MoEClust-package |
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component |
ais |
Australian Institute of Sport data |
aitken |
Aitken Acceleration |
as.Mclust |
Convert MoEClust objects to the Mclust class |
as.Mclust.MoEClust |
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 |
force_posiDiag |
Force diagonal elements of a triangular matrix to be positive |
MoEClust |
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component |
MoE_clust |
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component |
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_cstep |
C-step for MoEClust Models |
MoE_dens |
Density for MoEClust Mixture Models |
MoE_estep |
E-step for MoEClust Models |
MoE_gpairs |
Generalised Pairs Plots for MoEClust Mixture Models |
MoE_mahala |
Mahalanobis Distance Outlier Detection for Multivariate Response |
MoE_news |
Show the NEWS file |
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_stepwise |
Stepwise model/variable selection for MoEClust models |
MoE_Uncertainty |
Plot Clustering Uncertainties |
noise_vol |
Approximate Hypervolume Estimate |
plot.MoEClust |
Plot MoEClust Results |
predict.MoEClust |
Predictions for MoEClust models |
print.MoEClust |
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component |
print.MoECompare |
Choose the best MoEClust model |
quant_clust |
Quantile-Based Clustering for Univariate Data |
residuals.MoEClust |
Predictions for MoEClust models |
summary.MoEClust |
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component |