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.

Author | N. Benjamin Erichson [aut, cre] |

Date of publication | 2016-07-29 06:41:14 |

Maintainer | N. Benjamin Erichson <nbe@st-andrews.ac.uk> |

License | GPL (>= 2) |

Version | 0.6 |

https://github.com/Benli11/rSVD |

**ggbiplot:** Biplot for 'rPCA' using ggplot2

**ggcorplot:** Correlation plot

**ggscreeplot:** Pretty Screeplot

**plot.rpca:** Screeplot

**reigen:** Randomized Spectral Decomposition of a matrix (reigen).

**rpca:** Randomized principal component analysis (rpca).

**rrpca:** Randomized robust principal component analysis (rrpca).

**rsvd:** Randomized Singular Value Decomposition (rsvd).

**tiger:** Tiger

rsvd

rsvd/tests

rsvd/tests/testthat

rsvd/tests/testthat/test_rrpca.R

rsvd/tests/testthat/test_rsvd.R

rsvd/tests/testthat/test_rpca.R

rsvd/tests/testthat/test_dependency.R

rsvd/tests/testthat/test_reigen.R

rsvd/NAMESPACE

rsvd/data

rsvd/data/tiger.RData

rsvd/data/datalist

rsvd/R

rsvd/R/reigen.R
rsvd/R/rrpca.R
rsvd/R/ggbiplot.R
rsvd/R/tiger.R
rsvd/R/plots.R
rsvd/R/rpca.R
rsvd/R/wrapper_function.R
rsvd/R/rsvd.R
rsvd/MD5

rsvd/DESCRIPTION

rsvd/man

rsvd/man/tiger.Rd
rsvd/man/rrpca.Rd
rsvd/man/plot.rpca.Rd
rsvd/man/rpca.Rd
rsvd/man/reigen.Rd
rsvd/man/ggscreeplot.Rd
rsvd/man/ggcorplot.Rd
rsvd/man/rsvd.Rd
rsvd/man/ggbiplot.Rd
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