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: 2026-07-14 20:02:00 UTC; root
Author: Gregor Zens [aut, cre]
Maintainer: Gregor Zens <zens@iiasa.ac.at>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-07-14 18:33:59 UTC
RemoteUrl: https://github.com/cran/DynCount
RemoteRef: HEAD
RemoteSha: bbeb2e8206c18b0d6684da2806d13da6e223b188