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>.
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curl https://depscope.dev/api/check/conda/r-bgmmFirst published · 2021-05-28 03:17:40.877000+00:00
Last updated · 2025-09-18 07:16:06.604000+00:00