Model-Based Clustering / Normal Mixture Modeling

Documentation for package ‘mclust’ version 3.4.11

DESCRIPTION file.

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A B C D E H I M N P R S U W misc

-- A --

adjustedRandIndex

Adjusted Rand Index

-- B --

bic	BIC for Parameterized Gaussian Mixture Models
bicEMtrain	Select models in discriminant analysis using BIC

-- C --

cdens	Component Density for Parameterized MVN Mixture Models
cdensE	Component Density for a Parameterized MVN Mixture Model
cdensEEE	Component Density for a Parameterized MVN Mixture Model
cdensEEI	Component Density for a Parameterized MVN Mixture Model
cdensEEV	Component Density for a Parameterized MVN Mixture Model
cdensEII	Component Density for a Parameterized MVN Mixture Model
cdensEVI	Component Density for a Parameterized MVN Mixture Model
cdensV	Component Density for a Parameterized MVN Mixture Model
cdensVEI	Component Density for a Parameterized MVN Mixture Model
cdensVEV	Component Density for a Parameterized MVN Mixture Model
cdensVII	Component Density for a Parameterized MVN Mixture Model
cdensVVI	Component Density for a Parameterized MVN Mixture Model
cdensVVV	Component Density for a Parameterized MVN Mixture Model
chevron	Simulated minefield data
classError	Classification error.
clPairs	Pairwise Scatter Plots showing Classification
coordProj	Coordinate projections of multidimensional data modeled by an MVN mixture.
cross	Simulated Cross Data
cv1EMtrain	Select discriminant models using cross validation

-- D --

decomp2sigma	Convert mixture component covariances to matrix form.
defaultPrior	Default conjugate prior for Gaussian mixtures.
Defaults.Mclust	List of values controlling defaults for some MCLUST functions.
dens	Density for Parameterized MVN Mixtures
densityMclust	Density Estimation via Model-Based Clustering
diabetes	Diabetes data
diabetes.class	Diabetes data

-- E --

em	EM algorithm starting with E-step for parameterized Gaussian mixture models.
EMclust	BIC for Model-Based Clustering
emControl	Set control values for use with the EM algorithm.
emE	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEE	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEI	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEV	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEII	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEVI	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emV	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVEI	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVEV	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVII	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVVI	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVVV	EM algorithm starting with E-step for a parameterized Gaussian mixture model.
estep	E-step for parameterized Gaussian mixture models.
estepE	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEE	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEI	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEV	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEII	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEVI	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepV	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVEI	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVEV	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVII	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVVI	E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVVV	E-step in the EM algorithm for a parameterized Gaussian mixture model.

-- H --

hc	Model-based Hierarchical Clustering
hcE	Model-based Hierarchical Clustering
hcEEE	Model-based Hierarchical Clustering
hcEII	Model-based Hierarchical Clustering
hclass	Classifications from Hierarchical Agglomeration
hcV	Model-based Hierarchical Clustering
hcVII	Model-based Hierarchical Clustering
hcVVV	Model-based Hierarchical Clustering
hypvol	Aproximate Hypervolume for Multivariate Data

-- I --

imputeData	Missing Data Imputation via the mix package
imputePairs	Pairwise Scatter Plots showing Missing Data Imputations

-- M --

map	Classification given Probabilities
mapClass	Correspondence between classifications.
Mclust	Model-Based Clustering
mclust1Dplot	Plot one-dimensional data modeled by an MVN mixture.
mclust2Dplot	Plot two-dimensional data modelled by an MVN mixture.
mclustBIC	BIC for Model-Based Clustering
mclustDA	MclustDA discriminant analysis.
mclustDAtest	MclustDA Testing
mclustDAtrain	MclustDA Training
mclustModel	Best model based on BIC.
mclustModelNames	MCLUST Model Names
mclustOptions	Set default values for use with MCLUST.
mclustVariance	Template for variance specification for parameterized Gaussian mixture models.
me	EM algorithm starting with M-step for parameterized MVN mixture models.
meE	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEE	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEI	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEV	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEII	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEVI	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meV	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVEI	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVEV	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVII	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVVI	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVVV	EM algorithm starting with M-step for a parameterized Gaussian mixture model.
mstep	M-step for parameterized Gaussian mixture models.
mstepE	M-step for a parameterized Gaussian mixture model.
mstepEEE	M-step for a parameterized Gaussian mixture model.
mstepEEI	M-step for a parameterized Gaussian mixture model.
mstepEEV	M-step for a parameterized Gaussian mixture model.
mstepEII	M-step for a parameterized Gaussian mixture model.
mstepEVI	M-step for a parameterized Gaussian mixture model.
mstepV	M-step for a parameterized Gaussian mixture model.
mstepVEI	M-step for a parameterized Gaussian mixture model.
mstepVEV	M-step for a parameterized Gaussian mixture model.
mstepVII	M-step for a parameterized Gaussian mixture model.
mstepVVI	M-step for a parameterized Gaussian mixture model.
mstepVVV	M-step for a parameterized Gaussian mixture model.
mvn	Univariate or Multivariate Normal Fit
mvnX	Univariate or Multivariate Normal Fit
mvnXII	Univariate or Multivariate Normal Fit
mvnXXI	Univariate or Multivariate Normal Fit
mvnXXX	Univariate or Multivariate Normal Fit

-- N --

nVarParams

Number of Variance Parameters in Gaussian Mixture Models

-- P --

partconv	Numeric Encoding of a Partitioning
partuniq	Classifies Data According to Unique Observations
plot.densityMclust	Plot Univariate Mclust Density
plot.Mclust	Plot Model-Based Clustering Results
plot.mclustBIC	BIC Plot
plot.mclustDA	Plotting method for MclustDA discriminant analysis.
plot.mclustDAtrain	Plot mclustDA training models.
print.Mclust	Model-Based Clustering
print.mclustBIC	BIC for Model-Based Clustering
print.mclustDA	MclustDA discriminant analysis.
print.mclustDAtrain	MclustDA Training
print.summary.mclustBIC	Summary Function for model-based clustering.
printSummaryMclustBIC	Summary Function for model-based clustering.
printSummaryMclustBICn	Summary Function for model-based clustering.
priorControl	Conjugate Prior for Gaussian Mixtures.

-- R --

randProj

Random projections of multidimensional data modeled by an MVN mixture.

-- S --

sigma2decomp	Convert mixture component covariances to decomposition form.
sim	Simulate from Parameterized MVN Mixture Models
simE	Simulate from a Parameterized MVN Mixture Model
simEEE	Simulate from a Parameterized MVN Mixture Model
simEEI	Simulate from a Parameterized MVN Mixture Model
simEEV	Simulate from a Parameterized MVN Mixture Model
simEII	Simulate from a Parameterized MVN Mixture Model
simEVI	Simulate from a Parameterized MVN Mixture Model
simV	Simulate from a Parameterized MVN Mixture Model
simVEI	Simulate from a Parameterized MVN Mixture Model
simVEV	Simulate from a Parameterized MVN Mixture Model
simVII	Simulate from a Parameterized MVN Mixture Model
simVVI	Simulate from a Parameterized MVN Mixture Model
simVVV	Simulate from a Parameterized MVN Mixture Model
summary.mclustBIC	Summary Function for model-based clustering.
summary.mclustDAtest	Classification and posterior probability from mclustDAtest.
summary.mclustDAtrain	Models and classifications from mclustDAtrain
summary.mclustModel	Summary Function for MCLUST Models
summaryMclustBIC	Summary Function for model-based clustering.
summaryMclustBICn	Summary Function for model-based clustering.
surfacePlot	Density or uncertainty surface for bivariate mixtures.

-- U --

uncerPlot	Uncertainty Plot for Model-Based Clustering
unmap	Indicator Variables given Classification

-- W --

wreath

Data Simulated from a 14-Component Mixture

-- misc --

.Mclust

List of values controlling defaults for some MCLUST functions.