dgtf: Variational and Monte Carlo Inference for Bayesian State-Space Models of Count Data
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>.
| Version: | 0.1.0 |
| 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 |
| Published: | 2026-10-28 |
| DOI: | 10.32614/CRAN.package.dgtf (may not be active yet) |
| Author: | Meini Tang  [aut,
cre],
Raquel Prado
[aut] |
| Maintainer: | Meini Tang <meini.tang at gmail.com> |
| BugReports: | https://github.com/minimeini/dgtf/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/minimeini/dgtf |
| NeedsCompilation: | yes |
| SystemRequirements: | C++17 |
| Materials: | README |
| CRAN checks: | dgtf results |
Documentation:
| Reference manual: | dgtf.html , dgtf.pdf |
| Vignettes: |
COVID modeling with dgtf (source, R code) Getting started with dgtf (source, R code) |
Downloads:
| Package source: | dgtf_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Linking:
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