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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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Install
install.packages('mclust')
Monthly Downloads
57,522
Version
3.4.7
License
file LICENSE
Maintainer
Chris Fraley
Last Published
October 23rd, 2010
Functions in mclust (3.4.7)
Search functions
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.