ddpca-package

ddpca

 package

 ddpca 

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.

Fan Yang

Diagonally Dominant Principal Component Analysis

Tracy Ke

Lingzhou Xue

ddpca-package function

ddpca — ddpca-package

ddpca-package: ddpca

Description

Arguments

Details

References