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
cauchypca_1.3.tar.gz(r-4.5-noble)cauchypca_1.3.tar.gz(r-4.4-noble)
cauchypca_1.3.tgz(r-4.4-emscripten)cauchypca_1.3.tgz(r-4.3-emscripten)
cauchypca.pdf |cauchypca.html
cauchypca/json (API)

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

Peer review:

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

1.00 score 221 downloads 2 exports 12 dependencies

Last updated 11 months agofrom:3c21227cf7. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linuxOKNov 20 2024

Exports:cauchy.mlecauchy.pca

Dependencies:codetoolsdoParallelforeachiteratorsRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastRfast2Rnanoflann