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  "Title": "Power Logit Regression for Modeling Bounded Data",
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  "Authors@R": "c(\nperson(\"Felipe\", \"Queiroz\", email = \"ffelipeq@outlook.com\", role = c(\"aut\", \"cre\")),\nperson(\"Silvia\", \"Ferrari\", email = \"silviaferrari@usp.br\", role = \"aut\"))",
  "Description": "Power logit regression models for bounded continuous data,\nin which the density generator may be normal, Student-t, power\nexponential, slash, hyperbolic, sinh-normal, or type II\nlogistic. Diagnostic tools associated with the fitted model,\nsuch as the residuals, local influence measures, leverage\nmeasures, and goodness-of-fit statistics, are implemented. The\nestimation process follows the maximum likelihood approach and,\ncurrently, the package supports two types of estimators: the\nusual maximum likelihood estimator and the penalized maximum\nlikelihood estimator. More details about power logit regression\nmodels are described in Queiroz and Ferrari (2022)\n<arXiv:2202.01697>.",
  "URL": "https://github.com/ffqueiroz/PLreg",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "Date/Publication": "2023-02-14 17:22:47 UTC",
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