Package: DynCount
Title: Bayesian Dynamic Models for Count Time Series
Version: 0.2.0
Authors@R: 
    person("Gregor", "Zens", email = "zens@iiasa.ac.at", role = c("aut", "cre"))
Description: 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>.
License: MIT + file LICENSE
Language: en-GB
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: generics, stats, graphics, grDevices, utils
Suggests: coda, stochvol (>= 3.0.2), testthat (>= 3.0.0), knitr,
        rmarkdown
VignetteBuilder: knitr
LazyData: true
RoxygenNote: 7.3.1
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-09-28 09:51:36 UTC; Gregor
Author: Gregor Zens [aut, cre]
Maintainer: Gregor Zens <zens@iiasa.ac.at>
Repository: CRAN
Date/Publication: 2026-09-28 10:10:02 UTC
Built: R 4.6.1; ; 2026-09-28 11:18:27 UTC; unix
