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Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures | Canale | Journal of Statistical Software
Authors: Antonio Canale
Title: msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures
Abstract: msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric inference introduced by Canale and Dunson (2016). The method, based on mixtures of multiscale beta dictionary densities, overcomes the drawbacks of Pólya trees and inherits many of the advantages of Dirichlet process mixture models. The key idea is that an infinitely-deep binary tree is introduced, with a beta dictionary density assigned to each node of the tree. Using a multiscale stick-breaking characterization, stochastically decreasing weights are assigned to each node. The result is an infinite mixture model. The package msBP implements a series of basic functions to deal with this family of priors such as random densities and numbers generation, creation and manipulation of binary tree objects, and generic functions to plot and print the results. In addition, it implements the Gibbs samplers for posterior computation to perform multiscale density estimation and multiscale testing of group differences described in Canale and Dunson (2016).

Page views:: 812. Submitted: 2015-10-24. Published: 2017-06-07.
Paper: msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures     Download PDF (Downloads: 316)
Supplements:
msBP_1.3.tar.gz: R source package Download (Downloads: 28; 23KB)
v78i06.R: R replication code Download (Downloads: 31; 10KB)
indianliver.csv: Supplementary data (CSV format) Download (Downloads: 24; 8KB)

DOI: 10.18637/jss.v078.i06

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.