Robust and Flexible Model-Based Clustering for Data Sets with Missing Values at Random
Binary Classification Evaluation
Extractor function for MixtureMissing
Missing-Data Pattern Generation
Missing Values Generation
Cluster Initialization using a Heuristic Method
Multivariate Contaminated Normal Mixture (MCNM)
Mean Imputation
Multivariate Generalized Hyperbolic Mixture (MGHM)
MixtureMissing Plotting
Print for MixtureMissing
Mixture Model Selection
Summary for MixtureMissing
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, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). 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.