MixtureMissing (version 3.0.5)
Robust and Flexible Model-Based Clustering for Data Sets with
Missing Values at Random
Description
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, ) and
Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, ). 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.