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

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

dmvnorm()

Density of multivariate Gaussian distribution

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()

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

majorityVote()

Majority vote

map()

Classification given Probabilities

mapClass()

Correspondence between classifications

cv.MclustDA() cv1EMtrain() bicEMtrain()

Deprecated Functions in mclust package

mclust-package

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 MVN 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

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

Thyroid gland data

uncerPlot()

Uncertainty Plot for Model-Based Clustering

unmap()

Indicator Variables given Classification

wdbc

Wisconsin diagnostic breast cancer (WDBC) data

wreath

Data Simulated from a 14-Component Mixture