Journal of Statistical Software
The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets.
Classification, Discriminant analysis, EM algorithm, Generalized hyperbolic distribution, Model-based clustering
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This work is licensed under a Creative Commons Attribution 4.0 License.
Mathematics and Statistics
Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, and Paul D. McNicholas. "Model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution: MixGHD R package" Journal of Statistical Software (2021). https://doi.org/10.18637/jss.v098.i03