MixtureMissing: Robust Model-Based Clustering for Data Sets with Missing Values at Random

Implementation of robust model based cluster analysis with missing data. The models used are: Multivariate Contaminated Normal Mixtures (MCNM), Multivariate Student's t Mixtures (MtM), and Multivariate Normal Mixtures (MNM) for data sets with missing values at random. "Cluster analysis and outlier detection with missing data" Hung Tong, Cristina Tortora (2020) <arXiv:2012.05394>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: ContaminatedMixt (≥, mvtnorm (≥ 1.1-2), mnormt (≥ 2.0.2), cluster (≥ 2.1.2), rootSolve (≥, ggplot2 (≥ 3.3.5), GGally (≥ 2.0.0)
Suggests: mice (≥ 3.10.0)
Published: 2021-10-20
Author: Hung Tong [aut, cre], Cristina Tortora [aut, ths, dgs]
Maintainer: Hung Tong <hungtongmx at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MixtureMissing results


Reference manual: MixtureMissing.pdf


Package source: MixtureMissing_1.0.0.tar.gz
Windows binaries: r-devel: MixtureMissing_1.0.0.zip, r-release: MixtureMissing_1.0.0.zip, r-oldrel: MixtureMissing_1.0.0.zip
macOS binaries: r-release (arm64): MixtureMissing_1.0.0.tgz, r-release (x86_64): MixtureMissing_1.0.0.tgz, r-oldrel: MixtureMissing_1.0.0.tgz


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