MoTBFs: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'.

Version: 1.4.1
Depends: R (≥ 3.2.0)
Imports: quadprog, lpSolve, bnlearn, methods, ggm, Matrix
Published: 2022-04-18
DOI: 10.32614/CRAN.package.MoTBFs
Author: Inmaculada Pérez-Bernabé, Antonio Salmerón, Thomas D. Nielsen, Ana D. Maldonado
Maintainer: Ana D. Maldonado <ana.d.maldonado at>
License: LGPL-3
NeedsCompilation: yes
CRAN checks: MoTBFs results


Reference manual: MoTBFs.pdf


Package source: MoTBFs_1.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MoTBFs_1.4.1.tgz, r-oldrel (arm64): MoTBFs_1.4.1.tgz, r-release (x86_64): MoTBFs_1.4.1.tgz, r-oldrel (x86_64): MoTBFs_1.4.1.tgz
Old sources: MoTBFs archive


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