sparsesurv-package: sparsesurv: Forecasting and Early Outbreak Detection for...

In sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data

sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data

Description

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.

Author(s)

Maintainer: Alexandros Angelakis [e-mail]

Authors:

• Bryan Nyawanda

• Penelope Vounatsou

See Also

Useful links:

• https://github.com/alexangelakis-ang/sparsesurv

• Report bugs at https://github.com/alexangelakis-ang/sparsesurv/issues

sparsesurv documentation built on Sept. 11, 2025, 9:11 a.m.