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
MoEClust_1.6.0.zip(r-4.7-any)MoEClust_1.6.0.zip(r-4.6-any)MoEClust_1.6.0.zip(r-4.5-any)
MoEClust_1.6.0.tgz(r-4.6-any)MoEClust_1.6.0.tgz(r-4.5-any)
MoEClust_1.6.0.tar.gz(r-4.7-any)MoEClust_1.6.0.tar.gz(r-4.6-any)
MoEClust_1.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MoEClust/json (API)

# 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:

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

On CRAN:

gaussian-mixture-models mixture-of-experts model-based-clustering

5.65 score 7 stars 1 packages 43 scripts 539 downloads 26 exports 13 dependencies

Last updated from:653ca18ee4. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	149
source / vignettes	OK	188
linux-release	OK	149
macos-release	OK	169
macos-oldrel	OK	166
windows-devel	OK	136
windows-release	OK	126
windows-oldrel	OK	122
wasm-release	OK	112

Exports:aitken drop_constants drop_levels expert_covar FARI force_posiDiag MoE_AvePP MoE_clust MoE_compare MoE_control MoE_crit MoE_cstep MoE_dens MoE_entropy MoE_estep MoE_gpairs MoE_mahala MoE_news MoE_plotCrit MoE_plotGate MoE_plotLogLik MoE_Similarity MoE_stepwise MoE_Uncertainty noise_vol quant_clust

Dependencies:BH colorspace lattice lmtest MASS matrixStats mclust mvnfast nnet Rcpp RcppArmadillo vcd zoo

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

Introduction | Installing MoEClust | CO2 Data | AIS Data | References

Rendered fromMoEClust.Rmdusingknitr::rmarkdown

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

Help page	Topics
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component	MoEClust-package MoEClust
Australian Institute of Sport data	ais
Aitken Acceleration	aitken
Convert MoEClust objects to the Mclust class	as.Mclust as.Mclust.MoEClust
GNP and CO2 Data Set	CO2data
Drop constant variables from a formula	drop_constants
Drop unused factor levels to predict from unseen data	drop_levels
Account for extra variability in covariance matrices with expert covariates	expert_covar
Compute the Frobenius (adjusted) Rand index	FARI
Force diagonal elements of a triangular matrix to be positive	force_posiDiag
Average posterior probabilities of a fitted MoEClust model	MoE_AvePP
MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component	MoE_clust print.MoEClust summary.MoEClust
Choose the best MoEClust model	MoE_compare print.MoECompare
Set control values for use with MoEClust	MoE_control
MoEClust BIC, ICL, and AIC Model-Selection Criteria	MoE_crit
C-step for MoEClust Models	MoE_cstep
Density for MoEClust Mixture Models	MoE_dens
Entropy of a fitted MoEClust model	MoE_entropy
E-step for MoEClust Models	MoE_estep
Generalised Pairs Plots for MoEClust Mixture Models	MoE_gpairs
Mahalanobis Distance Outlier Detection for Multivariate Response	MoE_mahala
Show the NEWS file	MoE_news
Model Selection Criteria Plot for MoEClust Mixture Models	MoE_plotCrit
Plot MoEClust Gating Network	MoE_plotGate
Plot the Log-Likelihood of a MoEClust Mixture Model	MoE_plotLogLik
Plot the Similarity Matrix of a MoEClust Mixture Model	MoE_Similarity
Stepwise model/variable selection for MoEClust models	MoE_stepwise
Plot Clustering Uncertainties	MoE_Uncertainty
Approximate Hypervolume Estimate	noise_vol
Plot MoEClust Results	plot.MoEClust
Predictions from MoEClust expert networks	fitted.MoE_expert predict.MoE_expert residuals.MoE_expert
Predictions from MoEClust gating networks	fitted.MoE_gating predict.MoE_gating residuals.MoE_gating
Predictions for MoEClust models	fitted.MoEClust predict.MoEClust residuals.MoEClust
Quantile-Based Clustering for Univariate Data	quant_clust

Package: MoEClust 1.6.0

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

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