Package: SCE
Title: Stepwise Clustered Ensemble
Version: 1.1.2
Authors@R: 
    person(given = "Kailong",
           family = "Li",
           email = "lkl98509509@gmail.com",
           role = c("aut", "cre"))
Description: 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>.
URL: https://doi.org/10.5194/hess-25-4947-2021
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.3
Depends: R (>= 3.5.0)
Imports: stats (>= 3.5.0), utils (>= 3.5.0)
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
NeedsCompilation: no
Packaged: 2025-10-04 21:49:51 UTC; lkl98
Author: Kailong Li [aut, cre]
Maintainer: Kailong Li <lkl98509509@gmail.com>
Repository: CRAN
Date/Publication: 2025-10-04 22:10:02 UTC
Built: R 4.6.0; ; 2026-04-25 22:10:55 UTC; unix
