Recursive Partitioning for Structural Equation Models
Quantify bio diversity of a SEM Forest
Return the parameter estimates of a given leaf of a SEM tree
Wrapper function for computing the maxLR corrected p value from strucc...
Diversity Matrix
Average Deviance of a Dataset given a Forest
Compute the Negative Two-Loglikelihood of some data given a model (eit...
Evaluate Tree -2LL
Find Other Node Split Values
Fit multigroup model for evaluating a candidate split
Get the depth (or, height) a tree.
Determine Height of a Tree
Get a list of all leafs in a tree
Get Node By Id
Tree Size
Return list with parameter differences of a forest
Return table with parameter differences of a tree
Returns all leafs of a tree
Test whether a semtree object is a leaf.
Distances
Merge two SEM forests
Returns all estimates of a tree
Find outliers based on case proximity
SEMtrees Parameter Estimates Table
Compute partial dependence
Create dataset to compute partial dependence
Compute partial dependence for latent growth models
Plot parameter differences
Plot parameter differences
Plot tree structure
Predict method for semtree and semforest
Compute proximity matrix
Prune a SEM Tree or SEM Forest
SEMtrees Parameter Estimates Standard Error Table
SEM Forest Control Object
Create a SEM Forest
SEM Tree Package
SEM Tree Constraints Object
SEM Tree Control Object
SEM Tree: Recursive Partitioning for Structural Equation Models
Retain only basic tree structure
Creates subsets of trees from forests
SEMtree Partitioning Tool
Tabular Representation of a SEM Tree
SEM Forest Variable Importance
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>.