This site provides C++ code implementing Metropolis Hastings MCMC methods for exploring spaces of Bayesian classification and regression tree models, and developing posterior and predictive inference using Bayesian model averaging over many tree models. The code implements models and methods as described in
The code, with a readme file of details, is freely available to anyone interested. The readme includes details of compilation and use, including a small example with input data and parameter files, and description of output files. Enquiries or question to casper@stat.duke.edu Research underlying the software presented here was performed under partial support from the National Science Foundation through the SAMSI grant DMS-0112069, on grants DMS-0102227 and 0342172 to Duke University, and by grants from the Keck Foundation and NIH.
This software is made freely available to any interested user. The authors can provide no support nor assistance with implementations beyond the details and examples here, nor extensions of the code for other purposes. The download has been tested to confirm all details are operational as described here. It is understood by the user that neither the authors nor Duke University bear any responsibility nor assume any liability for any end-use of this software. It is expected that appropriate credit/acknowledgement be given should the software be included as an element in other software development or in publications.
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