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

Documentation:

Reference manual:

NobBS.pdf

Downloads:

Package source:	NobBS_1.0.0.tar.gz
Windows binaries:	r-devel: NobBS_1.0.0.zip, r-release: NobBS_1.0.0.zip, r-oldrel: NobBS_1.0.0.zip
macOS binaries:	r-devel (arm64): NobBS_1.0.0.tgz, r-release (arm64): NobBS_1.0.0.tgz, r-oldrel (arm64): NobBS_1.0.0.tgz, r-devel (x86_64): NobBS_1.0.0.tgz, r-release (x86_64): NobBS_1.0.0.tgz, r-oldrel (x86_64): NobBS_1.0.0.tgz
Old sources:	NobBS archive

Linking:

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