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cff-version: 1.2.0
message: 'To cite package "npfseir" in publications use:'
type: software
license: MIT
title: 'npfseir: Nested Particle Filter for Stochastic SEIR Epidemic Models'
version: 0.2.1
doi: 10.32614/CRAN.package.npfseir
abstract: 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) <https://doi.org/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) <https://doi.org/10.1093/aje/kwt133>. The package also provides utilities
  for simulation, posterior summarisation, and forecasting.
authors:
- family-names: Wang
  given-names: Weinan
  email: ww@ou.edu
repository: https://wwang-math.r-universe.dev
commit: 6b776c32d3bb18ba8c28e0670326dffea6914fa5
date-released: '2026-04-22'
contact:
- family-names: Wang
  given-names: Weinan
  email: ww@ou.edu