Mixture Models for Clustering and Classification
TPCM Internal C++ Call
VGPCM Internal C++ Call
Maximum a posterori
Mixture Models for Clustering and Classification
Parsimonious Clustering Models
Skew-t Parsimonious Clustering Models
Student T Parsimonious Clustering Models
Variance Gamma Parsimonious Clustering Models
K-means Initialization
Random Hard Initialization
Random Soft Initialization
Adjusted Rand Index
Expectation Step
Best Model Extractor
Generalized Hyperbolic Parsimonious Clustering Models
Gaussian Parsimonious Clustering Models
GPCM Internal C++ Call
GHPCM Internal C++ Call
STPCM Internal C++ Call
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.