epca-package: Exploratory Principal Component Analysis
In epca: Exploratory Principal Component Analysis
epca-package R Documentation
Exploratory Principal Component Analysis
Description
epca is for comprehending any data matrix that contains low-rank and sparse underlying signals of interest.
The package currently features two key tools: (1) sca for sparse principal component analysis and
(2) sma for sparse matrix approximation, a two-way data analysis for simultaneously row and column dimensionality reductions.
References
Chen, F. and Rohe K. (2020) "A New Basis for Sparse PCA".
epca documentation built on July 26, 2023, 5:47 p.m.
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| epca-package | R Documentation |
Exploratory Principal Component Analysis
Description
epca is for comprehending any data matrix that contains low-rank and sparse underlying signals of interest.
The package currently features two key tools: (1) sca for sparse principal component analysis and
(2) sma for sparse matrix approximation, a two-way data analysis for simultaneously row and column dimensionality reductions.
References
Chen, F. and Rohe K. (2020) "A New Basis for Sparse PCA".
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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