Package: dgtf
Type: Package
Title: Variational and Monte Carlo Inference for Bayesian State-Space
        Models of Count Data
Version: 0.1.0
Authors@R: c(
    person(given = "Meini", family = "Tang", role = c("aut", "cre"),
           email = "meini.tang@gmail.com",
           comment = c(ORCID = "0000-0001-5338-8772")),
    person(given = "Raquel", family = "Prado", role = "aut",
           email = "rprado@ucsc.edu",
           comment = c(ORCID = "0000-0001-6709-7191"))
    )
Description: 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)
        <doi:10.1016/j.csda.2026.108368>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
SystemRequirements: C++17
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.10), stats, utils, graphics, grDevices, ggplot2
        (>= 3.3.0)
LinkingTo: Rcpp, RcppArmadillo, BH, RcppProgress, pg
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, EpiEstim
Config/testthat/edition: 3
URL: https://github.com/minimeini/dgtf
BugReports: https://github.com/minimeini/dgtf/issues
VignetteBuilder: knitr
Config/roxygen2/version: 8.1.0
Packaged: 2026-10-28 19:30:37 UTC; root
Author: Meini Tang [aut, cre] (ORCID:
        <https://orcid.org/0000-0001-5338-8772>), Raquel Prado [aut]
        (ORCID: <https://orcid.org/0000-0001-6709-7191>)
Maintainer: Meini Tang <meini.tang@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-28 12:20:12 UTC
RemoteUrl: https://github.com/cran/dgtf
RemoteRef: HEAD
RemoteSha: fddeb62e87768793e8d609be48439a010fa79080