Package: qountstat
Type: Package
Title: Statistical Analysis of Count Data and Quantal Data
Version: 0.2.0
Authors@R: c(
    	person("Benjamin", "Daniels", comment = c(ORCID = "0000-0002-8344-6141"),  email = "benjamin.daniels@rwth-aachen.de", role = c("cre", "aut"),),
	person("Christian", "Dietrich", role = "aut"),
	person("Thomas", "Graeff", role = "aut"),
	person("Magnus", "Wang", role = "aut"))
Maintainer: Benjamin Daniels <benjamin.daniels@rwth-aachen.de>
Description: Methods for statistical analysis of count data and quantal data.
    For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using either a Quasi-Poisson, a negative binomial or a Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).
    For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated.
    All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.
    A model-agnostic, simulation-calibrated diagnostic for overdispersion in grouped count data is provided to support the choice between the implemented methods.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: MASS, multcomp
Suggests: knitr, rmarkdown, testthat
RoxygenNote: 7.3.2
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2026-10-16 15:26:38 UTC; upbeat-charming-dijkstra
Author: Benjamin Daniels [cre, aut] (ORCID:
    <https://orcid.org/0000-0002-8344-6141>),
  Christian Dietrich [aut],
  Thomas Graeff [aut],
  Magnus Wang [aut]
Repository: CRAN
Date/Publication: 2026-10-16 16:20:02 UTC
