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: João Vitor Dias Monteiro, Sudipto Banerjee, Gurumurthy Ramachandran
Title: B2Z: R Package for Bayesian Two-Zone Models
Abstract: A primary issue in industrial hygiene is the estimation of a worker's exposure to chemical, physical and biological agents. Mathematical modeling is increasingly being used as a method for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Recently, Zhang, Banerjee, Yang, Lungu, and Ramachandran (2009) proposed Bayesian hierarchical models for estimating parameters and exposure concentrations for the two-zone differential equation models and for predicting concentrations in a zone near and far away from the source of contamination.

Bayesian estimation, however, can often require substantial amounts of user-defined code and tuning. In this paper, we introduce a statistical software package, B2Z, built upon the R statistical computing platform that implements a Bayesian model for estimating model parameters and exposure concentrations in two-zone models. We discuss the algorithms behind our package and illustrate its use with simulated and real data examples.

Page views:: 3136. Submitted: 2010-06-09. Published: 2011-07-23.
Paper: B2Z: R Package for Bayesian Two-Zone Models     Download PDF (Downloads: 2774)
B2Z_1.4.tar.gz: R source package Download (Downloads: 656; 32KB)
v43i02.R: R example code from the paper Download (Downloads: 680; 6KB)
realdata.txt: Example data in ASCII format Download (Downloads: 762; 3KB)

DOI: 10.18637/jss.v043.i02

This work is licensed under the licenses
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.