naming of the arguments specifying prior distributions for the different parameters/components of the models changed (priors_mu -> priors_effect, priors_tau -> priors_heterogeneity, and priors_omega -> priors_bias),
prior distributions for specifying weight functions now use a dedicated function (prior(distribution = "two.sided", parameters = ...) -> prior_weightfunction(distribution = "two.sided", parameters = ...)),
new dedicated function for specifying no publication bias adjustment component / no heterogeneity component (prior_none()),
new dedicated functions for specifying models with the PET and PEESE publication bias adjustments (prior_PET(distribution = "Cauchy", parameters = ...) and prior_PEESE(distribution = "Cauchy", parameters = ...)),
new default prior distribution specification for the publication bias adjustment part of the models (corresponding to the RoBMA-PSMA model from Bartoš et al., 2021 preprint),
new model_type argument allowing to specify different "pre-canned" models ("PSMA" = RoBMA-PSMA, "PP" = RoBMA-PP, "2w" = corresponding to Maier et al., in press , manuscript),
combine_data function allows combination of different effect sizes / variability measures into a common effect size measure (also used from within the RoBMA function),
better and improved automatic fitting procedure now enabled by default (can be turned of with autofit = FALSE)
prior distributions can be specified on the different scale than the supplied effect sizes (the package fits the model on Fisher's z scale and back transforms the results back to the scale that was used for prior distributions specification, Cohen's d by default, but both of them can be overwritten with the prior_scale and transformation arguments),
new prior distributions, e.g., beta or fixed weight functions,
estimates from individual models are now plotted with the plot_models() function and the forest plot can be obtained with the forest() function,
the posterior distribution plots for the individual weights are no able supported, however, the weightfunction and the PET-PEESE publication bias adjustments can be visualized with the plot.RoBMA() function and parameter = "weightfunction" and parameter = "PET-PEESE".
Changes
priors_mu->priors_effect,priors_tau->priors_heterogeneity, andpriors_omega->priors_bias),prior(distribution = "two.sided", parameters = ...)->prior_weightfunction(distribution = "two.sided", parameters = ...)),prior_none()),prior_PET(distribution = "Cauchy", parameters = ...)andprior_PEESE(distribution = "Cauchy", parameters = ...)),model_typeargument allowing to specify different "pre-canned" models ("PSMA"= RoBMA-PSMA,"PP"= RoBMA-PP,"2w"= corresponding to Maier et al., in press , manuscript),combine_datafunction allows combination of different effect sizes / variability measures into a common effect size measure (also used from within theRoBMAfunction),autofit = FALSE)prior_scaleandtransformationarguments),plot_models()function and the forest plot can be obtained with theforest()function,plot.RoBMA()function andparameter = "weightfunction"andparameter = "PET-PEESE".