semtree: Recursive Partitioning for Structural Equation Models

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: 0.9.17
Depends: R (≥ 2.10), OpenMx (≥ 2.6.9)
Imports: bitops, sets, digest, rpart, rpart.plot (≥ 3.0.6), plotrix, cluster, stringr, lavaan, ggplot2, tidyr, methods, strucchange, sandwich, zoo, crayon, clisymbols, future.apply
Suggests: knitr, rmarkdown, viridis, MASS, psychTools, testthat
Published: 2021-07-30
Author: Andreas M. Brandmaier [aut, cre], John J. Prindle [aut], Manuel Arnold [aut]
Maintainer: Andreas M. Brandmaier <andy at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
In views: Psychometrics
CRAN checks: semtree results


Reference manual: semtree.pdf
Vignettes: Constraints in semtree
SEM Forests
Getting Started with the semtree package
Score-based Tests
Focus parameters in SEM forests


Package source: semtree_0.9.17.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): semtree_0.9.17.tgz, r-release (x86_64): semtree_0.9.17.tgz, r-oldrel: semtree_0.9.17.tgz
Old sources: semtree archive


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