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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "SCE" in publications use:'
type: software
license: GPL-3.0-only
title: 'SCE: Stepwise Clustered Ensemble'
version: 1.1.4
doi: 10.32614/CRAN.package.SCE
abstract: Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster
  Analysis (SCA) for multivariate data analysis. The package provides comprehensive
  tools for feature selection, model training, prediction, and evaluation in hydrological
  and environmental modeling applications. Key functionalities include recursive feature
  elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag
  (OOB) validation, and ensemble prediction capabilities. The package supports both
  single and multivariate response variables, making it suitable for complex environmental
  modeling scenarios. For more details see Li et al. (2021) <https://doi.org/10.5194/hess-25-4947-2021>.
authors:
- family-names: Li
  given-names: Kailong
  email: lkl98509509@gmail.com
repository: https://loong2020.r-universe.dev
commit: 1f2ca5da62f821eafde2ffba744016c0708978fb
url: https://doi.org/10.5194/hess-25-4947-2021
date-released: '2026-05-11'
contact:
- family-names: Li
  given-names: Kailong
  email: lkl98509509@gmail.com