Nested Particle Filter for Stochastic SEIR Epidemic Models // 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.