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  "Package": "npbr",
  "Type": "Package",
  "Title": "Nonparametric Boundary Regression",
  "Version": "1.8",
  "Date": "2023-03-21",
  "Author": "Abdelaati Daouia <Abdelaati.Daouia@tse-fr.eu>, Thibault Laurent\n<thibault.laurent@univ-tlse1.fr>, Hohsuk Noh\n<word5810@gmail.com>",
  "Maintainer": "Thibault Laurent <thibault.laurent@univ-tlse1.fr>",
  "Description": "A variety of functions for the best known and most\ninnovative approaches to nonparametric boundary estimation. The\nselected methods are concerned with empirical, smoothed,\nunrestricted as well as constrained fits under both separate\nand multiple shape constraints. They cover robust approaches to\noutliers as well as data envelopment techniques based on\npiecewise polynomials, splines, local linear fitting, extreme\nvalues and kernel smoothing. The package also seamlessly allows\nfor Monte Carlo comparisons among these different estimation\nmethods.  Its use is illustrated via a number of empirical\napplications and simulated examples.",
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  "NeedsCompilation": "yes",
  "Packaged": {
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      "title": "Nonparametric boundary regression",
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        "npbr"
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