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 ORCID iD [aut, cre], Luis Nieto-Barajas ORCID iD [aut], Antonio Canale ORCID iD [ctb]
Maintainer: Christian Carmona <carmona at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: BNPMIXcluster results


Reference manual: BNPMIXcluster.pdf


Package source: BNPMIXcluster_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BNPMIXcluster_1.3.tgz, r-release (x86_64): BNPMIXcluster_1.3.tgz, r-oldrel: BNPMIXcluster_1.3.tgz
Old sources: BNPMIXcluster archive


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