Parsimonious Model-Based Clustering with Covariates


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Documentation for package ‘MoEClust’ version 1.0.0

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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
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