| mixture {mixture} | R Documentation |
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 <mcnicholas@math.mcmaster.ca>
See Also
Details, examples, and references are given under gpcm, tpcm, ghpcm, stpcm, and vgpcm.