Package: MoEClust 1.6.0

MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Authors:Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]

MoEClust_1.6.0.tar.gz
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MoEClust_1.6.0.tgz(r-4.6-any)MoEClust_1.6.0.tgz(r-4.5-any)
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MoEClust_1.6.0.tgz(r-4.5-emscripten)
MoEClust.pdf |MoEClust.html✨
MoEClust/json (API)
NEWS

# Install 'MoEClust' in R:
install.packages('MoEClust', repos = c('https://keefe-murphy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/keefe-murphy/moeclust/issues

Datasets:
  • CO2data - GNP and CO2 Data Set
  • ais - Australian Institute of Sport data

On CRAN:

Conda:

gaussian-mixture-modelsmixture-of-expertsmodel-based-clustering

5.66 score 7 stars 1 packages 44 scripts 452 downloads 26 exports 13 dependencies

Last updated from:653ca18ee4. Checks:8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK148
source / vignettesOK250
linux-release-x86_64OK162
macos-devel-arm64OK94
macos-release-arm64OK92
windows-develOK115
windows-releaseOK123
wasm-releaseOK135

Exports:aitkendrop_constantsdrop_levelsexpert_covarFARIforce_posiDiagMoE_AvePPMoE_clustMoE_compareMoE_controlMoE_critMoE_cstepMoE_densMoE_entropyMoE_estepMoE_gpairsMoE_mahalaMoE_newsMoE_plotCritMoE_plotGateMoE_plotLogLikMoE_SimilarityMoE_stepwiseMoE_Uncertaintynoise_volquant_clust

Dependencies:BHcolorspacelatticelmtestMASSmatrixStatsmclustmvnfastnnetRcppRcppArmadillovcdzoo

MoEClust: Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates and a Noise Component

Keefe Murphy

Rendered fromMoEClust.Rmdusingknitr::rmarkdownon Mar 26 2026.

Last update: 2024-06-14
Started: 2017-09-06

Readme and manuals

Help Manual

Help pageTopics
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise ComponentMoEClust-package MoEClust
Australian Institute of Sport dataais
Aitken Accelerationaitken
Convert MoEClust objects to the Mclust classas.Mclust as.Mclust.MoEClust
GNP and CO2 Data SetCO2data
Drop constant variables from a formuladrop_constants
Drop unused factor levels to predict from unseen datadrop_levels
Account for extra variability in covariance matrices with expert covariatesexpert_covar
Compute the Frobenius (adjusted) Rand indexFARI
Force diagonal elements of a triangular matrix to be positiveforce_posiDiag
Average posterior probabilities of a fitted MoEClust modelMoE_AvePP
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise ComponentMoE_clust print.MoEClust summary.MoEClust
Choose the best MoEClust modelMoE_compare print.MoECompare
Set control values for use with MoEClustMoE_control
MoEClust BIC, ICL, and AIC Model-Selection CriteriaMoE_crit
C-step for MoEClust ModelsMoE_cstep
Density for MoEClust Mixture ModelsMoE_dens
Entropy of a fitted MoEClust modelMoE_entropy
E-step for MoEClust ModelsMoE_estep
Generalised Pairs Plots for MoEClust Mixture ModelsMoE_gpairs
Mahalanobis Distance Outlier Detection for Multivariate ResponseMoE_mahala
Show the NEWS fileMoE_news
Model Selection Criteria Plot for MoEClust Mixture ModelsMoE_plotCrit
Plot MoEClust Gating NetworkMoE_plotGate
Plot the Log-Likelihood of a MoEClust Mixture ModelMoE_plotLogLik
Plot the Similarity Matrix of a MoEClust Mixture ModelMoE_Similarity
Stepwise model/variable selection for MoEClust modelsMoE_stepwise
Plot Clustering UncertaintiesMoE_Uncertainty
Approximate Hypervolume Estimatenoise_vol
Plot MoEClust Resultsplot.MoEClust
Predictions from MoEClust expert networksfitted.MoE_expert predict.MoE_expert residuals.MoE_expert
Predictions from MoEClust gating networksfitted.MoE_gating predict.MoE_gating residuals.MoE_gating
Predictions for MoEClust modelsfitted.MoEClust predict.MoEClust residuals.MoEClust
Quantile-Based Clustering for Univariate Dataquant_clust