Bayesian multivariate receptor modeling software: BNFA and bayesMRM

Eun Sug Park; Eun Kyung Lee; Man Suk Oh

doi:10.1016/j.chemolab.2021.104280

Bayesian multivariate receptor modeling software: BNFA and bayesMRM

Eun Sug Park, Eun Kyung Lee, Man Suk Oh

Department of Statistics

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

We present user-friendly software tools to implement Bayesian multivariate receptor modeling in the form of a MATLAB function (BNFA) and an R package (bayesMRM). A basic model and a Markov chain Monte Carlo algorithm underlying BNFA and bayesMRM are given. An example of implementation based on real air pollution data is also provided. Users can freely choose between BNFA and bayesMRM depending on their computing platform. These tools are expected to facilitate implementation of Bayesian multivariate receptor models and/or Bayesian nonnegative factor analysis models and promote their use in chemometrics.

Original language	English
Article number	104280
Journal	Chemometrics and Intelligent Laboratory Systems
Volume	211
DOIs	https://doi.org/10.1016/j.chemolab.2021.104280
State	Published - 15 Apr 2021

Bibliographical note

Funding Information:
Man-Suk Oh’s research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government ( NRF-2019R1A2C1003086 ). Eun Sug Park would like to dedicate BNFA to the memory of her long-time friend and mentor, Cliff Spiegelman, who first introduced multivariate receptor modeling and MATLAB to Eun Sug as well as inspiring her throughout collaborative research. The authors are grateful to Prof. Byron Gajewski for testing BNFA and bayesMRM.

Publisher Copyright:
© 2021 The Author(s)

Keywords

Bayesian factor analysis
JAGS
MATLAB
Multivariate receptor modeling
R
Software
Source apportionment

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10.1016/j.chemolab.2021.104280

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title = "Bayesian multivariate receptor modeling software: BNFA and bayesMRM",

abstract = "We present user-friendly software tools to implement Bayesian multivariate receptor modeling in the form of a MATLAB function (BNFA) and an R package (bayesMRM). A basic model and a Markov chain Monte Carlo algorithm underlying BNFA and bayesMRM are given. An example of implementation based on real air pollution data is also provided. Users can freely choose between BNFA and bayesMRM depending on their computing platform. These tools are expected to facilitate implementation of Bayesian multivariate receptor models and/or Bayesian nonnegative factor analysis models and promote their use in chemometrics.",

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KW - Software

KW - Source apportionment

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Bayesian multivariate receptor modeling software: BNFA and bayesMRM

Abstract

Bibliographical note

Keywords

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