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: 2019-09-14 15:08:01 UTC; fanyang
Repository: CRAN
Date/Publication: 2019-09-14 20:50:02 UTC
Built: R 4.3.3; ; 2025-01-24 15:27:00 UTC; unix
