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 30 articles )

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  1. Mair, Patrick: Modern psychometrics with R (2018)
  2. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  3. Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H.: High-dimensional consistency in score-based and hybrid structure learning (2018)
  4. Bryon Aragam, Jiaying Gu, Qing Zhou: Learning Large-Scale Bayesian Networks with the sparsebn Package (2017) arXiv
  5. Datta, Sagnik; Gayraud, Ghislaine; Leclerc, Eric; Bois, Frederic Y.: \itGraph_sampler: a simple tool for fully Bayesian analyses of DAG-models (2017)
  6. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  7. Suzuki, Joe: A novel Chow-Liu algorithm and its application to gene differential analysis (2017)
  8. Almudevar, Anthony: An information theoretic approach to pedigree reconstruction (2016)
  9. Hobæk Haff, Ingrid; Aas, Kjersti; Frigessi, Arnoldo; Lacal, Virginia: Structure learning in Bayesian networks using regular vines (2016)
  10. Pensar, Johan; Nyman, Henrik; Lintusaari, Jarno; Corander, Jukka: The role of local partial independence in learning of Bayesian networks (2016)
  11. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  12. Young, William Chad; Raftery, Adrian E.; Yeung, Ka Yee: A posterior probability approach for gene regulatory network inference in genetic perturbation data (2016)
  13. Aragam, Bryon; Zhou, Qing: Concave penalized estimation of sparse Gaussian Bayesian networks (2015)
  14. Bontempi, Gianluca; Flauder, Maxime: From dependency to causality: a machine learning approach (2015)
  15. Córdoba-Sánchez, Irene; Bielza, Concha; Larrañaga, Pedro: Towards Gaussian Bayesian network fusion (2015)
  16. Dinwoodie, Ian H.; Pandya, Kruti: Exact tests for singular network data (2015)
  17. Gartner, Daniel; Kolisch, Rainer; Neill, Daniel B.; Padman, Rema: Machine learning approaches for early DRG classification and resource allocation (2015)
  18. Kuipers, Jack; Moffa, Giusi: Uniform random generation of large acyclic digraphs (2015)
  19. Mohammadi, A.; Wit, E.C.: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models (2015) arXiv
  20. Pircalabelu, Eugen; Claeskens, Gerda; Waldorp, Lourens: A focused information criterion for graphical models (2015)

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