CRAN/E | hdpca

hdpca

Principal Component Analysis in High-Dimensional Data

Installation

About

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.

Key Metrics

Version 1.1.5
R ≥ 3.0.0
Published 2021-01-13 1206 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Rounak Dey

deyrnk@umich.edu

Authors

Rounak Dey
Seunggeun Lee

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

hdpca archive

Depends

R ≥ 3.0.0

Imports

lpSolve
boot