Package: ltsspca 0.1.0

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
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ltsspca.pdf |ltsspca.html
ltsspca/json (API)

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

Peer review:

Bug tracker:https://github.com/yixinww/ltsspca/issues

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

On CRAN:

3.70 score 4 scripts 105 downloads 7 exports 3 dependencies

Last updated 5 years agofrom:8901400d0f. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64NOTENov 06 2024
R-4.5-linux-x86_64NOTENov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

Exports:AngledataSimltspcaltsspcaltsspcaRwmydiagPlotsPCA_rSVD

Dependencies:pracmaRcppRcppArmadillo

LTS-SPCA example

Rendered fromexample.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2019-10-07
Started: 2019-10-07