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  "Title": "Nested Particle Filter for Stochastic SEIR Epidemic Models",
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  "Description": "Implements the online Bayesian inference framework for\njoint state and parameter estimation in a stochastic\nSusceptible-Exposed-Infectious-Recovered (SEIR) epidemic model\nwith a time-varying transmission rate. The log-transmission\nrate is modelled as a latent Ornstein-Uhlenbeck (OU) process\nwith exact Gaussian discrete-time transitions. Inference is\nperformed via the nested particle filter (NPF) of Crisan and\nMiguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer\nparticle layer over the OU hyperparameters and, for each outer\nparticle, an inner bootstrap filter over epidemic states. The\nCori-style renewal-equation estimator follows Cori et al.\n(2013) <doi:10.1093/aje/kwt133>. The package also provides\nutilities for simulation, posterior summarisation, and\nforecasting.",
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      "title": "npfseir: Nested Particle Filter for Stochastic SEIR Epidemic Models",
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        "npfseir"
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      "title": "Cori-style renewal-equation R_t estimator",
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      "title": "Run the Nested Particle Filter for a stochastic SEIR model",
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        "npf_seir"
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        "1. Simulate a synthetic epidemic",
        "2. Run the nested particle filter",
        "3. Inspect results",
        "4. Plot the R_t trajectory",
        "5. Forecast",
        "6. Cori-style R_t comparison (illustrative only)",
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