Package: SmartSifter
Title: Online Unsupervised Outlier Detection Using Finite Mixtures with
        Discounting Learning Algorithms
Version: 0.1.0
Date: 2016-09-14
Author: Lizhen Nie <nie_lizhen@yahoo.com>
Maintainer: Lizhen Nie <nie_lizhen@yahoo.com>
Description: 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) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. 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.  
Depends: R (>= 3.3.1)
Imports: mvtnorm, rootSolve
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 5.0.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2016-09-14 01:13:35 UTC; mac
Repository: CRAN
Date/Publication: 2016-09-14 18:50:50
