"Call:"
"RoBMA(y = d, se = d_se, priors_bias = list(prior_weightfunction(\"two-sided\", "
"    list(c(0.1), c(1, 1))), prior_PET(\"normal\", list(0, 1))), "
"    parallel = TRUE, seed = 1)"
""
"Robust Bayesian meta-analysis"
"Diagnostics overview:"
" Model Prior Effect Prior Heterogeneity                  Prior Bias                 max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)"
"     1     Spike(0)            Spike(0)                                                           NA                  NA       NA         NA"
"     2     Spike(0)            Spike(0) omega[two-sided: .1] ~ CumDirichlet(1, 1)            0.00388               0.015     4722      1.000"
"     3     Spike(0)            Spike(0)                  PET ~ Normal(0, 1)[0, Inf]          0.00390               0.009    13829      1.000"
"     4     Spike(0)   InvGamma(1, 0.15)                                                      0.00302               0.011     8468      1.007"
"     5     Spike(0)   InvGamma(1, 0.15) omega[two-sided: .1] ~ CumDirichlet(1, 1)            0.00373               0.014     4932      1.000"
"     6     Spike(0)   InvGamma(1, 0.15)                  PET ~ Normal(0, 1)[0, Inf]          0.00385               0.010     9260      1.001"
"     7 Normal(0, 1)            Spike(0)                                                      0.00184               0.008    14006      1.000"
"     8 Normal(0, 1)            Spike(0) omega[two-sided: .1] ~ CumDirichlet(1, 1)            0.00377               0.015     4444      1.000"
"     9 Normal(0, 1)            Spike(0)                  PET ~ Normal(0, 1)[0, Inf]          0.00645               0.013     6099      1.001"
"    10 Normal(0, 1)   InvGamma(1, 0.15)                                                      0.00247               0.012     7460      1.001"
"    11 Normal(0, 1)   InvGamma(1, 0.15) omega[two-sided: .1] ~ CumDirichlet(1, 1)            0.00357               0.014     4864      1.000"
"    12 Normal(0, 1)   InvGamma(1, 0.15)                  PET ~ Normal(0, 1)[0, Inf]          0.00647               0.012     6625      1.001"
