% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/npfseir-package.R
\docType{package}
\name{npfseir-package}
\alias{npfseir}
\alias{npfseir-package}
\title{npfseir: Nested Particle Filter for Stochastic SEIR Epidemic Models}
\description{
Implements the online Bayesian inference framework for stochastic SEIR
epidemic models with latent Ornstein-Uhlenbeck transmission dynamics,
as described in Feng and Wang (2025).
}
\section{Main functions}{

\describe{
\item{\code{\link{npf_seir}}}{Run the nested particle filter.}
\item{\code{\link{seir_simulate}}}{Simulate a stochastic SEIR trajectory.}
\item{\code{\link{cori_rt}}}{Cori-style renewal R_t estimator (illustrative).}
\item{\code{\link{ou_params}}}{Exact OU discrete-time transition parameters.}
}
}

\section{S3 methods for \code{npf_seir} objects}{

\code{print}, \code{summary}, \code{plot}, \code{predict}.
}

\references{
Feng, W. and Wang, W. (2025). "Bayesian sequential inference for a stochastic SEIR model
with latent transmission dynamics." \emph{Preprint}.

Crisan, D. and Miguez, J. (2018). "Nested particle filters for online
parameter estimation in discrete-time state-space Markov models."
\emph{Bernoulli}, 24(4B):3039--3086. \doi{10.3150/17-BEJ954}
}
\author{
\strong{Maintainer}: Weinan Wang \email{ww@ou.edu}

}
