Package: FPCdpca 0.4.0

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>.

Authors:Guangbao Guo [aut, cre], Jiarui Li [aut]

FPCdpca_0.4.0.tar.gz
FPCdpca_0.4.0.zip(r-4.7-any)FPCdpca_0.4.0.zip(r-4.6-any)FPCdpca_0.4.0.zip(r-4.5-any)
FPCdpca_0.4.0.tgz(r-4.6-any)FPCdpca_0.4.0.tgz(r-4.5-any)
FPCdpca_0.4.0.tar.gz(r-4.7-any)FPCdpca_0.4.0.tar.gz(r-4.6-any)
FPCdpca_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
FPCdpca/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 200 downloads 7 exports 4 dependencies

Last updated from:e6d457581b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK136
source / vignettesOK165
linux-release-x86_64OK173
macos-release-arm64OK87
macos-oldrel-arm64OK72
windows-develOK72
windows-releaseOK73
windows-oldrelOK82
wasm-releaseOK117

Exports:DepcaDpcaDrpDrpcaDrsvdDsvdFPC

Dependencies:latticeMatrixmatrixcalcrsvd