bayesvl: BayesVL: Visual Learning and Bayesian Statistical Analysis in...

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

BayesVL: Visual Learning and Bayesian Statistical Analysis in R

Description

An R package for visually constructing graphical models of Bayesian networks and performing Hamiltonian Monte Carlo (HMC) via Stan, using functions such as bvl_model2Stan and bvl_modelFit.

Details

Package:	bayesvl
Type:	Package
Version:	0.8.0
Date:	2019-05-13
License:	GPL-3
Website:	https://github.com/sshpa/bayesvl

Author(s)

Quan-Hoang Vuong, Viet-Phuong La

References

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

• https://github.com/sshpa/bayesvl/tree/master/References

For case studies using the package in research articles, see:

• https://www.nature.com/articles/s41599-020-0442-3

See Also

bayesvl-class, bvl_modelFit, bvl_model2Stan

Examples

# Create a new model
model <- bayesvl()

# Add observed data nodes
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 directed arcs
model <- bvl_addArc(model, "B", "Lie", "slope")
model <- bvl_addArc(model, "C", "Lie", "slope")
model <- bvl_addArc(model, "T", "Lie", "slope")

# View model summary
summary(model)

bayesvl documentation built on June 8, 2025, 10:38 a.m.