Algorithms for Pitman-Yor Process Mixtures

Contains different algorithms to both univariate and multivariate Pitman-Yor process mixture models, and Griffiths-Milne Dependent Dirichlet process mixture models. Pitman-Yor process mixture models are flexible Bayesian nonparametric models to deal with density estimation. Estimation could be done via importance conditional sampler, or via slice sampler, as done by Walker (2007) , or using a marginal sampler, as in Escobar and West (1995) and extensions. The package contains also the procedures to estimate via importance conditional sampler a GM-Dependent Dirichlet process mixture model.


Reference manual

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0.1.1 by Riccardo Corradin, 20 days ago

Browse source code at

Authors: Riccardo Corradin

Documentation:   PDF Manual  

LGPL-3 | file LICENSE license

Imports methods, ggplot2

Linking to RcppArmadillo, Rcpp

See at CRAN