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mclust (version 3.0-0)
Model-Based Clustering / Normal Mixture Modeling
Description
Model-based clustering and normal mixture modeling including Bayesian regularization
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Install
install.packages('mclust')
Monthly Downloads
87,167
Version
3.0-0
License
See http://www.stat.washington.edu/mclust/license.txt
Maintainer
Chris Fraley
Last Published
October 31st, 2025
Functions in mclust (3.0-0)
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Defaults.Mclust
List of values controlling defaults for some MCLUST functions.
defaultPrior
Default conjugate prior for Gaussian mixtures.
cdensE
Component Density for a Parameterized MVN Mixture Model
meE
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
me
EM algorithm starting with M-step for parameterized MVN mixture models.
mclustVariance
Template for variance specification for parameterized Gaussian mixture models.
cv1EMtrain
Select discriminant models using cross validation
hcE
Model-based Hierarchical Clustering
Mclust
Model-Based Clustering
mstepE
M-step for a parameterized Gaussian mixture model.
mclustDAtest
MclustDA Testing
priorControl
Conjugate Prior for Gaussian Mixtures.
summary.mclustDAtest
Classification and posterior probability from mclustDAtest.
mapClass
Correspondence between classifications.
randProj
Random projections of multidimensional data modeled by an MVN mixture.
summary.mclustBIC
Summary Function for model-based clustering.
mclustDA
MclustDA discriminant analysis.
mclustBIC
BIC for Model-Based Clustering
mclustOptions
Set default values for use with MCLUST.
mvnX
Univariate or Multivariate Normal Fit
summary.mclustDAtrain
Models and classifications from mclustDAtrain
emControl
Set control values for use with the EM algorithm.
plot.mclustDAtrain
Plot mclustDA training models.
mvn
Univariate or Multivariate Normal Fit
uncerPlot
Uncertainty Plot for Model-Based Clustering
decomp2sigma
Convert mixture component covariances to matrix form.
estep
E-step for parameterized Gaussian mixture models.
simE
Simulate from a Parameterized MVN Mixture Model
unmap
Indicator Variables given Classification
partuniq
Classifies Data According to Unique Observations
hypvol
Aproximate Hypervolume for Multivariate Data
hclass
Classifications from Hierarchical Agglomeration
nVarParams
Number of Variance Parameters in Gaussian Mixture Models
map
Classification given Probabilities
bic
BIC for Parameterized Gaussian Mixture Models
sigma2decomp
Convert mixture component covariances to decomposition form.
summary.mclustModel
Summary Function for MCLUST Models
sim
Simulate from Parameterized MVN Mixture Models
em
EM algorithm starting with E-step for parameterized Gaussian mixture models.
dens
Density for Parameterized MVN Mixtures
coordProj
Coordinate projections of multidimensional data modeled by an MVN mixture.
adjustedRandIndex
Adjusted Rand Index
hc
Model-based Hierarchical Clustering
mclust-internal
Internal MCLUST functions
mclust1Dplot
Plot one-dimensional data modeled by an MVN mixture.
emE
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
plot.Mclust
Plot Model-Based Clustering Results
cdens
Component Density for Parameterized MVN Mixture Models
mstep
M-step for parameterized Gaussian mixture models.
plot.mclustDA
Plotting method for MclustDA discriminant analysis.
mclustDAtrain
MclustDA Training
partconv
Numeric Encoding of a Partitioning
mclust2Dplot
Plot two-dimensional data modelled by an MVN mixture.
surfacePlot
Density or uncertainty surface for two dimensional mixtures.
bicEMtrain
Select models in discriminant analysis using BIC
clPairs
Pairwise Scatter Plots showing Classification
classError
Classification error.
estepE
E-step in the EM algorithm for a parameterized Gaussian mixture model.
mclustModelNames
MCLUST Model Names
mclustModel
Best model based on BIC.
cross
Simulated Cross Data
diabetes
Diabetes data
wreath
Data Simulated from a 14-Component Mixture
chevron
Simulated minefield data