Package: NobBS 1.1.1

Rodrigo Zepeda Tello

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, ctb], Nicolas Menzies [aut], Marc Lipsitch [aut], Michael Johansson [aut], Rami Yaari [aut, ctb], Rodrigo Zepeda Tello [cre, aut, ctb], Teresa Yamana [ctb], Matteo Perini [ctb]

NobBS_1.1.1.tar.gz
NobBS_1.1.1.zip(r-4.7-any)NobBS_1.1.1.zip(r-4.6-any)
NobBS_1.1.1.tar.gz(r-4.7-any)NobBS_1.1.1.tar.gz(r-4.6-any)
NobBS_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
NobBS/json (API)

# Install 'NobBS' in R:
install.packages('NobBS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
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
  • mpoxdat - Mpoxdat: Mpox reporting data from the 2022 New York City Outbreak

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jagscpp

4.45 score 47 scripts 524 downloads 4 mentions 2 exports 18 dependencies

Last updated from:b42dc53fb3. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK123
source / vignettesOK299
linux-releaseOK122
windows-develOK102
windows-releaseOK88
wasm-releaseOK107

Exports:NobBSNobBS.strat

Dependencies:clicodadplyrgenericsgluelatticelifecyclemagrittrpillarpkgconfigR6rjagsrlangtibbletidyselectutf8vctrswithr

Accounting for Day-of-the-Week Effect in Nowcast Models
Introduction | Comparing the Performance with and without DoW Effect | DoW Coefficients Estimates | Employing DoW Estimates as Priors for Future Nowcasts | Conclusion
Rendered fromdow_effect.Rmdusingknitr::rmarkdown

Last update: 2025-05-07
Started: 2025-05-07

Calculating Weighted Interval Score for Nowcast Models
Introduction | Nowcasting Scenarios | Comparing the Nowcast Estimates of the Poisson and negative binomial Models | Extracting Quantile Estimates | Calculating and Plotting the Weighted Interval Score | Conclusion
Rendered fromwis_calc.Rmdusingknitr::rmarkdown

Last update: 2025-05-07
Started: 2025-05-07

Handling Batched Reporting in Nowcast Models
Introduction | Data Preparation | Nowcasting Scenarios | Delay Distribution Estimates | Incidence Estimates | Conclusion
Rendered frombatched_reports.Rmdusingknitr::rmarkdown

Last update: 2025-05-07
Started: 2025-05-07