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: | 0.3.0 |
| Depends: | Matrix |
| Imports: | RSpectra, psych, fBasics, R.matlab, glmnet, MASS, matrixStats, timeSeries, stats, matlabr, spls, pracma, matlab |
| Published: | 2020-03-04 |
| 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 |
| Reference manual: | SuperPCA.pdf |
| Package source: | SuperPCA_0.3.0.tar.gz |
| Windows binaries: | r-devel: SuperPCA_0.3.0.zip, r-release: SuperPCA_0.3.0.zip, r-oldrel: SuperPCA_0.3.0.zip |
| macOS binaries: | r-release: SuperPCA_0.3.0.tgz, r-oldrel: SuperPCA_0.3.0.tgz |
| Old sources: | SuperPCA archive |
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