Outlier Detection

To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 < https://www.jstor.org/stable/2347159>), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 < http://cs.uef.fi/~franti/papers.html>), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 < https://link.springer.com/chapter/10.1007/0-387-25465-X_7>), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 < http://www.aaai.org/Library/KDD/1998/kdd98-038.php>) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 < https://dl.acm.org/citation.cfm?id=3001507>). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate, bivariate and trivariate data, visualization is also provided.


Reference manual

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0.1.1 by Vinay Tiwari, a year ago

Browse source code at https://github.com/cran/OutlierDetection

Authors: Vinay Tiwari , Akanksha Kashikar

Documentation:   PDF Manual  

GPL-2 license

Imports ggplot2, DDoutlier, depth, depthTools, ldbod, spatstat, plotly

See at CRAN