Package: EESPCA 0.7.0
H. Robert Frost
EESPCA: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) <doi:10.1080/10618600.2021.1987254>.
Authors:
EESPCA_0.7.0.tar.gz
EESPCA_0.7.0.tar.gz(r-4.5-noble)EESPCA_0.7.0.tar.gz(r-4.4-noble)
EESPCA_0.7.0.tgz(r-4.4-emscripten)EESPCA_0.7.0.tgz(r-4.3-emscripten)
EESPCA.pdf |EESPCA.html✨
EESPCA/json (API)
| # Install 'EESPCA' in R: |
| install.packages('EESPCA', 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 3 years agofrom:bec773c6aa. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 11 2024 |
| R-4.5-linux | NOTE | Dec 11 2024 |
Exports:computeApproxNormSquaredEigenvectorcomputeResidualMatrixeespcaeespcaCVeespcaForKpowerIterationreconstructreconstructionErrorrifleInitriflePCACVtpowertpowerPCACV
