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 |