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cauchypca  

Robust Principal Component Analysis Using the Cauchy Distribution
View on CRAN: Click here


Download and install cauchypca package within the R console
Install from CRAN:
install.packages("cauchypca")

Install from Github:
library("remotes")
install_github("cran/cauchypca")

Install by package version:
library("remotes")
install_version("cauchypca", "1.3")



Attach the package and use:
library("cauchypca")
Maintained by
Michail Tsagris
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-09
Latest Update: 2024-01-24
Description:
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. .
How to cite:
Michail Tsagris (2022). cauchypca: Robust Principal Component Analysis Using the Cauchy Distribution. R package version 1.3, https://cran.r-project.org/web/packages/cauchypca. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2022-11-09 16:50), 1.1 (2023-09-15 01:50), 1.2 (2023-10-22 14:40)
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Complete documentation for cauchypca
Functions, R codes and Examples using the cauchypca R package
Some associated functions: cauchy.mle . cauchy.pca . cauchypca-package . 
Some associated R codes: cauchy.mle.R . cauchy.pca.R . onAttach.R .  Full cauchypca package functions and examples
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