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 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.
|Author||Andreas M. Brandmaier [aut, cre], John J. Prindle [aut]|
|Maintainer||Andreas M. Brandmaier <[email protected]>|
|Package repository||View on GitHub|
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