BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures

Abstract

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.

Software for Bayesian Statistics

“…For simulating values from the posterior distributions, they use the BUGS language via JAGS (see e.g., Bonner et al 2021;Erler et al 2021;Mayrink et al 2021;Weber et al 2021), Stan (see e.g., Bürkner 2021;Merkle et al 2021;Weber et al 2021), or nimble (Michaud et al 2021;Bonner et al 2021), interfaced with R by means of the corresponding packages rjags (Plummer, Stukalov, and Denwood 2021), rstan (Stan Development Team 2021) and nimble (de . Alternatively, other papers (e.g., Corradin et al 2021;Hosszejni and Kastner 2021;Knaus et al 2021;Venturini and Piccarreta 2021) write sampling functions in C++ which are then integrated into R by using the Rcpp (Eddelbuettel and François 2011) and RcppArmadillo (Eddelbuettel and Sanderson 2014) packages. Finally, some papers do not use MCMC but numerical approximations such as Eggleston et al (2021), Fasiolo et al (2021 and Van Niekerk et al (2021).…”

“…This distribution if often employed to model the baseline hazard function when fitting Cox proportional hazards models in survival analysis. Corradin, Canale, and Nipoti (2021) present the BNPmix R package for efficient Bayesian inference on nonparametric mixture models used for density estimation and clustering. Methods for model-based clustering of binary dissimilarity matrices implemented in the dmbc package are described in Venturini and Piccarreta (2021).…”

Truncated two‐parameter Poisson–Dirichlet approximation for Pitman–Yor process hierarchical models

“…)” represent the dependent and independent slice‐efficient sampler, and “Truncation” stands for the truncated stick‐breaking process. We would like to remind the reader of the remarkable BNPmix package developed by Corradin et al (2021), which contains the ICS and slice‐efficient samplers. On the other hand, the “Truncation” method is obtained by replacing Step 1 of Algorithm 5 with the posterior of the truncated stick‐breaking process, which can be found in Suppl.…”