Package: ddpca
Type: Package
Title: Diagonally Dominant Principal Component Analysis
Version: 1.0
Date: 2019-07-17
Author: Zheng Tracy Ke <zke@fas.harvard.edu>
        Lingzhou Xue <lingzhou@psu.edu>
        Fan Yang <fyang1@uchicago.edu>
Maintainer: Fan Yang <fyang1@uchicago.edu>
Description: Consider the problem of decomposing a large covariance matrix into a low rank matrix plus a diagonally dominant matrix. This problem is called Diagonally Dominant Principal Component Analysis (DD-PCA) in the reference Ke, Z., Xue, L. and Yang, F. (2019) <arXiv:1906.00051>. DD-PCA can be used in covariance matrix estimation and global detection in multiple testing. This package implements DD-PCA using both convex approach and non-convex approach; Convex approach refers to solving a convex relaxation of the original problem using Alternating Direction Method of Multipliers (ADMM), while non-convex approach resorts to an iterative projection algorithm. This package also implements two global testing methods proposed in the reference. 
License: GPL-2
Imports: RSpectra, Matrix, quantreg, MASS
NeedsCompilation: no
Packaged: 2019-07-17 15:03:47 UTC; fanyang
Repository: CRAN
Date/Publication: 2019-07-19 08:30:02 UTC
