Original author(s)Martyn Plummer
Initial releaseDecember 11, 2007 (2007-12-11)
Stable release
4.3.2[1] Edit this on Wikidata / 4 March 2023; 12 months ago (4 March 2023)
Written inC++
Operating systemUnix-like, Microsoft Windows, Mac OS X
PlatformIntel x86 - 32-bit, x64
Size1.7 MB
TypeStatistical package
LicenseGNU General Public License

Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer. JAGS has been employed for statistical work in many fields, for example ecology, management, and genetics.[2][3][4]

JAGS aims for compatibility with WinBUGS/OpenBUGS through the use of a dialect of the same modeling language (informally, BUGS), but it provides no GUI for model building and MCMC sample postprocessing, which must therefore be treated in a separate program (for example calling JAGS from R through a library such as rjags and post-processing MCMC output in R).[5]

The main advantage of JAGS in comparison to the members of the original BUGS family (WinBUGS and OpenBUGS) is its platform independence. It is written in C++, while the BUGS family is written in Component Pascal, a less widely known programming language.[6][7] In addition, JAGS is already part of many repositories of Linux distributions such as Ubuntu. It can also be compiled as a 64-bit application on 64-bit platforms, thus making all the addressable space available to BUGS models.

JAGS can be used via the command line or run in batch mode through script files. This means that there is no need to redo the settings with every run and that the program can be called and controlled from within another program (e.g. from R via rjags as outlined above).

JAGS is licensed under the GNU General Public License.

See also


  1. ^ "JAGS". 4 March 2023. Retrieved 24 February 2024.
  2. ^ Semmens, B. X.; Ward, E. J.; Moore, J. W.; Darimont, C. T. (2009). Getz, Wayne M (ed.). "Quantifying Inter- and Intra-Population Niche Variability Using Hierarchical Bayesian Stable Isotope Mixing Models". PLOS ONE. 4 (7): e6187. Bibcode:2009PLoSO...4.6187S. doi:10.1371/journal.pone.0006187. PMC 2704373. PMID 19587790.
  3. ^ Johnson, T. R.; Kuhn, K. M. (2013). "Bayesian Thurstonian models for ranking data using JAGS". Behavior Research Methods. 45 (3): 857–872. doi:10.3758/s13428-012-0300-3. PMID 23539504. S2CID 42660145.
  4. ^ McKeigue, P. M.; Campbell, H.; Wild, S.; Vitart, V.; Hayward, C.; Rudan, I.; Wright, A. F.; Wilson, J. F. (2010). "Bayesian methods for instrumental variable analysis with genetic instruments ('Mendelian randomization'): Example with urate transporter SLC2A9 as an instrumental variable for effect of urate levels on metabolic syndrome". International Journal of Epidemiology. 39 (3): 907–918. doi:10.1093/ije/dyp397. PMC 2878456. PMID 20348110.
  5. ^ Martyn Plummer (2003). JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20–22, Vienna, Austria. ISSN 1609-395X.
  6. ^ Lunn, David; Spiegelhalter, David; Thomas, Andrew; Best, Nicky (2009). "The BUGS project: Evolution, critique and future directions" (PDF). Statistics in Medicine. 28 (25): 3049–3067. doi:10.1002/sim.3680. PMID 19630097. Archived from the original (PDF) on 2012-09-15.
  7. ^ Simon Jackman (2009). Bayesian Analysis for the Social Sciences. Wiley Series in Probability and Statistics, volume 845. John Wiley and Sons. ISBN 978-0-470-01154-6.