Implementation of Probabilistic Regression Trees (PRTree),
providing functions for model fitting and prediction, with specific adaptations
to handle missing values. The main computations are implemented in 'Fortran'
for high efficiency. The package is based on the PRTree methodology described in
Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees"
< https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
Details on the treatment of missing data and implementation aspects are presented
in Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025),
"Handling Missing Data in Probabilistic Regression Trees: Methods and Implementation in R"