Description: Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.

Title: Learning Hybrid Bayesian Networks using Mixtures of Truncated
Author: Inmaculada Pérez-Bernabé, Antonio Salmerón
Description: 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'.
Imports: quadprog, lpSolve, bnlearn, methods

Package: MoTBFs
Type: Package
Title: Learning Hybrid Bayesian Networks using Mixtures of Truncated
Basis Functions
Version: 1.1
Author: Inmaculada Pérez-Bernabé, Antonio Salmerón, Thomas D. Nielsen, Ana D. Maldonado
Maintainer: Ana D. Maldonado <ana.d.maldonado@ual.es>
Description: 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'.
Depends: R (>= 3.2.0)
Imports: quadprog, lpSolve, bnlearn, methods, ggm
Encoding: UTF-8
License: LGPL-3
NeedsCompilation: yes
Repository: CRAN
Packaged: 2019-10-21 17:41:07 UTC; amg
Date/Publication: 2019-10-21 18:20:02 UTC
RoxygenNote: 6.1.99.9001