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For the analysis of\nquantal data an implementation of the Closure Principle\nFisher–Freeman–Halton test (\"CPFISH\") is provided (Lehmann, R\net al. (2018) ). P-values and\nno/lowest observed (adverse) effect concentration values are\ncalculated. All implemented methods include further functions\nto evaluate the power and the minimum detectable difference\nusing a bootstrapping approach. A model-agnostic,\nsimulation-calibrated diagnostic for overdispersion in grouped\ncount data is provided to support the choice between the\nimplemented methods.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "Repository": "https://bennidaniels.r-universe.dev", "Date/Publication": "2026-10-19 11:01:21 UTC", "RemoteUrl": "https://github.com/bennidaniels/qountstat", "RemoteRef": "HEAD", "RemoteSha": "74c16d5c4ba37186aecfd504ea7535ab1c52d0ae", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-20 10:31:05 UTC", "User": "root" }, "Author": "Benjamin Daniels [cre, aut] (ORCID:\n),\nChristian Dietrich [aut],\nThomas Graeff [aut],\nMagnus Wang [aut]", "_user": "bennidaniels", "_type": "src", "_file": "qountstat_0.2.0.tar.gz", "_fileid": "https://r2.ropensci.org/d17b61c5522730f6535d36cee25a20c16afdcaddae2610a089ec3987faa8ffcf", "_filesize": 199832, "_sha256": "d17b61c5522730f6535d36cee25a20c16afdcaddae2610a089ec3987faa8ffcf", 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