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>.
Version: 0.9.20 Depends: R (≥ 2.10), OpenMx (≥ 2.6.9) Imports: rpart, rpart.plot (≥ 3.0.6), lavaan, cluster, ggplot2, tidyr, methods, strucchange, sandwich, zoo, crayon, clisymbols, future.apply, data.table, expm, gridBase Suggests: knitr, rmarkdown, viridis, MASS, psychTools, testthat, future, ctsemOMX Published: 2024-04-08 DOI: 10.32614/CRAN.package.semtree Author: Andreas M. Brandmaier [aut, cre], John J. Prindle [aut], Manuel Arnold [aut], Caspar J. Van Lissa [aut] Maintainer: Andreas M. Brandmaier <andy at brandmaier.de> BugReports: https://github.com/brandmaier/semtree/issues License: GPL-3 URL: https://github.com/brandmaier/semtree NeedsCompilation: no Language: en-US Materials: NEWS In views: MachineLearning, MixedModels, Psychometrics CRAN checks: semtree results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=semtree to link to this page.
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