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  "Package": "sparsesurv",
  "Title": "Forecasting and Early Outbreak Detection for Sparse Count Data",
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  "Description": "Functions for fitting, forecasting, and early detection of\noutbreaks in sparse surveillance count time series. Supports\nnegative binomial (NB), self-exciting NB, generalise\nautoregressive moving average (GARMA) NB , zero-inflated NB\n(ZINB), self-exciting ZINB, generalise autoregressive moving\naverage ZINB, and hurdle formulations. Climatic and\nenvironmental covariates can be included in the regression\ncomponent and/or the zero-modified components. Includes\noutbreak-detection algorithms for NB, ZINB, and hurdle models,\nwith utilities for prediction and diagnostics.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "Config/testthat/edition": "3",
  "SystemRequirements": "JAGS (>= 4.x)",
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  "BugReports": "https://github.com/alexangelakis-ang/sparsesurv/issues",
  "RoxygenNote": "7.3.2",
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    "User": "root"
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  "Author": "Alexandros Angelakis [aut, cre], Bryan Nyawanda [aut], Penelope\nVounatsou [aut]",
  "Maintainer": "Alexandros Angelakis <alexandros.angelakis@swisstph.ch>",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-09-09 14:10:02 UTC",
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      "title": "Fit Negative Binomial GARMA Model with Prediction",
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      "title": "Fit Negative Binomial Model with Arbitrary Covariates",
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      "title": "Fit an outbreak detection Hurdle negative binomial outbreak model",
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      ]
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      "title": "Fit an outbreak detection negative binomial outbreak model",
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      ]
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    {
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      "title": "Fit an outbreak detection zero‐inflated negative binomial outbreak model",
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    {
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      "title": "Fit Self-Exciting Negative Binomial Model with Prediction",
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        "SENB"
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    {
      "page": "SEZINB",
      "title": "Fit Self exciting Zero-Inflated Negative Binomial Model with Arbitrary Covariates and Prediction",
      "topics": [
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      ]
    },
    {
      "page": "ZINB",
      "title": "Fit Zero-Inflated Negative Binomial Model with Arbitrary Covariates and Prediction",
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        "ZINB"
      ]
    }
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