Title: Cluster High Dimensional Categorical Datasets
Authors@R: c(person("Constantin", "Ahlmann-Eltze", email = "artjom31415@googlemail.com", role = c("aut", "cre")),
Description: Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet
(Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
Suggests: testthat, tibble, purrr, dplyr, rmutil, pheatmap, mcclust,
Packaged: 2018-06-18 10:35:01 UTC; constantin
Author: Constantin Ahlmann-Eltze [aut, cre],

applicable to large datasets. It is described and tested in the accompanying paper by
Packaged: 2019-03-11 17:14:06 UTC; constantin

Authors@R: c(person("Constantin", "Ahlmann-Eltze", email = "artjom31415@googlemail.com", role = c("aut", "cre"),

Title: Finite Mixture Distribution Models
Author: Peter Macdonald <pdmmac@mcmaster.ca>, with contributions
Description: This package contains functions for fitting finite mixture
distribution models to grouped data and conditional data by the method
of maximum likelihood using a combination of a Newton-type algorithm and
Packaged: Fri May  2 15:53:27 2008; Amy Liu

Author: Peter Macdonald <pdmmac@mcmaster.ca>, with contributions from
distribution models to grouped data and conditional data by the
method of maximum likelihood using a combination of a
Newton-type algorithm and the EM algorithm.

Author: Peter Macdonald <pdmmac@mcmaster.ca>, with contributions from Juan Du <duduyy@hotmail.com>
Description: Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.