EpiLPS: A Bayesian Tool for Fast and Flexible Estimation of the Reproduction Number

Estimation of the instantaneous reproduction number with Laplacian-P-splines following the methodology of Gressani et al. (2022) <doi:10.1371/journal.pcbi.1010618>. The negative binomial distribution is used to model the time series of incidence data. Two methods are available for inference : (1) a sampling-free approach based on a maximum a posteriori calibration of the hyperparameter vector and (2) a fully stochastic approach with a Metropolis-adjusted Langevin algorithm for efficient sampling of the posterior distribution.

Version: 1.1.0
Depends: R (≥ 4.1.0)
Imports: Rcpp (≥ 1.0.7), coda (≥ 0.19-4), EpiEstim (≥ 2.2-4), ggplot2 (≥ 3.3.5), gridExtra (≥ 2.3)
LinkingTo: RcppArmadillo, Rcpp
Suggests: rmarkdown, knitr
Published: 2023-04-05
Author: Oswaldo Gressani ORCID iD [aut, cre]
Maintainer: Oswaldo Gressani <oswaldo_gressani at hotmail.fr>
BugReports: https://github.com/oswaldogressani/EpiLPS/issues
License: GPL-3
Copyright: see file COPYRIGHTS
URL: <https://github.com/oswaldogressani/EpiLPS>
NeedsCompilation: yes
Citation: EpiLPS citation info
Materials: README NEWS
CRAN checks: EpiLPS results


Reference manual: EpiLPS.pdf
Vignettes: EpiLPS-vignette


Package source: EpiLPS_1.1.0.tar.gz
Windows binaries: r-devel: EpiLPS_1.1.0.zip, r-release: EpiLPS_1.1.0.zip, r-oldrel: EpiLPS_1.1.0.zip
macOS binaries: r-release (arm64): EpiLPS_1.1.0.tgz, r-oldrel (arm64): EpiLPS_1.1.0.tgz, r-release (x86_64): EpiLPS_1.1.0.tgz, r-oldrel (x86_64): EpiLPS_1.1.0.tgz
Old sources: EpiLPS archive


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