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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PRTree" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'PRTree: Probabilistic Regression Trees'
version: 1.0.3
doi: 10.32614/CRAN.package.PRTree
abstract: '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" <https://doi.org/10.48550/arXiv.2510.03634>.'
authors:
- family-names: Neimaier
  given-names: Alisson Silva
  email: alissonneimaier@hotmail.com
  orcid: https://orcid.org/0000-0002-7524-0776
- family-names: Prass
  given-names: Taiane Schaedler
  email: taianeprass@gmail.com
  orcid: https://orcid.org/0000-0003-3136-909X
repository: https://tsprass.r-universe.dev
commit: c89de6e938b3c10f96cebffab6a36f7f9389a839
date-released: '2026-02-18'
contact:
- family-names: Prass
  given-names: Taiane Schaedler
  email: taianeprass@gmail.com
  orcid: https://orcid.org/0000-0003-3136-909X