[aut,
cre],
Jinhua Wang [ctb]Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. Woo et al. (2016) <doi:10.1186/s12864-016-2871-3>.
| Version: | 0.2.0 |
| Depends: | R (≥ 3.6.0) |
| Imports: | methods, stats, utils, Rcpp (≥ 0.12.16), pROC, RColorBrewer |
| LinkingTo: | Rcpp |
| Suggests: | glmnet, BiocManager, Biostrings |
| Published: | 2019-10-28 |
| Author: | Jun Woo [aut,
cre],
Jinhua Wang [ctb] |
| Maintainer: | Jun Woo <jwoo at umn.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | bbl results |
| Reference manual: | bbl.pdf |
| Vignettes: |
bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R |
| Package source: | bbl_0.2.0.tar.gz |
| Windows binaries: | r-devel: bbl_0.2.0.zip, r-release: bbl_0.2.0.zip, r-oldrel: not available |
| OS X binaries: | r-release: bbl_0.1.5.tgz, r-oldrel: not available |
| Old sources: | bbl archive |
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