Package: quickOutlier
Title: Detect and Treat Outliers in Data Mining
Version: 0.1.5
Authors@R: 
    person(
      given  = "Daniel",
      family = "López Pérez",
      email  = "dlopez350@icloud.com",
      role   = c("aut", "cre"),
      comment = c(ORCID = "0009-0008-1695-9837"))
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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: dbscan, ggplot2, isotree, plotly, stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
URL: https://github.com/daniellop1/quickOutlier
BugReports: https://github.com/daniellop1/quickOutlier/issues
NeedsCompilation: no
Packaged: 2026-02-13 19:27:34 UTC; dlopez
Author: Daniel López Pérez [aut, cre] (ORCID:
    <https://orcid.org/0009-0008-1695-9837>)
Maintainer: Daniel López Pérez <dlopez350@icloud.com>
Repository: CRAN
Date/Publication: 2026-02-13 19:40:02 UTC
Built: R 4.4.3; ; 2026-03-06 04:51:17 UTC; windows
