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.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.3.1) |
| Imports: | mvtnorm, rootSolve |
| Suggests: | testthat |
| Published: | 2016-09-14 |
| Author: | Lizhen Nie |
| Maintainer: | Lizhen Nie <nie_lizhen at yahoo.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | SmartSifter citation info |
| CRAN checks: | SmartSifter results |
| Reference manual: | SmartSifter.pdf |
| Package source: | SmartSifter_0.1.0.tar.gz |
| Windows binaries: | r-devel: SmartSifter_0.1.0.zip, r-release: SmartSifter_0.1.0.zip, r-oldrel: SmartSifter_0.1.0.zip |
| macOS binaries: | r-release: SmartSifter_0.1.0.tgz, r-oldrel: SmartSifter_0.1.0.tgz |
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