Package: ensembleML
Type: Package
Title: Unified Interface for Ensemble Machine Learning Methods
Version: 0.2.5
Date: 2026-05-20
Authors@R: 
    person(
        given = "Sadikul",
        family = "Islam",
        email = "sadikul.islamiasri@gmail.com",
        role = c("aut", "cre"),
        comment = c(ORCID = "0000-0003-2924-7122")
    )
Description: 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>.
Language: en-US
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
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
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-01 07:29:28 UTC; acer
Author: Sadikul Islam [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2924-7122>)
Maintainer: Sadikul Islam <sadikul.islamiasri@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-05 15:00:07 UTC
Built: R 4.4.3; ; 2026-06-05 17:07:40 UTC; unix
