bbl: Boltzmann Bayes Learner

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 ORCID iD [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

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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_0.2.0.tgz, r-oldrel: not available
Old sources: bbl archive

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