Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'
Class bayesvl: Object Class for BayesVL Models
BayesVL: Visual Learning and Bayesian Statistical Analysis in R
News for Package 'bayesvl'
Utilities to manipulate graphs
Plot utilities for bayesvl objects
Build Stan Models from Directed Acyclic Graphs
bnlearn interface for bayesvl objects
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) <doi:10.31219/osf.io/w5dx6> The 'bayesvl' R package. Open Science Framework (May 18).