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.

Package details

AuthorTracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]
MaintainerFan Yang <fyang1@uchicago.edu>
Package repositoryView on CRAN
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ddpca documentation built on Sept. 15, 2019, 1:03 a.m.