Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal lowrank 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.
Package details 


Author  N. Benjamin Erichson [aut, cre] 
Date of publication  20160729 06:41:14 
Maintainer  N. Benjamin Erichson <[email protected]> 
License  GPL (>= 2) 
Version  0.6 
URL  https://github.com/Benli11/rSVD 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.