runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS

By M. Denwood. 2016

686 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7774423b57aa591d4fea83eb9aed79a2f2e0b087

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Anuj Sharma
2. Mo Zhao
3. Martyn Plummer
4. Yves Rosseel
5. John K. Kruschke
6. Edgar C. Merkle
7. Wei Li
8. Chenhui Liu

In Statistics and Probability

1. Anuj Sharma
2. Mo Zhao
3. Martyn Plummer
4. Yves Rosseel
5. John K. Kruschke
6. Edgar C. Merkle
7. Wei Li
8. Chenhui Liu

Citations in the corpus

• Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, methodology): “7 of Kruschke, 2015) and computed using the JAGS software (Plummer, 2003, 2017) with the runjags package in R (Denwood, 2016). HDI limits were computed from the MCMC chain using the method explained in Section 25.2.3 of Kruschke (2015) and with an effective sample size that exceeded 10,000, as recom”
• Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, methodology): “JAGS was run in R using the ‘runjags’ package (Denwood, 2016), by which the parallel computation could be easily realized.”
• JAGS Version 4.0.0 user manual (2015, methodology): “The runjags package [Denwood, 2016] includes many enhancements to JAGS, including a custom JAGS module that contains some additional distributions in the Pareto family.”
• blavaan: Bayesian structural equation models via parameter expansion (2015, methodology): “These pieces can be used to run the model “manually” via, e.g., package rjags (Plummer 2015a) or runjags (Denwood 2016).”