ddpca (1.1)

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Diagonally Dominant Principal Component Analysis.


Efficient procedures for fitting the DD-PCA (Ke et al., 2019, ) 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.

Maintainer: Fan Yang
Author(s): Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]

License: GPL-2

Uses: MASS, Matrix, quantreg, RSpectra

Released 2 months ago.

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