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Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <doi:10.48550/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 |
| DOI: | 10.32614/CRAN.package.ddpca |
| 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 |
| Reference manual: | ddpca.pdf |
| Package source: | ddpca_1.1.tar.gz |
| Windows binaries: | r-devel: ddpca_1.1.zip, r-release: ddpca_1.1.zip, r-oldrel: ddpca_1.1.zip |
| macOS binaries: | r-release (arm64): ddpca_1.1.tgz, r-oldrel (arm64): ddpca_1.1.tgz, r-release (x86_64): ddpca_1.1.tgz, r-oldrel (x86_64): ddpca_1.1.tgz |
| Old sources: | ddpca archive |
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These binaries (installable software) and packages are in development.
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