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. 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: |
2025-10-23 |
| 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 |
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