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E2E: An R Package for Easy-to-Build Ensemble Models

E2E is a comprehensive R package designed to streamline the development, evaluation, and interpretation of machine learning models for both diagnostic (classification) and prognostic (survival analysis) tasks. It provides a robust, extensible framework for training individual models and building powerful ensembles—including Bagging, Voting, and Stacking—with minimal code. The package also includes integrated tools for visualization and model explanation via SHAP values.

Author: Shanjie Luan (ORCID: 0009-0002-8569-8526, First and Corresponding Author), Ximing Wang

Citation: If you use E2E in your research, please cite it as: “Shanjie Luan (2025). E2E: An R Package for Easy-to-Build Ensemble Models. https://github.com/XIAOJIE0519/E2E”

Note: The article is in the process of being written/submitted and is undergoing review by CRAN and further revisions. If you have any questions, please contact Luan20050519@163.com.

Documentation

For complete documentation, tutorials, and function references, please visit our pkgdown website:

https://XIAOJIE0519.github.io/E2E/

Installation

The development version of E2E can be installed directly from GitHub using remotes.

# If you don't have remotes, install it first:
# install.packages("remotes")
remotes::install_github("XIAOJIE0519/E2E")

After installation, load the package into your R session:

Methodological Framework

Workflow
Workflow