Package: FPCdpca 0.1.0

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. (2020) <doi:10.1007/s00180-020-00974-4>.

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

FPCdpca_0.1.0.tar.gz
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FPCdpca_0.1.0.tgz(r-4.5-any)FPCdpca_0.1.0.tgz(r-4.4-any)FPCdpca_0.1.0.tgz(r-4.3-any)
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FPCdpca_0.1.0.tgz(r-4.4-emscripten)FPCdpca_0.1.0.tgz(r-4.3-emscripten)
FPCdpca.pdf |FPCdpca.html
FPCdpca/json (API)

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

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 162 downloads 7 exports 71 dependencies

Last updated 10 months agofrom:033270ac6f. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-winOKMar 24 2025
R-4.5-macOKMar 24 2025
R-4.5-linuxOKMar 24 2025
R-4.4-winOKMar 24 2025
R-4.4-macOKMar 24 2025
R-4.4-linuxOKMar 24 2025
R-4.3-winOKMar 24 2025
R-4.3-macOKMar 24 2025

Exports:DepcaDpcaDrpDrpcaDrsvdDsvdFPC

Dependencies:ADMMbase64encbitbit64bslibcachemcliclustercodetoolsCVXRdbscandigestdoParallelECOSolveRevaluatefastclusterfastmapfontawesomeforeachfsgenericsgluegmphighrhtmltoolshtmlwidgetsiteratorsjquerylibjsonliteknitrlabdsvlatticelifecyclemagrittrmaotaiMASSMatrixmatrixcalcmclustcompmemoisemgcvmimeminpack.lmnlmeosqppracmaR6RANNrappdirsrbibutilsRcppRcppArmadilloRcppDERcppDistRcppEigenRdimtoolsRdpackrglrlangrmarkdownRmpfrRSpectrarsvdRtsnesassscatterplot3dscsshapestinytexxfunyaml