Stepwise Clustered Ensemble
Air Quality Dataset
Evaluate SCE and SCA Model Performance
Variable Importance for SCE and SCA Models
Plot Recursive Feature Elimination Results
Predict Using SCE and SCA Models
Print SCE and SCA Model Objects
Recursive Feature Elimination for SCE Models
Stepwise Cluster Analysis (SCA)
Stepwise Clustered Ensemble (SCE)
Streamflow Dataset
Summary methods for SCE and SCA models
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>.