Author: Riccardo Corradin [aut, cre], Antonio Canale [ctb], Bernardo Nipoti [ctb]
Description: 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.

Title: Bayesian Nonparametric Mixture Models
Description: 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.

Description: 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.
Packaged: 2019-09-25 11:55:20 UTC; riccardocorradin

Packaged: 2019-12-06 15:01:40 UTC; riccardocorradin

Imports: methods, stats, ggplot2, coda, Rcpp, ggpubr

LinkingTo: RcppArmadillo, Rcpp(>= 0.12.13), RcppDist

Description: 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.

Title: Bayesian Network Structure Learning
Description: From a given dataframe, this package learns its Bayesian network structure based on a selected score.

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

Title: Bayesian Non- and Semi-parametric Model Fitting
Description: Markov chain Monte Carlo algorithms for non- and semi-parametric models

Title: Bayesian Non- and Semi-Parametric Model Fitting
Description: Markov chain Monte Carlo algorithms for non- and semi-parametric models.

Title: Bayesian Non- And Semi-Parametric Model Fitting
Description: MCMC for Dirichlet process mixtures.

Imports: coda, ggplot2, plot3D, threejs, gridExtra

Description: Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for variable selection in mean/variance regression models.