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 case counts. 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-within-Gibbs algorithm and Langevin diffusions for
efficient sampling of the posterior distribution.
| Version: |
1.0.7 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
Rcpp (≥ 1.0.7), coda (≥ 0.19-4), progress (≥ 1.2.2), crayon (≥ 1.4.1), MASS (≥ 7.3-54), EpiEstim (≥ 2.2-4), ggplot2 (≥
3.3.5), grDevices (≥ 4.1.0), gridExtra (≥ 2.3) |
| LinkingTo: |
RcppArmadillo, Rcpp |
| Suggests: |
rmarkdown, knitr |
| Published: |
2023-01-18 |
| Author: |
Oswaldo Gressani 
[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 |
Documentation:
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