Package: rsvd
Type: Package
Title: Randomized Singular Value Decomposition
Version: 0.6
Date: 2016-07-28
Authors@R: c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "nbe@st-andrews.ac.uk"))
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <nbe@st-andrews.ac.uk>
Description: 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.
Depends: R (>= 3.2.2)
License: GPL (>= 2)
LazyData: TRUE
URL: https://github.com/Benli11/rSVD
BugReports: https://github.com/Benli11/rSVD
RoxygenNote: 5.0.1
Suggests: ggplot2, plyr, scales, grid, testthat, knitr, rmarkdown
NeedsCompilation: no
Packaged: 2016-07-29 00:05:31 UTC; ben
Repository: CRAN
Date/Publication: 2016-07-29 06:41:14
Built: R 3.2.5; ; 2017-04-20 06:46:36 UTC; windows
