JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: (1) To have a cross-platform engine for the BUGS language. (2) To be extensible, allowing users to write their own functions, distributions and samplers. (3) To be a plaftorm for experimentation with ideas in Bayesian modelling. JAGS is licensed under the GNU General Public License. You may freely modify and redistribute it under certain conditions (see the file COPYING for details).

References in zbMATH (referenced in 89 articles )

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  1. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018)
  2. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018)
  3. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018)
  4. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018)
  5. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J.: Bayesian regression modeling with INLA (2018)
  6. Benavoli, Alessio; Corani, Giorgio; Demšar, Janez; Zaffalon, Marco: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis (2017)
  7. Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell: Stan: A Probabilistic Programming Language (2017)
  8. Boca, Simina M.; Pfeiffer, Ruth M.; Sampson, Joshua N.: Multivariate meta-analysis with an increasing number of parameters (2017)
  9. Chen Dong; Michel Wedel: BANOVA: An R Package for Hierarchical Bayesian ANOVA (2017)
  10. 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)
  11. Corani, Giorgio; Benavoli, Alessio; Demšar, Janez; Mangili, Francesca; Zaffalon, Marco: Statistical comparison of classifiers through Bayesian hierarchical modelling (2017)
  12. Draper, David; Terenin, Alexander: Comment: A brief survey of the current state of play for Bayesian computation in data science at big-data scale (2017)
  13. Gronau, Quentin F.; Sarafoglou, Alexandra; Matzke, Dora; Ly, Alexander; Boehm, Udo; Marsman, Maarten; Leslie, David S.; Forster, Jonathan J.; Wagenmakers, Eric-Jan; Steingroever, Helen: A tutorial on bridge sampling (2017)
  14. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  15. Jeon, Minjeong; Rijmen, Frank; Rabe-Hesketh, Sophia: A variational maximization-maximization algorithm for generalized linear mixed models with crossed random effects (2017)
  16. Lahoz-Monfort, José J.; Harris, Michael P.; Wanless, Sarah; Freeman, Stephen N.; Morgan, Byron J. T.: Bringing it all together: multi-species integrated population modelling of a breeding community (2017)
  17. Lanzarone, E.; Pasquali, S.; Gilioli, G.; Marchesini, E.: A Bayesian estimation approach for the mortality in a stage-structured demographic model (2017)
  18. Ligtvoet, Rudy: Exact one-sided Bayes factors for 2 by 2 contingency tables (2017)
  19. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  20. Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh: How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters (2017)

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