Diagnostics and Models for Underdispersed Count Data R package underdisp version 0.1.2 Tools for detecting and modeling underdispersion in count data (conditional variance below the conditional mean), the case the Poisson and negative binomial defaults cannot represent. Provides a screening diagnostic that benchmarks at-risk dispersion against a zero-truncated Poisson, regression-adjusted tests of equidispersion, and a dispersion profile that compares the variance-to-mean curves of competing families against the data; the continuous parameter binomial (CPB) and generalized event count (Katz) regressions with zero-truncated, hurdle, and zero-inflated forms and high-dimensional fixed effects with a split-panel jackknife bias correction; matched Poisson, negative binomial, COM-Poisson (rate- and mean-parameterized), generalized Poisson, gamma-count, and double Poisson regressions through the same interface, with frequency weights, offsets, and analytic, robust, and cluster-robust standard errors; bootstrap and profile-likelihood inference; proper scoring rules, rootograms, PIT histograms, and simulation methods; and quantities of interest including predicted distributions, the implied ceiling, rate ratios, and first differences with an extensive/intensive decomposition. The likelihoods are implemented in C++. Software Depends: R (>= 4.0) Imports: Rcpp, stats, MASS, VGAM, graphics, methods, numDeriv, parallel LinkingTo: Rcpp Suggests: sandwich, pscl, DHARMa, testthat (>= 3.0.0), knitr, rmarkdown, broom, modelsummary, texreg, gamlss.dist, rmutil, glmmTMB, COMPoissonReg, Ecdat, wooldridge Benjamin E. Bagozzi <bagozzib@udel.edu> Comprehensive R Archive Network (CRAN) Benjamin E. Bagozzi [aut, cre] (ORCID: <https://orcid.org/0000-0002-6233-6453>) GPL-3 2026-09-30 application/tgz https://CRAN.R-project.org/package=underdisp doi:10.32614/CRAN.package.underdisp