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mclust (version 3.4.7)

Model-Based Clustering / Normal Mixture Modeling

Description

Model-based clustering and normal mixture modeling including Bayesian regularization

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Version

Install

install.packages('mclust')

Monthly Downloads

59,191

Version

3.4.7

License

file LICENSE

Maintainer

Chris Fraley

Last Published

October 23rd, 2010

Functions in mclust (3.4.7)

cdens

Component Density for Parameterized MVN Mixture Models
defaultPrior

Default conjugate prior for Gaussian mixtures.
mclustModel

Best model based on BIC.
plot.densityMclust

Plot Univariate Mclust Density
hclass

Classifications from Hierarchical Agglomeration
hc

Model-based Hierarchical Clustering
decomp2sigma

Convert mixture component covariances to matrix form.
cv1EMtrain

Select discriminant models using cross validation
Mclust

Model-Based Clustering
classError

Classification error.
mstep

M-step for parameterized Gaussian mixture models.
mclust-internal

Internal MCLUST functions
meE

EM algorithm starting with M-step for a parameterized Gaussian mixture model.
bicEMtrain

Select models in discriminant analysis using BIC
Defaults.Mclust

List of values controlling defaults for some MCLUST functions.
mapClass

Correspondence between classifications.
hypvol

Aproximate Hypervolume for Multivariate Data
mclustDAtrain

MclustDA Training
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
randProj

Random projections of multidimensional data modeled by an MVN mixture.
mclustBIC

BIC for Model-Based Clustering
partconv

Numeric Encoding of a Partitioning
plot.mclustDA

Plotting method for MclustDA discriminant analysis.
clPairs

Pairwise Scatter Plots showing Classification
surfacePlot

Density or uncertainty surface for bivariate mixtures.
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture.
map

Classification given Probabilities
unmap

Indicator Variables given Classification
mvnX

Univariate or Multivariate Normal Fit
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
mstepE

M-step for a parameterized Gaussian mixture model.
plot.mclustDAtrain

Plot mclustDA training models.
bic

BIC for Parameterized Gaussian Mixture Models
dens

Density for Parameterized MVN Mixtures
wreath

Data Simulated from a 14-Component Mixture
hcE

Model-based Hierarchical Clustering
chevron

Simulated minefield data
me

EM algorithm starting with M-step for parameterized MVN mixture models.
estep

E-step for parameterized Gaussian mixture models.
summary.mclustBIC

Summary Function for model-based clustering.
sigma2decomp

Convert mixture component covariances to decomposition form.
plot.mclustBIC

BIC Plot
diabetes

Diabetes data
em

EM algorithm starting with E-step for parameterized Gaussian mixture models.
densityMclust

Density Estimation via Model-Based Clustering
mclustDA

MclustDA discriminant analysis.
cross

Simulated Cross Data
mclustVariance

Template for variance specification for parameterized Gaussian mixture models.
plot.Mclust

Plot Model-Based Clustering Results
mclustModelNames

MCLUST Model Names
mclust1Dplot

Plot one-dimensional data modeled by an MVN mixture.
cdensE

Component Density for a Parameterized MVN Mixture Model
coordProj

Coordinate projections of multidimensional data modeled by an MVN mixture.
sim

Simulate from Parameterized MVN Mixture Models
mvn

Univariate or Multivariate Normal Fit
estepE

E-step in the EM algorithm for a parameterized Gaussian mixture model.
uncerPlot

Uncertainty Plot for Model-Based Clustering
priorControl

Conjugate Prior for Gaussian Mixtures.
mclustDAtest

MclustDA Testing
adjustedRandIndex

Adjusted Rand Index
emControl

Set control values for use with the EM algorithm.
imputeData

Missing Data Imputation via the mix package
mclustOptions

Set default values for use with MCLUST.
partuniq

Classifies Data According to Unique Observations
emE

EM algorithm starting with E-step for a parameterized Gaussian mixture model.
simE

Simulate from a Parameterized MVN Mixture Model
summary.mclustModel

Summary Function for MCLUST Models
summary.mclustDAtrain

Models and classifications from mclustDAtrain
summary.mclustDAtest

Classification and posterior probability from mclustDAtest.