bnclassify: Learning Bayesian Network Classifiers
Bojan Mihaljević, Concha Bielza, Pedro Larrañaga · The R Journal · 2019
The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data.For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators.It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayesspecific methods based on discriminative score optimization and Bayesian model averaging.The implementation is efficient enough to allow for time-consuming discriminative scores on mediumsized data sets.The bnclassify package provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network plotting via the Rgraphviz package.It is extensively tested, with over 200 automated tests that give a code coverage of 94%.Here we present the main functionalities, illustrate them with a number of data sets, and comment on related software.