ddpca-package {ddpca}

Diagonally Dominant Principal Component Analysis

Description

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.

Details

The DESCRIPTION file:

Index of help topics:

DDHC                    DD-HC test
DDPCA_convex            Diagonally Dominant Principal Component
                        Analysis using Convex approach
DDPCA_nonconvex         Diagonally Dominant Principal Component
                        Analysis using Nonconvex approach
HCdetection             Higher Criticism for detecting rare and weak
                        signals
IHCDD                   IHC-DD test
ProjDD                  Projection onto the Diagonally Dominant Cone
ProjSDD                 Projection onto the Symmetric Diagonally
                        Dominant Cone
ddpca-package           Diagonally Dominant Principal Component
                        Analysis

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.

Note

Please cite the reference paper to cite this R package.

Author(s)

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

Maintainer: Fan Yang <[e-mail]>

References

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