{
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  "Package": "ConNEcT",
  "Title": "Contingency Measure-Based Networks for Binary Time Series",
  "Version": "0.7.27",
  "Authors@R": "c(\nperson(\"Nadja\", \"Bodner\", email = \"nadja.bodner@kuleuven.be\",role = c(\"aut\", \"cre\")),\nperson(\"Eva\", \"Ceulemans\", email = \"eva.ceulemans@kuleuven.be\",role = c(\"ctb\",\"rev\")))",
  "Maintainer": "Nadja Bodner <nadja.bodner@kuleuven.be>",
  "Description": "The ConNEcT approach investigates the pairwise association\nstrength of binary time series by calculating contingency\nmeasures and depicts the results in a network. The package\nincludes features to explore and visualize the data. To\ncalculate the pairwise concurrent or temporal sequenced\nrelationship between the variables, the package provides seven\ncontingency measures (proportion of agreement, classical &\ncorrected Jaccard, Cohen's kappa, phi correlation coefficient,\nodds ratio, and log odds ratio), however, others can easily be\nimplemented. The package also includes non-parametric\nsignificance tests, that can be applied to test whether the\ncontingency value quantifying the relationship between the\nvariables is significantly higher than chance level. Most\nimportantly this test accounts for auto-dependence and relative\nfrequency.See Bodner et al.(2021) <doi:\n10.1111/bmsp.12222>.Finally, a network can be drawn. Variables\ndepicted the nodes of the network, with the node size adapted\nto the prevalence. The association strength between the\nvariables defines the undirected (concurrent) or directed\n(temporal sequenced) links between the nodes. The results of\nthe non-parametric significance test can be included by\ndepicting either all links or only the significant ones.\nTutorial see Bodner et al.(2021)\n<doi:10.3758/s13428-021-01760-w>.",
  "License": "GPL (>= 2)",
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    "conData",
    "conMx",
    "conNEcT",
    "conProf",
    "conTest",
    "funClassJacc",
    "funCorrJacc",
    "funKappa",
    "funLogOdds",
    "funOdds",
    "funPhiCC",
    "funPropAgree",
    "getProb",
    "lagthemats",
    "modelAD",
    "modelNO",
    "permutAD",
    "permutNO",
    "qgraph.conNEcT"
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      "name": "AttachmentData",
      "title": "Attachment-related mother-child interaction dataset",
      "object": "AttachmentData",
      "class": [
        "data.frame"
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        "Mneg",
        "MAlone",
        "Togth",
        "Cpos",
        "Cneg",
        "CAlone"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "FamilyData",
      "title": "Affective family interaction dataset",
      "object": "FamilyData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "moanger",
        "faanger",
        "adanger",
        "modysph",
        "fadysph",
        "addysph",
        "mohappy",
        "fahappy",
        "adhappy"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "SymptomData",
      "title": "Depression symptom dataset",
      "object": "SymptomData",
      "class": [
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        "array"
      ],
      "fields": [
        "Core",
        "Energy",
        "Eat",
        "Sleep",
        "Motor",
        "Guilt",
        "Cogn",
        "Death"
      ],
      "rows": 145,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "AttachmentData",
      "title": "Attachment-related mother-child interaction dataset",
      "topics": [
        "AttachmentData"
      ]
    },
    {
      "page": "barplot.conData",
      "title": "Depict the relative frequencies (and conditional probabilities) of a binary time series in a barplot",
      "topics": [
        "barplot.conData"
      ]
    },
    {
      "page": "conData",
      "title": "Explore and tidy raw data",
      "topics": [
        "conData"
      ]
    },
    {
      "page": "conMx",
      "title": "Calculate contingency measure values of a (lagged) time series matrix",
      "topics": [
        "conMx"
      ]
    },
    {
      "page": "conNEcT",
      "title": "Calculate the link strength between multiple behaviors and return them as a matrix (optionally discarting all non-significant links)",
      "topics": [
        "conNEcT"
      ]
    },
    {
      "page": "conProf",
      "title": "Compare different lags in a contingency profile",
      "topics": [
        "conProf"
      ]
    },
    {
      "page": "conTest",
      "title": "Test significance",
      "topics": [
        "conTest"
      ]
    },
    {
      "page": "FamilyData",
      "title": "Affective family interaction dataset",
      "topics": [
        "FamilyData"
      ]
    },
    {
      "page": "funClassJacc",
      "title": "Calculate the classic Jaccard index between two vectors",
      "topics": [
        "funClassJacc"
      ]
    },
    {
      "page": "funCorrJacc",
      "title": "Calculate the corrected Jaccard index between two vectors",
      "topics": [
        "funCorrJacc"
      ]
    },
    {
      "page": "funKappa",
      "title": "Calculate Cohen's kappa between two vectors",
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        "funKappa"
      ]
    },
    {
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      ]
    },
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      "title": "Calculate the odds ratio between two vectors",
      "topics": [
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      ]
    },
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      ]
    },
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      ]
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      "title": "Retrieve (conditional) probabilities from binary time series",
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      ]
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      "page": "hist.conTest",
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      ]
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      "topics": [
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