{
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  "Type": "Package",
  "Title": "Statistical Analysis of Count Data and Quantal Data",
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  "Authors@R": "c(\nperson(\"Benjamin\", \"Daniels\", comment = c(ORCID = \"0000-0002-8344-6141\"),  email = \"Benjamin.Daniels@uba.de\", role = c(\"cre\", \"ctb\"),),\nperson(\"Christian\", \"Dietrich\", role = \"aut\"),\nperson(\"Thomas\", \"Graeff\", role = \"ctb\"),\nperson(\"Magnus\", \"Wang\", role = \"ctb\"))",
  "Maintainer": "Benjamin Daniels <Benjamin.Daniels@uba.de>",
  "Description": "Methods for statistical analysis of count data and quantal\ndata. For the analysis of count data an implementation of the\nClosure Principle Computational Approach Test (\"CPCAT\") is\nprovided (Lehmann, R et al. (2016)\n<doi:10.1007/s00477-015-1079-4>), as well as an implementation\nof a \"Dunnett GLM\" approach using a Quasi-Poisson regression\n(Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).\nFor the analysis of quantal data an implementation of the\nClosure Principle Fisher–Freeman–Halton test (\"CPFISH\") is\nprovided (Lehmann, R et al. (2018)\n<doi:10.1007/s00477-017-1392-1>). P-values and no/lowest\nobserved (adverse) effect concentration values are calculated.\nAll implemented methods include further functions to evaluate\nthe power and the minimum detectable difference using a\nbootstrapping approach.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "RoxygenNote": "7.3.2",
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    "User": "root"
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  "Author": "Benjamin Daniels [cre, ctb]\n(<https://orcid.org/0000-0002-8344-6141>), Christian Dietrich\n[aut], Thomas Graeff [ctb], Magnus Wang [ctb]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-02-21 11:30:25 UTC",
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      "title": "CPCAT",
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        "CPCAT"
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      "title": "CPCAT bootstrap MDD (bMDD)",
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      "title": "CPCAT Poisson test",
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      "title": "CPCAT power",
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      "title": "CPFISH",
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    },
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      "title": "CPFISH bootstrap MDD (bMDD)",
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      "title": "Daphnia.counts is a dataset to showcase analyses with CPCAT and Dunnett.GLM",
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