Note The McMasterPandemic project is evolving
- All new development work is happening in the macpan2 repository.
- The McMasterPandemic repository is in maintenance mode to support current users.
- If you are a new user, thank you for your interest! Please start here at macpan2.
Compartmental epidemic models for forecasting and analysis of infectious disease pandemics: contributions from Ben Bolker, Jonathan Dushoff, David Earn, Weiguang Guan, Morgan Kain, Michael Li, Irena Papst, Steve Walker (in alphabetical order). Feedback is welcome at the issues list, or e-mail us.
The repository contains an R package and various workflows/analyses. This repository is not on CRAN so you will need to either fork/clone the repository (from here) or install directly from GitHub. Either option will (may?) require you to first install two R packages that are also not on CRAN.
remotes::install_github("bbolker/bbmle")
remotes::install_github("johndharrison/semver")
Note that these commands depend on having the remotes package, which you can get from CRAN with the following command from an R prompt.
install.packages('remotes')
To install McMasterPandemic itself you follow the same formula.
remotes::install_github("mac-theobio/McMasterPandemic")
If this command fails it may be because your R installation is not set up to compile C++ code. Windows users should be able to get past this issue by installing Rtools.
The classic McMasterPandemic functionality described here does not depend on C++ code, and you can get access to this functionality by installing with this command.
remotes::install_github("mac-theobio/McMasterPandemic@v0.0.20.1")
Getting access to experimental features can sometimes be achieved with this command.
remotes::install_github("mac-theobio/McMasterPandemic@tmb-condense")
All use of this package is at your own risk. Quantitative forecasts are only as good as their parameter estimates.