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>).
|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|
|Author:||Andreas M. Brandmaier [aut, cre], John J. Prindle [aut], Manuel Arnold [aut]|
|Maintainer:||Andreas M. Brandmaier <andy at brandmaier.de>|
|CRAN checks:||semtree results|
Constraints in semtree
Getting Started with the semtree package
Focus parameters in SEM forests
|Windows binaries:||r-devel: semtree_0.9.17.zip, r-release: semtree_0.9.17.zip, r-oldrel: semtree_0.9.17.zip|
|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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