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Bayesian network structure learning, parameter learning and inference
Marco Scutari
GPL (>= 2)
Bayesian network structure learning (via constraint-based, score-based and hybrid algorithms), parameter learning (via ML and Bayesian estimators) and inference. This package implements the Grow-Shrink (GS) algorithm, the Incremental Association (IAMB) algorithm, the Interleaved-IAMB (Inter-IAMB) algorithm, the Fast-IAMB (Fast-IAMB) algorithm, the Max-Min Parents and Children (MMPC) algorithm, the Hiton-PC algorithm, the ARACNE and Chow-Liu algorithms, the Hill-Climbing (HC) greedy search algorithm, the Tabu Search (TABU) algorithm, the Max-Min Hill-Climbing (MMHC) algorithm and the two-stage Restricted Maximization (RSMAX2) algorithm 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 and inference, conditional probability queries and cross-validation.
Package Version Released
bnlearn 3.4 1 year 31 weeks ago
bnlearn 3.3 1 year 51 weeks ago
bnlearn 3.2 2 years 14 weeks ago
bnlearn 3.1 2 years 22 weeks ago
bnlearn 3.0 2 years 34 weeks ago
bnlearn 2.9 2 years 51 weeks ago
bnlearn 2.8 3 years 12 weeks ago
bnlearn 2.7 3 years 20 weeks ago
bnlearn 2.6 3 years 26 weeks ago
bnlearn 2.5 3 years 39 weeks ago
Quick bayes net learning, cooperation with deal, maybe gRain, MASTINO (outside CRAN). I will write about it, after I try to test sth.
Your rating: None Overall: 4 (135 votes)
Your rating: None Documentation: 4 (135 votes)


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