runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Standard

runjags : an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. / Denwood, Matt.

I: Journal of Statistical Software, Bind 71, Nr. 9, 2016, s. 1-25.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Denwood, M 2016, 'runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS', Journal of Statistical Software, bind 71, nr. 9, s. 1-25. https://doi.org/10.18637/jss.v071.i09

APA

Denwood, M. (2016). runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software, 71(9), 1-25. https://doi.org/10.18637/jss.v071.i09

Vancouver

Denwood M. runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software. 2016;71(9):1-25. https://doi.org/10.18637/jss.v071.i09

Author

Denwood, Matt. / runjags : an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. I: Journal of Statistical Software. 2016 ; Bind 71, Nr. 9. s. 1-25.

Bibtex

@article{960f9129599e4eb1842cd554298bfe62,
title = "runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS",
abstract = "The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to commonly used graphical outputs and summary statistics, and parallelized methods of running JAGS are provided. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. Automated simulation study functions are implemented to facilitate model performance assessment, as well as drop-k type cross-validation studies, using high performance computing clusters such as those provided by parallel. A module extension for JAGS is also included within runjags, providing the Pareto family of distributions and a series of minimally-informative priors including the DuMouchel and half-Cauchy priors. This paper outlines the primary functions of this package, and gives an illustration of a simulation study to assess the sensitivity of two equivalent model formulations to different prior distributions. ",
keywords = "MCMC, Bayesian, graphical models, interface utilities, JAGS, BUGS, R",
author = "Matt Denwood",
year = "2016",
doi = "10.18637/jss.v071.i09",
language = "English",
volume = "71",
pages = "1--25",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "The Foundation for Open Access Statistics",
number = "9",

}

RIS

TY - JOUR

T1 - runjags

T2 - an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS

AU - Denwood, Matt

PY - 2016

Y1 - 2016

N2 - The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to commonly used graphical outputs and summary statistics, and parallelized methods of running JAGS are provided. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. Automated simulation study functions are implemented to facilitate model performance assessment, as well as drop-k type cross-validation studies, using high performance computing clusters such as those provided by parallel. A module extension for JAGS is also included within runjags, providing the Pareto family of distributions and a series of minimally-informative priors including the DuMouchel and half-Cauchy priors. This paper outlines the primary functions of this package, and gives an illustration of a simulation study to assess the sensitivity of two equivalent model formulations to different prior distributions.

AB - The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to commonly used graphical outputs and summary statistics, and parallelized methods of running JAGS are provided. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. Automated simulation study functions are implemented to facilitate model performance assessment, as well as drop-k type cross-validation studies, using high performance computing clusters such as those provided by parallel. A module extension for JAGS is also included within runjags, providing the Pareto family of distributions and a series of minimally-informative priors including the DuMouchel and half-Cauchy priors. This paper outlines the primary functions of this package, and gives an illustration of a simulation study to assess the sensitivity of two equivalent model formulations to different prior distributions.

KW - MCMC

KW - Bayesian

KW - graphical models

KW - interface utilities

KW - JAGS

KW - BUGS

KW - R

U2 - 10.18637/jss.v071.i09

DO - 10.18637/jss.v071.i09

M3 - Journal article

VL - 71

SP - 1

EP - 25

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 9

ER -

ID: 168325760