"Call:"
"RoBMA(d = d, se = d_se, model_type = \"PP\", parallel = TRUE, seed = 1)"
""
"Robust Bayesian meta-analysis"
"Models overview:"
" Model Prior Effect Prior Heterogeneity          Prior Bias          Prior prob. log(marglik) Post. prob. Inclusion BF"
"     1     Spike(0)            Spike(0)                                    0.125        -0.83       0.092        0.706"
"     2     Spike(0)            Spike(0)   PET ~ Cauchy(0, 1)[0, Inf]       0.062         0.16       0.123        2.108"
"     3     Spike(0)            Spike(0) PEESE ~ Cauchy(0, 5)[0, Inf]       0.062        -0.86       0.045        0.700"
"     4     Spike(0)   InvGamma(1, 0.15)                                    0.125        -0.62       0.114        0.899"
"     5     Spike(0)   InvGamma(1, 0.15)   PET ~ Cauchy(0, 1)[0, Inf]       0.062        -0.11       0.095        1.569"
"     6     Spike(0)   InvGamma(1, 0.15) PEESE ~ Cauchy(0, 5)[0, Inf]       0.062        -0.97       0.040        0.626"
"     7 Normal(0, 1)            Spike(0)                                    0.125        -0.03       0.205        1.805"
"     8 Normal(0, 1)            Spike(0)   PET ~ Cauchy(0, 1)[0, Inf]       0.062        -0.59       0.058        0.929"
"     9 Normal(0, 1)            Spike(0) PEESE ~ Cauchy(0, 5)[0, Inf]       0.062        -1.53       0.023        0.351"
"    10 Normal(0, 1)   InvGamma(1, 0.15)                                    0.125        -0.39       0.143        1.164"
"    11 Normal(0, 1)   InvGamma(1, 0.15)   PET ~ Cauchy(0, 1)[0, Inf]       0.062        -0.87       0.044        0.695"
"    12 Normal(0, 1)   InvGamma(1, 0.15) PEESE ~ Cauchy(0, 5)[0, Inf]       0.062        -1.71       0.019        0.291"
