HDoutliers (1.0)
Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers.
https://www.r-project.org
https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf
http://cran.r-project.org/web/packages/HDoutliers
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See .
Maintainer:
Chris Fraley
Author(s): Chris Fraley [aut, cre], Leland Wilkinson [ctb]
License: MIT + file LICENSE
Uses: FactoMineR, FNN, mclust
Released almost 2 years ago.
7 previous versions
- HDoutliers_0.15. Released almost 3 years ago.
- HDoutliers_0.14. Released about 3 years ago.
- HDoutliers_0.12. Released about 3 years ago.
- HDoutliers_0.9. Released about 3 years ago.
- HDoutliers_0.8. Released about 3 years ago.
- HDoutliers_0.5. Released over 3 years ago.
- HDoutliers_0.2. Released over 3 years ago.
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