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) .


News

amelie 0.2.0.9000

  • Added multivariate option for probability density function calculation.
  • Added Matthews correlation coefficient as score for epsilon optimization.
  • Added pdfunc for computing density separately.
  • Added steps argument to ad function.
  • Added multivariate section to introduction vignette.
  • Added a NEWS.md file.

amelie 0.1.0

  • Initial release supporting the univariate Gaussian approach with F1 score.

Reference manual

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install.packages("amelie")

0.2.1 by Dmitriy Bolotov, 3 months ago


Browse source code at https://github.com/cran/amelie


Authors: Dmitriy Bolotov [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports stats

Suggests testthat, knitr, rmarkdown


See at CRAN