seroepirecipes implements and links to R packages implementing commonly used mathematical and statistical models for analyzing serological data. The package implements a range of methods in R vignettes, from fitting antibody kinetics models to longitudinal antibody titer data, estimating the force of infection using serocatalytic models, and inferring infection histories using time-since-infection methods. This codebase accompanies a literature review of analytical methods for seroepidemiology.
All of the tutorials use either simulated datasets from the serosim R-package or publicly available datasets.
- Include Stan models within source code rather than vignette-specific scripts.
- Attach example datasets
- Move the jahR antibody kinetics functions here
- (WIP) Description of datasets included in seroepirecipes
- (WIP) Common considerations when preparing serological data for analysis
- (WIP) Fitting a hierarchical antibody kinetics model to longitudinal antibody measurements
- (WIP) Using mixture models to classify serostatus
- Estimating the force of infection using serocatalytic models
- (WIP) Inferring infection histories using serosolver
Upcoming vignettes:
- (WIP) Estimating the force of infection using antibody acquisition models
- (WIP) Infection times using reversible jump MCMC
- (WIP) Time-since-infection machine learning classifier
You can install the development version of seroepirecipes with:
if(!require("remotes")) install.packages("remotes")
remotes::install_github("seroanalytics/seroepirecipes")Please report bugs and requests on the issues link.
<< TBC >>
We welcome contributions and ideas for additional vignettes and model implementations. Please follow the package contributing guide.