---
title: "explainer-vignette"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{explainer-vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

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

## Introduction to Explainer Package

This vignette serves as a basic introduction to the functionalities of the Explainer package. Explainer provides a comprehensive toolkit for explaining and interpreting machine learning models.

### Functions Overview

#### SHAPclust

SHAP values are used to cluster data samples using the k-means method to identify subgroups of individuals with specific patterns of feature contributions.

#### ShapFeaturePlot

plots SHAP values in association with feature values

#### ShapPartialPlot

Generates an interactive partial dependence plot based on SHAP values, visualizing the marginal effect of one or two features on the predicted outcome of a machine learning model.

#### eCM_plot

This function generates an enhanced confusion matrix plot using the CVMS package. The plot includes visualizations of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

#### eDecisionCurve

Decision curve analysis is a statistical method used in medical research to evaluate and compare the clinical utility of different diagnostic or predictive models. It assesses the net benefit of a model across a range of decision thresholds, aiding in the selection of the most informative and practical approach for guiding clinical decisions.

#### eFairness

This function generates Precision-Recall and ROC curves for sample subgroups, facilitating fairness analysis of a binary classification model.

#### ePerformance

This function generates Precision-Recall and ROC curves, including threshold information for binary classification models.

#### eROC_plot

This function generates Precision-Recall and ROC curves for binary classification models.

#### eSHAP_plot

The SHAP plot for classification models is a visualization tool that uses the Shapley value, an approach from cooperative game theory, to compute feature contributions for single predictions. The Shapley value fairly distributes the difference of the instance’s prediction and the datasets average prediction among the features. This method is available from the iml package.

#### eSHAP_plot_reg

The SHAP plot for regression models is a visualization tool that uses the Shapley value, an approach from cooperative game theory, to compute feature contributions for single predictions. The Shapley value fairly distributes the difference of the instance’s prediction and the datasets average prediction among the features. This method is available from the iml package.

#### range01

Scale the data to the range of 0 to 1. It uses the Hampel filter to adjust outliers, followed by min-max normalization.

#### regressmdl_eval

Provides calculations of measures to evaluate regression models.