Package: hdpca
Type: Package
Title: Principal Component Analysis in High-Dimensional Data
Version: 1.1.5
Date: 2021-01-13
Author: Rounak Dey, Seunggeun Lee
Maintainer: Rounak Dey <deyrnk@umich.edu>
Description: 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>.
Depends: R (>= 3.0.0)
License: GPL (>= 2)
Imports: lpSolve, boot
NeedsCompilation: no
Packaged: 2026-07-15 06:51:39 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2021-01-13 17:40:07 UTC
RemoteUrl: https://github.com/cran/hdpca
RemoteRef: HEAD
RemoteSha: 53883668a0e9bf5a5e8c6af7e9921fbc709548d0