Packaged: 2011-10-07 16:39:11 UTC; EBARRIOSZ

Description: Ferguson-Klass type algorithm for posterior normalized
Packaged: 2012-08-02 00:28:06 UTC; EBARRIOSZ

Author: I. Prunster and L. E. Nieto. R implementation by Ernesto Barrios
Description: Ferguson-Klass type algorithm for posterior normalized random measures

Title: Ferguson-Klass Type Algorithm for Posterior Normalized Random
Author: Ernesto Barrios, Antonio Lijoi, Luis E. Nieto-Barajas and Igor Prünster. R implementation by Ernesto Barrios and Luis E. Nieto-Barajas.
Description: Modeling mixtures with normalized random measures by a Ferguson-Klass type algorithm for posterior normalized random measures.

person("Ernesto", "Barrios", role = c("aut", "cre"), email = "ebarrios@itam.mx"),
person("Antonio", "Lijoi", role = "aut"),
person("Luis E.", "Nieto-Barajas", role = "aut"),
person("Igor", "Prünster", role = "aut"),
person("Guillaume", "Kon Kam King", role = "ctb")
Description: Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.

person("Julyan", "Arbel", role = c("ctb")),
person("Ernesto", "Barrios", role = c("aut"), email = "ebarrios@itam.mx"),
person("Guillaume", "Kon Kam King", role = c("aut", "cre"), email = "guillaume.konkamking.work@gmail.com"),
person("Igor", "Prünster", role = "aut")
Maintainer: Guillaume Kon Kam King <guillaume.konkamking.work@gmail.com>
Imports: ggplot2, gridExtra, survival, coda, dplyr, tidyr, viridis
Packaged: 2019-07-09 20:40:26 UTC; guillaume

Packaged: 2019-09-11 10:46:36 UTC; guillaume

Packaged: 2019-09-18 15:50:07 UTC; guillaume

Suggests: GreedyEPL, Rmpfr, gmp, knitr, rmarkdown, testthat
Packaged: 2020-03-08 09:00:00 UTC; gkonkamking

Suggests: GreedyEPL, Rmpfr, gmp, knitr, rmarkdown, testthat, BNPmix
Packaged: 2021-05-13 10:13:14 UTC; gkonkamking

Title: Algorithms for Pitman-Yor Process Mixtures
Description: 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.