Implements the online Bayesian inference framework for joint state and parameter estimation in a stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model with a time-varying transmission rate. The log-transmission rate is modelled as a latent Ornstein-Uhlenbeck (OU) process with exact Gaussian discrete-time transitions. Inference is performed via the nested particle filter (NPF) of Crisan and Miguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer particle layer over the OU hyperparameters and, for each outer particle, an inner bootstrap filter over epidemic states. The Cori-style renewal-equation estimator follows Cori et al. (2013) <doi:10.1093/aje/kwt133>. The package also provides utilities for simulation, posterior summarisation, and forecasting.
| Version: | 0.2.1 |
| Imports: | stats, graphics, grDevices |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-04-24 |
| DOI: | 10.32614/CRAN.package.npfseir |
| Author: | Weinan Wang [aut, cre] |
| Maintainer: | Weinan Wang <ww at ou.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | npfseir results |
| Reference manual: | npfseir.html , npfseir.pdf |
| Vignettes: |
Getting started with npfseir (source, R code) |
| Package source: | npfseir_0.2.1.tar.gz |
| Windows binaries: | r-devel: npfseir_0.2.1.zip, r-release: npfseir_0.2.1.zip, r-oldrel: npfseir_0.2.1.zip |
| macOS binaries: | r-release (arm64): npfseir_0.2.1.tgz, r-oldrel (arm64): npfseir_0.2.1.tgz, r-release (x86_64): npfseir_0.2.1.tgz, r-oldrel (x86_64): npfseir_0.2.1.tgz |
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