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  "Package": "quickOutlier",
  "Title": "Detect and Treat Outliers in Data Mining",
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  "Description": "Implements a suite of tools for outlier detection and\ntreatment in data mining. It includes univariate methods\n(Z-score, Interquartile Range), multivariate detection using\nMahalanobis distance, and density-based detection (Local\nOutlier Factor) via the 'dbscan' package. It also provides\nfunctions for visualization using 'ggplot2' and data cleaning\nvia Winsorization.",
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  "Repository": "https://daniellop1.r-universe.dev",
  "Date/Publication": "2025-12-19 16:23:31 UTC",
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      "title": "Detect Outliers using Isolation Forest (Machine Learning)",
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      "page": "detect_outliers",
      "title": "Detect Anomalies in a Data Frame",
      "topics": [
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      ]
    },
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      "page": "detect_ts_outliers",
      "title": "Detect Anomalies in Time Series using STL Decomposition",
      "topics": [
        "detect_ts_outliers"
      ]
    },
    {
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      "title": "Diagnose Influential Points in Linear Models (Cook's Distance)",
      "topics": [
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    {
      "page": "plot_interactive",
      "title": "Create an Interactive Outlier Plot",
      "topics": [
        "plot_interactive"
      ]
    },
    {
      "page": "plot_outliers",
      "title": "Plot Outliers with ggplot2",
      "topics": [
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    },
    {
      "page": "scan_data",
      "title": "Scan Entire Dataset for Outliers",
      "topics": [
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      ]
    },
    {
      "page": "treat_outliers",
      "title": "Treat Outliers (Winsorization/Capping)",
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        "Scanning the Dataset",
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