---
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)

**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](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](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)
```