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  "Title": "Tools for Deriving Income Inequality Estimates from Grouped\nIncome Data",
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  "Authors@R": "person(given = \"Andrew\",\nfamily = \"Carr\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"andrew.carr@duke.edu\")",
  "Description": "Provides two methods of estimating income inequality\nstatistics from binned income data, such as the income data\nprovided in the Census. These methods use different\ninterpolation techniques to infer the distribution of incomes\nwithin income bins.  One method is an implementation of\nJargowsky and Wheeler's mean-constrained integration over\nbrackets (MCIB).  The other method is based on a new technique,\nLorenz interpolation, which estimates income inequality by\nconstructing an interpolated Lorenz curve based on the binned\nincome data.  These methods can be used to estimate three\nincome inequality measures: the Gini (the default measure\nreturned), the Theil, and the Atkinson's index. Jargowsky and\nWheeler (2018) <doi:10.1177/0081175018782579>.",
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  "Author": "Andrew Carr [aut, cre, cph]",
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