New publication in leading Social Networks journal
(Department of Social Statistics) has published a new article in the prestigious journal Social Networks.
In the paper, Leifeld and his colleagues present a Bayesian framework for testing scientific expectations in Exponential Random Graph Models.
Their framework intends to overcome some of the limitations affecting classical settings, such as inconsistent behaviour when the null hypothesis is true, their inability to quantify evidence in favour of a null hypothesis, and their inability to test multiple hypotheses with competing equality and/or order constraints on the parameters of interest in a direct manner.
To tackle these shortcomings, this new publication presents Bayes factors and posterior probabilities for testing scientific expectations under a Bayesian framework. The methodology is implemented in the R package BFpack. The applicability of the methodology is illustrated using empirical collaboration networks and policy networks.