hdpca: Principal Component Analysis in High-Dimensional Data

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
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

Documentation:

Reference manual: hdpca.pdf

Downloads:

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-release (x86_64): hdpca_1.1.5.tgz, r-oldrel: hdpca_1.1.5.tgz
Old sources: hdpca archive

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