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  "Package": "grassr",
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  "Title": "Context-Conditioned Reporting for Binary Rater Reliability",
  "Version": "0.7.4",
  "Authors@R": "c(\nperson(\"Austin\", \"Semmel\", email = \"austinsemmel@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Rachel\", \"Gidaro\", role = \"aut\"))",
  "Description": "Generates a Report Card for rater reliability on binary\noutcomes from an N x k subject-by-rater rating matrix, on both\nthe inter-rater and intra-rater axes. Each panel coefficient is\npositioned on a data-generating-process-calibrated reference\nsurface conditioned on the study's rater count, sample size,\nand prevalence, yielding a pooled percentile (the coefficient's\nposition within the design's achievable agreement range)\ntogether with a consistency band on panel quality: the quality\nlevels whose sampling distributions are consistent with the\nobserved value at that design. The panel coefficients are the\nprevalence-adjusted bias-adjusted kappa (PABAK) of Byrt,\nBishop, and Carlin (1993) <doi:10.1016/0895-4356(93)90018-V>,\nthe first-order agreement coefficient (AC1) of Gwet (2008)\n<doi:10.1348/000711006X126600>, the multi-rater kappa of Fleiss\n(1971) <doi:10.1037/h0031619>, and the observed intraclass\ncorrelation. A cross-coefficient discordance diagnostic\n(delta-hat) reports the spread of the coefficients' implied\npanel qualities and flags panels for which no single\ncoefficient is a stable summary by the spread's percentile on a\nmatched null distribution; for such divergent panels the report\nroutes to a pairwise PABAK matrix and per-rater sensitivity and\nspecificity recovered from the latent-class model of Dawid and\nSkene (1979) <doi:10.2307/2346806>, with the two-rater bounds\nof Hui and Walter (1980) <doi:10.2307/2530508>.",
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      "title": "Per-rater Se/Sp asymmetry diagnostic",
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      "title": "Open calibration blocks",
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      "title": "Estimate prevalence of the positive class from rater data",
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      "title": "Return the full internal reference table",
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        "grass_reference_table"
      ]
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      "title": "Generate a GRASS Report Card from a rating matrix",
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        "grass_report"
      ]
    },
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      "page": "grass_roadmap",
      "title": "The grass ecosystem roadmap",
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        "grass_roadmap"
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      "title": "grassr: rater reliability on binary outcomes, from rating matrix to Report Card",
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        "The problem: the label tracks the study, not the raters",
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