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A generalization of principal component analysis for integrative analysis. The method finds principal components that describe single matrices or that are common to several matrices. The solutions are sparse. Rank of solutions is automatically selected using cross validation. The method is described in Kallus et al. (2019) <doi:10.48550/arXiv.1911.04927>.
Version: | 2.0.3 |
Depends: | R (≥ 3.3.0) |
Imports: | digest (≥ 0.6.0), Rcpp (≥ 1.0.8) |
LinkingTo: | Rcpp, RcppEigen, RcppGSL |
Published: | 2022-11-15 |
DOI: | 10.32614/CRAN.package.mmpca |
Author: | Jonatan Kallus [aut], Felix Held [ctb, cre] |
Maintainer: | Felix Held <felix.held at gmail.com> |
BugReports: | https://github.com/cyianor/mmpca/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/cyianor/mmpca |
NeedsCompilation: | yes |
SystemRequirements: | C++14 |
Materials: | README NEWS |
CRAN checks: | mmpca results |
Reference manual: | mmpca.pdf |
Package source: | mmpca_2.0.3.tar.gz |
Windows binaries: | r-devel: mmpca_2.0.3.zip, r-release: mmpca_2.0.3.zip, r-oldrel: mmpca_2.0.3.zip |
macOS binaries: | r-release (arm64): mmpca_2.0.3.tgz, r-oldrel (arm64): mmpca_2.0.3.tgz, r-release (x86_64): mmpca_2.0.3.tgz, r-oldrel (x86_64): mmpca_2.0.3.tgz |
Old sources: | mmpca archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.