SuperPCA: Supervised Principal Component Analysis

Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, <doi:10.1111/biom.12698>.

Version: 0.2.0
Depends: Matrix
Imports: RSpectra, psych, fBasics, R.matlab, glmnet, MASS, matrixStats, timeSeries, stats, matlabr, spls, pracma, matlab
Published: 2019-05-24
Author: Gen Li, Haocheng Ding, Jiayi Ji
Maintainer: Jiayi Ji <jj2876 at caa.columbia.edu>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: SuperPCA results

Downloads:

Reference manual: SuperPCA.pdf
Package source: SuperPCA_0.2.0.tar.gz
Windows binaries: r-devel: SuperPCA_0.2.0.zip, r-devel-gcc8: SuperPCA_0.2.0.zip, r-release: SuperPCA_0.2.0.zip, r-oldrel: SuperPCA_0.2.0.zip
OS X binaries: r-release: SuperPCA_0.2.0.tgz, r-oldrel: SuperPCA_0.2.0.tgz
Old sources: SuperPCA archive

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