Package: amelie 0.2.1

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>.

Authors:Dmitriy Bolotov [aut, cre]

amelie_0.2.1.tar.gz
amelie_0.2.1.zip(r-4.7)amelie_0.2.1.zip(r-4.6)amelie_0.2.1.zip(r-4.5)
amelie_0.2.1.tgz(r-4.6-any)amelie_0.2.1.tgz(r-4.5-any)
amelie_0.2.1.tar.gz(r-4.7-any)amelie_0.2.1.tar.gz(r-4.6-any)
amelie_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
amelie/json (API)
NEWS

# Install 'amelie' in R:
install.packages('amelie', repos = c('https://dbolotov.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dbolotov/amelie/issues

On CRAN:

Conda:

anomaly-detection

3.70 score 6 scripts 244 downloads 1 mentions 2 exports 0 dependencies

Last updated from:82d3d7bdc6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK118
source / vignettesOK155
linux-release-x86_64OK98
macos-release-arm64OK139
macos-oldrel-arm64OK152
windows-develOK75
windows-releaseOK77
windows-oldrelOK69
wasm-releaseOK80

Exports:adpdfunc

Dependencies:

Amelie Introduction

Rendered fromamelie-introduction.Rmdusingknitr::rmarkdownon May 17 2026.

Last update: 2018-03-23
Started: 2018-02-19