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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
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: quickOutlier_0.1.0.zip, r-release: not available, r-oldrel: quickOutlier_0.1.0.zip
macOS binaries: r-release (arm64): quickOutlier_0.1.0.tgz, r-oldrel (arm64): quickOutlier_0.1.0.tgz, r-release (x86_64): quickOutlier_0.1.0.tgz, r-oldrel (x86_64): quickOutlier_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=quickOutlier to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.