Package: tensorBF 1.0.2
tensorBF: Bayesian Tensor Factorization
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete 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 method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Authors:
tensorBF_1.0.2.tar.gz
tensorBF_1.0.2.zip(r-4.5)tensorBF_1.0.2.zip(r-4.4)tensorBF_1.0.2.zip(r-4.3)
tensorBF_1.0.2.tgz(r-4.5-any)tensorBF_1.0.2.tgz(r-4.4-any)tensorBF_1.0.2.tgz(r-4.3-any)
tensorBF_1.0.2.tar.gz(r-4.5-noble)tensorBF_1.0.2.tar.gz(r-4.4-noble)
tensorBF_1.0.2.tgz(r-4.4-emscripten)tensorBF_1.0.2.tgz(r-4.3-emscripten)
tensorBF.pdf |tensorBF.html✨
tensorBF/json (API)
NEWS
| # Install 'tensorBF' in R: |
| install.packages('tensorBF', repos = c('https://suleimank.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 6 years agofrom:bf051a0197. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 17 2025 |
| R-4.5-win | OK | Feb 17 2025 |
| R-4.5-mac | OK | Feb 17 2025 |
| R-4.5-linux | OK | Feb 17 2025 |
| R-4.4-win | OK | Feb 17 2025 |
| R-4.4-mac | OK | Feb 17 2025 |
| R-4.3-win | OK | Feb 17 2025 |
| R-4.3-mac | OK | Feb 17 2025 |
Exports:getDefaultOptsnormFiberCenteringnormSlabScalingplotTensorBFpredictTensorBFreconstructTensorBFtensorBFundoFiberCenteringundoSlabScaling
Dependencies:tensor
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| A function for generating a default set of parameters for Bayesian Tensor Factorization methods | getDefaultOpts |
| Preprocessing: fiber Centering | normFiberCentering |
| Preprocessing: Slab Scaling | normSlabScaling |
| Plot Tensor Components | plotTensorBF |
| Predict Missing Values using the Bayesian tensor factorization model | predictTensorBF |
| Reconstruct the data based on posterior samples | reconstructTensorBF |
| Bayesian Factorization of a Tensor | tensorBF |
| Postprocessing: Undo fiber Centering | undoFiberCentering |
| Postprocessing: Undo Slab Scaling | undoSlabScaling |
