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 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.

Version: 0.9.15
Depends: R (≥ 2.10), OpenMx (≥ 2.6.9)
Imports: bitops, sets, digest, rpart, rpart.plot (≥ 3.0.6), parallel, plotrix, cluster, stringr, lavaan, expm, ggplot2, viridis, tidyr, methods, strucchange, sandwich, zoo, crayon, clisymbols, testthat
Suggests: knitr, rmarkdown, MASS, psychTools
Published: 2021-04-28
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: README 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_0.9.15.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: semtree_0.9.15.tgz, r-oldrel: semtree_0.9.15.tgz
Old sources: semtree archive


Please use the canonical form to link to this page.