Model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution: MixGHD R package

Tortora, Cristina
Browne, Ryan P.
ElSherbiny, Aisha
Franczak, Brian C.
McNicholas, Paul D.
Faculty Advisor
model-based clustering , classification , discriminant analysis , EM algorithm , generalized hyperbolic distribution
Abstract (summary)
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. Software: GPL General Public License version 2 or version 3 or a GPL-compatible license.
Publication Information
Tortora C., Browne R.P, ElSherbiny A., Franczak B.C., and McNicholas P.D. Model-Based Clustering, Classification and Discriminant Analysis using the Generalized Hyperbolic Distribution: MixGHD R package. (2021). Journal of Statistical Software. 98(3): 1 - 24. DOI: 10.18637/jss.v098.i03
Item Type
Attribution (CC BY)