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bayesclust: An R Package for Testing and Searching for Significant Clusters

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  • 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 : = 1 versus H₁ : = k in a Bayesian setting, where 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.
  • This is the publisher’s final pdf. The published article is copyrighted by American Statistical Association and can be found at: http://www.jstatsoft.org/.
  • Keywords: clustering, R, hierarchical, Bayes
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  • Gopal, V., Fuentes, C., & Casella, G. (2012). Bayesclust: An R package for testing and searching for significant clusters. Journal of Statistical Software, 47(14), 1-21.
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  • 47
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  • 14
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  • This research was partially supported by NSF-DBI grant 0606607 and NIH grant R01GM081704.
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