"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                                                                    "
" Model                1                Parameter prior distributions"
" Prior prob.      0.250             (mu) intercept ~ Normal(0, 1)   "
" log(marglik)   -115.99               (mu) mod_con ~ Normal(0, 0.05)"
" Post. prob.      0.000                        tau ~ Spike(0)       "
" Inclusion BF     0.000                                             "
""
"Parameter estimates:"
"           Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"intercept 0.000 0.024 -0.046  0.000 0.047     0.00025          0.010 9623 1.000"
"mod_con   0.760 0.022  0.716  0.760 0.803     0.00023          0.011 8797 1.000"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
""
"                                                                              "
" Model                2                          Parameter prior distributions"
" Prior prob.      0.125                    (mu) intercept ~ Normal(0, 1)      "
" log(marglik)   -115.22                      (mu) mod_con ~ Normal(0, 0.05)   "
" Post. prob.      0.000                               tau ~ Spike(0)          "
" Inclusion BF     0.000             omega[two-sided: .05] ~ CumDirichlet(1, 1)"
""
"Parameter estimates:"
"               Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"intercept     0.000 0.024 -0.048  0.000 0.048     0.00025          0.010 9849 1.000"
"mod_con       0.759 0.022  0.715  0.759 0.801     0.00023          0.011 8983 1.000"
"omega[0,0.05] 1.000 0.000  1.000  1.000 1.000          NA             NA   NA    NA"
"omega[0.05,1] 0.394 0.262  0.036  0.342 0.942     0.00388          0.015 4536 1.000"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
""
"                                                                         "
" Model                3                     Parameter prior distributions"
" Prior prob.      0.125             (mu) intercept ~ Normal(0, 1)        "
" log(marglik)   -116.13               (mu) mod_con ~ Normal(0, 0.05)     "
" Post. prob.      0.000                        tau ~ Spike(0)            "
" Inclusion BF     0.000                        PET ~ Cauchy(0, 1)[0, Inf]"
""
"Parameter estimates:"
"            Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"intercept -0.121 0.149 -0.535 -0.078 0.019     0.00568          0.038   686 1.025"
"mod_con    0.760 0.022  0.717  0.760 0.803     0.00009          0.004 53589 1.000"
"PET        1.272 1.548  0.032  0.790 5.632     0.06108          0.039   642 1.001"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
""
"                                                                    "
" Model              4                  Parameter prior distributions"
" Prior prob.    0.250             (mu) intercept ~ Normal(0, 1)     "
" log(marglik)   17.07               (mu) mod_con ~ Normal(0.3, 0.15)"
" Post. prob.    0.453                        tau ~ Spike(0)         "
" Inclusion BF   2.489                                               "
""
"Parameter estimates:"
"           Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"intercept 0.000 0.025 -0.049  0.000 0.048     0.00027          0.011 8710 1.000"
"mod_con   0.924 0.024  0.878  0.924 0.973     0.00025          0.010 9345 1.000"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
""
"                                                                            "
" Model              5                          Parameter prior distributions"
" Prior prob.    0.125                    (mu) intercept ~ Normal(0, 1)      "
" log(marglik)   17.50                      (mu) mod_con ~ Normal(0.3, 0.15) "
" Post. prob.    0.350                               tau ~ Spike(0)          "
" Inclusion BF   3.774             omega[two-sided: .05] ~ CumDirichlet(1, 1)"
""
"Parameter estimates:"
"               Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"intercept     0.000 0.025 -0.048  0.000 0.048     0.00025          0.010 9801 1.000"
"mod_con       0.923 0.024  0.874  0.923 0.970     0.00026          0.011 8856 1.000"
"omega[0,0.05] 1.000 0.000  1.000  1.000 1.000          NA             NA   NA    NA"
"omega[0.05,1] 0.446 0.270  0.044  0.415 0.964     0.00404          0.015 4479 1.001"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
""
"                                                                       "
" Model              6                     Parameter prior distributions"
" Prior prob.    0.125             (mu) intercept ~ Normal(0, 1)        "
" log(marglik)   16.93               (mu) mod_con ~ Normal(0.3, 0.15)   "
" Post. prob.    0.196                        tau ~ Spike(0)            "
" Inclusion BF   1.709                        PET ~ Cauchy(0, 1)[0, Inf]"
""
"Parameter estimates:"
"            Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"intercept -0.123 0.140 -0.548 -0.080 0.017     0.00530          0.038   702 1.012"
"mod_con    0.924 0.024  0.877  0.924 0.971     0.00010          0.004 54313 1.000"
"PET        1.297 1.458  0.036  0.813 5.761     0.05603          0.038   677 1.001"
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale)."
