DPpackage: Bayesian Semi- and Nonparametric Modeling inR

Abstract

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification 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 non- and semi-parametric models in , DPpackage. Currently DPpackage includes models for marginal and conditional density estimation, ROC 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. To maximize computational efficiency, the actual sampling for each model is carried out using compiled FORTRAN.

Meta-Analysis Options for Inconsistent Nuclear Measurements

“…using a mixture of conditional Dirichlet processes. A somewhat similar approach is taken by Jara et al, 14 but for more general situations that are beyond our scope. The approach by Burr and Doss 12 allows the data~see Sec.…”

Bayesian Nonparametric Approaches for ROC Curve Inference

“…Finally, after obtaining MCMC samples for each of the parameters, we can plug-in, for each covariate x, each MCMC realization of F 0 (· | x) and F 1 (· | x) in (16.4) and compute the corresponding realization of the conditional ROC curve. The model previously described is implemented in the function LDDProc of the R library DPpackage (Jara et al 2011).…”

Bayesian Model Specification: Heuristics And Examples

“…The ensemble of R functions called DPpackage [14], available from CRAN, contains several functions that can fit model (1.9), including PTlm and PTdensity, and WinBUGS code for this model is available from Tim Hanson; this permits attention to shift away from the MCMC details and toward the modeling, where several surprises await (in relation to Your experience with parametric modeling).…”