# Detect and Treat Outliers in Data Mining

## Documentation for package ‘quickOutlier’ version 0.1.5

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

## Help Pages

detect_categorical_outliers | Detect Rare Categories (Categorical Outliers)
detect_density | Detect Density-Based Anomalies (LOF)
detect_iforest | Detect Outliers using Isolation Forest (Machine Learning)
detect_multivariate | Detect Multivariate Anomalies (Mahalanobis Distance)
detect_outliers | Detect Anomalies in a Data Frame
detect_ts_outliers | Detect Anomalies in Time Series using STL Decomposition
diagnose_influence | Diagnose Influential Points in Linear Models (Cook's Distance)
plot_interactive | Create an Interactive Outlier Plot
plot_outliers | Plot Outliers with ggplot2
scan_data | Scan Entire Dataset for Outliers
treat_outliers | Treat Outliers (Winsorization/Capping)