# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "LongituRF" in publications use:'
type: software
license: GPL-2.0-only
title: 'LongituRF: Random Forests for Longitudinal Data'
version: '0.9'
doi: 10.32614/CRAN.package.LongituRF
abstract: Random forests are a statistical learning method widely used in many areas
  of scientific research essentially for its ability to learn complex relationships
  between input and output variables and also its capacity to handle high-dimensional
  data. However, current random forests approaches are not flexible enough to handle
  longitudinal data. In this package, we propose a general approach of random forests
  for high-dimensional longitudinal data. It includes a flexible stochastic model
  which allows the covariance structure to vary over time. Furthermore, we introduce
  a new method which takes intra-individual covariance into consideration to build
  random forests. The method is fully detailled in Capitaine et.al. (2020) <https://doi.org/10.1177/0962280220946080>
  Random forests for high-dimensional longitudinal data.
authors:
- family-names: Capitaine
  given-names: Louis
  email: Louis.capitaine@u-bordeaux.fr
  orcid: https://orcid.org/0000-0001-6800-2342
repository: https://cranhaven.r-universe.dev
commit: 9c7ad1854657c86cb8ccf98b9ee557e2fd7f9ad0
date-released: '2026-05-14'
contact:
- family-names: Capitaine
  given-names: Louis
  email: Louis.capitaine@u-bordeaux.fr
  orcid: https://orcid.org/0000-0001-6800-2342