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 (<doi:10.48550/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
DOI: 10.32614/CRAN.package.rsvddpd
Author: Subhrajyoty Roy [aut, cre]
Maintainer: Subhrajyoty Roy <subhrajyotyroy at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: rsvddpd results


Reference manual: rsvddpd.pdf
Vignettes: Introduction to rSVDdpd


Package source: rsvddpd_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): rsvddpd_1.0.0.tgz, r-oldrel (arm64): rsvddpd_1.0.0.tgz, r-release (x86_64): rsvddpd_1.0.0.tgz, r-oldrel (x86_64): rsvddpd_1.0.0.tgz


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