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  "Title": "Recursive Partitioning for Structural Equation Models",
  "Authors@R": "c(person(\"Andreas M. Brandmaier\", email=\"andy@brandmaier.de\", role=c(\"aut\",\"cre\")), person(\"John J. Prindle\", email=\"jprindle@usc.edu\",role=c(\"aut\")),person(\"Manuel Arnold\", email=\"arnoldmz@hu-berlin.de\",role=c(\"aut\")),person(\"Caspar J. Van Lissa\", email=\"C.J.vanLissa@uu.nl\",role=c(\"aut\")), person(\"Moritz John\",role=\"ctb\"))",
  "Author": "Andreas M. Brandmaier [aut, cre], John J. Prindle [aut], Manuel\nArnold [aut], Caspar J. Van Lissa [aut], Moritz John [ctb]",
  "Maintainer": "Andreas M. Brandmaier <andy@brandmaier.de>",
  "Description": "SEM Trees and SEM Forests -- an extension of model-based\ndecision trees and forests to Structural Equation Models (SEM).\nSEM trees hierarchically split empirical data into homogeneous\ngroups each sharing similar data patterns with respect to a SEM\nby recursively selecting optimal predictors of these\ndifferences. SEM forests are an extension of SEM trees. They\nare ensembles of SEM trees each built on a random sample of the\noriginal data. By aggregating over a forest, we obtain measures\nof variable importance that are more robust than measures from\nsingle trees. A description of the method was published by\nBrandmaier, von Oertzen, McArdle, & Lindenberger (2013)\n<doi:10.1037/a0030001> and Arnold, Voelkle, & Brandmaier (2020)\n<doi:10.3389/fpsyg.2020.564403>.",
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    "diversityMatrix",
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    "getDepth",
    "getLeafs",
    "getNodeById",
    "getParDiffForest",
    "getParDiffTree",
    "getTerminalNodes",
    "hellinger",
    "isLeaf",
    "kl",
    "klsym",
    "modelEstimates",
    "outliers",
    "parameters",
    "partialDependence",
    "partialDependence_data",
    "partialDependence_growth",
    "plotParDiffForest",
    "plotParDiffTree",
    "plotTreeStructure",
    "proximity",
    "prune",
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    "semforest",
    "semforest_control",
    "semforest_score_control",
    "semforest.control",
    "semtree",
    "semtree_control",
    "semtree.constraints",
    "semtree.control",
    "strip",
    "subforest",
    "subtree",
    "thinOut",
    "toTable",
    "varimp",
    "varimpConvergencePlot"
  ],
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      "title": "Simulated Linear Latent Growth Curve Data",
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      "file": "lgcm.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "o1",
        "o2",
        "o3",
        "o4",
        "o5",
        "agegroup",
        "training",
        "noise"
      ],
      "rows": 400,
      "table": true,
      "tojson": true
    }
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      "page": "semtree-package",
      "title": "SEM Tree Package",
      "topics": [
        "semtree-package",
        ".SCALE_METRIC"
      ]
    },
    {
      "page": "aggregateVarimp",
      "title": "Aggregate Variable Importance Estimates",
      "topics": [
        "aggregateVarimp"
      ]
    },
    {
      "page": "biodiversity",
      "title": "Quantify bio diversity of a SEM Forest",
      "topics": [
        "biodiversity"
      ]
    },
    {
      "page": "boruta",
      "title": "Run the Boruta algorithm on a sem tree",
      "topics": [
        "boruta",
        "plot.boruta",
        "print.boruta"
      ]
    },
    {
      "page": "coef.semtree",
      "title": "Return the parameter estimates of a given leaf of a SEM tree",
      "topics": [
        "coef.semtree"
      ]
    },
    {
      "page": "computePval_maxLR",
      "title": "Wrapper function for computing the maxLR corrected p value from strucchange",
      "topics": [
        "computePval_maxLR"
      ]
    },
    {
      "page": "countPredictors",
      "title": "Count predictors",
      "topics": [
        "countPredictors",
        "countPredictors.semforest",
        "countPredictors.semtree"
      ]
    },
    {
      "page": "diversityMatrix",
      "title": "Diversity Matrix",
      "topics": [
        "diversityMatrix"
      ]
    },
    {
      "page": "evaluate",
      "title": "Average Deviance of a Dataset given a Forest",
      "topics": [
        "evaluate",
        "evaluate.semforest"
      ]
    },
    {
      "page": "evaluateDataLikelihood",
      "title": "Compute the Negative Two-Loglikelihood of some data given a model (either OpenMx or lavaan)",
      "topics": [
        "evaluateDataLikelihood"
      ]
    },
    {
      "page": "evaluateTree",
      "title": "Evaluate Tree -2LL",
      "topics": [
        "evaluateTree"
      ]
    },
    {
      "page": "findOtherSplits",
      "title": "Find Other Node Split Values",
      "topics": [
        "findOtherSplits"
      ]
    },
    {
      "page": "fitSubmodels",
      "title": "Fit multigroup model for evaluating a candidate split. This function has the fundamental logic for fitting two-group models in different variants",
      "topics": [
        "fitSubmodels"
      ]
    },
    {
      "page": "getDepth",
      "title": "Get the depth (or, height) a tree.",
      "topics": [
        "getDepth"
      ]
    },
    {
      "page": "getHeight",
      "title": "Determine Height of a Tree",
      "topics": [
        "getHeight"
      ]
    },
    {
      "page": "getLeafs",
      "title": "Get a list of all leafs in a tree",
      "topics": [
        "getLeafs"
      ]
    },
    {
      "page": "getNodeById",
      "title": "Get Node By Id",
      "topics": [
        "getNodeById"
      ]
    },
    {
      "page": "getNumNodes",
      "title": "Tree Size",
      "topics": [
        "getNumNodes"
      ]
    },
    {
      "page": "getParDiffForest",
      "title": "Return list with parameter differences of a forest",
      "topics": [
        "getParDiffForest"
      ]
    },
    {
      "page": "getParDiffTree",
      "title": "Return table with parameter differences of a tree",
      "topics": [
        "getParDiffTree"
      ]
    },
    {
      "page": "getTerminalNodes",
      "title": "Returns all leafs of a tree",
      "topics": [
        "getTerminalNodes"
      ]
    },
    {
      "page": "hellinger",
      "title": "Hellinger distance of two multivariate normal distributions",
      "topics": [
        "hellinger"
      ]
    },
    {
      "page": "isLeaf",
      "title": "Test whether a semtree object is a leaf.",
      "topics": [
        "isLeaf"
      ]
    },
    {
      "page": "kl",
      "title": "Kullback-Leiber Divergence",
      "topics": [
        "kl"
      ]
    },
    {
      "page": "klsym",
      "title": "Symmetric version of Kullback-Leiber Divergence",
      "topics": [
        "klsym"
      ]
    },
    {
      "page": "lgcm",
      "title": "Simulated Linear Latent Growth Curve Data",
      "topics": [
        "lgcm"
      ]
    },
    {
      "page": "merge.semforest",
      "title": "Merge two SEM forests",
      "topics": [
        "merge.semforest"
      ]
    },
    {
      "page": "modelEstimates",
      "title": "Returns all estimates of a tree",
      "topics": [
        "modelEstimates"
      ]
    },
    {
      "page": "outliers",
      "title": "Find outliers based on case proximity",
      "topics": [
        "outliers"
      ]
    },
    {
      "page": "parameters",
      "title": "SEMtrees Parameter Estimates Table",
      "topics": [
        "parameters"
      ]
    },
    {
      "page": "partialDependence",
      "title": "Compute partial dependence",
      "topics": [
        "partialDependence"
      ]
    },
    {
      "page": "partialDependence_data",
      "title": "Create dataset to compute partial dependence",
      "topics": [
        "partialDependence_data"
      ]
    },
    {
      "page": "partialDependence_growth",
      "title": "Compute partial dependence for latent growth models",
      "topics": [
        "partialDependence_growth"
      ]
    },
    {
      "page": "plot.semtree",
      "title": "Plot a SEM Tree",
      "topics": [
        "plot.semtree"
      ]
    },
    {
      "page": "plotParDiffForest",
      "title": "Plot parameter differences",
      "topics": [
        "plotParDiffForest"
      ]
    },
    {
      "page": "plotParDiffTree",
      "title": "Plot parameter differences",
      "topics": [
        "plotParDiffTree"
      ]
    },
    {
      "page": "plotTreeStructure",
      "title": "Plot tree structure",
      "topics": [
        "plotTreeStructure"
      ]
    },
    {
      "page": "predict.semforest",
      "title": "Predict method for semtree and semforest",
      "topics": [
        "predict.semforest"
      ]
    },
    {
      "page": "proximity",
      "title": "Compute proximity matrix",
      "topics": [
        "proximity"
      ]
    },
    {
      "page": "prune",
      "title": "Prune a SEM Tree or SEM Forest",
      "topics": [
        "prune",
        "prune.semforest",
        "prune.semtree"
      ]
    },
    {
      "page": "se",
      "title": "SEMtrees Parameter Estimates Standard Error Table",
      "topics": [
        "se"
      ]
    },
    {
      "page": "semforest",
      "title": "Create a SEM Forest",
      "topics": [
        "plot.semforest",
        "print.semforest",
        "semforest"
      ]
    },
    {
      "page": "semforest.control",
      "title": "SEM Forest Control Object",
      "topics": [
        "print.semforest.control",
        "semforest.control",
        "semforest_control",
        "semforest_score_control"
      ]
    },
    {
      "page": "semtree",
      "title": "SEM Tree: Recursive Partitioning for Structural Equation Models",
      "topics": [
        "nodeFunSemtree",
        "print.semtree",
        "semtree",
        "summary.semtree",
        "toLatex.semtree"
      ]
    },
    {
      "page": "semtree_control",
      "title": "SEM Tree Control Object",
      "topics": [
        "print.semtree.control",
        "semtree.control",
        "semtree_control"
      ]
    },
    {
      "page": "semtree.constraints",
      "title": "SEM Tree Constraints Object",
      "topics": [
        "semtree.constraints"
      ]
    },
    {
      "page": "strip",
      "title": "Retain only basic tree structure",
      "topics": [
        "strip"
      ]
    },
    {
      "page": "subforest",
      "title": "Creates subsets of trees from forests",
      "topics": [
        "subforest"
      ]
    },
    {
      "page": "subtree",
      "title": "SEMtree Partitioning Tool",
      "topics": [
        "subtree"
      ]
    },
    {
      "page": "surrogate",
      "title": "use the current data 'mydata' and find a surrogate split",
      "topics": [
        "surrogate"
      ]
    },
    {
      "page": "thinOut",
      "title": "Thin Out a Forest",
      "topics": [
        "thinOut"
      ]
    },
    {
      "page": "toTable",
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