# fit bayesian lasso
mod_1 <- blasso_net(dat, lasso_df = 2, chains = 2, prior_scale = 2)

# compute partial correlation matrix
par_mod_1 <- partial_corr(mod_1, prior_scale = 2, prob = .90)

# compute centrality
cent_info <- bayes_centrality(par_mod_1, scale = "std", rule = "interval",
                             prob_edge = .9, point_est_cent = "mean",
                             prob_cent = 0.90,  point_est_edge = "mean")

# plot centrality
centrality_plot(cent_info, shape = 17, size_1 = 4, size_2 = 3, width = .2)

# set some edges to zero based on some decison rule
edge_decide <- edge_decision_rule(par_mod_1, rule = "interval", point_est = "mean", prob = .90)

# plot
qgraph(edge_decide, layout = "spring")