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sparsesurv  

Forecasting and Early Outbreak Detection for Sparse Count Data
View on CRAN: Click here


Download and install sparsesurv package within the R console
Install from CRAN:
install.packages("sparsesurv")

Install from Github:
library("remotes")
install_github("cran/sparsesurv")

Install by package version:
library("remotes")
install_version("sparsesurv", "0.1.1")



Attach the package and use:
library("sparsesurv")
Maintained by
Alexandros Angelakis
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-09
Latest Update: 2025-09-09
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.
How to cite:
Alexandros Angelakis (2025). sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data. R package version 0.1.1, https://cran.r-project.org/web/packages/sparsesurv. Accessed 13 Sep. 2026.
Previous versions and publish date:
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Complete documentation for sparsesurv
Functions, R codes and Examples using the sparsesurv R package
Full sparsesurv package functions and examples
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