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mmpca: Integrative Analysis of Several Related Data Matrices

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

Documentation:

Reference manual: mmpca.pdf

Downloads:

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

Linking:

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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.
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