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.

Version: 1.1
Imports: RSpectra, Matrix, quantreg, MASS
Published: 2019-09-14
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

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Reference manual: ddpca.pdf
Package source: ddpca_1.1.tar.gz
Windows binaries: r-devel: ddpca_1.0.zip, r-release: ddpca_1.0.zip, r-oldrel: ddpca_1.1.zip
OS X binaries: r-release: ddpca_1.1.tgz, r-oldrel: ddpca_1.1.tgz
Old sources: ddpca archive

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