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 30 articles )

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  1. Antonio Canale: msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures (2017)
  2. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  3. Hong, Liang; Martin, Ryan: A review of Bayesian asymptotics in general insurance applications (2017)
  4. Jara, Alejandro: Theory and computations for the Dirichlet process and related models: an overview (2017)
  5. Kim, Chanmin; Daniels, Michael J.; Marcus, Bess H.; Roy, Jason A.: A framework for Bayesian nonparametric inference for causal effects of mediation (2017)
  6. Argiento, Raffaele; Bianchini, Ilaria; Guglielmi, Alessandra: Posterior sampling from $\epsilon$-approximation of normalized completely random measure mixtures (2016)
  7. 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)
  8. Gray-Davies, Tristan; Holmes, Chris C.; Caron, François: Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks (2016)
  9. Li, Dan; Wang, Xia; Lin, Lizhen; Dey, Dipak K.: Flexible link functions in nonparametric binary regression with Gaussian process priors (2016)
  10. Zhao, Lili; Feng, Dai; Chen, Guoan; Taylor, Jeremy M.G.: A unified Bayesian semiparametric approach to assess discrimination ability in survival analysis (2016)
  11. Bao, Junshu; Hanson, Timothy E.: Bayesian nonparametric multivariate ordinal regression (2015)
  12. Müller, Peter; Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim: Bayesian nonparametric data analysis (2015)
  13. Zhou, Haiming; Hanson, Timothy: Bayesian spatial survival models (2015)
  14. Zhou, Haiming; Hanson, Timothy; Jara, Alejandro; Zhang, Jiajia: Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model (2015)
  15. Zhou, Haiming; Hanson, Timothy; Knapp, Roland: Marginal Bayesian nonparametric model for time to disease arrival of threatened Amphibian populations (2015)
  16. Aktekin, Tevfik: Call center service process analysis: Bayesian parametric and semi-parametric mixture modeling (2014)
  17. Cameron, Ewan; Pettitt, Anthony: Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis (2014)
  18. Hanson, Timothy E.; Branscum, Adam J.; Johnson, Wesley O.: Informative $g$-priors for logistic regression (2014)
  19. Terrance Savitsky; Susan Paddock: Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R (2014)
  20. Wade, Sara; Walker, Stephen G.; Petrone, Sonia: A predictive study of Dirichlet process mixture models for curve fitting (2014)

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