deal
R package deal: Learning Bayesian Networks with Mixed Variables. Bayesian networks with continuous and/or discrete variables can be learned and compared from data.
(Source: http://freecode.com/)
Keywords for this software
References in zbMATH (referenced in 20 articles )
Showing results 1 to 20 of 20.
Sorted by year (- Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
- Anna V. Bubnova, Irina Deeva, Anna V. Kalyuzhnaya: MIxBN: library for learning Bayesian networks from mixed data (2021) arXiv
- Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
- Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
- Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
- İçen, Duygu; Ersel, Derya: A new approach for probability calculation of fuzzy events in Bayesian networks (2019)
- Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
- Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
- Datta, Sagnik; Gayraud, Ghislaine; Leclerc, Eric; Bois, Frederic Y.: \textitGraph_sampler: a simple tool for fully Bayesian analyses of DAG-models (2017)
- Masegosa, Andrés R.; Feelders, Ad J.; van der Gaag, Linda C.: Learning from incomplete data in Bayesian networks with qualitative influences (2016)
- Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
- Barclay, L. M.; Hutton, J. L.; Smith, J. Q.: Refining a Bayesian network using a chain event graph (2013)
- Cordone, Roberto; Lulli, Guglielmo: An integer optimization approach for reverse engineering of gene regulatory networks (2013)
- Højsgaard, Søren; Edwards, David; Lauritzen, Steffen: Graphical models with R. (2012)
- Markus Kalisch; Martin Mächler; Diego Colombo; Marloes Maathuis; Peter Bühlmann: Causal Inference Using Graphical Models with the R Package pcalg (2012) not zbMATH
- Jaroszewicz, Szymon; Scheffer, Tobias; Simovici, Dan A.: Scalable pattern mining with Bayesian networks as background knowledge (2009) ioport
- Marco Scutari: Learning Bayesian Networks with the bnlearn R Package (2009) arXiv
- Angelopoulos, Nicos; Cussens, James: Bayesian learning of Bayesian networks with informative priors (2008)
- Ben Hassen, Hanen; Masmoudi, Afif; Rebai, Ahmed: Causal inference in biomolecular pathways using a Bayesian network approach and an implicit method (2008)
- Claus Dethlefsen; Søren Højsgaard: A Common Platform for Graphical Models in R: The gRbase Package (2005) not zbMATH