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deal: A Package for Learning Bayesian Networks

Author

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  • Boettcher, Susanne G.
  • Dethlefsen, Claus

Abstract

deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

Suggested Citation

  • Boettcher, Susanne G. & Dethlefsen, Claus, 2003. "deal: A Package for Learning Bayesian Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i20).
    • Handle: RePEc:jss:jstsof:v:008:i20
    DOI: http://hdl.handle.net/10.18637/jss.v008.i20
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    Cited by:

    1. Carvalho, João Vinícius de França & Chiann, Chang, 2013. "Redes Bayesianas: Um método para avaliação de interdependência e contágio em séries temporais multivariadas," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(2), June.
    2. Dethlefsen, Claus & Højsgaard, Søren, 2005. "A Common Platform for Graphical Models in R: The gRbase Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i17).
    3. Scutari, Marco, 2017. "Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i02).
    4. Michael J McGeachie & Hsun-Hsien Chang & Scott T Weiss, 2014. "CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-7, June.
    5. Sagnik Datta & Ghislaine Gayraud & Eric Leclerc & Frederic Y. Bois, 2017. "Graph_sampler: a simple tool for fully Bayesian analyses of DAG-models," Computational Statistics, Springer, vol. 32(2), pages 691-716, June.
    6. Richard Howey & So-Youn Shin & Caroline Relton & George Davey Smith & Heather J Cordell, 2020. "Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data," PLOS Genetics, Public Library of Science, vol. 16(3), pages 1-35, March.
    7. repec:jss:jstsof:35:i03 is not listed on IDEAS
    8. repec:fgv:epgrbe:v:67:n:2:a:3 is not listed on IDEAS

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