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OutlierDetection (version 0.1.1)

Outlier Detection

Description

To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 ), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 ), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 ), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 ) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 ). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate, bivariate and trivariate data, visualization is also provided.

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Version

Install

install.packages('OutlierDetection')

Monthly Downloads

34

Version

0.1.1

License

GPL-2

Maintainer

Vinay Tiwari

Last Published

June 15th, 2019

Functions in OutlierDetection (0.1.1)

nn

Outlier detection using k Nearest Neighbours Distance method
maha

Outlier detection using Mahalanobis Distance
nnk

Outlier detection using kth Nearest Neighbour Distance method
PCOutlierDetection

Principal Component Outlier Detection(Intersection of all the methods applied on pc's)
OutlierDetection

Outlier Detection(Intersection of all the methods)
disp

Outlier detection using genralised dispersion
depthout

Outlier detection using depth based method
UnivariateOutlierDetection

Univariate Outlier Detection(Intersection of all the methods)
dens

Outlier detection using Robust Kernal-based Outlier Factor(RKOF) algorithm