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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1 |
rolling linux/jammy R-4.5 | ddpca_1.1.tar.gz |
48.2 KiB |
1.1 |
rolling linux/noble R-4.5 | ddpca_1.1.tar.gz |
48.1 KiB |
1.1 |
rolling source/ R- | ddpca_1.1.tar.gz |
11.2 KiB |
1.1 |
latest linux/jammy R-4.5 | ddpca_1.1.tar.gz |
48.2 KiB |
1.1 |
latest linux/noble R-4.5 | ddpca_1.1.tar.gz |
48.1 KiB |
1.1 |
latest source/ R- | ddpca_1.1.tar.gz |
11.2 KiB |
1.1 |
2026-04-26 source/ R- | ddpca_1.1.tar.gz |
11.2 KiB |
1.1 |
2026-04-23 source/ R- | ddpca_1.1.tar.gz |
11.2 KiB |
1.1 |
2026-04-09 windows/windows R-4.5 | ddpca_1.1.zip |
50.7 KiB |
1.1 |
2025-04-20 source/ R- | ddpca_1.1.tar.gz |
11.2 KiB |