Description Details Author(s) References See Also Examples

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

, `bvl_modelFit`

Package: | bayesvl |

Type: | Package |

Version: | 0.8.0 |

Date: | 13 May 2019 |

License: | GPL-3 |

Website: | Bayesvl |

Quan-Hoang Vuong, Viet-Phuong La

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

`bayesvl-class`

, `bvl_modelFit`

, `bvl_model2Stan`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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)
``` |

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