BNPMIXcluster: Bayesian Nonparametric Model for Clustering with Mixed Scale Variables

Model-based approach for clustering of multivariate data, capable of combining different types of variables (continuous, ordinal and nominal) and accommodating for different sampling probabilities in a complex survey design. The model is based on a location mixture model with a Poisson-Dirichlet process prior on the location parameters of the associated latent variables. Details of the underlying model is described in Carmona, C., Nieto-Barajas, L. E., Canale, A. (2016) <arXiv:1612.00083>.

Version:	1.3
Depends:	R (≥ 2.10)
Imports:	compiler, gplots, MASS, matrixcalc, mvtnorm, plyr, Rcpp (≥ 1.0.5), truncnorm
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	scatterplot3d
Published:	2020-11-30
Author:	Christian Carmona [aut, cre], Luis Nieto-Barajas [aut], Antonio Canale [ctb]
Maintainer:	Christian Carmona <carmona at stats.ox.ac.uk>
BugReports:	https://github.com/christianu7/BNPMIXcluster/issues
License:	MIT + file LICENSE
URL:	https://github.com/christianu7/BNPMIXcluster
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	BNPMIXcluster results

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