Package: ltsspca 0.1.0

Yixin Wang

ltsspca: Sparse Principal Component Based on Least Trimmed Squares

Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <doi:10.1080/00401706.2019.1671234>.

Authors:Yixin Wang [aut, cre], Stefan Van Aelst [aut], Holger Cevallos Valdiviezo [ctb], Tom Reynkens [ctb]

ltsspca_0.1.0.tar.gz
ltsspca_0.1.0.tar.gz(r-4.5-noble)ltsspca_0.1.0.tar.gz(r-4.4-noble)
ltsspca_0.1.0.tgz(r-4.4-emscripten)ltsspca_0.1.0.tgz(r-4.3-emscripten)
ltsspca.pdf |ltsspca.html✨
ltsspca/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 4 scripts 129 downloads 7 exports 3 dependencies

Last updated 5 years agofrom:c696ad6000. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64NOTENov 21 2024

Exports:AngledataSimltspcaltsspcaltsspcaRwmydiagPlotsPCA_rSVD

Dependencies:pracmaRcppRcppArmadillo

LTS-SPCA example

Yixin Wang

Rendered fromexample.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2019-10-09
Started: 2019-10-09