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  "Description": "Implementation of Probabilistic Regression Trees (PRTree),\nproviding functions for model fitting and prediction, with\nspecific adaptations to handle missing values. The main\ncomputations are implemented in 'Fortran' for high efficiency.\nThe package is based on the PRTree methodology described in\nAlkhoury et al. (2020), \"Smooth and Consistent Probabilistic\nRegression Trees\"\n<https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.\nDetails on the treatment of missing data and implementation\naspects are presented in Prass, T.S.; Neimaier, A.S.; Pumi, G.\n(2025), \"Handling Missing Data in Probabilistic Regression\nTrees: Methods and Implementation in R\"\n<doi:10.48550/arXiv.2510.03634>.",
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