c(person("Samuel", "Jenness",
role=c("cre","aut"), email="samuel.m.jenness@emory.edu"),
person("Steven M.","Goodreau",
role=c("aut"), email="goodreau@uw.edu"),
person("Martina", "Morris",
role=c("aut"), email="morrism@uw.edu"),
person("Emily", "Beylerian",
role=c("ctb"), email="emily.beylerian@gmail.com"),
person("Skye", "Bender-deMoll",
role=c("ctb"), email="skyebend@uw.edu"),
person("Kevin", "Weiss",
role=c("ctb"), email="kevin.weiss@emory.edu"))
Samuel Jenness [cre, aut],
Steven M. Goodreau [aut],
Martina Morris [aut],
Emily Beylerian [ctb],
Skye Bender-deMoll [ctb],
Kevin Weiss [ctb]

graphics, grDevices, stats, utils, doParallel, ergm (>=
3.7.1), foreach, network (>= 1.13.0), RColorBrewer, ape,
lazyeval

Mathematical Modeling of Infectious Disease Dynamics
Tools for simulating mathematical models of infectious disease dynamics.
Epidemic model classes include deterministic compartmental models, stochastic
agent-based 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 easy API for extending these templates to address novel scientific research aims.

graphics, grDevices, stats, utils, doParallel, ergm (>=
3.7.1), foreach, network (>= 1.13.0), RColorBrewer, ape,
lazyeval, ggplot2

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.
graphics, grDevices, stats, utils, doParallel, ergm (>= 3.9),
foreach, network (>= 1.13), RColorBrewer, ape, lazyeval,
ggplot2