"Call:"
"RoBMA.reg(formula = ~mod_con, data = df_reg, priors = list(mod_con = list(null = prior(\"normal\", "
"    list(0, 0.05)), alt = prior(\"normal\", list(0.3, 0.15)))), "
"    priors_heterogeneity = NULL, priors_bias = list(prior_weightfunction(distribution = \"two.sided\", "
"        parameters = list(alpha = c(1, 1), steps = c(0.05)), "
"        prior_weights = 1/2), prior_PET(distribution = \"Cauchy\", "
"        parameters = list(0, 1), truncation = list(0, Inf), prior_weights = 1/2)), "
"    priors_effect_null = NULL, parallel = TRUE, seed = 1)"
""
"Robust Bayesian meta-analysis"
"Diagnostics overview:"
" Model Prior intercept   Prior mod_con   Prior Heterogeneity                   Prior Bias                 max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)"
"     1    Normal(0, 1)   Normal(0, 0.05)            Spike(0)                                                       0.00025               0.011     8797      1.000"
"     2    Normal(0, 1)   Normal(0, 0.05)            Spike(0) omega[two-sided: .05] ~ CumDirichlet(1, 1)            0.00388               0.015     4536      1.000"
"     3    Normal(0, 1)   Normal(0, 0.05)            Spike(0)                   PET ~ Cauchy(0, 1)[0, Inf]          0.06108               0.039      642      1.025"
"     4    Normal(0, 1) Normal(0.3, 0.15)            Spike(0)                                                       0.00027               0.011     8710      1.000"
"     5    Normal(0, 1) Normal(0.3, 0.15)            Spike(0) omega[two-sided: .05] ~ CumDirichlet(1, 1)            0.00404               0.015     4479      1.001"
"     6    Normal(0, 1) Normal(0.3, 0.15)            Spike(0)                   PET ~ Cauchy(0, 1)[0, Inf]          0.05603               0.038      677      1.012"
