The R Journal: article published in 2020, volume 12:2

MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions
Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón and Thomas D. Nielsen , The R Journal (2020) 12:2, pages 321-341.

Abstract 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.

Received: 2020-04-05; online 2021-01-15, supplementary material, (1.9 KiB)
CRAN packages: MoTBFs, bnlearn, deal, pcalg, HydeNet, abn
CRAN Task Views implied by cited CRAN packages: gR, Bayesian, HighPerformanceComputing

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-019,
  author = {Inmaculada Pérez-Bernabé and Ana D. Maldonado and Antonio
          Salmerón and Thomas D. Nielsen},
  title = {{MoTBFs: An R Package for Learning Hybrid Bayesian Networks
          Using Mixtures of Truncated Basis Functions}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-019},
  url = {https://doi.org/10.32614/RJ-2021-019},
  pages = {321--341},
  volume = {12},
  number = {2}
}