R: Mixture Models for Clustering and Classification

# Mixture Models for Clustering and Classification

## Documentation for package ‘mixture’ version 2.2.0

- DESCRIPTION file

## Help Pages

mixture-package | Mixture Models for Clustering and Classification
ARI | Adjusted Rand Index
dmg | Density of multivariate Gaussian distribution
dmgh | Density of multivariate Generalized Hyperbolic distribution
dmst | Density of multivariate Skew-t distribution
dmvg | Density of multivariate Variance Gamma distribution
e_step | Expectation Step
get_best_model | Best Model Extractor
ghpcm | Generalized Hyperbolic Parsimonious Clustering Models
gpcm | Gaussian Parsimonious Clustering Models
main_loop | GPCM Internal C++ Call
main_loop_gh | GHPCM Internal C++ Call
main_loop_st | STPCM Internal C++ Call
main_loop_t | TPCM Internal C++ Call
main_loop_vg | VGPCM Internal C++ Call
MAP | Maximum _a posterori_
mixture | Mixture Models for Clustering and Classification
pcm | Parsimonious Clustering Models
stpcm | Skew-t Parsimonious Clustering Models
sx2 | Skewed Simulated Data 1
sx3 | Skewed Simulated Data 2
tpcm | Student T Parsimonious Clustering Models
vgpcm | Variance Gamma Parsimonious Clustering Models
x2 | Simulated Data
z_ig_kmeans | K-means Initialization
z_ig_random_hard | Random Hard Initialization
z_ig_random_soft | Random Soft Initialization