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
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Authors: Vahid Partovi Nia, Anthony C. Davison
Title: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust
Abstract: The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples.

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Paper: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust     Download PDF (Downloads: 8765)
Supplements:
bclust_1.3.tar.gz: R source package Download (Downloads: 607; 48KB)
v47i05.R: R example code from the paper Download (Downloads: 684; 4KB)
simG.RData: Simulated Gaussian data in R binary format Download (Downloads: 675; 15KB)
DOI: 10.18637/jss.v047.i05

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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.