JAGS

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 137 articles )

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  1. Amaral Turkman, Maria Antónia; Paulino, Carlos Daniel; Müller, Peter: Computational Bayesian statistics. An introduction (2019)
  2. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  3. Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.: \textsfRpackage \textsfrjmcmc: reversible jump MCMC using post-processing (2019)
  4. Heck, Daniel W.: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018) (2019)
  5. Heck, Daniel W.; Davis-Stober, Clintin P.: Multinomial models with linear inequality constraints: overview and improvements of computational methods for Bayesian inference (2019)
  6. Jona Lasinio, Giovanna; Pollice, Alessio; Fano, Elisa Anna: Generalized biodiversity assessment by Bayesian nested random effects models with spyke-and-slab priors (2019)
  7. Jure Demšar, Grega Repovš, Erik Štrumbelj: bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology (2019) arXiv
  8. Kim, Gwangsu: Posterior consistency in frailty models and simulation studies to test the presence of random effects (2019)
  9. Kinzy, Tyler G.; Starr, Timothy K.; Tseng, George C.; Ho, Yen-Yi: Meta-analytic framework for modeling genetic coexpression dynamics (2019)
  10. Lu, Zhao-Hua; Chow, Sy-Miin; Ram, Nilam; Cole, Pamela M.: Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data (2019)
  11. Osthus, Dave; Gattiker, James; Priedhorsky, Reid; Del Valle, Sara Y.: Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) (2019)
  12. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  13. Wiśniowski, Arkadiusz; Bijak, Jakub; Forster, Jonathan J.; Smith, Peter W. F.: Hierarchical model for forecasting the outcomes of binary referenda (2019)
  14. Betz, Jennifer; Kellner, Ralf; Rösch, Daniel: Systematic effects among loss given defaults and their implications on downturn estimation (2018)
  15. Boehm, Udo; Annis, Jeffrey; Frank, Michael J.; Hawkins, Guy E.; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D.; Palmeri, Thomas J.; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey J.; Voss, Andreas; Wiecki, Thomas V.; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations (2018)
  16. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  17. Casey Youngflesh: MCMCvis: Tools to Visualize, Manipulate, and Summarize MCMC Output (2018) not zbMATH
  18. Depaoli, Sarah; Liu, Yang: Book review of: R. Levy and R. J. Mislevy, Bayesian psychometric modeling (2018)
  19. Diniz, Márcio Augusto; Kim, Sungjin; Tighiouart, Mourad: A Bayesian adaptive design in cancer phase I trials using dose combinations in the presence of a baseline covariate (2018)
  20. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH

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