quickOutlier: Detect and Treat Outliers in Data Mining

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.

Version:	0.1.5
Imports:	dbscan, ggplot2, isotree, plotly, stats
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2026-02-13
DOI:	10.32614/CRAN.package.quickOutlier
Author:	Daniel López Pérez [aut, cre]
Maintainer:	Daniel López Pérez <dlopez350 at icloud.com>
BugReports:	https://github.com/daniellop1/quickOutlier/issues
License:	MIT + file LICENSE
URL:	https://github.com/daniellop1/quickOutlier
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	quickOutlier results

Documentation:

Reference manual:	quickOutlier.html , quickOutlier.pdf
Vignettes:	Getting Started with quickOutlier (source, R code)

Downloads:

Package source:	quickOutlier_0.1.5.tar.gz
Windows binaries:	r-devel: quickOutlier_0.1.5.zip, r-release: quickOutlier_0.1.5.zip, r-oldrel: quickOutlier_0.1.5.zip
macOS binaries:	r-release (arm64): quickOutlier_0.1.5.tgz, r-oldrel (arm64): quickOutlier_0.1.5.tgz, r-release (x86_64): quickOutlier_0.1.5.tgz, r-oldrel (x86_64): quickOutlier_0.1.5.tgz
Old sources:	quickOutlier archive

Linking:

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