Package: semtree 0.9.20
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>.
Authors:
semtree_0.9.20.tar.gz
semtree_0.9.20.zip(r-4.5)semtree_0.9.20.zip(r-4.4)semtree_0.9.20.zip(r-4.3)
semtree_0.9.20.tgz(r-4.4-any)semtree_0.9.20.tgz(r-4.3-any)
semtree_0.9.20.tar.gz(r-4.5-noble)semtree_0.9.20.tar.gz(r-4.4-noble)
semtree_0.9.20.tgz(r-4.4-emscripten)semtree_0.9.20.tgz(r-4.3-emscripten)
semtree.pdf |semtree.html✨
semtree/json (API)
NEWS
# Install 'semtree' in R: |
install.packages('semtree', repos = c('https://lifebrain.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/brandmaier/semtree/issues
- lgcm - Simulated Linear Latent Growth Curve Data
bigdatadecision-treeforestmultivariaterandomforestrecursive-partitioningsemstatistical-modelingstructural-equation-modelingstructural-equation-models
Last updated 3 days agofrom:985740e217. Checks:OK: 1 WARNING: 6. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 05 2024 |
R-4.5-win | WARNING | Sep 05 2024 |
R-4.5-linux | WARNING | Sep 05 2024 |
R-4.4-win | WARNING | Sep 05 2024 |
R-4.4-mac | WARNING | Sep 05 2024 |
R-4.3-win | WARNING | Sep 05 2024 |
R-4.3-mac | WARNING | Sep 05 2024 |
Exports:biodiversityborutadiversityMatrixevaluateTreefitSubmodelsgetDepthgetLeafsgetNodeByIdgetParDiffForestgetParDiffTreegetTerminalNodeshellingerisLeafklmodelEstimatesoutliersparameterspartialDependencepartialDependence_datapartialDependence_growthplotParDiffForestplotParDiffTreeplotTreeStructureproximityprunesesemforestsemforest_controlsemforest_score_controlsemforest.controlsemtreesemtree_controlsemtree.constraintssemtree.controlstripsubforestsubtreetoTablevarimpvarimpConvergencePlot
Dependencies:BHcliclisymbolsclustercodetoolscolorspacecpp11crayondata.tabledigestdplyrexpmfansifarverfuturefuture.applygenericsggplot2globalsgluegridBasegtableisobandlabelinglatticelavaanlifecyclelistenvmagrittrMASSMatrixmgcvmnormtmunsellmvtnormnlmenumDerivOpenMxparallellypbivnormpillarpkgconfigpurrrquadprogR6RColorBrewerRcppRcppEigenRcppParallelrlangrpartrpart.plotrpfsandwichscalesStanHeadersstringistringrstrucchangetibbletidyrtidyselectutf8vctrsviridisLitewithrzoo
Constraints in semtree
Rendered fromconstraints.Rmd
usingknitr::rmarkdown
on Sep 05 2024.Last update: 2023-11-24
Started: 2019-09-17
Focus parameters in SEM forests
Rendered fromsemforest-focus.Rmd
usingknitr::rmarkdown
on Sep 05 2024.Last update: 2023-11-24
Started: 2020-04-23
Getting Started with the semtree package
Rendered fromgetting-started.Rmd
usingknitr::rmarkdown
on Sep 05 2024.Last update: 2023-11-23
Started: 2019-09-12
SEM Trees with score-based tests
Rendered fromscore-based-tests.Rmd
usingknitr::rmarkdown
on Sep 05 2024.Last update: 2023-11-24
Started: 2020-04-17
SEM Forests
Rendered fromforests.Rmd
usingknitr::rmarkdown
on Sep 05 2024.Last update: 2024-03-26
Started: 2020-11-05