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
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

Documentation:

Reference manual:

sparsesurv.html , sparsesurv.pdf

Downloads:

Package source:	sparsesurv_0.1.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): sparsesurv_0.1.1.tgz, r-oldrel (arm64): sparsesurv_0.1.1.tgz, r-release (x86_64): sparsesurv_0.1.1.tgz, r-oldrel (x86_64): sparsesurv_0.1.1.tgz

Linking:

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