R package R2jags: Using R to Run ’JAGS’. Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.

References in zbMATH (referenced in 8 articles )

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  1. Coley, Rebecca Yates; Fisher, Aaron J.; Mamawala, Mufaddal; Carter, Herbert Ballentine; Pienta, Kenneth J.; Zeger, Scott L.: A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer (2017)
  2. Hong, Hwanhee; Rudolph, Kara E.; Stuart, Elizabeth A.: Bayesian approach for addressing differential covariate measurement error in propensity score methods (2017)
  3. Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv
  4. Arcuti, Simona; Pollice, Alessio; Ribecco, Nunziata; D’Onghia, Gianfranco: Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model (2016)
  5. Aregay, Mehreteab; Shkedy, Ziv; Molenberghs, Geert: Comparison of additive and multiplicative Bayesian models for longitudinal count data with overdispersion parameters: a simulation study (2015)
  6. Lunn, David; Jackson, Christopher; Best, Nicky; Thomas, Andrew; Spiegelhalter, David: The BUGS book. A practical introduction to Bayesian analysis (2013)
  7. Higgs, Megan D.; Hoef, Jay M.Ver: Discretized and aggregated: modeling dive depth of harbor seals from ordered categorical data with temporal autocorrelation (2012)
  8. Schofield, Matthew R.; Barker, Richard J.: Full open population capture-recapture models with individual covariates (2011)