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 = 'https://cloud.r-project.org')
Datasets:
  • hapmap - Example dataset - Hapmap Phase III

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 161 downloads 3 exports 2 dependencies

Last updated 4 years agofrom:53883668a0. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 16 2025
R-4.5-linuxOKMar 16 2025
R-4.4-linuxOKMar 16 2025

Exports:hdpc_estpc_adjustselect.nspike

Dependencies:bootlpSolve

Citation

To cite package ‘hdpca’ in publications use:

Dey R, Lee S (2021). hdpca: Principal Component Analysis in High-Dimensional Data. R package version 1.1.5, https://CRAN.R-project.org/package=hdpca.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {hdpca: Principal Component Analysis in High-Dimensional
      Data},
    author = {Rounak Dey and Seunggeun Lee},
    year = {2021},
    note = {R package version 1.1.5},
    url = {https://CRAN.R-project.org/package=hdpca},
  }