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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'.
| Version: | 1.4.1 |
| Depends: | R (≥ 3.2.0) |
| Imports: | quadprog, lpSolve, bnlearn, methods, ggm, Matrix |
| Published: | 2022-04-18 |
| DOI: | 10.32614/CRAN.package.MoTBFs |
| Author: | Inmaculada Pérez-Bernabé, Antonio Salmerón, Thomas D. Nielsen, Ana D. Maldonado |
| Maintainer: | Ana D. Maldonado <ana.d.maldonado at ual.es> |
| License: | LGPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | MoTBFs results |
| Reference manual: | MoTBFs.pdf |
| Package source: | MoTBFs_1.4.1.tar.gz |
| Windows binaries: | r-devel: MoTBFs_1.4.1.zip, r-release: MoTBFs_1.4.1.zip, r-oldrel: MoTBFs_1.4.1.zip |
| macOS binaries: | r-release (arm64): MoTBFs_1.4.1.tgz, r-oldrel (arm64): MoTBFs_1.4.1.tgz, r-release (x86_64): MoTBFs_1.4.1.tgz, r-oldrel (x86_64): MoTBFs_1.4.1.tgz |
| Old sources: | MoTBFs archive |
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These binaries (installable software) and packages are in development.
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