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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "DynCount" in publications use:'
type: software
license: MIT
title: 'DynCount: Bayesian Dynamic Models for Poisson and Binomial Time Series'
version: 0.1.0
doi: 10.1214/26-AOAS2171
abstract: '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) <https://doi.org/10.1214/26-AOAS2171>.'
authors:
- family-names: Zens
  given-names: Gregor
  email: zens@iiasa.ac.at
preferred-citation:
  type: article
  title: Dynamic Count Models with Flexible Innovation Processes for Irregular Maritime
    Migration
  authors:
  - family-names: Zens
    given-names: Gregor
    email: zens@iiasa.ac.at
  - family-names: Bijak
    given-names: Jakub
  journal: The Annals of Applied Statistics
  year: '2026'
  volume: '20'
  issue: '2'
  doi: 10.1214/26-AOAS2171
  start: '1671'
  end: '1690'
repository: https://cran.r-universe.dev
commit: bbeb2e8206c18b0d6684da2806d13da6e223b188
date-released: '2026-07-14'
contact:
- family-names: Zens
  given-names: Gregor
  email: zens@iiasa.ac.at