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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | sparsesurv_0.1.1.tar.gz |
126.7 KiB |
0.1.1 |
rolling linux/noble R-4.5 | sparsesurv_0.1.1.tar.gz |
126.6 KiB |
0.1.1 |
rolling source/ R- | sparsesurv_0.1.1.tar.gz |
24.7 KiB |
0.1.1 |
latest linux/jammy R-4.5 | sparsesurv_0.1.1.tar.gz |
126.7 KiB |
0.1.1 |
latest linux/noble R-4.5 | sparsesurv_0.1.1.tar.gz |
126.6 KiB |
0.1.1 |
latest source/ R- | sparsesurv_0.1.1.tar.gz |
24.7 KiB |
0.1.1 |
2026-04-26 source/ R- | sparsesurv_0.1.1.tar.gz |
24.7 KiB |
0.1.1 |
2026-04-23 source/ R- | sparsesurv_0.1.1.tar.gz |
24.7 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | sparsesurv_0.1.1.zip |
129.4 KiB |