DPpackage (version 1.1-4)
Bayesian nonparametric modeling in R
Description
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
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya Trees,
Linear Dependent Tailfree Processes,
Mixtures of Triangular distributions,
Random Bernstein polynomials priors and Dependent Bernstein Polynomials.
The package also includes
models considering Penalized B-Splines. Currently the package
includes semiparametric models for marginal and conditional density
estimation, ROC curve analysis, interval censored data, binary
regression models, generalized linear mixed models, IRT type
models, and generalized additive 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.