# XAItest: Enhancing Feature Discovery with eXplainable AI

## Documentation for package ‘XAItest’ version 1.3.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

.objXAI | ObjXAI class
addSimThresh | Add "simThresh" Simulated Threshold Feature
genScenario | Generate benchmark scenarios for feature discovery
getFeatImpThresholds | The getFeatImpThresholds function identifies the minimum level of feature importance required to exceed a specified significance threshold, which is determined by the p-value.
getMetricsTable | Get the Metrics Table
getMetricsTable-method | Get the Metrics Table
mapPvalImportance | The mapPvalImportance function displays a datatable with color-coded cells based on significance thresholds for feature importance and p-value columns.
modelsOverview | Models Overview
ObjXAI-class | ObjXAI class
plotModel | Plot the model
setMetricsTable | Set the Metrics Table
setMetricsTable-method | Set the Metrics Table
show-method | Show Method for ObjXAI
XAI.test | The XAI.test function complements t-test and correlation analyses in feature discovery by integrating eXplainable AI techniques such as feature importance, SHAP, LIME, or custom functions. It provides the option of automatic integration of simulated data to facilitate matching significance between p-values and feature importance.