rsvd: Randomized Singular Value Decomposition

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.

AuthorN. Benjamin Erichson [aut, cre]
Date of publication2016-07-29 06:41:14
MaintainerN. Benjamin Erichson <nbe@st-andrews.ac.uk>
LicenseGPL (>= 2)
Version0.6
https://github.com/Benli11/rSVD

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Files

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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