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>.

Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).

Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.

Using variational techniques we address some epidemiological
  problems as the incidence curve decomposition by inverting the renewal 
  equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> 
  and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of 
  the functional relationship between epidemiological indicators. We also 
  propose a learning method for the short time forecast of the trend 
  incidence curve  as described in 
  Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.

Contains tools for formatting inline code, renaming redundant
  columns, aggregating age categories, adding survey weights, finding the earliest
  date of an event, plotting z-curves, generating population counts and 
  formatting proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epi.github.io/sitrep/>.