ddpca-package: Diagonally Dominant Principal Component Analysis

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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.



Index: This package was not yet installed at build time.
This package contains DDPCA_nonconvex and DDPCA_convex function, which decomposes a positive semidefinite matrix into a low rank component, and a diagonally dominant component using either nonconvex approach or convex approach.


Please cite the reference paper to cite this R package.


Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]

Maintainer: Fan Yang <fyang1@uchicago.edu>


Ke, Z., Xue, L. and Yang, F., 2019. Diagonally Dominant Principal Component Analysis. Journal of Computational and Graphic Statistics, under review.

ddpca documentation built on Sept. 15, 2019, 1:03 a.m.