Guidelines interpretation output evaluating informative hypotheses

The information criteria GORIC and GORICA - referred to as GORIC(A) - are AIC-type information criteria and can evaluate one or more informative, theory-based hypotheses. Hence, you do not have to specify a null hypothesis nor (only) equality restrictions. You can (also) compare the size and/or ordering of mean parameters or of (standardized) regression-type parameters. You could, for example, evaluate the hypothesis Hm:μ1>μ2>μ3 (a simple ordering of mean parameters) or Hm:β1−β2>β3−β4 (a possible representation of an interaction effect in terms of regression parameters). The goal of the GORIC(A) is to select the best from a set of candidate hypotheses/models. The GORIC(A) can be applied using the R function goric() in the restriktor R package. This guidelines documents discusses what type of hypotheses one can evaluate and how the GORIC(A) output should be interpreted.