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overdisp  

Overdispersion in Count Data Multiple Regression Analysis
View on CRAN: Click here


Download and install overdisp package within the R console
Install from CRAN:
install.packages("overdisp")

Install from Github:
library("remotes")
install_github("cran/overdisp")

Install by package version:
library("remotes")
install_version("overdisp", "0.1.2")



Attach the package and use:
library("overdisp")
Maintained by
Rafael Freitas Souza
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-02-16
Latest Update: 2023-07-04
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) 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.
How to cite:
Rafael Freitas Souza (2020). overdisp: Overdispersion in Count Data Multiple Regression Analysis. R package version 0.1.2, https://cran.r-project.org/web/packages/overdisp
Previous versions and publish date:
0.1.0 (2020-02-16 14:30), 0.1.1 (2020-10-06 06:10)
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Functions, R codes and Examples using the overdisp R package
Some associated functions: README . 
Some associated R codes: overdisp.R .  Full overdisp package functions and examples
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