DPpackage: Bayesian Semi- and Nonparametric Modeling in R. 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 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 efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 12 articles )

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  1. Dinov, Ivo D.; Siegrist, Kyle; Pearl, Dennis K.; Kalinin, Alexandr; Christou, Nicolas: Probability \itDistributome: a web computational infrastructure for exploring the properties, interrelations, and applications of probability distributions (2016)
  2. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  3. Zhou, Haiming; Hanson, Timothy: Bayesian spatial survival models (2015)
  4. Aktekin, Tevfik: Call center service process analysis: Bayesian parametric and semi-parametric mixture modeling (2014)
  5. Hanson, Timothy E.; Branscum, Adam J.; Johnson, Wesley O.: Informative $g$-priors for logistic regression (2014)
  6. Wade, Sara; Walker, Stephen G.; Petrone, Sonia: A predictive study of Dirichlet process mixture models for curve fitting (2014)
  7. Lunn, David; Jackson, Christopher; Best, Nicky; Thomas, Andrew; Spiegelhalter, David: The BUGS book. A practical introduction to Bayesian analysis (2013)
  8. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  9. San Martín, Ernesto; Jara, Alejandro; Rolin, Jean-Marie; Mouchart, Michel: On the Bayesian nonparametric generalization of IRT-type models (2011)
  10. Christensen, Ronald; Johnson, Wesley Orin; Branscum, Adam; Hanson, Timothy E.: Bayesian ideas and data analysis. An introduction for scientists and statisticians. (2010)
  11. Jara, Alejandro; Lesaffre, Emmanuel; De Iorio, Maria; Quintana, Fernando: Bayesian semiparametric inference for multivariate doubly-interval-censored data (2010)
  12. Komárek, Arnošt; Lesaffre, Emmanuel: Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution (2008)