The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

To cite the ensembleML package in publications, use: The underlying algorithms should also be cited:

Islam S (2026). ensembleML: Unified Interface for Ensemble Machine Learning Methods. R package version 0.2.0, https://cran.r-project.org/package=ensembleML.

Breiman L (2001). “Random Forests.” Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324.

Chen T, Guestrin C (2016). “XGBoost: A Scalable Tree Boosting System.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. doi:10.1145/2939672.2939785.

Freund Y, Schapire R (1997). “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences, 55(1), 119–139. doi:10.1006/jcss.1997.1504.

Breiman L (1996). “Bagging Predictors.” Machine Learning, 24(2), 123–140. doi:10.1007/BF00058655.

Corresponding BibTeX entries:

  @Manual{,
    title = {{ensembleML}: Unified Interface for Ensemble Machine
      Learning Methods},
    author = {Sadikul Islam},
    year = {2026},
    note = {R package version 0.2.0},
    url = {https://cran.r-project.org/package=ensembleML},
  }
  @Article{,
    title = {Random Forests},
    author = {Leo Breiman},
    journal = {Machine Learning},
    year = {2001},
    volume = {45},
    number = {1},
    pages = {5--32},
    doi = {10.1023/A:1010933404324},
  }
  @InProceedings{,
    title = {{XGBoost}: A Scalable Tree Boosting System},
    author = {Tianqi Chen and Carlos Guestrin},
    booktitle = {Proceedings of the 22nd ACM SIGKDD International
      Conference on Knowledge Discovery and Data Mining},
    year = {2016},
    pages = {785--794},
    publisher = {ACM},
    doi = {10.1145/2939672.2939785},
  }
  @Article{,
    title = {A Decision-Theoretic Generalization of On-Line Learning
      and an Application to Boosting},
    author = {Yoav Freund and Robert E. Schapire},
    journal = {Journal of Computer and System Sciences},
    year = {1997},
    volume = {55},
    number = {1},
    pages = {119--139},
    doi = {10.1006/jcss.1997.1504},
  }
  @Article{,
    title = {Bagging Predictors},
    author = {Leo Breiman},
    journal = {Machine Learning},
    year = {1996},
    volume = {24},
    number = {2},
    pages = {123--140},
    doi = {10.1007/BF00058655},
  }

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.