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: 2023-07-04 14:42:33 UTC; fs-ra
Author: Rafael Freitas Souza [cre],
  Hamilton Luiz Correa [ctb],
  A. Colin Cameron [aut],
  Pravin Trivedi [aut]
Repository: CRAN
Date/Publication: 2023-07-04 15:00:02 UTC
Built: R 4.6.0; ; 2026-04-25 21:00:15 UTC; unix
