Unified Interface for Ensemble Machine Learning Methods R package ensembleML version 0.2.5 Provides a clean, unified interface for training, predicting, and evaluating ensemble machine learning models including Random Forest, Gradient Boosting ('XGBoost'), 'AdaBoost', and 'Bagging'. All algorithms share a consistent API: em_fit(), em_predict(), em_evaluate(), and em_tune(). Includes built-in cross-validation, feature importance, calibration diagnostics, partial dependence plots, and model comparison utilities. Methods: Breiman (2001) <doi:10.1023/A:1010933404324>; Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>; Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>; Breiman (1996) <doi:10.1007/BF00058655>. Software Depends: R (>= 4.1.0) Imports: randomForest (>= 4.7-1), xgboost (>= 1.7.0), adabag (>= 4.2), ggplot2 (>= 3.4.0), rlang (>= 1.1.0), stats, utils Suggests: pROC (>= 1.18.0), gridExtra (>= 2.3), testthat (>= 3.0.0), knitr, rmarkdown, mlbench Sadikul Islam <sadikul.islamiasri@gmail.com> Comprehensive R Archive Network (CRAN) Sadikul Islam [aut, cre] (ORCID: <https://orcid.org/0000-0003-2924-7122>) MIT + file LICENSE (https://CRAN.R-project.org/package=ensembleML/LICENSE) 2026-06-05 application/tgz https://CRAN.R-project.org/package=ensembleML doi:10.32614/CRAN.package.ensembleML en-US