NobBS: Nowcasting by Bayesian Smoothing
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 <sfm341 at mail.harvard.edu> |
| License: |
MIT + file LICENSE |
| 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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