FPCdpca: The FPCdpca Criterion on Distributed Principal Component Analysis

We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) <doi:10.1016/j.physa.2024.130308>.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: matrixcalc, rsvd, stats
Suggests: testthat (≥ 3.0.0)
Published: 2025-04-10
DOI: 10.32614/CRAN.package.FPCdpca
Author: Guangbao Guo ORCID iD [aut, cre], Jiarui Li [aut]
Maintainer: Guangbao Guo <ggb11111111 at 163.com>
License: Apache License (== 2.0)
NeedsCompilation: no
CRAN checks: FPCdpca results

Documentation:

Reference manual: FPCdpca.pdf

Downloads:

Package source: FPCdpca_0.2.0.tar.gz
Windows binaries: r-devel: FPCdpca_0.1.0.zip, r-release: FPCdpca_0.2.0.zip, r-oldrel: FPCdpca_0.1.0.zip
macOS binaries: r-devel (arm64): FPCdpca_0.2.0.tgz, r-release (arm64): FPCdpca_0.2.0.tgz, r-oldrel (arm64): FPCdpca_0.2.0.tgz, r-devel (x86_64): FPCdpca_0.2.0.tgz, r-release (x86_64): FPCdpca_0.2.0.tgz, r-oldrel (x86_64): FPCdpca_0.2.0.tgz
Old sources: FPCdpca archive

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