Package: mmpca 2.0.4

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>.

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

mmpca_2.0.4.tar.gz
mmpca_2.0.4.zip(r-4.7)mmpca_2.0.4.zip(r-4.6)mmpca_2.0.4.zip(r-4.5)
mmpca_2.0.4.tgz(r-4.6-x86_64)mmpca_2.0.4.tgz(r-4.6-arm64)mmpca_2.0.4.tgz(r-4.5-x86_64)mmpca_2.0.4.tgz(r-4.5-arm64)
mmpca_2.0.4.tar.gz(r-4.7-arm64)mmpca_2.0.4.tar.gz(r-4.7-x86_64)mmpca_2.0.4.tar.gz(r-4.6-arm64)mmpca_2.0.4.tar.gz(r-4.6-x86_64)
mmpca_2.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mmpca/json (API)

# Install 'mmpca' in R:

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

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 gsl cpp openmp

3.00 score 2 stars 365 downloads 1 exports 4 dependencies

Last updated from:2170f736f8. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	150
linux-devel-x86_64	OK	142
source / vignettes	OK	185
linux-release-arm64	OK	139
linux-release-x86_64	OK	141
macos-release-arm64	OK	97
macos-release-x86_64	OK	220
macos-oldrel-arm64	OK	94
macos-oldrel-x86_64	OK	187
windows-devel	OK	122
windows-release	OK	117
windows-oldrel	OK	125
wasm-release	OK	114