As part of ESRC grant RES-000-23-1190-A entitled "Sample Size, Identifiability and MCMC Efficiency in Complex Random Effect Models" we have been adding additional MCMC functionality to MLwiN (as well as developing the MLPowSim software package).
The MCMC functions added do not expand the models that can be fitted within MLwiN but instead offer alternative MCMC approaches for certain existing model classes that are faster or produce better mixing chains. An additional MCMC options menu item has been added which allows the user to select from various MCMC methods including parameter expansion, hierarchical centering, orthogonal parameterisations, structured MCMC and structured MVN formulations.
A new version of the book MCMC Estimation in MLwiN that accompanies the software has been produced. In this book the existing material has been updated and slightly reordered and we have added five additional chapters to cover each of the new MCMC methods that have been implemented.
If you use the new features and would like to give feedback, bug reports or a wish list please e-mail me (email@example.com). More details of the developments and links to journal articles linked to the project are available at seis.bris.ac.uk/~frwjb/esrc.html
Good luck with the software,
Prof W J Browne