DynCount: Bayesian Dynamic Models for Poisson and Binomial Time Series

Fits Bayesian state-space models for non-Gaussian time series using a latent log-rate (Poisson) or latent logit (binomial) formulation. The latent trajectory follows a first-order random walk or a stationary AR(1) process, sampled by Metropolis-within-Gibbs using the implied Gaussian Markov random field (GMRF) full conditionals. Four innovation structures are supported for the latent increments: constant-variance Gaussian, Student-t, a finite scale mixture of normals, and stochastic volatility. Both families support time-constant zero inflation. The package provides simulation, fitting, forecasting, summary and plotting tools. It implements and extends the methodology of Zens and Bijak (2026) <doi:10.1214/26-AOAS2171>.

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