Package: cauchypca 1.3

Michail Tsagris

cauchypca: Robust Principal Component Analysis Using the Cauchy Distribution

A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

Authors:Michail Tsagris [aut, cre], Aisha Fayomi [ctb], Yannis Pantazis [ctb], Andrew T.A. Wood [ctb]

cauchypca_1.3.tar.gz
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cauchypca.pdf |cauchypca.html✨
cauchypca/json (API)

# Install 'cauchypca' in R:
install.packages('cauchypca', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

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

1.00 score 236 downloads 2 exports 11 dependencies

Last updated 1 years agofrom:3c21227cf7. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-linuxOKMar 20 2025
R-4.4-linuxOKMar 20 2025

Exports:cauchy.mlecauchy.pca

Dependencies:codetoolsdoParallelforeachiteratorsRcppRcppArmadilloRcppParallelRfastRfast2Rnanoflannzigg

Citation

To cite package ‘cauchypca’ in publications use:

Tsagris M (2024). cauchypca: Robust Principal Component Analysis Using the Cauchy Distribution. R package version 1.3, https://CRAN.R-project.org/package=cauchypca.

Corresponding BibTeX entry: 

  @Manual{,
    title = {cauchypca: Robust Principal Component Analysis Using the
      Cauchy Distribution},
    author = {Michail Tsagris},
    year = {2024},
    note = {R package version 1.3},
    url = {https://CRAN.R-project.org/package=cauchypca},
  }