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. 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: 1.0
Depends: R (≥ 3.2.0)
Imports: quadprog, lpSolve, bnlearn, methods
Suggests: mnormt
Published: 2015-09-28
Author: Inmaculada Pérez-Bernabé, Antonio Salmerón
Maintainer: Inmaculada Pérez-Bernabé <iperezbernabe at gmail.com>
License: LGPL-3
NeedsCompilation: yes
CRAN checks: MoTBFs results

Downloads:

Reference manual: MoTBFs.pdf
Package source: MoTBFs_1.0.tar.gz
Windows binaries: r-devel: MoTBFs_1.0.zip, r-release: MoTBFs_1.0.zip, r-oldrel: MoTBFs_1.0.zip
OS X binaries: r-release: MoTBFs_1.0.tgz, r-oldrel: MoTBFs_1.0.tgz

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