{
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  "Title": "Causal Modeling with Coincidence Analysis",
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  "Date": "2025-06-02",
  "Authors@R": "c(person(\"Mathias\", \"Ambuehl\", role = c(\"aut\", \"cre\", \"cph\"), \nemail = \"mathias.ambuehl@consultag.ch\"),\nperson(\"Michael\", \"Baumgartner\", role = c(\"aut\", \"cph\"),\nemail = \"michael.baumgartner@uib.no\"),\nperson(\"Ruedi\", \"Epple\", role = \"ctb\"),\nperson(\"Veli-Pekka\", \"Parkkinen\", role = \"ctb\"),\nperson(\"Alrik\", \"Thiem\", role = \"ctb\")\n)",
  "Description": "Provides comprehensive functionalities for causal modeling\nwith Coincidence Analysis (CNA), which is a configurational\ncomparative method of causal data analysis that was first\nintroduced in Baumgartner (2009)\n<doi:10.1177/0049124109339369>, and generalized in Baumgartner\n& Ambuehl (2020) <doi:10.1017/psrm.2018.45>. CNA is designed to\nrecover INUS-causation from data, which is particularly\nrelevant for analyzing processes featuring conjunctural\ncausation (component causation) and equifinality (alternative\ncausation). CNA is currently the only method for INUS-discovery\nthat allows for multiple effects (outcomes/endogenous factors),\nmeaning it can analyze common-cause and causal chain\nstructures. Moreover, as of version 4.0, it is the only method\nof its kind that provides measures for model evaluation and\nselection that are custom-made for the problem of\nINUS-discovery.",
  "License": "GPL (>= 2)",
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  "Maintainer": "Mathias Ambuehl <mathias.ambuehl@consultag.ch>",
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    "redundant",
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    "rhs",
    "rreduce",
    "selectCases",
    "selectCases1",
    "showConCovMeasures",
    "showDetailMeasures",
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    "stdCond"
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    {
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      "title": "cna: A Package for Causal Modeling with Coincidence Analysis",
      "topics": [
        "cna-package"
      ]
    },
    {
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      "title": "Perform Coincidence Analysis",
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    {
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        "msc"
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        "coherence.default"
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    },
    {
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      "title": "Evaluate msc, asf, and csf on the level of cases/configurations in the data",
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        "condList",
        "condList.character",
        "condList.condTbl",
        "print.cond",
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    {
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      "title": "Methods for class \"condList\"",
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        "condList-methods",
        "group.by.outcome",
        "summary.condList"
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      "topics": [
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        "as.data.frame.condTbl",
        "condTbl",
        "print.condTbl"
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    },
    {
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      "title": "Assemble cases with identical configurations into a configuration table",
      "topics": [
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        "print.configTable"
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      "title": "Transform a configuration table into a data frame",
      "topics": [
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        "ct2df"
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    },
    {
      "page": "cyclic",
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      "topics": [
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    {
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      "topics": [
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      "topics": [
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    {
      "page": "detailMeasures",
      "title": "Calculate summary measures for msc, asf, and csf",
      "topics": [
        "detailMeasures"
      ]
    },
    {
      "page": "fs2cs",
      "title": "Convert fs data to cs data",
      "topics": [
        "fs2cs"
      ]
    },
    {
      "page": "full.ct",
      "title": "Generate the logically possible value configurations of a given set of factors",
      "topics": [
        "allCombs",
        "full.ct",
        "full.ct.condTbl",
        "full.ct.configTable",
        "full.ct.cti",
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