Detect and Treat Outliers in Data Mining

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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)