ddpca: Diagonally Dominant Principal Component Analysis

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <doi:10.48550/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.

Version:	1.1
Imports:	RSpectra, Matrix, quantreg, MASS
Published:	2019-09-14
DOI:	10.32614/CRAN.package.ddpca
Author:	Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]
Maintainer:	Fan Yang <fyang1 at uchicago.edu>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	ddpca results

Documentation:

Reference manual:

ddpca.pdf

Downloads:

Package source:	ddpca_1.1.tar.gz
Windows binaries:	r-devel: ddpca_1.1.zip, r-release: ddpca_1.1.zip, r-oldrel: ddpca_1.1.zip
macOS binaries:	r-release (arm64): ddpca_1.1.tgz, r-oldrel (arm64): ddpca_1.1.tgz, r-release (x86_64): ddpca_1.1.tgz, r-oldrel (x86_64): ddpca_1.1.tgz
Old sources:	ddpca archive

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