JAGS 4.3.0 is released

The source tarball for JAGS 4.3.0 is now available from Sourceforge. Binary distributions will be available later. See the updated manual for details of the features in the new version. This version is fully compatible with the current version of rjags on CRAN.

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New manual

I have spent the last couple of months updating the JAGS manual for the release of 4.3.0. I have uploaded the manual to Sourceforge so you can now download it. This is mainly for the benefit of people coming to my JAGS tutorial next week at useR! 2017.

This was the final task before releasing JAGS 4.3.0 so you can expect some news on that front fairly soon.

 

JAGS tutorial at useR! 2017

I am giving a pre-conference tutorial on JAGS at useR! 2017 in Brussels on 4 July. You can see the outline of the tutorial on the conference website along with the other tutorials being given the same day.

This is my first pre-conference tutorial. Last year I gave a three day course on JAGS at the University of Zurich, so I have plenty material. My main problem is not over-filling the 3-hour time slot.

You can always make it faster…

… but sometimes you just need a good example to show you how. Saana Isojunno from the University of Saint Andrews wrote to me with an example that stopped working when she upgraded from JAGS 3.4.0 to JAGS 4.2.0. Saana’s model showed the classic signs of a memory leak. Compilation was very slow and memory consumption increased until all available RAM was used and the process was killed by the operating system. Continue reading

PyJAGS

Editors note: I am very pleased to announce that Tomasz Mi─ůsko has created a Python interface to JAGS. The rest of this post is by Tomasz.

Nowadays Python users certainly cannot complain about a lack of MCMC packages. We have emcee, PyMC, PyMC3, and PyStan to mention a few. Recently this list was extended by one more; PyJAGS – a Python interface to JAGS. JAGS is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation, quite often used from within the R environment with the help of the rjags package. The PyJAGS package offers Python users a high-level API to JAGS, similar to the one found in rjags. Current rjags users interested in migrating to Python should feel at home. Of course, other interested in doing Bayesian data analysis may also find PyJAGS useful. Continue reading