R: aricode

aricode {aricode} | R Documentation

## aricode

### Description

A package for efficient computations of standard clustering comparison measures. Most of the available measures are described in the paper of Vinh et al, JMLR, 2009 (see reference below).

### Details

Traditional implementations (e.g., function `adjustedRandIndex` of package `mclust`) are in Omega(n + u v) where n is the size of the vectors the classifications of which are to be compared, u and v are the respective number of classes in each vectors. Here, the implementation is in Theta(n), plus the gain of speed due to the C++ code.

### Functions in aricode

The functions included in aricode are:

- ARI: computes the adjusted rand index

- Chi2: computes the Chi-square statistic

- MARI: computes the modified adjusted rand index (Sundqvist et al, in preparation)

- MARIraw: computes the raw version of the modified adjusted rand index

- RI: computes the rand index

- NVI: computes the normalized variation information

- NID: computes the normalized information distance

- NMI: computes the normalized mutual information

- AMI: computes the adjusted mutual information

- entropy: computes the conditional and joint entropies

- clustComp: computes all clustering comparison measures at once

### Author(s)

Julien Chiquet julien.chiquet@inrae.com

Guillem Rigaill guillem.rigaill@inrae.fr

Martina Sundqvist martina.sundqvist@agroparistech.fr

### References

Nguyen Xuan Vinh, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance." Journal of Machine Learning Research 11.Oct (2010): 2837-2854. as described in Vinh et al (2009)

### See Also

`ARI`, `RI`, `NID`, `NVI`, `AMI`, `NMI`, `entropy`, `clustComp`

[Package aricode version 1.0.0 Index]