Package: NobBS 1.0.0

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>.

Authors:Sarah McGough [aut, cre], Nicolas Menzies [aut], Marc Lipsitch [aut], Michael Johansson [aut]

NobBS_1.0.0.tar.gz
NobBS_1.0.0.zip(r-4.5)NobBS_1.0.0.zip(r-4.4)NobBS_1.0.0.zip(r-4.3)
NobBS_1.0.0.tgz(r-4.4-any)NobBS_1.0.0.tgz(r-4.3-any)
NobBS_1.0.0.tar.gz(r-4.5-noble)NobBS_1.0.0.tar.gz(r-4.4-noble)
NobBS_1.0.0.tgz(r-4.4-emscripten)NobBS_1.0.0.tgz(r-4.3-emscripten)
NobBS.pdf |NobBS.html✨
NobBS/json (API)
NEWS

# Install 'NobBS' in R:
install.packages('NobBS', repos = c('https://sarahhbellum.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sarahhbellum/nobbs/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • denguedat - Denguedat: Dengue fever reporting data from Puerto Rico

On CRAN:

5.54 score 18 stars 32 scripts 173 downloads 4 mentions 2 exports 19 dependencies

Last updated 11 months agofrom:7216e335ac. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winOKNov 04 2024
R-4.5-linuxOKNov 04 2024
R-4.4-winOKNov 04 2024
R-4.4-macOKNov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:NobBSNobBS.strat

Dependencies:clicodadplyrfansigenericsgluelatticelifecyclemagrittrpillarpkgconfigR6rjagsrlangtibbletidyselectutf8vctrswithr