Package: ddpca 1.1

Fan Yang

ddpca: Diagonally Dominant Principal Component Analysis

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arxiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

Authors:Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]

ddpca_1.1.tar.gz
ddpca_1.1.tar.gz(r-4.5-noble)ddpca_1.1.tar.gz(r-4.4-noble)
ddpca_1.1.tgz(r-4.4-emscripten)ddpca_1.1.tgz(r-4.3-emscripten)
ddpca.pdf |ddpca.html✨
ddpca/json (API)

# Install 'ddpca' in R:
install.packages('ddpca', repos = 'https://cloud.r-project.org')

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 174 downloads 7 exports 10 dependencies

Last updated 6 years agofrom:518926e09c. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 14 2025
R-4.5-linuxOKMar 14 2025
R-4.4-linuxOKMar 14 2025

Exports:DDHCDDPCA_convexDDPCA_nonconvexHCdetectionIHCDDProjDDProjSDD

Dependencies:latticeMASSMatrixMatrixModelsquantregRcppRcppEigenRSpectraSparseMsurvival

Citation

To cite package ‘ddpca’ in publications use:

Ke T, Xue L, Yang F (2019). ddpca: Diagonally Dominant Principal Component Analysis. R package version 1.1, https://CRAN.R-project.org/package=ddpca.

Corresponding BibTeX entry: 

  @Manual{,
    title = {ddpca: Diagonally Dominant Principal Component Analysis},
    author = {Tracy Ke and Lingzhou Xue and Fan Yang},
    year = {2019},
    note = {R package version 1.1},
    url = {https://CRAN.R-project.org/package=ddpca},
  }