rospca

Robust Sparse PCA using the ROSPCA Algorithm


Keywords
robust-pca, sparse-data
Licenses
CNRI-Python-GPL-Compatible/CNRI-Python-GPL-Compatible

Documentation

rospca

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The rospca package contains the implementation of robust sparse PCA using the ROSPCA algorithm of Hubert et al. (2016). Moreover, the simulation study and glass dataset discussed in this paper are included.

This package relies heavily on the code from Valentin Todorov for rrcov and on the mrfDepth package.

The latest development version of rospca can be installed from GitHub using

install.packages("devtools")

devtools::install_github("TReynkens/rospca")

If you work on Windows, make sure first that Rtools is installed when installing the development version from GitHub.

References

Hubert, Mia, Tom Reynkens, Eric Schmitt, and Tim Verdonck. 2016. “Sparse PCA for High-Dimensional Data With Outliers.” Technometrics 58 (4): 424–34.