Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions R package MoTBFs version 1.4.2 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'. Software Depends: R (>= 3.2.0) Imports: quadprog, lpSolve, bnlearn, methods, ggm, Matrix Ana D. Maldonado <ana.d.maldonado@ual.es> Comprehensive R Archive Network (CRAN) Inmaculada Pérez-Bernabé [aut], Antonio Salmerón [aut], Thomas D. Nielsen [aut], Ana D. Maldonado [aut, cre] LGPL-3 2025-07-22 application/tgz https://CRAN.R-project.org/package=MoTBFs doi:10.32614/CRAN.package.MoTBFs