DynCount: Bayesian Dynamic Models for Poisson and Binomial Time Series

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>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	stats, graphics, grDevices, utils
Suggests:	stochvol, testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-07-14
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
CRAN checks:	DynCount results

Documentation:

Reference manual:	DynCount.html , DynCount.pdf
Vignettes:	Dynamic Models for Poisson and Binomial Time Series (source, R code)

Downloads:

Package source:	DynCount_0.1.0.tar.gz
Windows binaries:	r-devel: DynCount_0.1.0.zip, r-release: DynCount_0.1.0.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): DynCount_0.1.0.tgz, r-oldrel (arm64): DynCount_0.1.0.tgz, r-release (x86_64): DynCount_0.1.0.tgz, r-oldrel (x86_64): DynCount_0.1.0.tgz

Linking:

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