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 72 articles , 1 standard article )

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  1. Canale, Antonio; Corradin, Riccardo; Nipoti, Bernardo: Importance conditional sampling for Pitman-Yor mixtures (2022)
  2. Zhao, Yuanying; Xu, Dengke; Duan, Xingde; Du, Jiang: A semiparametric Bayesian approach to binomial distribution logistic mixed-effects models for longitudinal data (2022)
  3. Corradin, R., Canale, A.,Nipoti, B: BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures (2021) not zbMATH
  4. Jongbloed, Geurt; van der Meulen, Frank; Pang, Lixue: Nonparametric Bayesian estimation of a concave distribution function with mixed interval censored data (2021)
  5. Rosner, Gary L.; Laud, Purushottam W.; Johnson, Wesley O.: Bayesian thinking in biostatistics (2021)
  6. Stefanucci, Marco; Canale, Antonio: Multiscale stick-breaking mixture models (2021)
  7. Wehrhahn, Claudia; Jara, Alejandro; Barrientos, Andrés F.: On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals (2021)
  8. Adhikari, Samrachana; Rose, Sherri; Normand, Sharon-Lise: Nonparametric Bayesian instrumental variable analysis: evaluating heterogeneous effects of coronary arterial access site strategies (2020)
  9. Antoniano-Villalobos, Isadora; Borgonovo, Emanuele; Lu, Xuefei: Nonparametric estimation of probabilistic sensitivity measures (2020)
  10. Bianchini, Ilaria; Guglielmi, Alessandra; Quintana, Fernando A.: Determinantal point process mixtures via spectral density approach (2020)
  11. Ganjali, Mojtaba; Baghfalaki, Taban; Fagbamigbe, Adeniyi Francis: A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models (2020)
  12. Shen, Jieli; Liu, Regina Y.; Xie, Min-ge: (i)Fusion: individualized fusion learning (2020)
  13. Tonellato, Stefano F.: Bayesian nonparametric clustering as a community detection problem (2020)
  14. Arbel, Julyan; De Blasi, Pierpaolo; Prünster, Igor: Stochastic approximations to the Pitman-Yor process (2019)
  15. Conversano, Claudio; Cannas, Massimo; Mola, Francesco; Sironi, Emiliano: Random effects clustering in multilevel modeling: choosing a proper partition (2019)
  16. Edwards, Matthew C.; Meyer, Renate; Christensen, Nelson: Bayesian nonparametric spectral density estimation using B-spline priors (2019)
  17. Klein, Nadja; Smith, Michael Stanley: Implicit copulas from Bayesian regularized regression smoothers (2019)
  18. Li, Yuelin; Schofield, Elizabeth; Gönen, Mithat: A tutorial on Dirichlet process mixture modeling (2019)
  19. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  20. Shi, Yushu; Martens, Michael; Banerjee, Anjishnu; Laud, Purushottam: Low information omnibus (LIO) priors for Dirichlet process mixture models (2019)

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