Package: GFA 1.0.5
GFA: Group Factor Analysis
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <doi:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <doi:10.1093/bioinformatics/btw207>; for details, see the citation info.
Authors:
GFA_1.0.5.tar.gz
GFA_1.0.5.zip(r-4.5)GFA_1.0.5.zip(r-4.4)GFA_1.0.5.zip(r-4.3)
GFA_1.0.5.tgz(r-4.4-any)GFA_1.0.5.tgz(r-4.3-any)
GFA_1.0.5.tar.gz(r-4.5-noble)GFA_1.0.5.tar.gz(r-4.4-noble)
GFA_1.0.5.tgz(r-4.4-emscripten)GFA_1.0.5.tgz(r-4.3-emscripten)
GFA.pdf |GFA.html✨
GFA/json (API)
NEWS
| # Install 'GFA' in R: |
| install.packages('GFA', repos = c('https://eemeii.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 11 months agofrom:d83e91adc3. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-win | OK | Sep 17 2024 |
| R-4.5-linux | OK | Sep 17 2024 |
| R-4.4-win | OK | Sep 17 2024 |
| R-4.4-mac | OK | Sep 17 2024 |
| R-4.3-win | OK | Sep 17 2024 |
| R-4.3-mac | OK | Sep 17 2024 |
Exports:getDefaultOptsgfainformativeNoisePriornormalizeDatareconstructionrobustComponentssequentialGfaPredictionundoNormalizeDatavisualizeComponents
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Group factor analysis. | GFA-package GFA |
| A function for generating the default priors of GFA model | getDefaultOpts |
| Gibbs sampling for group factor analysis | gfa |
| Informative noise residual prior | informativeNoisePrior |
| Normalize data to be used by GFA | normalizeData |
| Full data reconstruction based on posterior samples | reconstruction |
| Robust GFA components | robustComponents |
| Sequential prediction of new samples from observed data views to unobserved | sequentialGfaPrediction |
| A function for returning predictions into the original data space | undoNormalizeData |
| Visualize GFA components | visualizeComponents |
