Package: overdisp
Type: Package
Title: Overdispersion in Count Data Multiple Regression Analysis
Version: 0.1.2
Authors@R: c(person(given = "Rafael", family = "Freitas Souza", role = "cre", email = "fsrafael@usp.br"), person(given = "Hamilton Luiz", family = "Correa", role = "ctb"), person(given = "A. Colin", family = "Cameron", role = "aut"), person(given = "Pravin", family = "Trivedi", role = "aut"))
Maintainer: Rafael Freitas Souza <fsrafael@usp.br>
Description: 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.
License: GPL (>= 2)
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-10-07 10:07:25 UTC; root
Author: Rafael Freitas Souza [cre], Hamilton Luiz Correa [ctb], A.
        Colin Cameron [aut], Pravin Trivedi [aut]
Repository: https://fsrafael25.r-universe.dev
Date/Publication: 2023-07-04 14:00:02 UTC
RemoteUrl: https://github.com/cran/overdisp
RemoteRef: HEAD
RemoteSha: e10846bb681030d847a3932b0cdc931a5c2c1ba9