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  "Title": "Flexible Extended State-Space Epidemiological Models with Modern\nInference",
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  "Authors@R": "person(\"Subir\", \"Hait\",\nemail = \"haitsubi@msu.edu\",\nrole  = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0004-9871-9677\"))",
  "Description": "An extended epidemiological modelling framework that goes\nbeyond the classical SIR (Susceptible-Infectious-Recovered)\nmodel. Supports SEIR\n(Susceptible-Exposed-Infectious-Recovered), SEIRD\n(Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD\n(Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased),\nand age-stratified compartmental models with flexible\nintervention functions (spline-based, Gaussian process, or\nuser-defined). Inference is available via maximum likelihood or\nsequential Monte Carlo (SMC, also known as particle filtering)\nwith no external binary dependencies. Includes a\ndependency-free real-time effective reproduction number (Rt)\nestimator, spatial multi-patch models with gravity-model\nmobility, ensemble forecasting via Bayesian model averaging\n(BMA), and proper scoring rules including CRPS (Continuous\nRanked Probability Score), coverage, and MAE (Mean Absolute\nError) for forecast evaluation. Methods follow Anderson and May\n(1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon\n(2001) <doi:10.1007/978-1-4757-3437-9>, Cori et al. (2013)\n<doi:10.1093/aje/kwt133>, and Gneiting and Raftery (2007)\n<doi:10.1198/016214506000001437>.",
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  "Repository": "https://causalfragility-lab.r-universe.dev",
  "Date/Publication": "2026-04-21 22:47:01 UTC",
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