E2E
Ensemble Learning Framework for Diagnostic and Prognostic Modeling
Provides a framework to build and evaluate diagnosis or
prognosis models using stacking, voting, and bagging ensemble
techniques with various base learners. The package also includes
tools for visualization and interpretation of models. The development
version of the package is available on 'GitHub' at
< https://github.com/xiaojie0519/E2E>. The methods
are based on the foundational work of Breiman (1996)
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: "Luan, S. and Wang, X. (2025), E2E: An R Package for Easy-to-Build Ensemble Models. Med Research. https://doi.org/10.1002/mdr2.70030"
Note: The article is open source on CRAN and Github and is free to use, but you have to cite our article if you use E2E in your research. If you have any questions, please contact [email protected].
Documentation
For complete documentation, tutorials, and function references, please visit our pkgdown website:
https://XIAOJIE0519.github.io/E2E/
back to our github website:
https://github.com/XIAOJIE0519/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:
library(E2E)