Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Brian J. Smith
Title: boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference
Abstract: Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint posterior distributions in Bayesian analysis. The idea of MCMC is to iteratively produce parameter values that are representative samples from the joint posterior. Unlike frequentist analysis where iterative model fitting routines are monitored for convergence to a single point, MCMC output is monitored for convergence to a distribution. Thus, specialized diagnostic tools are needed in the Bayesian setting. To this end, the R package boa was created. This manuscript presents the user's manual for boa, which outlines the use of and methodology upon which the software is based. Included is a description of the menu system, data management capabilities, and statistical/graphical methods for convergence assessment and posterior inference. Throughout the manual, a linear regression example is used to illustrate the software.

Page views:: 19000. Submitted: 2007-04-27. Published: 2007-11-13.
Paper: boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference     Download PDF (Downloads: 21865)
boa_1.1.6-1.tar.gz: R source package Download (Downloads: 1387; 421KB)

DOI: 10.18637/jss.v021.i11

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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.