Anomaly Detection with Normal Probability Functions
Released Mar 18, 2019 by Dmitriy Bolotov
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:
This package can be included as a dependency from a Java or Scala project by including
the following your project's
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>amelie</artifactId> <version>0.2.1-b1</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
If you're using Renjin from the command line, you load this library by invoking:
This package was last tested against Renjin 0.9.2725 on Mar 26, 2019.