underdisp: Diagnostics and Models for Underdispersed Count Data
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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0) |
| Imports: |
Rcpp, stats, MASS, VGAM, graphics, methods, numDeriv |
| LinkingTo: |
Rcpp |
| Suggests: |
sandwich, pscl, DHARMa, testthat (≥ 3.0.0), knitr, rmarkdown, broom, modelsummary, texreg |
| Published: |
2026-08-20 |
| DOI: |
10.32614/CRAN.package.underdisp (may not be active yet) |
| Author: |
Benjamin E. Bagozzi [aut, cre] |
| Maintainer: |
Benjamin E. Bagozzi <bagozzib at udel.edu> |
| BugReports: |
https://github.com/bagozzib/underdisp/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/bagozzib/underdisp |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
underdisp results |
Documentation:
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