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 74 articles )

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  1. Azzimonti, Laura; Corani, Giorgio; Scutari, Marco: A Bayesian hierarchical score for structure learning from related data sets (2022)
  2. Rodríguez-López, Verónica; Sucar, Luis Enrique: Knowledge transfer for causal discovery (2022)
  3. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  4. Anna V. Bubnova, Irina Deeva, Anna V. Kalyuzhnaya: MIxBN: library for learning Bayesian networks from mixed data (2021) arXiv
  5. Constantinou, Anthony C.; Liu, Yang; Chobtham, Kiattikun; Guo, Zhigao; Kitson, Neville K.: Large-scale empirical validation of Bayesian network structure learning algorithms with noisy data (2021)
  6. Javidian, Mohammad Ali; Valtorta, Marco: A decomposition-based algorithm for learning the structure of multivariate regression chain graphs (2021)
  7. Manuele Leonelli, Ramsiya Ramanathan, Rachel Wilkerson: Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package (2021) arXiv
  8. Marcot, Bruce G.; Hanea, Anca M.: What is an optimal value of (k) in (k)-fold cross-validation in discrete Bayesian network analysis? (2021)
  9. Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
  10. Wang, Bingling; Zhou, Qing: Causal network learning with non-invertible functional relationships (2021)
  11. Watson, David S.; Wright, Marvin N.: Testing conditional independence in supervised learning algorithms (2021)
  12. Winn, Emily T.; Vazquez, Marilyn; Loliencar, Prachi; Taipale, Kaisa; Wang, Xu; Heo, Giseon: A survey of statistical learning techniques as applied to inexpensive pediatric obstructive sleep apnea data (2021)
  13. Zhaolong Ling, Kui Yu, Yiwen Zhang, Lin Liu, Jiuyong Li: Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning (2021) arXiv
  14. Gherardo Varando, Federico Carli, Manuele Leonelli, Eva Riccomagno: The R Package stagedtrees for Structural Learning of Stratified Staged Trees (2020) arXiv
  15. Görgen, Christiane; Leonelli, Manuele: Model-preserving sensitivity analysis for families of Gaussian distributions (2020)
  16. Gu, Jiaying; Zhou, Qing: Learning big Gaussian Bayesian networks: partition, estimation and fusion (2020)
  17. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  18. Javidian, Mohammad Ali; Valtorta, Marco; Jamshidi, Pooyan: AMP chain graphs: minimal separators and structure learning algorithms (2020)
  19. Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik: Causal discovery toolbox: uncovering causal relationships in Python (2020)
  20. Kim, Gang-Hoo; Kim, Sung-Ho: Marginal information for structure learning (2020)

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