The aim of the MoTBFs package is to provide a free and accessible implementation of algorithms for learning MoTBFs from data. The package implements learning algorithms for univariate, conditional, and joint MoTBF densities, explained in the Mixtures of Truncated Basis Functions vignette. By extension, functionality is also provided for learning MoTBF-based Bayesian networks by leveraging functionality from the bnlearn package. Furthermore, the MoTBFs package also provides methods for integrating prior domain knowledge in the learning process, thus also supporting data sparse domains. An extensive example included in the Workflow of MoTBFs pacakge vignette illustrates the main functionality of the package.
New functionalities are included to the MoTBFs package, including the implementation of the variable elimination algorithm for inference, and the adaptation of the Chow-Liu algorithm to MoTBF densities to learn the structure of TAN models.