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All functions

Baudry_etal_2010_JCGS_examples
Simulated Example Datasets From Baudry et al. (2010)
BrierScore()
Brier score to assess the accuracy of probabilistic predictions
EuroUnemployment
Unemployment data for European countries in 2014
GvHD
GvHD Dataset
Mclust()
Model-Based Clustering
MclustBootstrap()
Resampling-based Inference for Gaussian finite mixture models
MclustDA()
MclustDA discriminant analysis
MclustDR()
Dimension reduction for model-based clustering and classification
MclustDRsubsel()
Subset selection for GMMDR directions based on BIC
MclustSSC()
MclustSSC semi-supervised classification
acidity
Acidity data
adjustedRandIndex()
Adjusted Rand Index
banknote
Swiss banknotes data
bic()
BIC for Parameterized Gaussian Mixture Models
cdens()
Component Density for Parameterized MVN Mixture Models
cdensE() cdensV() cdensX() cdensEII() cdensVII() cdensEEI() cdensVEI() cdensEVI() cdensVVI() cdensEEE() cdensEEV() cdensVEV() cdensVVV() cdensEVE() cdensEVV() cdensVEE() cdensVVE() cdensXII() cdensXXI() cdensXXX()
Component Density for a Parameterized MVN Mixture Model
cdfMclust() quantileMclust()
Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
chevron
Simulated minefield data
clPairs() clPairsLegend()
Pairwise Scatter Plots showing Classification
classError()
Classification error
classPriorProbs()
Estimation of class prior probabilities by EM algorithm
clustCombi()
Combining Gaussian Mixture Components for Clustering
clustCombiOptim()
Optimal number of clusters obtained by combining mixture components
combMat()
Combining Matrix
combiPlot()
Plot Classifications Corresponding to Successive Combined Solutions
combiTree()
Tree structure obtained from combining mixture components
coordProj()
Coordinate projections of multidimensional data modeled by an MVN mixture.
covw()
Weighted means, covariance and scattering matrices conditioning on a weighted matrix
crimcoords() summary(<crimcoords>) plot(<crimcoords>)
Discriminant coordinates data projection
cross
Simulated Cross Data
cvMclustDA()
MclustDA cross-validation
decomp2sigma()
Convert mixture component covariances to matrix form
defaultPrior()
Default conjugate prior for Gaussian mixtures
dens()
Density for Parameterized MVN Mixtures
densityMclust()
Density Estimation via Model-Based Clustering
densityMclust.diagnostic()
Diagnostic plots for mclustDensity estimation
diabetes
Diabetes Data (flawed)
dmvnorm()
Density of multivariate Gaussian distribution
dupPartition()
Partition the data by grouping together duplicated data
em()
EM algorithm starting with E-step for parameterized Gaussian mixture models
emControl()
Set control values for use with the EM algorithm
emE() emV() emX() emEII() emVII() emEEI() emVEI() emEVI() emVVI() emEEE() emVEE() emEVE() emVVE() emEEV() emVEV() emEVV() emVVV() emXII() emXXI() emXXX()
EM algorithm starting with E-step for a parameterized Gaussian mixture model
entPlot()
Plot Entropy Plots
errorBars()
Draw error bars on a plot
estep()
E-step for parameterized Gaussian mixture models.
estepE() estepV() estepEII() estepVII() estepEEI() estepVEI() estepEVI() estepVVI() estepEEE() estepEEV() estepVEV() estepVVV() estepEVE() estepEVV() estepVEE() estepVVE()
E-step in the EM algorithm for a parameterized Gaussian mixture model.
gmmhd() plot(<gmmhd>)
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
hc() as.hclust(<hc>)
Model-based Agglomerative Hierarchical Clustering
hcE() hcV() hcEII() hcVII() hcEEE() hcVVV()
Model-based Hierarchical Clustering
hcRandomPairs()
Random hierarchical structure
hclass()
Classifications from Hierarchical Agglomeration
hdrlevels()
Highest Density Region (HDR) Levels
hypvol()
Aproximate Hypervolume for Multivariate Data
icl()
ICL for an estimated Gaussian Mixture Model
imputeData()
Missing data imputation via the mix package
imputePairs()
Pairwise Scatter Plots showing Missing Data Imputations
logLik(<Mclust>)
Log-Likelihood of a Mclust object
logLik(<MclustDA>)
Log-Likelihood of a MclustDA object
logsumexp()
Log sum of exponentials
majorityVote()
Majority vote
map()
Classification given Probabilities
mapClass()
Correspondence between classifications
cv.MclustDA() cv1EMtrain() bicEMtrain()
Deprecated Functions in mclust package
mclust-package mclust
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
mclust.options()
Default values for use with MCLUST package
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
mclustBICupdate()
Update BIC values for parameterized Gaussian mixture models
mclustBootstrapLRT() print(<mclustBootstrapLRT>) plot(<mclustBootstrapLRT>)
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
mclustICL() summary(<mclustICL>)
ICL Criterion for Model-Based Clustering
mclustLoglik()
Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
mclustModel()
Best model based on BIC
mclustModelNames()
MCLUST Model Names
mclustVariance()
Template for variance specification for parameterized Gaussian mixture models
me()
EM algorithm starting with M-step for parameterized MVN mixture models
me.weighted()
EM algorithm with weights starting with M-step for parameterized Gaussian mixture models
meE() meV() meX() meEII() meVII() meEEI() meVEI() meEVI() meVVI() meEEE() meVEE() meEVE() meVVE() meEEV() meVEV() meEVV() meVVV() meXII() meXXI() meXXX()
EM algorithm starting with M-step for a parameterized Gaussian mixture model
mstep()
M-step for parameterized Gaussian mixture models
mstepE() mstepV() mstepEII() mstepVII() mstepEEI() mstepVEI() mstepEVI() mstepVVI() mstepEEE() mstepEEV() mstepVEV() mstepVVV() mstepEVE() mstepEVV() mstepVEE() mstepVVE()
M-step for a parameterized Gaussian mixture model
mvn()
Univariate or Multivariate Normal Fit
mvnX() mvnXII() mvnXXI() mvnXXX()
Univariate or Multivariate Normal Fit
nMclustParams()
Number of Estimated Parameters in Gaussian Mixture Models
nVarParams()
Number of Variance Parameters in Gaussian Mixture Models
partconv()
Numeric Encoding of a Partitioning
partuniq()
Classifies Data According to Unique Observations
plot(<Mclust>)
Plotting method for Mclust model-based clustering
plot(<MclustBootstrap>)
Plot of bootstrap distributions for mixture model parameters
plot(<MclustDA>)
Plotting method for MclustDA discriminant analysis
plot(<MclustDR>)
Plotting method for dimension reduction for model-based clustering and classification
plot(<MclustSSC>)
Plotting method for MclustSSC semi-supervised classification
plot(<clustCombi>)
Plot Combined Clusterings Results
plot(<densityMclust>) plotDensityMclust1() plotDensityMclust2() plotDensityMclustd()
Plots for Mixture-Based Density Estimate
plot(<hc>)
Dendrograms for Model-based Agglomerative Hierarchical Clustering
plot(<mclustBIC>)
BIC Plot for Model-Based Clustering
plot(<mclustICL>)
ICL Plot for Model-Based Clustering
predict(<Mclust>)
Cluster multivariate observations by Gaussian finite mixture modeling
predict(<MclustDA>)
Classify multivariate observations by Gaussian finite mixture modeling
predict(<MclustDR>)
Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
predict(<MclustSSC>)
Classification of multivariate observations by semi-supervised Gaussian finite mixtures
predict(<densityMclust>)
Density estimate of multivariate observations by Gaussian finite mixture modeling
priorControl()
Conjugate Prior for Gaussian Mixtures.
randProj()
Random projections of multidimensional data modeled by an MVN mixture
randomOrthogonalMatrix()
Random orthogonal matrix
sigma2decomp()
Convert mixture component covariances to decomposition form.
sim()
Simulate from Parameterized MVN Mixture Models
simE() simV() simEII() simVII() simEEI() simVEI() simEVI() simVVI() simEEE() simVEE() simEVE() simVVE() simEEV() simVEV() simEVV() simVVV()
Simulate from a Parameterized MVN Mixture Model
softmax()
Softmax function
summary(<Mclust>) print(<summary.Mclust>)
Summarizing Gaussian Finite Mixture Model Fits
summary(<MclustBootstrap>)
Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
summary(<MclustDA>) print(<summary.MclustDA>)
Summarizing discriminant analysis based on Gaussian finite mixture modeling
summary(<MclustDR>) print(<summary.MclustDR>)
Summarizing dimension reduction method for model-based clustering and classification
summary(<MclustSSC>) print(<summary.MclustSSC>)
Summarizing semi-supervised classification model based on Gaussian finite mixtures
summary(<mclustBIC>)
Summary function for model-based clustering via BIC
surfacePlot()
Density or uncertainty surface for bivariate mixtures
thyroid
UCI Thyroid Gland Data
uncerPlot()
Uncertainty Plot for Model-Based Clustering
unmap()
Indicator Variables given Classification
wdbc
UCI Wisconsin Diagnostic Breast Cancer Data
wreath
Data Simulated from a 14-Component Mixture