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  "Type": "Package",
  "Title": "Scalable Causal Discovery and Model Selection on Mixed Datasets\nwith 'rCausalMGM'",
  "Version": "1.0.1",
  "Date": "2026-03-13",
  "Author": "Tyler C Lovelace [aut], Max Dudek [aut], Jack Fiore [aut],\nPanayiotis V Benos [aut, cre]",
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  "Maintainer": "Panayiotis V Benos <pbenos@ufl.edu>",
  "Description": "Scalable methods for learning causal graphical models from\nmixed data, including continuous, discrete, and censored\nvariables. The package implements CausalMGM, which combines a\nconvex, score-based approach for learning an initial moralized\ngraph with a producer-consumer scheme that enables efficient\nparallel conditional independence testing in constraint-based\ncausal discovery algorithms. The implementation supports\nhigh-dimensional datasets and provides individual access to\ncore components of the workflow, including MGM and the\nPC-Stable and FCI-Stable causal discovery algorithms. To\nsupport practical applications, the package includes multiple\nmodel selection strategies, including information criteria\nbased on likelihood and model complexity, cross-validation for\nout-of-sample likelihood estimation, and stability-based\napproaches that assess graph robustness across subsamples.",
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    "pcStable",
    "pcStars",
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    "plot.graphCV",
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    "plot.graphSTARS",
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    "print.graph",
    "print.graphCV",
    "print.graphPath",
    "print.graphSTARS",
    "print.graphSTEPS",
    "print.knowledge",
    "printGraph",
    "prMetrics",
    "prMetricsAdjacency",
    "prMetricsCausal",
    "prMetricsOrientation",
    "saveGraph",
    "SHD",
    "simRandomDAG",
    "skeleton",
    "steps"
  ],
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    {
      "page": "adjMat2Graph",
      "title": "Convert an adjacency matrix into a graph",
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      ]
    },
    {
      "page": "allMetrics",
      "title": "Combined graph recovery metrics",
      "topics": [
        "allMetrics"
      ]
    },
    {
      "page": "bootstrap",
      "title": "Runs bootstrapping for a causal graph on the dataset.",
      "topics": [
        "bootstrap"
      ]
    },
    {
      "page": "boss",
      "title": "Runs the BOSS causal discovery algorithm on the dataset",
      "topics": [
        "boss"
      ]
    },
    {
      "page": "coxmgm",
      "title": "Calculate the CoxMGM graph on a dataset.",
      "topics": [
        "coxmgm"
      ]
    },
    {
      "page": "coxmgmCV",
      "title": "Implements k-fold cross-validation for CoxMGM",
      "topics": [
        "coxmgmCV"
      ]
    },
    {
      "page": "coxmgmPath",
      "title": "Estimates a solution path for CoxMGM",
      "topics": [
        "coxmgmPath"
      ]
    },
    {
      "page": "cpdag",
      "title": "Calculate the CPDAG for a given DAG",
      "topics": [
        "cpdag"
      ]
    },
    {
      "page": "createKnowledge",
      "title": "A function to create a prior knowledge object for use with causal discovery algorithms",
      "topics": [
        "createKnowledge"
      ]
    },
    {
      "page": "fciCV",
      "title": "Implements k-fold cross-validation for FCI-Stable",
      "topics": [
        "fciCV"
      ]
    },
    {
      "page": "fciStable",
      "title": "Runs the causal discovery algorithm FCI-Stable on a dataset.",
      "topics": [
        "fciStable"
      ]
    },
    {
      "page": "fciStars",
      "title": "Implements StARS for FCI-Stable",
      "topics": [
        "fciStars"
      ]
    },
    {
      "page": "graphTable",
      "title": "A function to generate a data.frame for objects from graph class. It incorporates adjacency and orientation frequency if estimates of edge stability are available.",
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      ]
    },
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      "title": "Runs the GRaSP causal discovery algorithm on the dataset",
      "topics": [
        "grasp"
      ]
    },
    {
      "page": "growShrinkMB",
      "title": "Implements Grow-Shrink algorithm for Markov blanket identification",
      "topics": [
        "growShrinkMB"
      ]
    },
    {
      "page": "loadGraph",
      "title": "Load a graph from a \".txt\" file",
      "topics": [
        "loadGraph"
      ]
    },
    {
      "page": "mgm",
      "title": "Calculate the Mixed Graphical Model (MGM) graph on a dataset.",
      "topics": [
        "mgm"
      ]
    },
    {
      "page": "mgmCV",
      "title": "Implements k-fold cross-validation for MGM",
      "topics": [
        "mgmCV"
      ]
    },
    {
      "page": "mgmfciCV",
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      "topics": [
        "mgmfciCV"
      ]
    },
    {
      "page": "mgmPath",
      "title": "Estimates a solution path for MGM",
      "topics": [
        "mgmPath"
      ]
    },
    {
      "page": "mgmpcCV",
      "title": "Implements k-fold cross-validation for MGM-PC-Stable",
      "topics": [
        "mgmpcCV"
      ]
    },
    {
      "page": "moral",
      "title": "Calculate the moral graph for a given DAG",
      "topics": [
        "moral"
      ]
    },
    {
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      "title": "Calculate the PAG for a given DAG and set of latent variables",
      "topics": [
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      ]
    },
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      "title": "Implements k-fold cross-validation for PC-Stable",
      "topics": [
        "pcCV"
      ]
    },
    {
      "page": "pcStable",
      "title": "Runs the causal discovery algorithm PC-Stable on a dataset.",
      "topics": [
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      ]
    },
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      "title": "Implements StARS for PC-Stable",
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      "title": "A plot override function for the graph class",
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      "title": "A plot override function for the graphCV class",
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    },
    {
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      "title": "A plot override function for the graphPath class",
      "topics": [
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    },
    {
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      "title": "A plot override function for the graphSTARS class",
      "topics": [
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    },
    {
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      "title": "A plot override function for the graphSTEPS class",
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      "title": "A print override function for the graph class",
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      "title": "A print override function for the graphCV class",
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