{
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  "Package": "binequality",
  "Type": "Package",
  "Title": "Methods for Analyzing Binned Income Data",
  "Version": "1.0.4",
  "Date": "2018-11-05",
  "Author": "Samuel V. Scarpino, Paul von Hippel, and Igor Holas",
  "Maintainer": "Samuel V. Scarpino <s.scarpino@northeastern.edu>",
  "Description": "Methods for model selection, model averaging, and\ncalculating metrics, such as the Gini, Theil, Mean Log\nDeviation, etc, on binned income data where the topmost bin is\nright-censored.  We provide both a non-parametric method,\ntermed the bounded midpoint estimator (BME), which assigns\ncases to their bin midpoints; except for the censored bins,\nwhere cases are assigned to an income estimated by fitting a\nPareto distribution. Because the usual Pareto estimate can be\ninaccurate or undefined, especially in small samples, we\nimplement a bounded Pareto estimate that yields much better\nresults.  We also provide a parametric approach, which fits\ndistributions from the generalized beta (GB) family. Because\nsome GB distributions can have poor fit or undefined estimates,\nwe fit 10 GB-family distributions and use multimodel inference\nto obtain definite estimates from the best-fitting\ndistributions. We also provide binned income data from all\nUnited States of America school districts, counties, and\nstates.",
  "License": "GPL (>= 3.0)",
  "LazyLoad": "yes",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-07-12 05:06:08 UTC",
    "User": "root"
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2018-11-05 13:20:03 UTC",
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  "_created": "2026-07-12T05:06:08.000Z",
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    "author": "Samuel V. Scarpino <s.scarpino@northeastern.edu>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0.4\n",
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    "name": "Samuel V. Scarpino",
    "email": "s.scarpino@northeastern.edu"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/binequality"
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  "_assets": [
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    "extra/citation.html",
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    "extra/contents.json",
    "manual.pdf"
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  "_releases": [
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      "date": "2014-09-15"
    },
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      "date": "2016-12-17"
    },
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      "date": "2017-07-09"
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      "date": "2018-02-18"
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    "fitFunc",
    "getMids",
    "getQuantilesParams",
    "giniCoef",
    "LRT",
    "makeFitComb",
    "makeInt",
    "makeIntWeight",
    "makeWeightsAIC",
    "mAvg",
    "midStats",
    "MLD",
    "modelAvg",
    "paramFilt",
    "run_GB_family",
    "SDL",
    "theilInd"
  ],
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    {
      "name": "county_bins",
      "title": "A data set containing binned income for US counties",
      "object": "county_bins",
      "file": "county_bins.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "bin_num",
        "count_pop",
        "count_pop_moe",
        "bin_min",
        "bin_max",
        "count_sample",
        "county",
        "state",
        "county_fips",
        "state_fips"
      ],
      "rows": 51536,
      "table": true,
      "tojson": true
    },
    {
      "name": "school_district_bins",
      "title": "A data set containing the school district data.",
      "object": "school_district_bins",
      "file": "school_district_bins.rda",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "state_bins",
      "title": "A data set containing the binned state data.",
      "object": "state_bins",
      "file": "state_bins.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "fips_state",
        "bin_min",
        "bin_max",
        "State",
        "households_pop",
        "moe_households_pop",
        "households_sample",
        "hb"
      ],
      "rows": 832,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "binequality-package",
      "title": "Methods for Analyzing Binned Income Data",
      "topics": [
        "binequality-package",
        "binequality"
      ]
    },
    {
      "page": "county_bins",
      "title": "A data set containing binned income for US counties",
      "topics": [
        "county_bins"
      ]
    },
    {
      "page": "fitFunc",
      "title": "A function to fit a parametric distribution to binned data.",
      "topics": [
        "fitFunc"
      ]
    },
    {
      "page": "getMids",
      "title": "A function to calculate the bin midpoints.",
      "topics": [
        "getMids"
      ]
    },
    {
      "page": "getQuantilesParams",
      "title": "A function to extract the quantiles and parameters",
      "topics": [
        "getQuantilesParams"
      ]
    },
    {
      "page": "giniCoef",
      "title": "Calculates the Gini coefficient from quantiles",
      "topics": [
        "giniCoef"
      ]
    },
    {
      "page": "LRT",
      "title": "A function to perform likelihood ratio tests",
      "topics": [
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      ]
    },
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      "page": "makeFitComb",
      "title": "A function to transform a list into a dataframe",
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      "page": "makeInt",
      "title": "A function to create a survival object from bin counts.",
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      "page": "makeIntWeight",
      "title": "A function to create a survival object from bin counts and normalized bin weights.",
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    {
      "page": "makeWeightsAIC",
      "title": "A function to calculate AIC weights",
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    {
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      "title": "A simple function to perfom model averaging using pre-calculated weights.",
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      "title": "A function to calculate statistics using bin midpoints",
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      ]
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      "title": "A function to calculate the MLD",
      "topics": [
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      ]
    },
    {
      "page": "modelAvg",
      "title": "A function to calculate model averages",
      "topics": [
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    },
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      "title": "A function to filter models based on estimated parameters",
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      "page": "run_GB_family",
      "title": "A function to fit a parametric distributions to binned data.",
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      "title": "A data set containing the school district data.",
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    },
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      "title": "A function to calculate the SDL",
      "topics": [
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      ]
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    {
      "page": "state_bins",
      "title": "A data set containing the binned state data.",
      "topics": [
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      ]
    },
    {
      "page": "theilInd",
      "title": "A function to calculate the Theil",
      "topics": [
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      ]
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