---
title: "1. Getting Started"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{1. Getting Started}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# 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), Ximing Wang

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

**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](mailto:Luan20050519@163.com).

## Installation

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

```{r, eval=FALSE}
# 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:

```{r setup}
library(E2E)
```

## Core Concepts

E2E operates on two parallel tracks: **Diagnostic Models** and **Prognostic Models**. Before using functions from either track, you **must initialize** the corresponding system. This step registers a suite of pre-defined, commonly used models.

### Sample Data

To follow the examples, you'll need sample data files. There are four data frames included in the package for you to try: `train_dia`, `test_dia`, `train_pro`, `test_pro`. 

`train_dia` and `test_dia` are for diagnosis, with column names sample, outcome, variable 1, 2, 3. 

```{r}
head(train_dia)
```

`train_pro` and `test_pro` are for prognosis, with column names sample, outcome, time, variable 1, 2, 3.

```{r}
head(train_pro)
```