runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS
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runjags : an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. / Denwood, Matt.
In: Journal of Statistical Software, Vol. 71, No. 9, 2016, p. 1-25.Research output: Contribution to journal › Journal article › Research › peer-review
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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