{
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  "Package": "alphaOutlier",
  "Type": "Package",
  "Title": "Obtain Alpha-Outlier Regions for Well-Known Probability\nDistributions",
  "Version": "1.2.2",
  "Date": "2026-04-26",
  "Authors@R": "c(person(given = \"Andre\",\nfamily = \"Rehage\",\nrole = c(\"aut\", \"cre\"),\nemail = \"andre.rehage@gmx.de\"),\nperson(given = \"Sonja\",\nfamily = \"Kuhnt\",\nrole = \"aut\"))",
  "Maintainer": "Andre Rehage <andre.rehage@gmx.de>",
  "Description": "Given the parameters of a distribution, the package uses\nthe concept of alpha-outliers by Davies and Gather (1993) to\nflag outliers in a data set. See Davies, L.; Gather, U. (1993):\nThe identification of multiple outliers, JASA, 88 423, 782-792,\n<doi:10.1080/01621459.1993.10476339> for details.",
  "License": "GPL-3",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-27 06:27:16 UTC",
    "User": "root"
  },
  "Author": "Andre Rehage [aut, cre], Sonja Kuhnt [aut]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-04-27 09:33:33 UTC",
  "RemoteUrl": "https://github.com/cran/alphaOutlier",
  "RemoteRef": "HEAD",
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  "_published": "2026-05-27T06:30:10.238Z",
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    "author": "Andre Rehage <andre.rehage@gmx.de>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.2.2\n",
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    "name": "Andre Rehage",
    "email": "andre.rehage@gmx.de"
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  "_dependencies": [
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      "package": "Rsolnp",
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  "_updates": [
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  "_tags": [
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      "date": "2026-04-27"
    }
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  "_userbio": {
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    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/alphaOutlier"
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "manual.pdf"
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  "_realowner": "cran",
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      "date": "2015-05-07"
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      "date": "2015-05-15"
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      "date": "2016-09-09"
    },
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    "aout.cg",
    "aout.chisq",
    "aout.conttab",
    "aout.exp",
    "aout.gandh",
    "aout.hyper",
    "aout.kernel",
    "aout.laplace",
    "aout.logis",
    "aout.mvnorm",
    "aout.nbinom",
    "aout.norm",
    "aout.pareto",
    "aout.pois",
    "aout.weibull",
    "createDesMat"
  ],
  "_datasets": [
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      "name": "citiesData",
      "title": "Population of the 999 largest German cities",
      "object": "citiesData",
      "file": "citiesData.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Population.of.biggest.German.cities"
      ],
      "rows": 999,
      "table": true,
      "tojson": true
    },
    {
      "name": "daysabs",
      "title": "Number of absence days of students",
      "object": "daysabs",
      "file": "daysabs.RData",
      "class": [
        "numeric"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "alphaOutlier-package",
      "title": "Obtain alpha-outlier regions for well-known probability distributions",
      "concept": [
        "distribution",
        "robust statistics"
      ],
      "topics": [
        "alphaOutlier-package",
        "alphaOutlier"
      ]
    },
    {
      "page": "aout.binom",
      "title": "Find alpha-outliers in Binomial data",
      "topics": [
        "aout.binom"
      ]
    },
    {
      "page": "aout.cg",
      "title": "Find alpha-outliers in conditional Gaussian data",
      "topics": [
        "aout.cg"
      ]
    },
    {
      "page": "aout.chisq",
      "title": "Find alpha-outliers in chi^2 data",
      "topics": [
        "aout.chisq"
      ]
    },
    {
      "page": "aout.conttab",
      "title": "Find alpha-outliers in two-way contingency tables",
      "topics": [
        "aout.conttab"
      ]
    },
    {
      "page": "aout.exp",
      "title": "Find alpha-outliers in exponentially distributed data",
      "topics": [
        "aout.exp"
      ]
    },
    {
      "page": "aout.gandh",
      "title": "Find alpha-outliers in data from the family of g-and-h distributions",
      "topics": [
        "aout.gandh"
      ]
    },
    {
      "page": "aout.hyper",
      "title": "Find alpha-outliers in hypergeometric data",
      "topics": [
        "aout.hyper"
      ]
    },
    {
      "page": "aout.kernel",
      "title": "Find alpha-outliers in arbitrary univariate data using kernel density estimation",
      "topics": [
        "aout.kernel"
      ]
    },
    {
      "page": "aout.laplace",
      "title": "Find alpha-outliers in Laplace / double exponential data",
      "topics": [
        "aout.laplace"
      ]
    },
    {
      "page": "aout.logis",
      "title": "Find alpha-outliers in logistic data",
      "topics": [
        "aout.logis"
      ]
    },
    {
      "page": "aout.mvnorm",
      "title": "Find alpha-outliers in multivariate normal data",
      "topics": [
        "aout.mvnorm"
      ]
    },
    {
      "page": "aout.nbinom",
      "title": "Find alpha-outliers in negative Binomial data",
      "topics": [
        "aout.nbinom"
      ]
    },
    {
      "page": "aout.norm",
      "title": "Find alpha-outliers in normal data",
      "topics": [
        "aout.norm"
      ]
    },
    {
      "page": "aout.pareto",
      "title": "Find alpha-outliers in Pareto data",
      "topics": [
        "aout.pareto"
      ]
    },
    {
      "page": "aout.pois",
      "title": "Find alpha-outliers in Poisson count data",
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      ]
    },
    {
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      "title": "Find alpha-outliers in Weibull data",
      "topics": [
        "aout.weibull"
      ]
    },
    {
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      "topics": [
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      ]
    },
    {
      "page": "createDesMat",
      "title": "Create design matrix for log-linear models of contingency tables",
      "topics": [
        "createDesMat"
      ]
    },
    {
      "page": "daysabs",
      "title": "Number of absence days of students",
      "topics": [
        "daysabs"
      ]
    }
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