Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random [R package MixtureMissing version 3.0.5]

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年08月01日6>). 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. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

Version: 3.0.5

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年10月23日

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