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.

Install the latest version of this package by entering the following in R:
install.packages("rsvd")
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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