OutlierDetection: 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.

Version: 0.1.1
Imports: ggplot2, DDoutlier, depth, depthTools, ldbod, spatstat, plotly
Published: 2019-06-15
Author: Vinay Tiwari, Akanksha Kashikar
Maintainer: Vinay Tiwari <vinaystiwari786 at gmail.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: OutlierDetection results


Reference manual: OutlierDetection.pdf
Package source: OutlierDetection_0.1.1.tar.gz
Windows binaries: r-devel: OutlierDetection_0.1.1.zip, r-release: OutlierDetection_0.1.1.zip, r-oldrel: OutlierDetection_0.1.1.zip
macOS binaries: r-release: OutlierDetection_0.1.1.tgz, r-oldrel: OutlierDetection_0.1.1.tgz
Old sources: OutlierDetection archive


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