{
  "_id": "6a1026e4acfb0bcc41c8fbda",
  "Package": "r4lineups",
  "Title": "Statistical Inference on Lineup Fairness",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Colin\", \"Tredoux\", email = \"colin.tredoux@uct.ac.za\", role = c(\"aut\", \"cre\")),\nperson(\"Tamsyn\", \"Naylor\", email = \"tmnaylor@outlook.com\", role = \"aut\"))",
  "Maintainer": "Colin Tredoux <colin.tredoux@uct.ac.za>",
  "Description": "Since the early 1970s eyewitness testimony researchers\nhave recognised the importance of estimating properties such as\nlineup bias (is the lineup biased against the suspect, leading\nto a rate of choosing higher than one would expect by chance?),\nand lineup size (how many reasonable choices are in fact\navailable to the witness? A lineup is supposed to consist of a\nsuspect and a number of additional members, or foils, whom a\npoor-quality witness might mistake for the perpetrator). Lineup\nmeasures are descriptive, in the first instance, but since the\nearliest articles in the literature researchers have recognised\nthe importance of reasoning inferentially about them. This\npackage contains functions to compute various properties of\nlaboratory or police lineups, and is intended for use by\nresearchers in forensic psychology and/or eyewitness testimony\nresearch. Among others, the r4lineups package includes\nfunctions for calculating lineup proportion, functional size,\nvarious estimates of effective size, diagnosticity ratio,\nhomogeneity of the diagnosticity ratio, ROC curves for\nconfidence x accuracy data and the degree of similarity of\nfaces in a lineup.",
  "License": "CC0",
  "Encoding": "UTF-8",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-13 08:54:59 UTC",
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  "Author": "Colin Tredoux [aut, cre], Tamsyn Naylor [aut]",
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  "Repository": "https://colintredoux.r-universe.dev",
  "Date/Publication": "2018-07-18 12:20:02 UTC",
  "RemoteUrl": "https://github.com/cran/r4lineups",
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  "_created": "2026-05-13T08:54:59.000Z",
  "_published": "2026-05-22T09:50:28.546Z",
  "_distro": "noble",
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        "lineup_2",
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      "title": "Confidence Intervals for Proportion",
      "topics": [
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      ]
    },
    {
      "page": "allfoilbias",
      "title": "Bias for each lineup member",
      "topics": [
        "allfoilbias"
      ]
    },
    {
      "page": "allprop",
      "title": "Lineup proportion for all lineup members",
      "topics": [
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      ]
    },
    {
      "page": "chi_diag",
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      "page": "compare_eff_sizes.boot",
      "title": "Comparing Effective Size: Base function for bootstrapping",
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    },
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      "title": "Mean diagnosticity ratio for k lineup pairs",
      "topics": [
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    },
    {
      "page": "d_weights",
      "title": "Diagnosticity ratio weights",
      "topics": [
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    {
      "page": "datacheck1",
      "title": "Helper function",
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      ]
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      "page": "datacheck2",
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      "topics": [
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    },
    {
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      "topics": [
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    {
      "page": "eff_size_per_foils",
      "title": "Effective Size per Foils",
      "topics": [
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      ]
    },
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      "title": "Master Function: Comparing Effective Size",
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      "title": "Effective Size",
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    },
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    },
    {
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      "title": "Compute similarity of faces in a lineup; experimental function",
      "topics": [
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      "title": "Functional Size",
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      "title": "Lineup vector",
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      "title": "I Component of Effective Size(Tredoux, 1998)",
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      "title": "Ln of Diagnosticity Ratio",
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      "page": "make_roc",
      "title": "Compute and plot ROC curve for lineup accuracy ~ confidence",
      "topics": [
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    {
      "page": "make_rocdata",
      "title": "Helper functions: Compute and plot ROC curve for lineup accuracy ~ confidence",
      "topics": [
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      "topics": [
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      "page": "rot_vector",
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      "page": "show_lineup",
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      "title": "r4lineups",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Data Format",
        "r4lineups Sample Datasets",
        "Lineup Proportion",
        "Calculating proportion for a single target/lineup member:",
        "Calculating proportion for each lineup member",
        "Functional Size",
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        "ROC Curve (Confidence ~ Accuracy)",
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