Package: rsvd
Type: Package
Title: Randomized Singular Value Decomposition
Version: 2.0.0
Authors@R: c(person("N. Benjamin", "Erichson", role = c("aut", "cre"), email = "erichson@icsi.berkeley.edu"))
Author: N. Benjamin Erichson [aut, cre]
Maintainer: N. Benjamin Erichson <erichson@icsi.berkeley.edu>
Description: Computes low-rank matrix approximations for dimension reduction,
  data analysis, and compression. Randomized algorithms use random weighted
  combinations of the input to find a small subspace, then factorize the
  projected matrix. This can reduce the work when the requested rank is small
  relative to the matrix dimensions and the input has a useful low-rank
  approximation. Methods include singular value decomposition, principal
  component analysis, QB factorization, and interpolative and CUR
  decompositions. Robust principal component analysis separates a matrix into
  low-rank and entrywise sparse components. Selected routines support sparse
  matrices, including principal component analysis with implicit centering
  and scaling. Plotting functions display scores, loadings, and explained
  variance. See Erichson et al. (2019) <doi:10.18637/jss.v089.i11> for the
  algorithms and examples of their use.
Depends: R (>= 4.4.0)
Imports: Matrix (>= 1.7-0), methods
License: GPL (>= 3)
LazyData: TRUE
LazyDataCompression: xz
URL: https://github.com/erichson/rSVD
BugReports: https://github.com/erichson/rSVD/issues
Suggests: ggplot2, testthat (>= 3.0.0)
NeedsCompilation: no
Encoding: UTF-8
Config/roxygen2/version: 8.1.1
Packaged: 2026-11-02 01:13:18 UTC; runner
Repository: CRAN
Date/Publication: 2026-11-02 09:30:02 UTC
