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.


Reference manual

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1.1 by Fan Yang, 2 years ago

Browse source code at https://github.com/cran/ddpca

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

Documentation:   PDF Manual  

GPL-2 license

Imports RSpectra, Matrix, quantreg, MASS

See at CRAN