SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013; <doi:10.1037/a0030001>) and Arnold, Voelkle, & Brandmaier (2020; <doi:10.3389/fpsyg.2020.564403>).
|Author||Andreas M. Brandmaier [aut, cre], John J. Prindle [aut], Manuel Arnold [aut]|
|Maintainer||Andreas M. Brandmaier <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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