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spca: Least Squares Sparse Principal Components Analysis

Implements least-squares sparse principal component analysis (LS-SPCA). The approach follows Merola (2015) <doi:10.1111/anzs.12128> and Merola and Chen (2019) <doi:10.1016/j.jmva.2019.04.001>.

Version: 1.1.1
Depends: R (≥ 4.3)
Imports: Rcpp (≥ 1.0.14), ggplot2 (≥ 4.0.0), RMTstat (≥ 0.3.1), scales, rlang
LinkingTo: Rcpp, RcppEigen
Suggests: testthat (≥ 3.0.0), peakRAM (≥ 1.0.2), knitr, rmarkdown, bench, R.rsp
Published: 2026-07-10
DOI: 10.32614/CRAN.package.spca
Author: Giovanni Maria Merola [aut, cre]
Maintainer: Giovanni Maria Merola <merolagio at gmail.com>
BugReports: https://github.com/merolagio/spca/issues
License: AGPL-3
URL: https://github.com/merolagio/spca
NeedsCompilation: yes
Citation: spca citation info
Materials: README, NEWS
CRAN checks: spca results

Documentation:

Reference manual: spca.html , spca.pdf
Vignettes: Computing Least Squares Sparse Principal Components with spca (source, R code)
Introduction to the spca package (source, R code)

Downloads:

Package source: spca_1.1.1.tar.gz
Windows binaries: r-devel: spca_1.1.1.zip, r-release: spca_1.1.1.zip, r-oldrel: spca_1.1.1.zip
macOS binaries: r-release (arm64): spca_1.1.1.tgz, r-oldrel (arm64): spca_1.1.1.tgz, r-release (x86_64): spca_1.1.1.tgz, r-oldrel (x86_64): spca_1.1.1.tgz
Old sources: spca 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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