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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>.
Getting started
Browse package contents
Package details |
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| Author | Rounak Dey, Seunggeun Lee |
| Maintainer | Rounak Dey <deyrnk@umich.edu> |
| License | GPL (>= 2) |
| Version | 1.1.5 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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