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

Implementation of robust model-based cluster analysis for data sets with missing values at random. The models used are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>), Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>), Multivariate Skew's t Mixture (MStM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>), Multivariate t Mixture (MtM, Wang et al., 2004, <doi:10.1016/j.patrec.2004.01.010>), and Multivariate Normal Mixture (MNM, Ghahramani and Jordan, 1994, <doi:10.21236/ADA295618>).

Version: 2.0.0
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)
Suggests: mice (≥ 3.10.0)
Published: 2023-04-13
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


Reference manual: MixtureMissing.pdf


Package source: MixtureMissing_2.0.0.tar.gz
Windows binaries: r-devel: MixtureMissing_2.0.0.zip, r-release: MixtureMissing_2.0.0.zip, r-oldrel: MixtureMissing_2.0.0.zip
macOS binaries: r-release (arm64): MixtureMissing_2.0.0.tgz, r-oldrel (arm64): MixtureMissing_2.0.0.tgz, r-release (x86_64): MixtureMissing_2.0.0.tgz, r-oldrel (x86_64): MixtureMissing_2.0.0.tgz
Old sources: MixtureMissing archive


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