Analysis and visualization of plant disease progress curve data. Functions for
    fitting two-parameter population dynamics models (exponential, monomolecular, logistic
    and Gompertz) to proportion data for single or multiple epidemics using either linear
    or no-linear regression.  Statistical and visual outputs are provided to aid in model
    selection. Synthetic curves can be simulated for any of the models given the parameters.
    See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>
    for further information on the methods.

Provides functions and classes designed to handle and visualise
    epidemiological flows between locations. Also contains a statistical method
    for predicting disease spread from flow data initially described in
    Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>.
    This package is part of the RECON (<https://www.repidemicsconsortium.org/>)
    toolkit for outbreak analysis.

This is a collection of assorted functions and examples collected 
    from various projects. Currently we have functionalities for simplifying 
    overlapping time intervals, Charlson comorbidity score constructors for 
    Danish data, getting frequency for multiple variables, getting standardized
    output from logistic and log-linear regressions, sibling design linear 
    regression functionalities a method for calculating the confidence intervals 
    for functions of parameters from a GLM, Bayes equivalent for hypothesis 
    testing with asymptotic Bayes factor, and several help functions for 
    generalized random forest analysis using 'grf'.

The interface package to access data from the
    'EpiGraphDB' <https://epigraphdb.org> platform.
    It provides easy access to the 'EpiGraphDB' platform with functions that
    query the corresponding REST endpoints on the API <https://api.epigraphdb.org>
    and return the response data in the 'tibble' data frame format.

Maximum likelihood estimation of nonlinear mixed effects models of
	epidemic growth using Template Model Builder ('TMB').  Enables
	joint estimation for collections of disease incidence time series,
	including time series that describe multiple epidemic waves.
	Supports a set of widely used phenomenological models: exponential,
	logistic, Richards (generalized logistic), subexponential,
	and Gompertz.  Provides methods for interrogating model objects
	and several auxiliary functions, including one for computing basic
	reproduction numbers from fitted values of the initial exponential
	growth rate.
	Preliminary versions of this software were applied
	in Ma et al. (2014) <doi:10.1007/s11538-013-9918-2> and
	in Earn et al. (2020) <doi:10.1073/pnas.2004904117>.