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 |
| 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 |