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In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.
| Version: | 1.1.5 |
| Depends: | R (≥ 3.0.0) |
| Imports: | lpSolve, boot |
| Published: | 2021-01-13 |
| DOI: | 10.32614/CRAN.package.hdpca |
| Author: | Rounak Dey, Seunggeun Lee |
| Maintainer: | Rounak Dey <deyrnk at umich.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | hdpca results |
| Reference manual: | hdpca.pdf |
| Package source: | hdpca_1.1.5.tar.gz |
| Windows binaries: | r-devel: hdpca_1.1.5.zip, r-release: hdpca_1.1.5.zip, r-oldrel: hdpca_1.1.5.zip |
| macOS binaries: | r-release (arm64): hdpca_1.1.5.tgz, r-oldrel (arm64): hdpca_1.1.5.tgz, r-release (x86_64): hdpca_1.1.5.tgz, r-oldrel (x86_64): hdpca_1.1.5.tgz |
| Old sources: | hdpca archive |
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