emmeans, GGally, ggplot2, ggridges, KernSmooth, knitr, lme4,
logspline, performance, rmarkdown, rstan, rstanarm, see,

Imports: insight (>= 0.5.0), methods, stats, utils

Imports: insight (>= 0.6.0), methods, stats, utils
emmeans, GGally, ggplot2, ggridges, KernSmooth, knitr, MASS,
mclust, lme4, logspline, performance, rmarkdown, rstan,

Imports: insight (>= 0.8.0), methods, stats, utils
mclust, modelbased, lme4, logspline, performance, rmarkdown,
rstan, rstanarm, see, stringr, testthat, tidyr, tweedie

Imports: insight (>= 0.8.4), methods, stats, utils
Suggests: BayesFactor, bayesQR, bridgesampling, brms, broom, covr,
dplyr, emmeans, GGally, ggplot2, ggridges, KernSmooth, knitr,
MASS, mclust, modelbased, lme4, logspline, performance,
rmarkdown, rstan, rstanarm, see, stringr, testthat, tidyr,

MASS, mclust, modelbased, lme4, logspline, mediation,
parameters, performance, rmarkdown, rstan, rstanarm, see,

Imports: insight (>= 0.9.6.1), methods, stats, utils
dplyr, effectsize, emmeans, GGally, ggplot2, ggridges, httr,
KernSmooth, knitr, MASS, mclust, modelbased, lme4, logspline,
mediation, parameters, performance, rmarkdown, rstan, rstanarm,

Imports: insight (>= 0.11.0), methods, stats, utils
KernSmooth, knitr, MASS, mclust, modelbased, lavaan, lme4,
logspline, mediation, parameters, performance, rmarkdown,

Imports: insight (>= 0.12.0), methods, stats, utils

Title: Visually Learning the Graphical Structure of Bayesian Networks
Authors@R: c(person("Viet-Phuong", "La", role = c("aut", "cre"), email = "lvphuong@gmail.com"), person("Quan-Hoang", "Vuong", role = c("aut"), email = "hoang.vuongquan@phenikaa-uni.edu.vn"))
Imports: coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyr
Description: 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).