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 15:54:50 UTC; root
Author: Gregor Zens [aut, cre]
Maintainer: Gregor Zens <zens@iiasa.ac.at>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-28 10:10:02 UTC
RemoteUrl: https://github.com/cran/DynCount
RemoteRef: HEAD
RemoteSha: fbcadd55407b7bfc76b3067dcd0d053ab15bb950