| PackageDescription | Low-rank matrix decompositions are fundamental tools and widely used
for data analysis, dimension reduction, and data compression.
Classically, highly accurate deterministic matrix algorithms are used
for this task. However, the emergence of large-scale data has severely
challenged our computational ability to analyze big data. The concept
of randomness has been demonstrated as an effective strategy to quickly
produce approximate answers to familiar problems such as the singular
value decomposition (SVD). The rsvd package provides several randomized
matrix algorithms such as the randomized singular value decomposition
(rsvd), randomized principal component analysis (rpca), randomized
robust principal component analysis (rrpca), randomized interpolative
decomposition (rid), and the randomized CUR decomposition (rcur). In
addition several plot functions are provided. |