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>.
|Author||Jun Woo [aut, cre] (<https://orcid.org/0000-0003-3220-2064>), Jinhua Wang [ctb]|
|Maintainer||Jun Woo <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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