bnlearn

R package bnlearn: Bayesian network structure learning, parameter learning and inference. Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms for both discrete and Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries and cross-validation. Development snapshots with the latest bugfixes are available from www.bnlearn.com.


References in zbMATH (referenced in 41 articles )

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  1. Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco: Hierarchical estimation of parameters in Bayesian networks (2019)
  2. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  3. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  4. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  5. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  6. Scutari, Marco; Vitolo, Claudia; Tucker, Allan: Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation (2019)
  7. Zhao, Jianjun; Ho, Shen-Shyang: Improving Bayesian network local structure learning via data-driven symmetry correction methods (2019)
  8. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
  9. Geilke, Michael; Karwath, Andreas; Frank, Eibe; Kramer, Stefan: Online estimation of discrete, continuous, and conditional joint densities using classifier chains (2018)
  10. Hoegh, Andrew; Leman, Scotland: Correlated model fusion (2018)
  11. Liu, Xu-Qing; Liu, Xin-Sheng: Markov blanket and Markov boundary of multiple variables (2018)
  12. Mair, Patrick: Modern psychometrics with R (2018)
  13. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  14. Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H.: High-dimensional consistency in score-based and hybrid structure learning (2018)
  15. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
  16. Datta, Sagnik; Gayraud, Ghislaine; Leclerc, Eric; Bois, Frederic Y.: \textitGraph_sampler: a simple tool for fully Bayesian analyses of DAG-models (2017)
  17. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  18. Suzuki, Joe: A novel Chow-Liu algorithm and its application to gene differential analysis (2017)
  19. Almudevar, Anthony: An information theoretic approach to pedigree reconstruction (2016)
  20. Hobæk Haff, Ingrid; Aas, Kjersti; Frigessi, Arnoldo; Lacal, Virginia: Structure learning in Bayesian networks using regular vines (2016)

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