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: 3.0.4 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: 2025-02-04 DOI: 10.32614/CRAN.package.MixtureMissing 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: Cluster, MissingData CRAN checks: MixtureMissing results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=MixtureMissing to link to this page.
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