Bayesian Dynamic Models for Count Time Series R package DynCount version 0.2.0 Fits Bayesian state-space models for count time series using a latent log-rate (Poisson), latent logit (binomial) or latent additive-log-ratio (multinomial choice counts) formulation. Each latent trajectory follows a first-order random walk or a stationary AR(1) process and is sampled by Metropolis-within-Gibbs using the implied Gaussian Markov random field full conditionals. The latent increments can be Gaussian, Student-t, a finite scale mixture of normals, or follow a stochastic volatility process, and the Poisson and binomial families support zero inflation. It implements and extends the methodology of Zens and Bijak (2026) <doi:10.1214/26-AOAS2171>. Software Depends: R (>= 3.5.0) Imports: generics, stats, graphics, grDevices, utils Suggests: coda, stochvol (>= 3.0.2), testthat (>= 3.0.0), knitr, rmarkdown Gregor Zens <zens@iiasa.ac.at> Comprehensive R Archive Network (CRAN) Gregor Zens [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=DynCount/LICENSE) 2026-09-28 application/tgz https://CRAN.R-project.org/package=DynCount doi:10.32614/CRAN.package.DynCount en-GB