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PRTree: Probabilistic Regression Trees
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" <doi:10.48550/arXiv.2510.03634>.
| Version: |
1.0.2 |
| Depends: |
R (≥ 4.3.0) |
| Published: |
2025-10-26 |
| DOI: |
10.32614/CRAN.package.PRTree |
| Author: |
Alisson Silva Neimaier
 [aut],
Taiane Schaedler Prass
[aut, ths,
cre] |
| Maintainer: |
Taiane Schaedler Prass <taianeprass at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| CRAN checks: |
PRTree results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=PRTree
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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