Semiparametric Bayesian Analysis using Dirichlet process priors and Extensions

Bayesian Nonparametric and Semiparametric Analysis
This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian nonparametric and
semiparametric models. Although the name of the package was motivated
by the Dirichlet Process prior, the package considers and will consider
other priors on functional spaces. So far, DPpackage includes models
considering Dirichlet Process and Polya Tree priors. Currently the package
includes semiparametric models for density estimation, ROC curve analysis,
interval censored data, binary regression models, generalized linear mixed
models, and IRT type models. The package also contains functions to compute
Pseudo-Bayes factors for model comparison, and to elicitate the precision
parameter of the Dirichlet Process. To maximize computational efficiency,
the actual sampling for each model is done in compiled FORTRAN. The functions
return objects which can be subsequently analyzed with functions provided in
the coda package.

This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian nonparametric and
semiparametric models. Although the name of the package was motivated
by the Dirichlet Process prior, the package considers and will consider
other priors on functional spaces. So far, DPpackage includes models
considering Dirichlet Process, Dirichlet Process Mixtures, Polya Trees, and
Random Bernstein polynomials priors.
Currently the package includes semiparametric models for density estimation,
ROC curve analysis, interval censored data, binary regression models,
generalized linear mixed models, and IRT type models. The package also contains
functions to compute Pseudo-Bayes factors for model comparison, and to elicitate
the precision parameter of the Dirichlet Process. To maximize computational efficiency,
the actual sampling for each model is done in compiled FORTRAN. The functions
return objects which can be subsequently analyzed with functions provided in
the coda package.