Package: qountstat 0.1.1

Benjamin Daniels

qountstat: Statistical Analysis of Count Data and Quantal Data

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 a Quasi-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.

Authors:Benjamin Daniels [cre, ctb], Christian Dietrich [aut], Thomas Graeff [ctb], Magnus Wang [ctb]

qountstat_0.1.1.tar.gz
qountstat_0.1.1.tar.gz(r-4.5-noble)qountstat_0.1.1.tar.gz(r-4.4-noble)
qountstat_0.1.1.tgz(r-4.4-emscripten)qountstat_0.1.1.tgz(r-4.3-emscripten)
qountstat.pdf |qountstat.html
qountstat/json (API)

# Install 'qountstat' in R:
install.packages('qountstat', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 408 downloads 9 exports 10 dependencies

Last updated 1 months agofrom:717ec49e27. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linuxOKMar 23 2025
R-4.4-linuxOKMar 23 2025

Exports:CPCATCPCAT.bMDDCPCAT.powerCPFISHCPFISH.bMDDCPFISH.powerDunnett.GLMDunnett.GLM.bMDDDunnett.GLM.power

Dependencies:codetoolslatticeMASSMatrixmultcompmvtnormsandwichsurvivalTH.datazoo