NobBS: Nowcasting by Bayesian Smoothing (original) (raw)
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.
| Version: | 1.0.0 |
|---|---|
| Depends: | R (≥ 3.3.0) |
| Imports: | dplyr, rlang, rjags, coda, magrittr |
| Published: | 2024-01-08 |
| DOI: | 10.32614/CRAN.package.NobBS |
| Author: | Sarah McGough [aut, cre], Nicolas Menzies [aut], Marc Lipsitch [aut], Michael Johansson [aut] |
| Maintainer: | Sarah McGough |
| License: | MIT + file |
| NeedsCompilation: | no |
| SystemRequirements: | JAGS (http://mcmc-jags.sourceforge.net/) for analysis of Bayesian hierarchical models |
| Materials: | README NEWS |
| CRAN checks: | NobBS results |
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