mixture: Mixture Models for Clustering and Classification

In mixture: Mixture Models for Clustering and Classification

Mixture Models for Clustering and Classification

Description

An implementation of 14 parsimonious clustering models for finite mixtures with components that are Gaussian, generalized hyperbolic, variance-gamma, Student's t, or skew-t, for model-based clustering and model-based classification, even with missing data.

Details

Package:	mixture
Type:	Package
Version:	2.1.1
Date:	2024-01-29
License:	GPL (>=2)

This package contains the functions gpcm, tpcm, ghpcm, vgpcm, stpcm, e_step, ARI, and get_best_model, plus three simulated data sets.

This package also contains advanced functions for large system use which are: main_loop main_loop_vg , main_loop_gh, main_loop_t , main_loop_st ,z_ig_random_soft, z_ig_random_hard, z_ig_kmeans.

Author(s)

Nik Pocuca, Ryan P. Browne, and Paul D. McNicholas.

Maintainer: Paul D. McNicholas <[e-mail]>

See Also

Details, examples, and references are given under gpcm, tpcm, ghpcm, stpcm, and vgpcm.

mixture documentation built on May 29, 2024, 1:47 a.m.