rsvddpd: Robust Singular Value Decomposition using Density Power Divergence

Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (<arXiv:2109.10680>). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms.

Version: 1.0.0
Imports: Rcpp (≥ 1.0.5), MASS, stats, utils, matrixStats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, microbenchmark, pcaMethods
Published: 2021-10-27
Author: Subhrajyoty Roy [aut, cre]
Maintainer: Subhrajyoty Roy <subhrajyotyroy at gmail.com>
BugReports: https://github.com/subroy13/rsvddpd/issues
License: MIT + file LICENSE
URL: https://github.com/subroy13/rsvddpd
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: rsvddpd results

Documentation:

Reference manual: rsvddpd.pdf
Vignettes: Introduction to rSVDdpd

Downloads:

Package source: rsvddpd_1.0.0.tar.gz
Windows binaries: r-devel: rsvddpd_1.0.0.zip, r-devel-UCRT: rsvddpd_1.0.0.zip, r-release: not available, r-oldrel: rsvddpd_1.0.0.zip
macOS binaries: r-release (arm64): rsvddpd_1.0.0.tgz, r-release (x86_64): rsvddpd_1.0.0.tgz, r-oldrel: rsvddpd_1.0.0.tgz

Linking:

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