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sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data
Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.
Getting started
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Package details |
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| Author | Alexandros Angelakis [aut, cre], Bryan Nyawanda [aut], Penelope Vounatsou [aut] |
| Maintainer | Alexandros Angelakis <alexandros.angelakis@swisstph.ch> |
| License | GPL (>= 3) |
| Version | 0.1.1 |
| URL | https://github.com/alexangelakis-ang/sparsesurv |
| Package repository | View on CRAN |
| Installation |
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