Package: PMA 1.2-3
PMA: Penalized Multivariate Analysis
Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data <doi:10.2202/1544-6115.1470>.
Authors:
PMA_1.2-3.tar.gz
PMA_1.2-3.zip(r-4.5)PMA_1.2-3.zip(r-4.4)PMA_1.2-3.zip(r-4.3)
PMA_1.2-3.tgz(r-4.4-x86_64)PMA_1.2-3.tgz(r-4.4-arm64)PMA_1.2-3.tgz(r-4.3-x86_64)PMA_1.2-3.tgz(r-4.3-arm64)
PMA_1.2-3.tar.gz(r-4.5-noble)PMA_1.2-3.tar.gz(r-4.4-noble)
PMA_1.2-3.tgz(r-4.4-emscripten)PMA_1.2-3.tgz(r-4.3-emscripten)
PMA.pdf |PMA.html✨
PMA/json (API)
NEWS
| # Install 'PMA' in R: |
| install.packages('PMA', repos = c('https://bnaras.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bnaras/pma/issues
Last updated 12 months agofrom:0d60b40495. Checks:9 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 22 2025 |
| R-4.5-win-x86_64 | OK | Oct 24 2024 |
| R-4.5-linux-x86_64 | OK | Oct 24 2024 |
| R-4.4-win-x86_64 | OK | Jan 22 2025 |
| R-4.4-mac-x86_64 | OK | Jan 22 2025 |
| R-4.4-mac-aarch64 | OK | Jan 22 2025 |
| R-4.3-win-x86_64 | OK | Jan 22 2025 |
| R-4.3-mac-x86_64 | OK | Jan 22 2025 |
| R-4.3-mac-aarch64 | OK | Jan 22 2025 |
Exports:CCACCA.permutedownload_breast_dataMultiCCAMultiCCA.permutePlotCGHPMDPMD.cvSPCSPC.cv
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Penalized Multivariate Analysis | PMA-package PMA |
| Perform sparse canonical correlation analysis using the penalized matrix decomposition. | CCA print.CCA |
| Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition. | CCA.permute |
| Download and return the breast data | download_breast_data |
| Perform sparse multiple canonical correlation analysis. | MultiCCA print.MultiCCA |
| Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition. | MultiCCA.permute plot.MultiCCA.permute print.MultiCCA.permute |
| Plot CGH data | PlotCGH |
| Get a penalized matrix decomposition for a data matrix. | PMD |
| Do tuning parameter selection for PMD via cross-validation | PMD.cv |
| Perform sparse principal component analysis | print.SPC SPC |
| Perform cross-validation on sparse principal component analysis | plot.SPC.cv print.SPC.cv SPC.cv |
