Changes in version 0.1.5 (2026-02-13)                  

  - Added a NEWS.md file to track package changes.

Functions

  - detect_categorical_outliers(): Detects low-frequency outliers in
    categorical variables based on a percentage threshold.
  - detect_lof(): Implements density-based outlier detection using the
    Local Outlier Factor (LOF) algorithm (via dbscan).
  - detect_iforest(): Detects outliers using the Isolation Forest
    algorithm (via isotree), effective for high-dimensional data.
  - detect_multivariate(): Identifies multivariate outliers using
    Mahalanobis distance with a Chi-square threshold.
  - detect_outliers_univ(): Performs univariate outlier detection using
    either Z-score or Interquartile Range (IQR) methods.
  - detect_ts_outliers(): Identifies anomalies in time series data using
    STL decomposition.
  - diagnose_influence(): Diagnoses influential observations in linear
    regression models using Cook's distance.
  - plot_interactive(): Creates interactive scatter plots using plotly
    to visualize multivariate outliers.
  - plot_outliers(): Generates static ggplot2 visualizations combining
    boxplots and jittered points to show outliers.
  - scan_data(): Scans the entire dataset and provides a summary table
    of outlier counts and percentages for all numeric columns.
  - treat_outliers(): Implements Winsorization (capping) to treat
    outliers by replacing extreme values with calculated thresholds.