Package: hdpca 1.1.5
Rounak Dey
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>.
Authors:
hdpca_1.1.5.tar.gz
hdpca_1.1.5.tar.gz(r-4.5-noble)hdpca_1.1.5.tar.gz(r-4.4-noble)
hdpca_1.1.5.tgz(r-4.4-emscripten)hdpca_1.1.5.tgz(r-4.3-emscripten)
hdpca.pdf |hdpca.html✨
hdpca/json (API)
| # Install 'hdpca' in R: |
| install.packages('hdpca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- hapmap - Example dataset - Hapmap Phase III
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:53883668a0. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-linux | OK | Sep 17 2024 |
Exports:hdpc_estpc_adjustselect.nspike
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example dataset - Hapmap Phase III | hapmap |
| High-dimensional PCA estimation | hdpc_est |
| Adjusting shrinkage in PC scores | pc_adjust |
| Finding Distant Spikes | select.nspike |