ensembleML: Unified Interface for Ensemble Machine Learning Methods

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>.

Version:	0.2.5
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
Published:	2026-06-05
DOI:	10.32614/CRAN.package.ensembleML (may not be active yet)
Author:	Sadikul Islam [aut, cre]
Maintainer:	Sadikul Islam <sadikul.islamiasri at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Language:	en-US
Citation:	ensembleML citation info
CRAN checks:	ensembleML results

Documentation:

Reference manual:	ensembleML.html , ensembleML.pdf
Vignettes:	Getting Started with ensembleML (source, R code)

Downloads:

Package source:	ensembleML_0.2.5.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): ensembleML_0.2.5.tgz, r-oldrel (arm64): ensembleML_0.2.5.tgz, r-release (x86_64): ensembleML_0.2.5.tgz, r-oldrel (x86_64): ensembleML_0.2.5.tgz

Linking:

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