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