Package: rsvddpd 1.0.0

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.

Authors:Subhrajyoty Roy [aut, cre]

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rsvddpd.pdf |rsvddpd.html✨
rsvddpd/json (API)
NEWS

# Install 'rsvddpd' in R:
install.packages('rsvddpd', repos = c('https://subroy13.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/subroy13/rsvddpd/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

4 exports 3 stars 0.93 score 4 dependencies 6 scripts 250 downloads

Last updated 1 years agofrom:86685f2ea2. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-win-x86_64NOTESep 03 2024
R-4.5-linux-x86_64NOTESep 03 2024
R-4.4-win-x86_64NOTESep 03 2024
R-4.4-mac-x86_64NOTESep 03 2024
R-4.4-mac-aarch64NOTESep 03 2024
R-4.3-win-x86_64NOTESep 03 2024
R-4.3-mac-x86_64NOTESep 03 2024
R-4.3-mac-aarch64NOTESep 03 2024

Exports:AddOutliercv.alpharSVDdpdsimSVD

Dependencies:MASSmatrixStatsRcppRcppArmadillo

Introduction to rSVDdpd

Rendered fromrSVDdpd-intro.Rmdusingknitr::rmarkdownon Sep 03 2024.

Last update: 2021-05-17
Started: 2020-10-12