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 28 articles )

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  1. Fullerton, Andrew S.; Xu, Jun: Ordered regression models. Parallel, partial, and non-parallel alternatives (2016)
  2. Hilbe, Joseph M.: Practical guide to logistic regression (2016)
  3. Kary, Arthur; Taylor, Robert; Donkin, Chris: Using Bayes factors to test the predictions of models: a case study in visual working memory (2016)
  4. Li, Longhai; Qiu, Shi; Zhang, Bei; Feng, Cindy X.: Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC (2016)
  5. Okada, Kensuke; Lee, Michael D.: A Bayesian approach to modeling group and individual differences in multidimensional scaling (2016)
  6. Shiffrin, Richard M.; Chandramouli, Suyog H.; Grünwald, Peter D.: Bayes factors, relations to minimum description length, and overlapping model classes (2016)
  7. Anders, Royce; Batchelder, William H.: Cultural consensus theory for the ordinal data case (2015)
  8. Jabot, Franck: Why preferring parametric forecasting to nonparametric methods? (2015)
  9. Matzke, Dora; Dolan, Conor V.; Batchelder, William H.; Wagenmakers, Eric-Jan: Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items (2015)
  10. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  11. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  12. Vincent, Benjamin T.: A tutorial on Bayesian models of perception (2015)
  13. Anders, R.; Oravecz, Z.; Batchelder, W.H.: Cultural consensus theory for continuous responses: a latent appraisal model for information pooling (2014)
  14. Josse, Julie; van Eeuwijk, Fred; Piepho, Hans-Peter; Denis, Jean-Baptiste: Another look at Bayesian analysis of AMMI models for genotype-environment data (2014)
  15. Kruschke, John: Doing Bayesian data analysis. A tutorial introduction with R, JAGS, and Stan (2014)
  16. Turner, Brandon M.; Steyvers, Mark; Merkle, Edgar C.; Budescu, David V.; Wallsten, Thomas S.: Forecast aggregation via recalibration (2014)
  17. Vandekerckhove, Joachim: A cognitive latent variable model for the simultaneous analysis of behavioral and personality data (2014)
  18. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  19. Higgs, Megan D.; Link, William A.; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.: Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year (2013)
  20. Jiang, Xun; Dey, Dipak K.; Prunier, Rachel; Wilson, Adam M.; Holsinger, Kent E.: A new class of flexible link functions with application to species co-occurrence in Cape Floristic region (2013)

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