DPpackage

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 59 articles , 1 standard article )

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  1. Antoniano-Villalobos, Isadora; Borgonovo, Emanuele; Lu, Xuefei: Nonparametric estimation of probabilistic sensitivity measures (2020)
  2. Bianchini, Ilaria; Guglielmi, Alessandra; Quintana, Fernando A.: Determinantal point process mixtures via spectral density approach (2020)
  3. Arbel, Julyan; De Blasi, Pierpaolo; Prünster, Igor: Stochastic approximations to the Pitman-Yor process (2019)
  4. Conversano, Claudio; Cannas, Massimo; Mola, Francesco; Sironi, Emiliano: Random effects clustering in multilevel modeling: choosing a proper partition (2019)
  5. Edwards, Matthew C.; Meyer, Renate; Christensen, Nelson: Bayesian nonparametric spectral density estimation using B-spline priors (2019)
  6. Klein, Nadja; Smith, Michael Stanley: Implicit copulas from Bayesian regularized regression smoothers (2019)
  7. Li, Yuelin; Schofield, Elizabeth; Gönen, Mithat: A tutorial on Dirichlet process mixture modeling (2019)
  8. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  9. Shi, Yushu; Martens, Michael; Banerjee, Anjishnu; Laud, Purushottam: Low information omnibus (LIO) priors for Dirichlet process mixture models (2019)
  10. Soriano, Jacopo; Ma, Li: Mixture modeling on related samples by (\psi)-stick breaking and kernel perturbation (2019)
  11. van der Vaart, Aad: Comment: “Bayes, oracle Bayes and empirical Bayes” (2019)
  12. Das, Priyam; Ghosal, Subhashis: Bayesian non-parametric simultaneous quantile regression for complete and grid data (2018)
  13. Hahn, P. Richard; Martin, Ryan; Walker, Stephen G.: On recursive Bayesian predictive distributions (2018)
  14. Jo, Youngin; Jo, Seongil; Lee, Yung-Seop; Lee, Jaeyong: Bayesian temporal density estimation with autoregressive species sampling models (2018)
  15. Müeller, Peter; Quintana, Fernando A.; Page, Garritt: Nonparametric Bayesian inference in applications (2018)
  16. Quinlan, José J.; Page, Garritt L.; Quintana, Fernando A.: Density regression using repulsive distributions (2018)
  17. Richardson, Robert; Hartman, Brian: Bayesian nonparametric regression models for modeling and predicting healthcare claims (2018)
  18. Antonio Canale: msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures (2017) not zbMATH
  19. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  20. Hong, Liang; Martin, Ryan: A review of Bayesian asymptotics in general insurance applications (2017)

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