In bnclassify: Learning Discrete Bayesian Network Classifiers from Data

\abstract{The \pkg{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-Bayes-specific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on medium-sized data sets. The \pkg{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 \pkg{igraph} 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.}