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  "Package": "bullseye",
  "Title": "Visualising Multiple Pairwise Variable Correlations and Other\nScores",
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  "Authors@R": "c(person(given = \"Amit\", family = \"Chinwan\", email = \"amit.chinwan.2019@mumail.ie\", role = c(\"aut\")),\nperson(given = \"Catherine\", family = \"Hurley\", email = \"catherine.hurley@mu.ie\", role = c(\"aut\", \"cre\")))",
  "Description": "We provide a tidy data structure and visualisations for\nmultiple or grouped variable correlations, general association\nmeasures scagnostics and other pairwise scores suitable for\nnumerical, ordinal and nominal variables. Supported measures\ninclude distance correlation, maximal information, ace\ncorrelation, Kendall's tau, and polychoric correlation.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "https://cbhurley.github.io/bullseye/,\nhttps://github.com/cbhurley/bullseye",
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  "Repository": "https://cbhurley.r-universe.dev",
  "Date/Publication": "2026-03-04 10:32:00 UTC",
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  "Maintainer": "Catherine Hurley <catherine.hurley@mu.ie>",
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    "add_nobs_to_pairwise",
    "as.pairwise",
    "pair_ace",
    "pair_cancor",
    "pair_chi",
    "pair_control",
    "pair_cor",
    "pair_dcor",
    "pair_gkGamma",
    "pair_gkTau",
    "pair_kendall",
    "pair_methods",
    "pair_mine",
    "pair_nmi",
    "pair_polychor",
    "pair_polyserial",
    "pair_scagnostics",
    "pair_spearman",
    "pair_tauA",
    "pair_tauB",
    "pair_tauC",
    "pair_tauW",
    "pair_uncertainty",
    "pairwise",
    "pairwise_by",
    "pairwise_multi",
    "pairwise_scores",
    "plot_pairwise",
    "plot_pairwise_linear"
  ],
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      "page": "ace_cor",
      "title": "Calculates ace based transformations and correlation, handling missing values and factors.",
      "topics": [
        "ace_cor"
      ]
    },
    {
      "page": "add_nobs_to_pairwise",
      "title": "Adds number of observations column to pairwise tibble",
      "topics": [
        "add_nobs_to_pairwise"
      ]
    },
    {
      "page": "as.matrix.pairwise",
      "title": "Converts a pairwise to a symmetric matrix. Uses the first entry for each (x,y) pair.",
      "topics": [
        "as.matrix.pairwise"
      ]
    },
    {
      "page": "pair_ace",
      "title": "Alternating conditional expectations correlation",
      "topics": [
        "pair_ace"
      ]
    },
    {
      "page": "pair_cancor",
      "title": "Canonical correlation",
      "topics": [
        "pair_cancor"
      ]
    },
    {
      "page": "pair_chi",
      "title": "Pearson's Contingency Coefficient for association between factors.",
      "topics": [
        "pair_chi"
      ]
    },
    {
      "page": "pair_control",
      "title": "Default scores calculated by 'pairwise_scores'",
      "topics": [
        "pair_control"
      ]
    },
    {
      "page": "pair_cor",
      "title": "Pearson, Spearman or Kendall correlation",
      "topics": [
        "pair_cor"
      ]
    },
    {
      "page": "pair_dcor",
      "title": "Distance correlation",
      "topics": [
        "pair_dcor"
      ]
    },
    {
      "page": "pair_gkGamma",
      "title": "Goodman Kruskal's Gamma for association between ordinal factors.",
      "topics": [
        "pair_gkGamma"
      ]
    },
    {
      "page": "pair_gkTau",
      "title": "Goodman Kruskal's Tau for association between ordinal factors.",
      "topics": [
        "pair_gkTau"
      ]
    },
    {
      "page": "pair_kendall",
      "title": "Kendall's correlation",
      "topics": [
        "pair_kendall"
      ]
    },
    {
      "page": "pair_methods",
      "title": "Pairwise score functions available in the package",
      "topics": [
        "pair_methods"
      ]
    },
    {
      "page": "pair_mine",
      "title": "MINE family values",
      "topics": [
        "pair_mine"
      ]
    },
    {
      "page": "pair_nmi",
      "title": "Normalized mutual information",
      "topics": [
        "pair_nmi"
      ]
    },
    {
      "page": "pair_polychor",
      "title": "Polychoric correlation",
      "topics": [
        "pair_polychor"
      ]
    },
    {
      "page": "pair_polyserial",
      "title": "Polyserial correlation",
      "topics": [
        "pair_polyserial"
      ]
    },
    {
      "page": "pair_scagnostics",
      "title": "Graph-theoretic scagnostics values",
      "topics": [
        "pair_scagnostics"
      ]
    },
    {
      "page": "pair_spearman",
      "title": "Spearman correlation",
      "topics": [
        "pair_spearman"
      ]
    },
    {
      "page": "pair_tauA",
      "title": "Kendall's tau A for association between ordinal factors.",
      "topics": [
        "pair_tauA"
      ]
    },
    {
      "page": "pair_tauB",
      "title": "Kendall's tau B for association between ordinal factors.",
      "topics": [
        "pair_tauB"
      ]
    },
    {
      "page": "pair_tauC",
      "title": "Stuarts's tau C for association between ordinal factors.",
      "topics": [
        "pair_tauC"
      ]
    },
    {
      "page": "pair_tauW",
      "title": "Kendall's W for association between ordinal factors.",
      "topics": [
        "pair_tauW"
      ]
    },
    {
      "page": "pair_uncertainty",
      "title": "Uncertainty coefficient for association between factors.",
      "topics": [
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      ]
    },
    {
      "page": "pairwise",
      "title": "A generic function to create a data structure for summarising variable pairs in a dataset",
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        "as.pairwise",
        "pairwise",
        "pairwise.data.frame",
        "pairwise.easycorrelation",
        "pairwise.matrix"
      ]
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      ]
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      ]
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      ]
    },
    {
      "page": "plot.pairwise",
      "title": "Plot method for class 'pairwise'.",
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      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cbhurley/actions/runs/26809867247"
    }
  ]
}