overdisp: Overdispersion in Count Data Multiple Regression Analysis

Detection of overdispersion in count data for multiple regression analysis. Log-linear count data regression is one of the most popular techniques for predictive modeling where there is a non-negative discrete quantitative dependent variable. In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language.

Version: 0.1.2
Suggests: testthat (≥ 3.0.0)
Published: 2023-07-04
Author: Rafael Freitas Souza [cre], Hamilton Luiz Correa [ctb], A. Colin Cameron [aut], Pravin Trivedi [aut]
Maintainer: Rafael Freitas Souza <fsrafael at usp.br>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: overdisp results


Reference manual: overdisp.pdf


Package source: overdisp_0.1.2.tar.gz
Windows binaries: r-prerel: overdisp_0.1.2.zip, r-release: overdisp_0.1.2.zip, r-oldrel: overdisp_0.1.2.zip
macOS binaries: r-prerel (arm64): overdisp_0.1.2.tgz, r-release (arm64): overdisp_0.1.2.tgz, r-oldrel (arm64): overdisp_0.1.2.tgz, r-prerel (x86_64): overdisp_0.1.2.tgz, r-release (x86_64): overdisp_0.1.2.tgz
Old sources: overdisp archive


Please use the canonical form https://CRAN.R-project.org/package=overdisp to link to this page.