Package: ddpca
Type: Package
Title: Diagonally Dominant Principal Component Analysis
Version: 1.1
Date: 2019-09-14
Author: Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]
Maintainer: Fan Yang <fyang1@uchicago.edu>
Authors@R: c(person("Tracy", "Ke", email="zke@fas.harvard.edu", role=c("aut")),
	 person("Lingzhou", "Xue", email="lzxue@psu.edu", role=c("aut")),
	 person("Fan", "Yang", email="fyang1@uchicago.edu", role=c("aut", "cre")))
Description: 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.
License: GPL-2
Imports: RSpectra, Matrix, quantreg, MASS
NeedsCompilation: no
Packaged: 2026-10-03 12:32:30 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2019-09-14 19:50:02 UTC
RemoteUrl: https://github.com/cran/ddpca
RemoteRef: HEAD
RemoteSha: 518926e09ca7294dcbd1a4421f7516b23aaec48b