source rsvd_0.6.tar.gz