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Probabilistic Regression Trees (PRTree). Functions for fitting and predicting PRTree models with some adaptations to handle missing values. The main calculations are performed in 'FORTRAN', resulting in highly efficient algorithms. This package's implementation is based on the PRTree methodology described in Alkhoury, S.; Devijver, E.; Clausel, M.; Tami, M.; Gaussier, E.; Oppenheim, G. (2020) - "Smooth And Consistent Probabilistic Regression Trees" <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
Version: | 0.1.2 |
Depends: | R (≥ 4.2.0) |
Published: | 2024-09-28 |
DOI: | 10.32614/CRAN.package.PRTree |
Author: | Alisson Silva Neimaier [aut, cre], Taiane Schaedler Prass [aut, ths] |
Maintainer: | Alisson Silva Neimaier <alissonneimaier at hotmail.com> |
License: | GPL (≥ 3) |
NeedsCompilation: | yes |
CRAN checks: | PRTree results |
Reference manual: | PRTree.pdf |
Package source: | PRTree_0.1.2.tar.gz |
Windows binaries: | r-devel: PRTree_0.1.2.zip, r-release: PRTree_0.1.2.zip, r-oldrel: PRTree_0.1.2.zip |
macOS binaries: | r-release (arm64): PRTree_0.1.2.tgz, r-oldrel (arm64): PRTree_0.1.2.tgz, r-release (x86_64): PRTree_0.1.2.tgz, r-oldrel (x86_64): PRTree_0.1.2.tgz |
Old sources: | PRTree archive |
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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.