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

References in zbMATH (referenced in 60 articles )

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  1. Zhaolong Ling, Kui Yu, Yiwen Zhang, Lin Liu, Jiuyong Li: Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning (2021) arXiv
  2. Gherardo Varando, Federico Carli, Manuele Leonelli, Eva Riccomagno: The R Package stagedtrees for Structural Learning of Stratified Staged Trees (2020) arXiv
  3. Görgen, Christiane; Leonelli, Manuele: Model-preserving sensitivity analysis for families of Gaussian distributions (2020)
  4. Gu, Jiaying; Zhou, Qing: Learning big Gaussian Bayesian networks: partition, estimation and fusion (2020)
  5. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  6. Javidian, Mohammad Ali; Valtorta, Marco; Jamshidi, Pooyan: AMP chain graphs: minimal separators and structure learning algorithms (2020)
  7. Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik: Causal discovery toolbox: uncovering causal relationships in Python (2020)
  8. Kim, Gang-Hoo; Kim, Sung-Ho: Marginal information for structure learning (2020)
  9. Park, Gunwoong: Identifiability of additive noise models using conditional variances (2020)
  10. Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
  11. Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco: Hierarchical estimation of parameters in Bayesian networks (2019)
  12. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  13. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  14. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  15. Musella, Flaminia; Vicard, Paola; Vitale, Vincenzina: Copula grow-shrink algorithm for structural learning (2019)
  16. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  17. Scutari, Marco; Graafland, Catharina Elisabeth; Gutiérrez, José Manuel: Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms (2019)
  18. Scutari, Marco; Vitolo, Claudia; Tucker, Allan: Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation (2019)
  19. Sondhi, Arjun; Shojaie, Ali: The reduced PC-algorithm: improved causal structure learning in large random networks (2019)
  20. Talvitie, Topi; Eggeling, Ralf; Koivisto, Mikko: Learning Bayesian networks with local structure, mixed variables, and exact algorithms (2019)

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