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  "Title": "Pervasiveness Functions for Correlational Data",
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  "Authors@R": "person(\"Denis\", \"Lajoie\", , \"denis.lajoie@umoncton.ca\", role = c(\"aut\", \"cre\"))",
  "Description": "Analysis of pervasiveness of effects in correlational\ndata. The Observed Proportion (or Percentage) of Concordant\nPairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric\ninstead of the traditional -1 to 1 metric to facilitate\ninterpretation. As its name implies, it represents the\nproportion of concordant pairs in a sample (with an adjustment\nfor ties). Pairs are concordant when a participant who has a\nlarger value on a variable than another participant also has a\nlarger value on a second variable. The OPCP is therefore an\neasily interpretable indicator of monotonicity. The pervasive\nfunctions are essentially wrappers for the 'arules' package by\nHahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and\nserve to count individuals who actually display the pattern(s)\nsuggested by a regression. For more details, see the paper\n\"Considering approaches to pervasiveness in the context of\npersonality psychology\" now accepted at the journal Personality\nScience.",
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  "Maintainer": "Denis Lajoie <denis.lajoie@umoncton.ca>",
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      "title": "Calculate Observed Proportion of Concordant Pairs (OPCP)",
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