mixdir: Cluster High Dimensional Categorical Datasets

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 applicable to large datasets.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: extraDistr, Rcpp
LinkingTo: Rcpp
Suggests: testthat, tibble, purrr, dplyr, rmutil, pheatmap, mcclust, ggplot2, tidyr, utils
Published: 2018-06-18
Author: Constantin Ahlmann-Eltze [aut, cre], Christopher Yau [ths]
Maintainer: Constantin Ahlmann-Eltze <artjom31415 at googlemail.com>
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: mixdir results

Downloads:

Reference manual: mixdir.pdf
Package source: mixdir_0.1.0.tar.gz
Windows binaries: r-devel: mixdir_0.1.0.zip, r-release: mixdir_0.1.0.zip, r-oldrel: mixdir_0.1.0.zip
OS X binaries: r-release: mixdir_0.1.0.tgz, r-oldrel: mixdir_0.1.0.tgz

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