amelie: Anomaly Detection with Normal Probability Functions

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: 0.2.0
Imports: stats
Suggests: testthat, knitr, rmarkdown
Published: 2018-04-08
Author: Dmitriy Bolotov [aut, cre]
Maintainer: Dmitriy Bolotov <dbolotov at live.com>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: NEWS
CRAN checks: amelie results

Downloads:

Reference manual: amelie.pdf
Vignettes: Introduction
Package source: amelie_0.2.0.tar.gz
Windows binaries: r-prerel: amelie_0.2.0.zip, r-release: amelie_0.2.0.zip, r-oldrel: amelie_0.2.0.zip
OS X binaries: r-prerel: amelie_0.2.0.tgz, r-release: amelie_0.2.0.tgz
Old sources: amelie archive

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