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mclust (version 3.4.4)
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
92,062
Version
3.4.4
License
file LICENSE
Maintainer
Chris Fraley
Last Published
April 8th, 2010
Functions in mclust (3.4.4)
Search all functions
mclustDAtrain
MclustDA Training
mvnX
Univariate or Multivariate Normal Fit
hcE
Model-based Hierarchical Clustering
priorControl
Conjugate Prior for Gaussian Mixtures.
summary.mclustDAtest
Classification and posterior probability from mclustDAtest.
classError
Classification error.
summary.mclustDAtrain
Models and classifications from mclustDAtrain
meE
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
plot.mclustDAtrain
Plot mclustDA training models.
chevron
Simulated minefield data
adjustedRandIndex
Adjusted Rand Index
cdens
Component Density for Parameterized MVN Mixture Models
wreath
Data Simulated from a 14-Component Mixture
mclust-internal
Internal MCLUST functions
hypvol
Aproximate Hypervolume for Multivariate Data
mclustDAtest
MclustDA Testing
dens
Density for Parameterized MVN Mixtures
hc
Model-based Hierarchical Clustering
Mclust
Model-Based Clustering
mclust2Dplot
Plot two-dimensional data modelled by an MVN mixture.
Defaults.Mclust
List of values controlling defaults for some MCLUST functions.
mclustBIC
BIC for Model-Based Clustering
mvn
Univariate or Multivariate Normal Fit
mclustModelNames
MCLUST Model Names
mapClass
Correspondence between classifications.
mstepE
M-step for a parameterized Gaussian mixture model.
sim
Simulate from Parameterized MVN Mixture Models
emControl
Set control values for use with the EM algorithm.
estepE
E-step in the EM algorithm for a parameterized Gaussian mixture model.
coordProj
Coordinate projections of multidimensional data modeled by an MVN mixture.
hclass
Classifications from Hierarchical Agglomeration
plot.mclustDA
Plotting method for MclustDA discriminant analysis.
bic
BIC for Parameterized Gaussian Mixture Models
defaultPrior
Default conjugate prior for Gaussian mixtures.
em
EM algorithm starting with E-step for parameterized Gaussian mixture models.
map
Classification given Probabilities
plot.Mclust
Plot Model-Based Clustering Results
mclust1Dplot
Plot one-dimensional data modeled by an MVN mixture.
summary.mclustBIC
Summary Function for model-based clustering.
partconv
Numeric Encoding of a Partitioning
clPairs
Pairwise Scatter Plots showing Classification
summary.mclustModel
Summary Function for MCLUST Models
mclustDA
MclustDA discriminant analysis.
unmap
Indicator Variables given Classification
mclustModel
Best model based on BIC.
mclustVariance
Template for variance specification for parameterized Gaussian mixture models.
diabetes
Diabetes data
simE
Simulate from a Parameterized MVN Mixture Model
imputePairs
Pairwise Scatter Plots showing Missing Data Imputations
cv1EMtrain
Select discriminant models using cross validation
imputeData
Missing Data Imputation via the mix package
mclustOptions
Set default values for use with MCLUST.
partuniq
Classifies Data According to Unique Observations
cross
Simulated Cross Data
me
EM algorithm starting with M-step for parameterized MVN mixture models.
sigma2decomp
Convert mixture component covariances to decomposition form.
uncerPlot
Uncertainty Plot for Model-Based Clustering
cdensE
Component Density for a Parameterized MVN Mixture Model
estep
E-step for parameterized Gaussian mixture models.
randProj
Random projections of multidimensional data modeled by an MVN mixture.
decomp2sigma
Convert mixture component covariances to matrix form.
bicEMtrain
Select models in discriminant analysis using BIC
emE
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
plot.mclustBIC
BIC Plot
surfacePlot
Density or uncertainty surface for two dimensional mixtures.
nVarParams
Number of Variance Parameters in Gaussian Mixture Models
mstep
M-step for parameterized Gaussian mixture models.