R package bnstruct: Bayesian Network Structure Learning from Data with Missing Values. Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
- Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
- Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
- Kallah-Dagadu, G.; Nkansah, B. K.; Howard, N.: Probabilistic graphical modelling of causal effects among the occurrences of transcription factors in DNA sequence (2018)