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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("OutlierDetection")

0.1.1 by Vinay Tiwari, 12 days 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