"Call:"
"RoBMA(d = d, se = d_se, model_type = \"PP\", 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)   PET ~ Cauchy(0, 1)[0, Inf]          0.00533               0.011     8257      1.001"
"     3     Spike(0)            Spike(0) PEESE ~ Cauchy(0, 5)[0, Inf]          0.01077               0.011     9037      1.001"
"     4     Spike(0)   InvGamma(1, 0.15)                                       0.00301               0.011     8101      1.004"
"     5     Spike(0)   InvGamma(1, 0.15)   PET ~ Cauchy(0, 1)[0, Inf]          0.00623               0.012     6972      1.000"
"     6     Spike(0)   InvGamma(1, 0.15) PEESE ~ Cauchy(0, 5)[0, Inf]          0.01282               0.012     7523      1.001"
"     7 Normal(0, 1)            Spike(0)                                       0.00218               0.010    10339      1.001"
"     8 Normal(0, 1)            Spike(0)   PET ~ Cauchy(0, 1)[0, Inf]          0.01348               0.022     1990      1.001"
"     9 Normal(0, 1)            Spike(0) PEESE ~ Cauchy(0, 5)[0, Inf]          0.01716               0.017     3522      1.000"
"    10 Normal(0, 1)   InvGamma(1, 0.15)                                       0.00316               0.012     6929      1.000"
"    11 Normal(0, 1)   InvGamma(1, 0.15)   PET ~ Cauchy(0, 1)[0, Inf]          0.01383               0.021     2184      1.003"
"    12 Normal(0, 1)   InvGamma(1, 0.15) PEESE ~ Cauchy(0, 5)[0, Inf]          0.02658               0.020     2480      1.001"
