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) .


Reference manual

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0.3.1 by Jun Woo, 5 months ago

Browse source code at

Authors: Jun Woo [aut, cre] , Jinhua Wang [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports methods, stats, utils, Rcpp, pROC, RColorBrewer

Suggests glmnet, BiocManager, Biostrings

Linking to Rcpp

See at CRAN