ddpca: Diagonally Dominant Principal Component Analysis
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.
| Version: |
1.1 |
| Imports: |
RSpectra, Matrix, quantreg, MASS |
| Published: |
2019-09-14 |
| Author: |
Tracy Ke [aut],
Lingzhou Xue [aut],
Fan Yang [aut, cre] |
| Maintainer: |
Fan Yang <fyang1 at uchicago.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
ddpca results |
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