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cff-version: 1.2.0
message: 'To cite package "dgtf" in publications use:'
type: software
license: MIT
title: 'dgtf: Variational and Monte Carlo Inference for Bayesian State-Space Models
  of Count Data'
version: 0.1.0
doi: 10.32614/CRAN.package.dgtf
abstract: Bayesian inference for a general class of state-space models for count data,
  providing a unified interface to variational Bayes and Markov chain Monte Carlo.
  Models are assembled from modular components for the observation distribution, link
  function, gain function, lag distribution, system equation, and system error, and
  support Poisson and negative binomial observations together with seasonality terms.
  The model class and the hybrid variational approximation are described in Tang and
  Prado (2026) <https://doi.org/10.1016/j.csda.2026.108368>.
authors:
- family-names: Tang
  given-names: Meini
  email: meini.tang@gmail.com
  orcid: https://orcid.org/0000-0001-5338-8772
- family-names: Prado
  given-names: Raquel
  email: rprado@ucsc.edu
  orcid: https://orcid.org/0000-0001-6709-7191
repository: https://minimeini.r-universe.dev
repository-code: https://github.com/minimeini/dgtf
commit: 89dc660f877392b10df70e30e295a1d03f13fc8b
url: https://github.com/minimeini/dgtf
date-released: '2026-10-23'
contact:
- family-names: Tang
  given-names: Meini
  email: meini.tang@gmail.com
  orcid: https://orcid.org/0000-0001-5338-8772