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. Full
    methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.

Estimates the time-varying reproduction number,
    rate of spread, and doubling time using a range of open-source tools
    (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and
    current best practices (Gostic et al. (2020)
    <doi:10.1101/2020.06.18.20134858>).  It aims to help users avoid some
    of the limitations of naive implementations in a framework that is
    informed by community feedback and is actively supported.

A collection of fast and flexible functions for analyzing
    omics data in observational studies. Multiple different approaches for
    integrating multiple environmental/genetic factors, omics data, and/or
    phenotype data are implemented. This includes functions for performing
    omics wide association studies with one or more variables of interest
    as the exposure or outcome; a function for performing a meet in the
    middle analysis for linking exposures, omics, and outcomes (as
    described by Chadeau-Hyam et al., (2010)
    <doi:10.3109/1354750X.2010.533285>); and a function for performing a
    mixtures analysis across all omics features using quantile-based
    g-Computation (as described by Keil et al., (2019)
    <doi:10.1289/EHP5838>).