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Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.
| Version: | 0.2.1 |
| Imports: | stats |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2019-03-18 |
| Author: | Dmitriy Bolotov [aut, cre] |
| Maintainer: | Dmitriy Bolotov <dbolotov at live.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | amelie results |
| Reference manual: | amelie.pdf |
| Vignettes: |
Introduction |
| Package source: | amelie_0.2.1.tar.gz |
| Windows binaries: | r-devel: amelie_0.2.1.zip, r-release: amelie_0.2.1.zip, r-oldrel: amelie_0.2.1.zip |
| macOS binaries: | r-release (arm64): amelie_0.2.1.tgz, r-oldrel (arm64): amelie_0.2.1.tgz, r-release (x86_64): amelie_0.2.1.tgz, r-oldrel (x86_64): amelie_0.2.1.tgz |
| Old sources: | amelie archive |
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These binaries (installable software) and packages are in development.
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