Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan' R package bayesvl version 1.0.0 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). Software Depends: R (>= 3.5.0), rstan (>= 2.10.0), StanHeaders (>= 2.18.0), stats, graphics, methods Imports: coda, bnlearn, ggplot2, bayesplot, viridis, reshape2 Suggests: loo (>= 2.0.0) Viet-Phuong La <lvphuong@gmail.com> Comprehensive R Archive Network (CRAN) Viet-Phuong La [aut, cre], Quan-Hoang Vuong [aut] GPL (>= 3) 2025-05-17 application/tgz https://CRAN.R-project.org/package=bayesvl doi:10.32614/CRAN.package.bayesvl