MoTBFs (1.1)

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Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions.

http://cran.r-project.org/web/packages/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. Prez-Bernab, A. Salmern, H. Langseth (2015) ; H. Langseth, T.D. Nielsen, I. Prez-Bernab, A. Salmern (2014) ; I. Prez-Bernab, A. Fernndez, R. Rum, A. Salmern (2016) ). 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'.

Maintainer: Ana D. Maldonado
Author(s): Inmaculada Prez-Bernab, Antonio Salmern, Thomas D. Nielsen, Ana D. Maldonado

License: LGPL-3

Uses: bnlearn, ggm, lpSolve, quadprog

Released 23 days ago.


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