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.
| Version: | 0.1.1 |
| Depends: | R (≥ 4.1) |
| Imports: | R2jags,coda, stats |
| Suggests: | testthat (≥ 3.0.0),knitr,rjags,rmarkdown,ggplot2,reshape2 |
| Published: | 2025-09-09 |
| DOI: | 10.32614/CRAN.package.sparsesurv |
| Author: | Alexandros Angelakis [aut, cre], Bryan Nyawanda [aut], Penelope Vounatsou [aut] |
| Maintainer: | Alexandros Angelakis <alexandros.angelakis at swisstph.ch> |
| BugReports: | https://github.com/alexangelakis-ang/sparsesurv/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/alexangelakis-ang/sparsesurv |
| NeedsCompilation: | no |
| SystemRequirements: | JAGS (>= 4.x) |
| Materials: | README,NEWS |
| CRAN checks: | sparsesurv results |
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