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: 2026-10-14 09:05:46 UTC; root
Author: Sadikul Islam [aut, cre] (ORCID:
        <https://orcid.org/0000-0003-2924-7122>)
Maintainer: Sadikul Islam <sadikul.islamiasri@gmail.com>
Config/pak/sysreqs: make libicu-dev libxml2-dev
Repository: https://sadikulislamiasri-hub.r-universe.dev
Date/Publication: 2026-06-05 15:00:07 UTC
RemoteUrl: https://github.com/cran/ensembleML
RemoteRef: HEAD
RemoteSha: 3d5598315619093e1e3401ec629fa97f5da89bd8