bayesvl: BayesVL package for Bayesian statistical analyses in R

In sshpa/bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

BayesVL package for Bayesian statistical analyses in R

Description

The R package for visually learning the graphical structures of Bayesian networks, and performing Hamiltonian MCMC with Stan through bvl_model2Stan, bvl_modelFit

Details

Package:	bayesvl
Type:	Package
Version:	0.8.0
Date:	13 May 2019
License:	GPL-3
Website:	Bayesvl

Author(s)

Quan-Hoang Vuong, Viet-Phuong La

References

For documentation, case studies and worked examples, and other tutorial information visit the References section on our Github:

• github.com/sshpa/bayesvl/master/References/bvl_ug_en08.pdf

See Also

bayesvl-class, bvl_modelFit, bvl_model2Stan

Examples

	# Design the model in directed acyclic graph
	model <- bayesvl()
	
	# add observed data nodes to the model
	model <- bvl_addNode(model, "Lie", "binom")
	model <- bvl_addNode(model, "B", "binom")
	model <- bvl_addNode(model, "C", "binom")
	model <- bvl_addNode(model, "T", "binom")
	
	# add path between nodes
	model <- bvl_addArc(model, "B", "Lie", "slope")
	model <- bvl_addArc(model, "C", "Lie", "slope")
	model <- bvl_addArc(model, "T", "Lie", "slope")
  
  summary(model)

sshpa/bayesvl documentation built on June 1, 2022, 12:50 a.m.