MixtureMissing: Robust Model-Based Clustering for Data Sets with Missing Values
at Random
Implementation of robust model based cluster analysis with missing data.
The models used are: Multivariate Contaminated Normal Mixtures (MCNM),
Multivariate Student's t Mixtures (MtM), and Multivariate Normal Mixtures (MNM)
for data sets with missing values at random.
See "Model-Based Clustering and Outlier Detection with Missing Data" by
Hung Tong and Cristina Tortora (2022) <doi:10.1007/s11634-021-00476-1>.
| Version: |
1.0.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
ContaminatedMixt (≥ 1.3.4.1), mvtnorm (≥ 1.1-2), mnormt (≥
2.0.2), cluster (≥ 2.1.2), rootSolve (≥ 1.8.2.2), MASS (≥
7.3) |
| Suggests: |
mice (≥ 3.10.0) |
| Published: |
2022-01-30 |
| 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 |
| CRAN checks: |
MixtureMissing results |
Documentation:
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