Package: DynCount
Title: Bayesian Dynamic Models for Poisson and Binomial Time Series
Version: 0.1.0
Authors@R: 
    person("Gregor", "Zens", email = "zens@iiasa.ac.at", role = c("aut", "cre"))
Description: 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>.
License: MIT + file LICENSE
Language: en-GB
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: stats, graphics, grDevices, utils
Suggests: stochvol, testthat (>= 3.0.0), knitr, rmarkdown
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 7.3.1
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-07-06 10:25:38 UTC; Gregor
Author: Gregor Zens [aut, cre]
Maintainer: Gregor Zens <zens@iiasa.ac.at>
Repository: CRAN
Date/Publication: 2026-07-14 17:00:02 UTC
Built: R 4.5.2; ; 2026-07-14 17:43:20 UTC; unix
