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>.
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
generics, stats, graphics, grDevices, utils |
| Suggests: |
coda, stochvol (≥ 3.0.2), testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-09-28 |
| DOI: |
10.32614/CRAN.package.DynCount |
| Author: |
Gregor Zens [aut, cre] |
| Maintainer: |
Gregor Zens <zens at iiasa.ac.at> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Citation: |
DynCount citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
DynCount results |
Documentation:
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