DynCount: Bayesian Dynamic Models for Count Time Series

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>.

install.packages("DynCount")

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