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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>.
| Version: | 0.1.0 |
| Imports: | matrixcalc, Rdimtools, rsvd, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2024-05-27 |
| DOI: | 10.32614/CRAN.package.FPCdpca |
| Author: | Guangbao Guo [aut, cre, cph], Jiarui Li [ctb] |
| Maintainer: | Guangbao Guo <ggb11111111 at 163.com> |
| License: | Apache License (== 2.0) |
| NeedsCompilation: | no |
| CRAN checks: | FPCdpca results |
| Reference manual: | FPCdpca.pdf |
| Package source: | FPCdpca_0.1.0.tar.gz |
| Windows binaries: | r-devel: FPCdpca_0.1.0.zip, r-release: FPCdpca_0.1.0.zip, r-oldrel: FPCdpca_0.1.0.zip |
| macOS binaries: | r-release (arm64): FPCdpca_0.1.0.tgz, r-oldrel (arm64): FPCdpca_0.1.0.tgz, r-release (x86_64): FPCdpca_0.1.0.tgz, r-oldrel (x86_64): FPCdpca_0.1.0.tgz |
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