SCE: Stepwise Clustered Ensemble

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) <doi:10.5194/hess-25-4947-2021>.

Version:	1.0.0
Depends:	R (≥ 3.5.0)
Imports:	stats (≥ 3.5.0), utils (≥ 3.5.0)
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2025-06-23
DOI:	10.32614/CRAN.package.SCE
Author:	Kailong Li [aut, cre]
Maintainer:	Kailong Li <lkl98509509 at gmail.com>
License:	GPL-3
URL:	https://doi.org/10.5194/hess-25-4947-2021
NeedsCompilation:	no
Materials:	README
CRAN checks:	SCE results

Documentation:

Reference manual:

SCE.pdf

Downloads:

Package source:	SCE_1.0.0.tar.gz
Windows binaries:	r-devel: SCE_1.0.0.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): SCE_1.0.0.tgz, r-oldrel (arm64): SCE_1.0.0.tgz, r-release (x86_64): SCE_1.0.0.tgz, r-oldrel (x86_64): SCE_1.0.0.tgz

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