An R package for personalized treatment selection at a single decision point.

Bayesian predictive model for personalized treatment selection for new untreated patients, which leverages known predictive and prognostic biomarkers. In particular, predictive biomarkers are exploited to inform a product partition model with covariates (PPMx) to obtain homogeneous clusters.

The implementation has been done in C++ through the use of Rcpp and RcppArmadillo.

Authors: Matteo Pedone, Raffaele Argiento, Francesco Stingo

Maintainer: Matteo Pedone.

You can install the development version of treatppmx from GitHub with:

# install.packages("devtools")
devtools::install_github("mattpedone/treatppmx")

NOTE that this package depends on vweights.

# install.packages("devtools")
devtools::install_github("mattpedone/vweights")

It has only been tested on a PC running Ubuntu 20.04.2 LTS.