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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: "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 Luan20050519@163.com.

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)

Methodological Framework

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Version

Install

install.packages('E2E')

Monthly Downloads

179

Version

0.1.2

License

MIT + file LICENSE

Maintainer

Shanjie Luan

Last Published

December 4th, 2025

Functions in E2E (0.1.2)

get_registered_models_pro

Get Registered Prognostic Models

Train an EasyEnsemble Model for Imbalanced Classification

Train a Multi-Layer Perceptron (Neural Network) Model for Classification

get_registered_models_dia

Get Registered Diagnostic Models

Train Gradient Boosting Machine (GBM) for Survival

print_model_summary_dia

Print Diagnostic Model Summary

Generic Prediction Interface for Prognostic Models

plot_integrated_results

Visualize Integrated Modeling Results

Train a Linear Discriminant Analysis (LDA) Model for Classification

evaluate_predictions_dia

Evaluate Predictions from a Data Frame

Train Lasso Cox Proportional Hazards Model

Train Stepwise Cox Model (AIC-based)

Train Partial Least Squares Cox (PLS-Cox)

Train a Random Forest Model for Classification

Train a Ridge (L2 Regularized Logistic Regression) Model for Classification

Train a Quadratic Discriminant Analysis (QDA) Model for Classification

register_model_pro

Register a Prognostic Model

print_model_summary_pro

Print Prognostic Model Summary

initialize_modeling_system_dia

Initialize Diagnostic Modeling System

register_model_dia

Register a Diagnostic Model Function

initialize_modeling_system_pro

Initialize Prognosis Modeling System

Run Multiple Diagnostic Models

Train Ridge Cox Model

Train Random Survival Forest (RSF)

Train a Voting Ensemble Diagnostic Model

Training Data for Diagnostic Models

Train a Support Vector Machine (Linear Kernel) Model for Classification

Train a Stacking Diagnostic Model

find_optimal_threshold_dia

Find Optimal Probability Threshold

min_max_normalize

Min-Max Normalization

Train a Gradient Boosting Machine (GBM) Model for Classification

Comprehensive Prognostic Modeling Pipeline

Train a Lasso (L1 Regularized Logistic Regression) Model for Classification

Train an XGBoost Tree Model for Classification

Train Stacking Ensemble for Prognosis

Train XGBoost Cox Model

load_and_prepare_data_dia

Load and Prepare Data for Diagnostic Models

Training Data for Prognostic (Survival) Models

Comprehensive Diagnostic Modeling Pipeline

Imbalanced Data Diagnostic Modeling Pipeline

Train a Naive Bayes Model for Classification

Run Multiple Prognostic Models

Test Data for Diagnostic Models

Test Data for Prognostic (Survival) Models

Train a Bagging Diagnostic Model

Train a Decision Tree Model for Classification

Train Elastic Net Cox Model

re-export Surv from survival

calculate_metrics_at_threshold_dia

Calculate Classification Metrics at a Specific Threshold

evaluate_model_dia

Evaluate Diagnostic Model Performance

Train Bagging Ensemble for Prognosis

Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classification

Apply Prognostic Model to New Data

Apply a Trained Model to New Data

Generate and Plot SHAP Explanation Figures

evaluate_predictions_pro

Evaluate External Predictions

Plot Prognostic Model Evaluation Figures

Plot Diagnostic Model Evaluation Figures

evaluate_model_pro

Evaluate Prognostic Model Performance