BayesVL

The R package for visually learning the graphical structures of Bayesian networks, and performing Hamiltonian Markov chain Monte Carlo (MCMC) with 'Stan'.

Features:

* Creating the (starting) graphical structure of Bayesian networks
* Creating one or more random Bayesian networks learned from dataset with customized constraints
* Generating Stan code for structures of Bayesian networks for sampling and parameter learning
* Plotting the Bayesian network graphs 
* Performing Markov chain Monte Carlo (MCMC) simulations and plotting various graphs for posteriors check
* Compatibility with R 3.4 or newer versions

Here is the CHANGELOG

Get started

Install

Getting started and installing the latest snapshot type in the R console:

> install.packages("devtools")
> devtools::install_github("sshpa/bayesvl")

Create appropriate Bayesian network structures

Creating a node for each variable in the proposed network

dag <- bayesvl()
dag <- bvl_addNode(dag, "B")
dag <- bvl_addNode(dag, "C")
dag <- bvl_addNode(dag, "T")
dag <- bvl_addNode(dag, "DC")
dag <- bvl_addNode(dag, "MD")

Starting to add arcs between variables (nodes) using the survey data

dag <- bvl_addArc(dag, "B", "DC")
dag <- bvl_addArc(dag, "C", "DC")
dag <- bvl_addArc(dag, "T", "DC")

Generate 'Stan' code

Generating the 'Stan' code required for building structures of the Bayesian networks required for sampling and parameter learning

stan_code <- bvl_model2Stan(dag)
cat(stan_code)

Getting the model's parameters

params <- bvl_stanParams(dag)
params

Sample and fit the 'Stan' model

Sampling the predefined 'Stan' model

stan_fit <- bvl_modelFit(dag, data, iter=20000 , warmup=2000 , chains=4 , cores=4)
summary(stan_fit)