DPpackage (version 1.1-7.4)
Bayesian Nonparametric Modeling in R
Description
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.
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. 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.