Package: metaEnsembleR
Type: Package
Title: Automated Intuitive Package for Meta-Ensemble Learning
Version: 0.1.0
Date: 2020-10-27
Author: Ajay Arunachalam
Maintainer: Ajay Arunachalam <ajay.arunachalam08@gmail.com>
Description: Extends the base classes and methods of 'caret' package
        for integration of base learners. The user can input the number
        of different base learners, and specify the final learner,
        along with the train-validation-test data partition split
        ratio. The predictions on the unseen new data is the resultant
        of the ensemble meta-learning
        <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/>
        of the heterogeneous learners aimed to reduce the
        generalization error in the predictive models. It significantly
        lowers the barrier for the practitioners to apply heterogeneous
        ensemble learning techniques in an amateur fashion to their
        everyday predictive problems.
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: no
Imports: caret, ggplot2, graphics, e1071, gbm, randomForest
Depends: gridExtra
Suggests: knitr, R.rsp
VignetteBuilder: R.rsp
RoxygenNote: 7.1.1
Packaged: 2026-10-12 23:14:33 UTC; root
Config/pak/sysreqs: libicu-dev
Repository: https://cranhaven.r-universe.dev
Date/Publication: 2026-10-12 12:02:00 UTC
RemoteUrl: https://github.com/cranhaven/cranhaven.r-universe.dev
RemoteRef: package/metaEnsembleR
RemoteSha: 0ac3f147ae732f35fdb07e81806be456c3e5682e
RemoteSubdir: metaEnsembleR