Nested Particle Filter for Stochastic SEIR Epidemic Models R package npfseir version 0.2.1 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. Software Imports: stats, graphics, grDevices Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Weinan Wang <ww@ou.edu> Comprehensive R Archive Network (CRAN) Weinan Wang [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=npfseir/LICENSE) 2026-04-24 application/tgz https://CRAN.R-project.org/package=npfseir doi:10.32614/CRAN.package.npfseir