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 st-andrews.ac.uk> |
| BugReports: | https://github.com/Benli11/rSVD |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/Benli11/rSVD |
| NeedsCompilation: | no |
| CRAN checks: | rsvd results |
| Reference manual: | rsvd.pdf |
| Package source: | rsvd_0.6.tar.gz |
| Windows binaries: | r-devel: rsvd_0.6.zip, r-release: rsvd_0.6.zip, r-oldrel: rsvd_0.6.zip |
| OS X Mavericks binaries: | r-release: rsvd_0.6.tgz, r-oldrel: rsvd_0.6.tgz |
| Old sources: | rsvd archive |
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