MoTBFs

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'.