B2Z: An R package for bayesian two-zone models

João Vitor Dias Monteiro, Sudipto Banerjee, Gurumurthy Ramachandran

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalJournal of Statistical Software
Volume43
Issue number2
DOIs
StatePublished - Jul 2011

Keywords

  • Bayesian inference
  • Markov chain monte carlo
  • R package
  • Two-zone models

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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