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DynCount  

Bayesian Dynamic Models for Poisson and Binomial Time Series
View on CRAN: Click here


Download and install DynCount package within the R console
Install from CRAN:
install.packages("DynCount")

Install from Github:
library("remotes")
install_github("cran/DynCount")

Install by package version:
library("remotes")
install_version("DynCount", "0.1.0")



Attach the package and use:
library("DynCount")
Maintained by
Gregor Zens
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-14
Latest Update: 2026-07-14
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>.
How to cite:
Gregor Zens (2026). DynCount: Bayesian Dynamic Models for Poisson and Binomial Time Series. R package version 0.1.0, https://cran.r-project.org/web/packages/DynCount. Accessed 25 Oct. 2026.
Previous versions and publish date:
0.1.0 (2026-07-14 19:00), (2026-09-28 12:10)
Other packages that cited DynCount R package
View DynCount citation profile
Other R packages that DynCount depends, imports, suggests or enhances
Complete documentation for DynCount
Functions, R codes and Examples using the DynCount R package
Full DynCount package functions and examples
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