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. See "Model-Based Clustering and Outlier Detection with Missing Data" by Hung Tong and Cristina Tortora (2022) <doi:10.1007/s11634-021-00476-1>.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: ContaminatedMixt (≥, mvtnorm (≥ 1.1-2), mnormt (≥ 2.0.2), cluster (≥ 2.1.2), rootSolve (≥, MASS (≥ 7.3)
Suggests: mice (≥ 3.10.0)
Published: 2022-01-30
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_1.0.2.tar.gz
Windows binaries: r-devel: MixtureMissing_1.0.2.zip, r-release: MixtureMissing_1.0.2.zip, r-oldrel: MixtureMissing_1.0.2.zip
macOS binaries: r-release (arm64): MixtureMissing_1.0.2.tgz, r-oldrel (arm64): MixtureMissing_1.0.2.tgz, r-release (x86_64): MixtureMissing_1.0.2.tgz, r-oldrel (x86_64): MixtureMissing_1.0.2.tgz
Old sources: MixtureMissing archive


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