Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Vikneswaran Gopal, Claudio Fuentes, George Casella
Title: bayesclust: An R Package for Testing and Searching for Significant Clusters
Abstract: The detection and determination of clusters has been of special interest among researchers from different fields for a long time. In particular, assessing whether the clusters are significant is a question that has been asked by a number of experimenters. In Fuentes and Casella (2009), the authors put forth a new methodology for analyzing clusters. It tests the hypothesis H0 : κ = 1 versus H1 : κ = k in a Bayesian setting, where κ denotes the number of clusters in a population. The bayesclust package implements this approach in R. Here we give an overview of the algorithm and a detailed description of the functions available in the package. The routines in bayesclust allow the user to test for the existence of clusters, and then pick out optimal partitionings of the data. We demonstrate the testing procedure with simulated datasets.

Page views:: 2980. Submitted: 2009-03-21. Published: 2012-05-17.
Paper: bayesclust: An R Package for Testing and Searching for Significant Clusters     Download PDF (Downloads: 2591)
bayesclust_3.1.tar.gz: R source package Download (Downloads: 526; 19KB)
v47i14.R: R example code from the paper Download (Downloads: 582; 1KB)

DOI: 10.18637/jss.v047.i14

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.