BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures
Dublin Core
Title
BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures
Subject
: Bayesian nonparametric mixture, C++, multivariate density estimation, clustering,
importance conditional sampler, slice sampler, marginal sampler.
importance conditional sampler, slice sampler, marginal sampler.
Description
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust
generalization of the popular class of Dirichlet process mixture models. A variety of
model specifications and state-of-the-art posterior samplers are implemented. In order to
achieve computational efficiency, all sampling methods are written in C++ and seamless
integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits
the ggplot2 capabilities and implements a series of generic functions to plot and print
summaries of posterior densities and induced clustering of the data.
generalization of the popular class of Dirichlet process mixture models. A variety of
model specifications and state-of-the-art posterior samplers are implemented. In order to
achieve computational efficiency, all sampling methods are written in C++ and seamless
integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits
the ggplot2 capabilities and implements a series of generic functions to plot and print
summaries of posterior densities and induced clustering of the data.
Creator
Riccardo Corradin
Source
https://www.jstatsoft.org/article/view/v100i15
Publisher
University of Milano-Bicocca
Date
November 2020
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Riccardo Corradin, “BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/8228.