Modeling of Epidemics using Hierarchical Bayesian Models

Flexibly specify and fit Bayesian statistical models for epidemics. 'epidemia' leverages Rs formula interface so that users can parameterize reproduction numbers and ascertainment rates in terms of predictors. Infections are propagated over time using self-exciting point processes. Multiple regions can be modeled simultaneously with multilevel models. The models and framework behind the package are described in Bhatt et al. (2021) . The design of the package has been inspired by, and has borrowed from, 'rstanarm' (Goodrich et al., 2020) <>. 'rstan' (Stan Development Team, 2020) <> is used as the back end for fitting models.


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1.0.0 by James Scott, a month ago

Browse source code at

Authors: James Scott [aut, cre] , Axel Gandy [aut] , Swapnil Mishra [aut] , Juliette Unwin [aut] , Seth Flaxman [aut] , Samir Bhatt [aut] , Jonathan Ish-Horowicz [ctb] , Leon Wu [ctb] , Ben Goodrich [ctb] , Jonah Gabry [ctb] , Trustees of Columbia University [cph] (R/helpers.R) , Paul-Christian Burkner [cph] (R/misc.R) , Triad Sou [cph] (R/geom_stepribbon.R) , Kengo Nagashima [cph] (R/geom_stepribbon.R)

Documentation:   PDF Manual  

GPL-3 license

Imports dplyr, methods, Rcpp, rstan, rstantools, magrittr, rstanarm, lme4, ggplot2, Matrix, scales, Rdpack, stats, zoo, tidyr, rlang, bayesplot, hrbrthemes

Suggests testthat, knitr, gridExtra, bookdown, EpiEstim, lubridate, kableExtra, extrafont

Linking to BH, Rcpp, RcppEigen, rstan, StanHeaders

System requirements: GNU make

See at CRAN