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 |
| 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 |
Documentation:
Downloads:
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