Nonparametric Bayesian analysis in R I am looking for a good tutorial on clustering data in R using hierarchical dirichlet process (HDP) (one of the recent and popular nonparametric Bayesian methods). 
There is DPpackage (IMHO, the most comprehensive of all the available ones) in R for nonparametric Bayesian analysis. But I am unable to understand the examples provided in R News or in the package reference manual well enough to code HDP.
Any help or pointer is appreciated.
A C++ implementation of HDP for topic modeling is available here (please look at the bottom for C++ code)
 A: These two links provide some R (and C) code examples of implementing a DP normal mixture:


*

*http://ice.uchicago.edu/2008_presentations/Rossi/density_estimation_with_DP_priors.ppt

*http://www.duke.edu/~neelo003/r/DP02.r
Found another reference.  Chapter 15 has DP winbugs code:


*

*http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html
A: Here are some online ressources I found interesting without going into detail (and I'm not a specialist of this topic):


*

*Hierarchical Dirichlet Processes, by Teh et al. (2005)

*Dirichlet Processes A gentle tutorial, by El-Arini (2008)

*Bayesian Nonparametrics, by Rosasco (2010)

*Non-parametric Bayesian Methods, by Ghahramani (2005)


The definitive reference seems to be

N. Hjort, C. Holmes, P. Müller, and S.
  Walker, editors. Bayesian
  Nonparametrics. Number 28 in
  Cambridge Series in Statistical and
  Probabilistic Mathematics. Cambridge
  University Press, 2010.

About R, there seems to be some other packages worth to explore if the DPpackage does not suit your needs, e.g. dpmixsim, BHC, or mbsc found on Rseek.org.
