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 227 downloads 7 exports 4 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK167
source / vignettesOK160
linux-releaseOK114
macos-releaseOK94
macos-oldrelOK86
windows-develOK70
windows-releaseOK71
windows-oldrelOK68
wasm-releaseOK106

Exports:DepcaDpcaDrpDrpcaDrsvdDsvdFPC

Dependencies:latticeMatrixmatrixcalcrsvd