GreedyEPL, Rmpfr, gmp, knitr, rmarkdown, testthat

GreedyEPL, Rmpfr, gmp, knitr, rmarkdown, testthat, BNPmix

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) <doi:10.1080/03610910601096262>, or using a marginal sampler, as in Escobar and West (1995) <doi:10.2307/2291069> and extensions. The package contains also the procedures to estimate via importance conditional sampler a GM-Dependent Dirichlet process mixture model.

Riccardo Corradin [aut, cre], Antonio Canale [ctb], Bernardo Nipoti [ctb]
Performs Bayesian nonparametric univariate and multivariate density estimation and clustering by means of Pitman-Yor mixtures and Dependent Dirichlet process mixtures for partially exchangeable data.

Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and Dependent Dirichlet process mixtures for partially exchangeable data.

Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data.

methods, stats, ggplot2, coda, Rcpp, ggpubr

Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.

From a given dataframe, this package learns its Bayesian network structure based on a selected score.

From a given data frame, this package learns its Bayesian network structure based on a selected score.