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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:3c21227cf7. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 20 2024 |
| R-4.5-linux | OK | Nov 20 2024 |
Exports:cauchy.mlecauchy.pca
Dependencies:codetoolsdoParallelforeachiteratorsRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastRfast2Rnanoflann
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Robust Principal Component Analysis Using the Cauchy Distribution | cauchypca-package |
| MLE of the Cauchy distribution | cauchy.mle |
| Robust PCA using the Cauchy distribution | cauchy.pca |