DPMM is an R package providing a library of model fitting functions, diagnostics tools for the fitted model and plotting functions. This package is developed from the work done in Cardoso, Dennis, Bowden, Shields and McKinley (2024) <https://doi.org/10.1186/s12911-023-02400-3>.
If you are just getting started with DPMM, we recommend starting with the tutorial vignettes, the examples throughout the package documentation, and the paper Dirichlet process mixture models to estimate outcomes for individuals with missing predictor data: application to predict optimal type 2 diabetes therapy in electronic health record data:
- Pedro Cardoso, John M. Dennis, Jack Bowden, Beverley M. Shields, Trevelyan J. McKinley the MASTERMIND Consortium. Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy. BMC Medical Informatics and Decision Making 24, 12 (2024), doi: <https://doi.org/10.1186/s12911-023-02400-3>.
- Install latest development version from GitHub (requires devtools package):
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("PM-Cardoso/DPMM", dependencies = TRUE, build_vignettes = FALSE)This installation won't include the vignettes (they take some time to build), but all of the vignettes are available online at https://pm-cardoso.github.io/DPMM/index.html.