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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.

Versions across snapshots

VersionRepositoryFileSize
0.1.2 rolling linux/jammy R-4.5 overdisp_0.1.2.tar.gz 14.9 KiB
0.1.2 rolling linux/noble R-4.5 overdisp_0.1.2.tar.gz 14.8 KiB
0.1.2 rolling source/ R- overdisp_0.1.2.tar.gz 4.2 KiB
0.1.2 latest linux/jammy R-4.5 overdisp_0.1.2.tar.gz 14.9 KiB
0.1.2 latest linux/noble R-4.5 overdisp_0.1.2.tar.gz 14.8 KiB
0.1.2 latest source/ R- overdisp_0.1.2.tar.gz 4.2 KiB
0.1.2 2026-04-26 source/ R- overdisp_0.1.2.tar.gz 4.2 KiB
0.1.2 2026-04-23 source/ R- overdisp_0.1.2.tar.gz 4.2 KiB
0.1.2 2026-04-09 windows/windows R-4.5 overdisp_0.1.2.zip 17.6 KiB
0.1.2 2025-04-20 source/ R- overdisp_0.1.2.tar.gz 4.2 KiB

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