epigrowthfit
| REF | Earn et al. (2020) |
| Docs | None |
| Github | github.com/davidearn/epigrowthfit |
| Last commit | Feb, 2025 |
| Installation | CRAN |
Brief description
Copied from the developer site.
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
Methods
Given the lack of package documentation it is difficult to fully assess which methods are being implemented.
Assessment
| Features | |
| Ability to nowcast/forecast | No |
| Incorporates delay distributions | No |
| Estimates expected cases | No |
| Communicates uncertainty | No |
| Validation | |
| Documentation of package methods | No |
| Documentation of package implementation | No |
Sample code
There is no available vignette or manual on how to use this package other than function descriptions.
This Github repository describes the methods used in Earn et al. (2020), but documentation is minimal.