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>
License: GPL (≥ 3)
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:, r-release:, r-oldrel:
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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