DPpackage: Bayesian semi- and nonparametric modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling exibility and robustness against mis-specication of the probability model. in the bayesian context,this is accomplished by placing a prior distribution on a function space,such as the space of all probability distributions or the space of all regression functions. unfortunately,posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. this paper provides an introduction to a simple,yet comprehensive,set of programs for the implementation of some bayesian nonparametric and semiparametric models in r,dppackage. currently,dppackage includes models for marginal and conditional density estimation,receiver operating characteristic curve analysis,interval-censored data,binary regression data,item response data,longitudinal and clustered data using generalized linear mixed models,and regression data using generalized additive models. the package also contains functions to compute pseudo-bayes factors for model comparison and for eliciting the precision parameter of the dirichlet process prior,and a general purpose metropolis sampling algorithm. to maximize computational eciency,the actual sampling for each model is carried out using compiled c,c++ or fortran code.