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Bayesian Networks in R with Applications in Systems Biology R. Nagarajan, M. Scutari and S. Lèbre (2013). Use R!, Vol. 48, Springer (US). ISBN-10: 1461464455 ISBN-13: 978-1461464457 Springer Website Amazon Website Errata Corrige page 3: “if a node vi precedes vj, there can be no arc from vj to vi” should be “if a node vi precedes vj, there can be no path from vj to vi”. page 3: it's true that leaf
Features bnlearn provides an open implementation of large parts of the literature on Bayesian networks: Classes of Bayesian networks: discrete (multinomial) Bayesian networks for discrete data, Gaussian Bayesian networks for continuous data and Conditional Gaussian networks for mixed data. Structure learning algorithms: constraint-based (PC Stable, Grow-Shrink, IAMB, Fast-IAMB, Inter-IAMB, IAMB-FD
bnlearn - an R package for Bayesian network learning and inference
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