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 200 articles )

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  6. Lee, Michael D.; Bock, Jason R.; Cushman, Isaiah; Shankle, William R.: An application of multinomial processing tree models and Bayesian methods to understanding memory impairment (2020)
  7. Ma, Zhihua; Chen, Guanghui: Bayesian semiparametric latent variable model with DP prior for joint analysis: implementation with nimble (2020)
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  9. Michalkiewicz, Martha; Horn, Sebastian S.; Bayen, Ute J.: Hierarchical multinomial modeling to explain individual differences in children’s clustering in free recall (2020)
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  12. Oravecz, Zita; Vandekerckhove, Joachim: A joint process model of consensus and longitudinal dynamics (2020)
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  16. Timothy D. Meehan, Nicole L. Michel, Håvard Rue: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R (2020) not zbMATH
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  18. Zhan, Peida; Wang, Wen-Chung; Li, Xiaomin: A partial mastery, higher-order latent structural model for polytomous attributes in cognitive diagnostic assessments (2020)
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  20. Amoros, Ruben; King, Ruth; Toyoda, Hidenori; Kumada, Takashi; Johnson, Philip J.; Bird, Thomas G.: A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma (2019)

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