Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.
copied from cf-post-staging / r-bgmm| Label | Latest Version |
|---|---|
| main | 1.8.5 |
| cf201901 | 1.8.3 |
| cf202003 | 1.8.4 |
| gcc7 | 1.8.3 |
