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underdisp  

Diagnostics and Models for Underdispersed Count Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("underdisp", "0.1.0")



Attach the package and use:
library("underdisp")
Maintained by
Benjamin E. Bagozzi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-20
Latest Update: 2026-08-20
Description:
Tools for detecting and modeling underdispersion in count data (conditional variance below the conditional mean), a phenomenon overlooked by the Poisson and negative binomial defaults. Provides a screening diagnostic that benchmarks at-risk dispersion against a zero-truncated Poisson; the continuous parameter binomial (CPB) regression and its zero-truncated variant, with an interpretable observation-specific bound and high-dimensional fixed-effects support; validated bootstrap (for coefficients) and profile-likelihood (for the dispersion parameter) inference; and quantities of interest including predicted probabilities and the implied ceiling. The likelihood is implemented in C++ for speed.
How to cite:
Benjamin E. Bagozzi (2026). underdisp: Diagnostics and Models for Underdispersed Count Data. R package version 0.1.0, https://cran.r-project.org/web/packages/underdisp. Accessed 03 Oct. 2026.
Previous versions and publish date:
(2026-09-30 23:21), 0.1.0 (2026-08-20 18:40), 0.1.1 (2026-09-28 17:30)
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Complete documentation for underdisp
Functions, R codes and Examples using the underdisp R package
Full underdisp package functions and examples
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