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.
| Version: | 1.0.2 |
| Imports: | tensor, methods |
| Published: | 2018-10-02 |
| DOI: | 10.32614/CRAN.package.tensorBF |
| Author: | Suleiman A Khan [aut, cre], Muhammad Ammad-ud-din [aut] |
| Maintainer: | Suleiman A Khan <khan.suleiman at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Citation: | tensorBF citation info |
| Materials: | NEWS |
| In views: | MissingData |
| CRAN checks: | tensorBF results |
| Reference manual: | tensorBF.pdf |
| Package source: | tensorBF_1.0.2.tar.gz |
| Windows binaries: | r-devel: tensorBF_1.0.2.zip, r-release: tensorBF_1.0.2.zip, r-oldrel: tensorBF_1.0.2.zip |
| macOS binaries: | r-release (arm64): tensorBF_1.0.2.tgz, r-oldrel (arm64): tensorBF_1.0.2.tgz, r-release (x86_64): tensorBF_1.0.2.tgz, r-oldrel (x86_64): tensorBF_1.0.2.tgz |
| Old sources: | tensorBF archive |
Please use the canonical form https://CRAN.R-project.org/package=tensorBF to link to this page.