"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"
"Models overview:"
" Model Prior Effect Prior Heterogeneity                  Prior Bias                 Prior prob. log(marglik) Post. prob. Inclusion BF"
"     1     Spike(0)            Spike(0)                                                   0.083        -2.90       0.043        0.499"
"     2     Spike(0)            Spike(0) omega[two-sided: .1] ~ CumDirichlet(1, 1)         0.083        -2.60       0.059        0.686"
"     3     Spike(0)            Spike(0)                  PET ~ Normal(0, 1)[0, Inf]       0.083        -1.46       0.185        2.492"
"     4     Spike(0)   InvGamma(1, 0.15)                                                   0.083        -2.66       0.056        0.647"
"     5     Spike(0)   InvGamma(1, 0.15) omega[two-sided: .1] ~ CumDirichlet(1, 1)         0.083        -2.61       0.058        0.683"
"     6     Spike(0)   InvGamma(1, 0.15)                  PET ~ Normal(0, 1)[0, Inf]       0.083        -1.73       0.141        1.799"
"     7 Normal(0, 1)            Spike(0)                                                   0.083        -2.01       0.107        1.313"
"     8 Normal(0, 1)            Spike(0) omega[two-sided: .1] ~ CumDirichlet(1, 1)         0.083        -2.28       0.081        0.969"
"     9 Normal(0, 1)            Spike(0)                  PET ~ Normal(0, 1)[0, Inf]       0.083        -2.25       0.083        0.999"
"    10 Normal(0, 1)   InvGamma(1, 0.15)                                                   0.083        -2.38       0.073        0.870"
"    11 Normal(0, 1)   InvGamma(1, 0.15) omega[two-sided: .1] ~ CumDirichlet(1, 1)         0.083        -2.70       0.053        0.621"
"    12 Normal(0, 1)   InvGamma(1, 0.15)                  PET ~ Normal(0, 1)[0, Inf]       0.083        -2.56       0.061        0.716"
