CRAN
modes 0.7.0
Find the Modes and Assess the Modality of Complex and Mixture Distributions, Especially with Big Datasets
Released Mar 7, 2016 by Sathish Deevi
Designed with a dual purpose of accurately estimating the mode (or modes) as well as characterizing the modality of data. The specific application area includes complex or mixture distributions particularly in a big data environment. The heterogeneous nature of (big) data may require deep introspective statistical and machine learning techniques, but these statistical tools often fail when applied without first understanding the data. In small datasets, this often isn't a big issue, but when dealing with large scale data analysis or big data thoroughly inspecting each dimension typically yields an O(n^n-1) problem. As such, dealing with big data require an alternative toolkit. This package not only identifies the mode or modes for various data types, it also provides a programmatic way of understanding the modality (i.e. unimodal, bimodal, etc.) of a dataset (whether it's big data or not). See
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies>
<dependency>
<groupId>org.renjin.cran</groupId>
<artifactId>modes</artifactId>
<version>0.7.0-b29</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>bedatadriven</id>
<name>bedatadriven public repo</name>
<url>https://nexus.bedatadriven.com/content/groups/public/</url>
</repository>
</repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:modes')
Test Results
This package was last tested against Renjin 0.9.2687 on Aug 24, 2018.