Parsimonious Model-Based Clustering with Covariates

Documentation for package ‘MoEClust’ version 1.1.0

MoEClust-package	MoEClust: Parsimonious Model-Based Clustering with Covariates
ais	Australian Institute of Sport data
as.Mclust	Convert MoEClust objects to the Mclust class
CO2data	GNP and CO2 Data Set
drop_constants	Drop constant variables from a formula
drop_levels	Drop unused factor levels to predict from unseen data
expert_covar	Account for extra variability in covariance matrices with expert covariates
MoEClust	MoEClust: Parsimonious Model-Based Clustering with Covariates
MoE_aitken	Aitken Acceleration
MoE_clust	MoEClust: Parsimonious Model-Based Clustering with Covariates
MoE_compare	Choose the best MoEClust model
MoE_control	Set control values for use with MoEClust
MoE_crit	MoEClust BIC, ICL, and AIC Model-Selection Criteria
MoE_dens	Density for MoEClust Mixture Models
MoE_estep	Compute the Responsility Matrix and Log-likelihood for MoEClust Mixture Models
MoE_gpairs	Generalised Pairs Plots for MoEClust Mixture Models
MoE_mahala	Mahalanobis Distance Outlier Detection for Multivariate Response
MoE_plotCrit	Model Selection Criteria Plot for MoEClust Mixture Models
MoE_plotGate	Plot MoEClust Gating Network
MoE_plotLogLik	Plot the Log-Likelihood of a MoEClust Mixture Model
MoE_qclass	Quantile-Based Clustering for Univariate Data
noise_vol	Approximate Hypervolume Estimate
plot.MoEClust	Plot MoEClust Results
print.MoEClust	MoEClust: Parsimonious Model-Based Clustering with Covariates
print.MoECompare	Choose the best MoEClust model
summary.MoEClust	MoEClust: Parsimonious Model-Based Clustering with Covariates