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 |
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