sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data (original) (raw)
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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