Package: msPCA 0.4.0

msPCA: Sparse Principal Component Analysis with Multiple Principal Components

Implements an algorithm for computing multiple sparse principal components of a dataset. The method is based on Cory-Wright and Pauphilet "Sparse PCA with Multiple Principal Components" (2026) <doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation heuristic with a truncated power method applied at each iteration to compute sparse principal components with controlled sparsity.

Authors:Ryan Cory-Wright [aut, cph], Jean Pauphilet [aut, cre, cph]

msPCA_0.4.0.tar.gz
msPCA_0.4.0.zip(r-4.7)msPCA_0.4.0.zip(r-4.6)msPCA_0.4.0.zip(r-4.5)
msPCA_0.4.0.tgz(r-4.6-x86_64)msPCA_0.4.0.tgz(r-4.6-arm64)msPCA_0.4.0.tgz(r-4.5-x86_64)msPCA_0.4.0.tgz(r-4.5-arm64)
msPCA_0.4.0.tar.gz(r-4.7-arm64)msPCA_0.4.0.tar.gz(r-4.7-x86_64)msPCA_0.4.0.tar.gz(r-4.6-arm64)msPCA_0.4.0.tar.gz(r-4.6-x86_64)
msPCA_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
msPCA/json (API)
NEWS

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

Bug tracker:https://github.com/jeanpauphilet/mspca/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

3.48 score 4 scripts 515 downloads 6 exports 2 dependencies

Last updated from:f243e30f22. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK118
linux-devel-x86_64OK114
source / vignettesOK163
linux-release-arm64OK106
linux-release-x86_64OK141
macos-release-arm64OK101
macos-release-x86_64OK181
macos-oldrel-arm64OK96
macos-oldrel-x86_64OK275
windows-develOK114
windows-releaseOK126
windows-oldrelOK97
wasm-releaseOK100

Exports:feasibility_violation_offfraction_variance_explainedfraction_variance_explained_perPCmspcaprint_mspcatpm

Dependencies:RcppRcppEigen

Readme and manuals

Help Manual

Help pageTopics
Feasibility violationfeasibility_violation_off
Fraction of variance explainedfraction_variance_explained
Fraction of variance explained per PCfraction_variance_explained_perPC
Multiple Sparse PCAmspca
Print mspca outputprint_mspca
Truncated Power Methodtpm
Variance explained per PCvariance_explained_perPC