c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "erichson@uw.edu"))
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.

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. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.

c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "erichson@berkeley.edu"))