Nonlinear Mixed Effects Models of Epidemic Growth
Extract the Residual Degrees of Freedom
Description of Objects of Class egf
Fit Nonlinear Mixed Effects Models of Epidemic Growth
Simulating Incidence Time Series
Extract Coefficients and Random Effect Covariance Parameters
Confidence Intervals
Compute a Packed Representation of a Covariance Matrix
Define Control Parameters
Define Control Parameters for Plotting
Test for Convergence
Test for Random Effects
Define a Top Level Nonlinear Model
Define an Optimization Method
Define a Parallelization Method
Prior Distributions
Top Level Nonlinear Model Parameter Names
Defunct Functions in Package epigrowthfit
Deprecated Functions in Package epigrowthfit
Not Yet Implemented Functions in Package epigrowthfit
Package epigrowthfit
Extract the (Generalized) AIC
Compute the Expected Epidemic Final Size
Fitted Values
Details about Fixed Effect Coefficients
Extract Model Formulae
Extract Model Calls
Generation Interval Distribution
Extract the Log (Marginal) Likelihood
Extract Model Frames
Extract Design Matrices
Extract the Number of Observations
Plot Nonlinear Mixed Effects Models of Epidemic Growth
Predicted Values
Printing Model Objects
Univariate Likelihood Profiles
Compute the Basic Reproduction Number
Details about Random Effect Coefficients
Simulation and Parametric Bootstrapping
Model Summaries
Model Terms
Compute the Characteristic Time Scale
Model Covariance Matrices
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>.
Useful links