The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
NEWS.md file to track package changes.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.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.