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mclust (version 3.1-0)
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
72,128
Version
3.1-0
License
See http://www.stat.washington.edu/mclust/license.txt
Maintainer
Chris Fraley
Last Published
February 23rd, 2024
Functions in mclust (3.1-0)
Search all functions
hcE
Model-based Hierarchical Clustering
cdensE
Component Density for a Parameterized MVN Mixture Model
mapClass
Correspondence between classifications.
nVarParams
Number of Variance Parameters in Gaussian Mixture Models
partuniq
Classifies Data According to Unique Observations
bicEMtrain
Select models in discriminant analysis using BIC
mclustOptions
Set default values for use with MCLUST.
emE
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
me
EM algorithm starting with M-step for parameterized MVN mixture models.
estep
E-step for parameterized Gaussian mixture models.
mclustModelNames
MCLUST Model Names
em
EM algorithm starting with E-step for parameterized Gaussian mixture models.
plot.mclustDAtrain
Plot mclustDA training models.
plot.Mclust
Plot Model-Based Clustering Results
mstep
M-step for parameterized Gaussian mixture models.
mclustDAtest
MclustDA Testing
adjustedRandIndex
Adjusted Rand Index
plot.mclustBIC
BIC Plot
mstepE
M-step for a parameterized Gaussian mixture model.
hclass
Classifications from Hierarchical Agglomeration
emControl
Set control values for use with the EM algorithm.
sigma2decomp
Convert mixture component covariances to decomposition form.
mclustVariance
Template for variance specification for parameterized Gaussian mixture models.
summary.mclustModel
Summary Function for MCLUST Models
meE
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
mclust2Dplot
Plot two-dimensional data modelled by an MVN mixture.
hc
Model-based Hierarchical Clustering
mclustBIC
BIC for Model-Based Clustering
sim
Simulate from Parameterized MVN Mixture Models
clPairs
Pairwise Scatter Plots showing Classification
coordProj
Coordinate projections of multidimensional data modeled by an MVN mixture.
cv1EMtrain
Select discriminant models using cross validation
summary.mclustDAtrain
Models and classifications from mclustDAtrain
uncerPlot
Uncertainty Plot for Model-Based Clustering
mvnX
Univariate or Multivariate Normal Fit
summary.mclustDAtest
Classification and posterior probability from mclustDAtest.
priorControl
Conjugate Prior for Gaussian Mixtures.
plot.mclustDA
Plotting method for MclustDA discriminant analysis.
mclust-internal
Internal MCLUST functions
partconv
Numeric Encoding of a Partitioning
mclustDAtrain
MclustDA Training
Defaults.Mclust
List of values controlling defaults for some MCLUST functions.
Mclust
Model-Based Clustering
classError
Classification error.
summary.mclustBIC
Summary Function for model-based clustering.
surfacePlot
Density or uncertainty surface for two dimensional mixtures.
cdens
Component Density for Parameterized MVN Mixture Models
simE
Simulate from a Parameterized MVN Mixture Model
dens
Density for Parameterized MVN Mixtures
mclustDA
MclustDA discriminant analysis.
mclustModel
Best model based on BIC.
bic
BIC for Parameterized Gaussian Mixture Models
estepE
E-step in the EM algorithm for a parameterized Gaussian mixture model.
map
Classification given Probabilities
mclust1Dplot
Plot one-dimensional data modeled by an MVN mixture.
randProj
Random projections of multidimensional data modeled by an MVN mixture.
hypvol
Aproximate Hypervolume for Multivariate Data
defaultPrior
Default conjugate prior for Gaussian mixtures.
decomp2sigma
Convert mixture component covariances to matrix form.
mvn
Univariate or Multivariate Normal Fit
unmap
Indicator Variables given Classification
cross
Simulated Cross Data
diabetes
Diabetes data
chevron
Simulated minefield data
wreath
Data Simulated from a 14-Component Mixture