This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions. This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases. The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables. Probabilistic inference using forward sampling is also implemented. Part of the functionality of the MoTBFs package relies on the bnlearn package, which includes functions for learning the structure of a Bayesian network from a data set. Leveraging this functionality, the MoTBFs package supports learning of MoTBF-based Bayesian networks over hybrid domains. We give a brief introduction to the methodological context and algorithms implemented in the package. An extensive illustrative example is used to describe the package, its functionality, and its usage.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-019.zip
MoTBFs, bnlearn, deal, pcalg, HydeNet, abn
gR, Bayesian, HighPerformanceComputing
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For attribution, please cite this work as
Pérez-Bernabé, et al., "The R Journal: MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions", The R Journal, 2021
BibTeX citation
@article{RJ-2021-019,
author = {Pérez-Bernabé, Inmaculada and Maldonado, Ana D. and Salmerón, Antonio and Nielsen, Thomas D.},
title = {The R Journal: MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-019},
doi = {10.32614/RJ-2021-019},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {321-341}
}