Package: ddpca 1.1
Fan Yang
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.
Authors:
ddpca_1.1.tar.gz
ddpca_1.1.tar.gz(r-4.5-noble)ddpca_1.1.tar.gz(r-4.4-noble)
ddpca_1.1.tgz(r-4.4-emscripten)ddpca_1.1.tgz(r-4.3-emscripten)
ddpca.pdf |ddpca.html✨
ddpca/json (API)
| # Install 'ddpca' in R: |
| install.packages('ddpca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:518926e09c. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 14 2024 |
| R-4.5-linux | OK | Nov 14 2024 |
Exports:DDHCDDPCA_convexDDPCA_nonconvexHCdetectionIHCDDProjDDProjSDD
Dependencies:latticeMASSMatrixMatrixModelsquantregRcppRcppEigenRSpectraSparseMsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Diagonally Dominant Principal Component Analysis | ddpca-package ddpca |
| DD-HC test | DDHC |
| Diagonally Dominant Principal Component Analysis using Convex approach | DDPCA_convex |
| Diagonally Dominant Principal Component Analysis using Nonconvex approach | DDPCA_nonconvex |
| Higher Criticism for detecting rare and weak signals | HCdetection |
| IHC-DD test | IHCDD |
| Projection onto the Diagonally Dominant Cone | ProjDD |
| Projection onto the Symmetric Diagonally Dominant Cone | ProjSDD |