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: 0.1.0
Imports: dbscan, ggplot2, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-12-19
DOI: 10.32614/CRAN.package.quickOutlier (may not be active yet)
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
CRAN checks: quickOutlier results

Documentation:

Reference manual: quickOutlier.html , quickOutlier.pdf
Vignettes: Introduction to quickOutlier (source, R code)

Downloads:

Package source: quickOutlier_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): quickOutlier_0.1.0.tgz

Linking:

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