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.
|Author||Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]|
|Maintainer||Fan Yang <[email protected]uchicago.edu>|
|Package repository||View on CRAN|
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