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. (2019) <doi:10.1101/663823>.

Version: 0.1.0
Depends: R (≥ 3.3.0)
Imports: dplyr, rjags, coda, magrittr
Published: 2020-03-03
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

Downloads:

Reference manual: NobBS.pdf
Package source: NobBS_0.1.0.tar.gz
Windows binaries: r-devel: NobBS_0.1.0.zip, r-devel-gcc8: NobBS_0.1.0.zip, r-release: NobBS_0.1.0.zip, r-oldrel: NobBS_0.1.0.zip
OS X binaries: r-release: NobBS_0.1.0.tgz, r-oldrel: NobBS_0.1.0.tgz

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