Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).


Reference manual

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0.8.5 by Viet-Phuong La, a year ago

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Authors: Viet-Phuong La [aut, cre] , Quan-Hoang Vuong [aut]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyr

Depends on rstan, StanHeaders, stats, graphics, methods

Suggests loo

See at CRAN