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:Rounak Dey, Seunggeun Lee

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'))

Peer review:

Datasets:
  • 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.

1.00 score 7 scripts 219 downloads 3 exports 2 dependencies

Last updated 4 years agofrom:53883668a0. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 16 2024
R-4.5-linuxOKDec 16 2024

Exports:hdpc_estpc_adjustselect.nspike

Dependencies:bootlpSolve