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.
- Version1.1.5
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release01/13/2021
Team
Rounak Dey
Seunggeun Lee
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports2 packages