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We hope to allow for a wider range of base predictors, \ni.e. deep learning methods, in future releases. A key feature of ``eipy`` is its built-in nested cross-validation approach, allowing for a \nfair comparison of a collection of user-defined ensemble methods.\n\nDocumentation including tutorials are available at `https://eipy.readthedocs.io/en/latest/ \u003chttps://eipy.readthedocs.io/en/latest/\u003e`_.\n\nInstallation\n------------\n\nAs usual it is recommended to set up a virtual environment prior to installation. \nYou can install ensemble-integration with pip:\n\n``pip install ensemble-integration``\n\nCitation\n--------\n\nIf you use ``ensemble-integration`` in a scientific publication please cite the following:\n\nJamie J. R. Bennett, Yan Chak Li and Gaurav Pandey. *An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles*, https://doi.org/10.48550/arXiv.2401.09582.\n\nYan Chak Li, Linhua Wang, Jeffrey N Law, T M Murali, Gaurav Pandey. *Integrating multimodal data through interpretable heterogeneous ensembles*, Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac065, https://doi.org/10.1093/bioadv/vbac065.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgauravpandeylab%2Feipy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgauravpandeylab%2Feipy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgauravpandeylab%2Feipy/lists"}