Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) . It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.

Documentation

Manual: SmartSifter.pdf
Vignette: None available.

Maintainer: Lizhen Nie <nie_lizhen at yahoo.com>

Author(s): Lizhen Nie <nie_lizhen at yahoo.com>

Install package and any missing dependencies by running this line in your R console:

install.packages("SmartSifter")

Depends R (>= 3.3.1)
Imports mvtnorm, rootSolve
Suggests testthat
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Package SmartSifter
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Version 0.1.0
Published 2016-09-14
License GPL (>= 2)
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NeedsCompilation no
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Package source SmartSifter_0.1.0.tar.gz