Simulation Intervention for Cross-Sectional Network Models: Based on the R Packages NodeIdentifyR and NIRApost

Traditional cross-sectional network-centrality metrics fail to distinguish causal directions between symptoms, leading to biases in selecting potential intervention targets. The nodeIdentifyR algorithm (NIRA) addresses this issue by using simulation-based interventions to identify projected optimal intervention target in cross-sectional networks. However, existing applications of NIRA typically overlook several recommended validation steps, which may reduce the robustness of its results. Specifically, a critical prerequisite for applying NIRA, testing for moderation effects to ensure the invariance of edge weights during simulated intervention, is consistently ignored. Moreover, they lack statistical significance testing for simulated intervention effects through permutation tests and stability assessment of NIRA outcomes via repeated simulations. In this article, we introduce the extended R package NIRApost , which supplements NIRA with these three recommended complementary procedures. We provide a comprehensive R tutorial demonstrating the implementation of both NIRA and these validation steps. Researchers applying NIRA are advised to conduct moderation-effect testing as a prerequisite, followed by permutation tests and stability analyses to ensure robust and interpretable findings. Upon completing this tutorial, readers are capable of properly applying NIRA and its validation procedures in their own data analyses.