Package: mmpca 2.0.3

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) <arxiv:1911.04927>.

Authors:Jonatan Kallus [aut], Felix Held [ctb, cre]

mmpca_2.0.3.tar.gz
mmpca_2.0.3.zip(r-4.5)mmpca_2.0.3.zip(r-4.4)mmpca_2.0.3.zip(r-4.3)
mmpca_2.0.3.tgz(r-4.4-x86_64)mmpca_2.0.3.tgz(r-4.4-arm64)mmpca_2.0.3.tgz(r-4.3-x86_64)mmpca_2.0.3.tgz(r-4.3-arm64)
mmpca_2.0.3.tar.gz(r-4.5-noble)mmpca_2.0.3.tar.gz(r-4.4-noble)
mmpca_2.0.3.tgz(r-4.4-emscripten)mmpca_2.0.3.tgz(r-4.3-emscripten)
mmpca.pdf |mmpca.html
mmpca/json (API)
NEWS

# Install 'mmpca' in R:
install.packages('mmpca', repos = c('https://cyianor.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cyianor/mmpca/issues

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

data-integration

3.00 score 2 stars 224 downloads 1 exports 4 dependencies

Last updated 2 years agofrom:273cdaecbe. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64NOTENov 05 2024
R-4.5-linux-x86_64NOTENov 05 2024
R-4.4-win-x86_64NOTENov 05 2024
R-4.4-mac-x86_64NOTENov 05 2024
R-4.4-mac-aarch64NOTENov 05 2024
R-4.3-win-x86_64NOTENov 05 2024
R-4.3-mac-x86_64NOTENov 05 2024
R-4.3-mac-aarch64NOTENov 05 2024

Exports:mmpca

Dependencies:digestRcppRcppEigenRcppGSL