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

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random. Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic.

Version: 3.0.2
Depends: R (≥ 3.5.0)
Imports: mvtnorm (≥ 1.1-2), mnormt (≥ 2.0.2), cluster (≥ 2.1.2), MASS (≥ 7.3), numDeriv (≥ 8.1.1), Bessel (≥ 0.6.0), mclust (≥ 5.0.0), mice (≥ 3.10.0)
Published: 2024-03-19
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
In views: MissingData
CRAN checks: MixtureMissing results

Documentation:

Reference manual: MixtureMissing.pdf

Downloads:

Package source: MixtureMissing_3.0.2.tar.gz
Windows binaries: r-devel: MixtureMissing_3.0.2.zip, r-release: MixtureMissing_3.0.2.zip, r-oldrel: MixtureMissing_3.0.2.zip
macOS binaries: r-release (arm64): MixtureMissing_3.0.2.tgz, r-oldrel (arm64): MixtureMissing_3.0.2.tgz, r-release (x86_64): MixtureMissing_3.0.2.tgz
Old sources: MixtureMissing archive

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