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  "Title": "Graphical Multiple Comparison Procedures",
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  "Authors@R": "c(\nperson(\"Dong\", \"Xi\", , \"dong.xi1@gilead.com\", role = c(\"aut\", \"cre\")),\nperson(\"Ethan\", \"Brockmann\", , \"ethan.brockmann@atorusresearch.com\", role = \"aut\"),\nperson(\"Gilead Sciences, Inc.\", role = c(\"cph\", \"fnd\"))\n)",
  "Description": "Multiple comparison procedures (MCPs) control the\nfamilywise error rate in clinical trials. Graphical MCPs\ninclude many commonly used procedures as special cases; see\nBretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016)\n<doi:10.1002/sim.6985>, and Xi et al. (2017)\n<doi:10.1002/bimj.201600233>. This package is a low-dependency\nimplementation of graphical MCPs which allow mixed types of\ntests. It also includes power simulations and visualization of\ngraphical MCPs.",
  "License": "Apache License (>= 2)",
  "URL": "https://github.com/openpharma/graphicalMCP",
  "BugReports": "https://github.com/openpharma/graphicalMCP/issues",
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  "Date/Publication": "2026-03-21 03:43:57 UTC",
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    "hommel",
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    "random_graph",
    "sidak",
    "simple_successive_1",
    "simple_successive_2",
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    "two_doses_two_primary_two_secondary"
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      "title": "Calculate adjusted p-values",
      "topics": [
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        "adjust_p_hochberg",
        "adjust_p_parametric",
        "adjust_p_simes"
      ]
    },
    {
      "page": "adjust_weights",
      "title": "Calculate adjusted hypothesis weights",
      "topics": [
        "adjust_weights_hochberg",
        "adjust_weights_parametric",
        "adjust_weights_simes"
      ]
    },
    {
      "page": "as_graph",
      "title": "Convert between graphicalMCP, gMCP, and igraph graph classes",
      "topics": [
        "as_graphMCP",
        "as_graphMCP.initial_graph",
        "as_igraph",
        "as_igraph.initial_graph",
        "as_initial_graph",
        "as_initial_graph.graphMCP",
        "as_initial_graph.igraph"
      ]
    },
    {
      "page": "example_graphs",
      "title": "Example graphs of commonly used multiple comparison procedures",
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        "bonferroni_holm",
        "bonferroni_holm_weighted",
        "bonferroni_weighted",
        "dunnett_closure_weighted",
        "dunnett_single_step",
        "dunnett_single_step_weighted",
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        "random_graph",
        "sidak",
        "simple_successive_1",
        "simple_successive_2",
        "three_doses_two_primary_two_secondary",
        "two_doses_two_primary_two_secondary"
      ]
    },
    {
      "page": "graph_calculate_power",
      "title": "Calculate power values for a graphical multiple comparison procedure",
      "topics": [
        "graph_calculate_power"
      ]
    },
    {
      "page": "graph_create",
      "title": "Create the initial graph for a multiple comparison procedure",
      "topics": [
        "graph_create"
      ]
    },
    {
      "page": "graph_generate_weights",
      "title": "Generate the weighting strategy based on a graphical multiple comparison procedure",
      "topics": [
        "graph_generate_weights"
      ]
    },
    {
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      "title": "Find alternate rejection orderings (sequences) for shortcut tests",
      "topics": [
        "graph_rejection_orderings"
      ]
    },
    {
      "page": "graph_test_closure",
      "title": "Perform closed graphical multiple comparison procedures",
      "topics": [
        "graph_test_closure"
      ]
    },
    {
      "page": "graph_test_shortcut",
      "title": "Perform shortcut (sequentially rejective) graphical multiple comparison procedures",
      "topics": [
        "graph_test_shortcut"
      ]
    },
    {
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      "title": "Obtain an updated graph by updating an initial graphical after deleting hypotheses",
      "topics": [
        "graph_update"
      ]
    },
    {
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      "title": "S3 plot method for class 'initial_graph'",
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        "plot.initial_graph"
      ]
    },
    {
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      "topics": [
        "plot.updated_graph"
      ]
    },
    {
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      "topics": [
        "print.graph_report"
      ]
    },
    {
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      "title": "S3 print method for the class 'initial_graph'",
      "topics": [
        "print.initial_graph"
      ]
    },
    {
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      "title": "S3 print method for the class 'power_report'",
      "topics": [
        "print.power_report"
      ]
    },
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        "Fallback procedure",
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        "Simes-based procedures",
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      "fileid": "b15c94fdfacbb87f8f1d4698fd0515c10f14f9a95fa889a30827f35c0b6fe2d3",
      "status": "success",
      "buildurl": "https://github.com/r-universe/openpharma/actions/runs/26144776322"
    }
  ]
}