JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling

By Martyn Plummer. 2003

1,545 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a4a1cf47f43a6dc520053846101648196d26f18b

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Citations in the corpus

Using MCMC chain outputs to efficiently estimate Bayes factors (2011, methodology): “Although we chose to implement the Gibbs sampler in R, any method of sampling from the marginal posterior distributions will enable the computation of the CMDE, including WinBUGS and JAGS.”
Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis (2015, methodology): “Estimation uses standard Markov chain Monte Carlo software, and the package requires one of JAGS (Plummer, 2003), OpenBUGS (Lunn et al ., 2009), or WinBUGS (Lunn et al ., 2000), to be installed, as well as the corresponding R package.”
Stan: A Probabilistic Programming Language (2017, unknown): “…linear models discussed in Part II of (Gelman and Hill 2007), which are structured with grouped and interacted predictors at multiple levels, hierarchical covariance priors, nonconjugate coeﬃcient priors, latent eﬀects as in item-response models, and varying output link functions and distributions.”
Deep Probabilistic Programming (2017, background): “On the other side, there are languages which emphasize efﬁciency (Spiegelhalter et al., 1995; Murphy, 2001; Plummer, 2003; Salvatier et al., 2015; Carpenter et al., 2016).”
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective (2017, unknown): “The most popular, and free of charge, are JAGS (Plummer, 2003, 2012), BUGS (2013), and Stan (Stan Development Team, 2012).”
bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “This article is structured as follows: First we describe the implementation details of the algorithm from bridgesampling ; second, we illustrate the functionality of the package using a simple Bayesian t -test example where posterior samples are obtained via JAGS .”
Do wild ungulates experience higher stress with humans than with large carnivores? (2018, methodology): “The models were implemented and run in JAGS 3.4.0 interfaced with R through rjags 3.14 and coda 0.16 R packages (Plummer, 2003; Plummer et al.”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, unknown): “Further, there are various packages in the R programming environment (Albert, 2009) such as STAN (Stan Development Team, 2014) and JAGS (Plummer, 2003) that implement Bayesian methods.”
Random effects structure for confirmatory hypothesis testing: Keep it maximal (2013, unknown): “, 2000), JAGS (Plummer, 2003), or MCMCglmm (Hadfield, 2010).”
runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS (2016, unknown): “Just Another Gibbs Sampler ( JAGS ; Plummer 2003) is a cross-platform alternative with a direct interface to R using rjags (Plummer 2016), which can be easily extended with user-speciĄed modules supporting additional distributions and random number generators (Wabersich and Vandekerckhove 2014).”
A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP (2019, unknown): “, 2017) or JAGS (Plummer, 2003), it is relatively straightforward to specify any distribution for the residuals.”
The JASP guidelines for conducting and reporting a Bayesian analysis (2019, unknown): “When dealing with more complex models that go beyond the “staple” analyses such as t-tests, there exist a number of software packages that are aimed at coding custom models, such as JAGS (Plummer, 2003) or Stan (Carpenter et al.”
Rejecting or Accepting Parameter Values in Bayesian Estimation (2018, unknown): “7 of Kruschke, 2015) and computed using the JAGS software (Plummer, 2003, 2017) with the runjags package in R (Denwood, 2016).”