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 247 articles )

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  1. Irena B Chen, Qiyuan Shi, Scott L Zeger, Zhenke Wu: baker: An R package for Nested Partially-Latent Class Models (2022) arXiv
  2. Zhou, Haiming; Huang, Xianzheng: Bayesian beta regression for bounded responses with unknown supports (2022)
  3. Alvares, Danilo; Armero, Carmen; Forte, Anabel; Chopin, Nicolas: Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data (2021)
  4. Barraquand, Frédéric; Gimenez, Olivier: Fitting stochastic predator-prey models using both population density and kill rate data (2021)
  5. Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
  6. Brandon P.M. Edwards, Adam C. Smith: bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data (2021) not zbMATH
  7. Chao, Fengqing; Gerland, Patrick; Cook, Alex R.; Alkema, Leontine: Global estimation and scenario-based projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model (2021)
  8. Eggleston, B. S., Ibrahim, J. G., McNeil, B., Catellier, D: BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data (2021) not zbMATH
  9. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  10. Gür, Sercan; Pötzelberger, Klaus: On the empirical estimator of the boundary in inverse first-exit problems (2021)
  11. Hartmann, Raphael; Klauer, Karl Christoph: Partial derivatives for the first-passage time distribution in Wiener diffusion models (2021)
  12. Hoseinzadeh, Akram; Maleki, Mohsen; Khodadadi, Zahra: Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions (2021)
  13. Jeffrey W. Doser, Andrew O. Finley, Marc Kery, Elise F. Zipkin: spOccupancy: An R package for single species, multispecies, and integrated spatial occupancy models (2021) arXiv
  14. Kazemi, Iraj; Hassanzadeh, Fatemeh: Marginalized random-effects models for clustered binomial data through innovative link functions (2021)
  15. Kelter, Riko: Bayesian model selection in the (\mathcalM)-open setting -- approximate posterior inference and subsampling for efficient large-scale leave-one-out cross-validation via the difference estimator (2021)
  16. Kelter, Riko: Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality (2021)
  17. Kuschnig, N., Vashold, L.: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R (2021) not zbMATH
  18. Mayrink, V. D., Duarte, J. D. N., Demarqui, F. N.: pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution (2021) not zbMATH
  19. Ma, Zhihua; Chen, Guanghui: Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data (2021)
  20. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH

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