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  "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.",
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  "Encoding": "UTF-8",
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  "Date/Publication": "2025-09-04 08:35:29 UTC",
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