PRTree: An R Package for Probabilistic Regression Trees with Built-in Missing Data Handling

PRTree is an R package for fitting Probabilistic Regression Trees (PRTrees), a class of regression trees that replaces deterministic splits with probabilistic associations to produce smooth prediction functions. The package implements both the original methodology and its recent extension for handling missing predictor values, allowing model fitting and prediction directly from incomplete datasets without prior imputation. It provides a unified framework for model fitting, prediction, visualization, smoothing-parameter selection, cross-validation, and model diagnostics through a standard R interface. Computationally intensive routines are implemented in FORTRAN, while the high-level interface follows the usual R workflow based on S3 classes and generic methods. This paper reviews the underlying methodology, describes the software architecture and main package components, and illustrates their use through reproducible examples.