Unpacking Heterogeneity in a Multivariate and Longitudinal Framework: Structural Equation Models with Boruta Feature Selection

With increasing access to large datasets, implementing complex multivariate models has become feasible. Yet, identifying important moderators amidst influential variables remains a challenge. Addressing this, we introduce SEM-Boruta, a novel adaptation of the Boruta algorithm, and demonstrate its robustness through application on simulated datasets in a multivariate and longitudinal framework.