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quickOutlier (version 0.1.5)

Detect and Treat Outliers in Data Mining

Description

Implements a suite of tools for outlier detection and treatment in data mining. It includes univariate methods (Z-score, Interquartile Range), multivariate detection using Mahalanobis distance, and density-based detection (Local Outlier Factor) via the 'dbscan' package. It also provides functions for visualization using 'ggplot2' and data cleaning via Winsorization.

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Install

install.packages('quickOutlier')

Version

0.1.5

License

MIT + file LICENSE

Issues

0

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Maintainer

Daniel López Pérez

Last Published

February 13th, 2026

Functions in quickOutlier (0.1.5)

detect_ts_outliers

Detect Anomalies in Time Series using STL Decomposition
detect_iforest

Detect Outliers using Isolation Forest (Machine Learning)
detect_categorical_outliers

Detect Rare Categories (Categorical Outliers)
detect_density

Detect Density-Based Anomalies (LOF)
plot_interactive

Create an Interactive Outlier Plot
diagnose_influence

Diagnose Influential Points in Linear Models (Cook's Distance)
detect_multivariate

Detect Multivariate Anomalies (Mahalanobis Distance)
detect_outliers

Detect Anomalies in a Data Frame
plot_outliers

Plot Outliers with ggplot2
scan_data

Scan Entire Dataset for Outliers
treat_outliers

Treat Outliers (Winsorization/Capping)