EpiModel: Mathematical Modeling of Infectious Disease Dynamics

Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims.

Version: 2.0.2
Depends: R (≥ 3.5), deSolve (≥ 1.21), networkDynamic (≥ 0.9), tergm (≥ 3.5), tergmLite (≥ 2.2.0)
Imports: graphics, grDevices, stats, utils, doParallel, ergm (≥ 3.10), foreach, network (≥ 1.13), RColorBrewer, ape, lazyeval, ggplot2
LinkingTo: ergm
Suggests: knitr, ndtv, rmarkdown, shiny, testthat
Published: 2020-08-05
Author: Samuel Jenness [cre, aut], Steven M. Goodreau [aut], Martina Morris [aut], Emily Beylerian [ctb], Skye Bender-deMoll [ctb], Kevin Weiss [ctb], Shawnee Anderson [ctb]
Maintainer: Samuel Jenness <samuel.m.jenness at emory.edu>
BugReports: https://github.com/statnet/EpiModel/issues
License: GPL-3
URL: http://epimodel.org/, http://github.com/statnet/EpiModel
NeedsCompilation: yes
Citation: EpiModel citation info
Materials: NEWS
CRAN checks: EpiModel results


Reference manual: EpiModel.pdf
Vignettes: EpiModel Introduction
Package source: EpiModel_2.0.2.tar.gz
Windows binaries: r-devel: EpiModel_2.0.2.zip, r-release: EpiModel_2.0.2.zip, r-oldrel: EpiModel_2.0.2.zip
macOS binaries: r-release: EpiModel_2.0.2.tgz, r-oldrel: EpiModel_2.0.2.tgz
Old sources: EpiModel archive

Reverse dependencies:

Reverse suggests: statnet, tergmLite


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