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mclust (version 4.1)

Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation

Description

Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.

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Version

Install

install.packages('mclust')

Monthly Downloads

74,509

Version

4.1

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

May 1st, 2013

Functions in mclust (4.1)

hclass

Classifications from Hierarchical Agglomeration
densityMclust

Density Estimation via Model-Based Clustering
emE

EM algorithm starting with E-step for a parameterized Gaussian mixture model.
diabetes

Diabetes data
coordProj

Coordinate projections of multidimensional data modeled by an MVN mixture.
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
bicEMtrain

Select models in discriminant analysis using BIC
mclust.options

Default values for use with MCLUST package
decomp2sigma

Convert mixture component covariances to matrix form.
Mclust

Model-Based Clustering
mclust-internal

Internal MCLUST functions
hc

Model-based Hierarchical Clustering
icl

ICL for an estimated Gaussian Mixture Model
entPlot

Plot Entropy Plots
cdens

Component Density for Parameterized MVN Mixture Models
MclustDR

Dimension reduction for model-based clustering and classification
mvn

Univariate or Multivariate Normal Fit
estep

E-step for parameterized Gaussian mixture models.
MclustDA

MclustDA discriminant analysis
defaultPrior

Default conjugate prior for Gaussian mixtures.
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
mstepE

M-step for a parameterized Gaussian mixture model.
print.clustCombi

Displays Combined Clusterings Results
bic

BIC for Parameterized Gaussian Mixture Models
Baudry_etal_2010_JCGS_examples

Simulated Example Datasets From Baudry et al. (2010)
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
me.weighted

EM algorithm with weights starting with M-step for parameterized MVN mixture models
cross

Simulated Cross Data
clustCombi-internal

Internal clustCombi functions
classError

Classification error
meE

EM algorithm starting with M-step for a parameterized Gaussian mixture model.
clPairs

Pairwise Scatter Plots showing Classification
cdensE

Component Density for a Parameterized MVN Mixture Model
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture.
combMat

Combining Matrix
plot.mclustBIC

BIC Plot for Model-Based Clustering
hcE

Model-based Hierarchical Clustering
mclust1Dplot

Plot one-dimensional data modeled by an MVN mixture.
partconv

Numeric Encoding of a Partitioning
dens

Density for Parameterized MVN Mixtures
cv.MclustDA

MclustDA cross-validation
mclustBIC

BIC for Model-Based Clustering
mclustVariance

Template for variance specification for parameterized Gaussian mixture models
plot.clustCombi

Plot Combined Clusterings Results
cdfMclust

Cumulative density function from mclustDensity estimation
cv1EMtrain

Select discriminant models using cross validation
mvnX

Univariate or Multivariate Normal Fit
emControl

Set control values for use with the EM algorithm.
GvHD

GvHD Dataset
mclustModel

Best model based on BIC
plot.Mclust

Plot Model-Based Clustering Results
map

Classification given Probabilities
plot.MclustDA

Plotting method for MclustDA discriminant analysis
clustCombi

Combining Gaussian Mixture Components for Clustering
em

EM algorithm starting with E-step for parameterized Gaussian mixture models.
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
mclustICL

ICL Criterion for Model-Based Clustering
adjustedRandIndex

Adjusted Rand Index
plot.mclustICL

ICL Plot for Model-Based Clustering
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
simE

Simulate from a Parameterized MVN Mixture Model
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
summary.MclustDA

Summarizing discriminant analysis based on Gaussian finite mixture modeling.
priorControl

Conjugate Prior for Gaussian Mixtures.
sigma2decomp

Convert mixture component covariances to decomposition form.
mstep

M-step for parameterized Gaussian mixture models.
surfacePlot

Density or uncertainty surface for bivariate mixtures.
sim

Simulate from Parameterized MVN Mixture Models
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
hypvol

Aproximate Hypervolume for Multivariate Data
uncerPlot

Uncertainty Plot for Model-Based Clustering
summary.mclustBIC

Summary Function for model-based clustering.
logLik.Mclust

Log-Likelihood of a Mclust object
predict.MclustDR

Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
summary.MclustDR

Summarizing dimension reduction method for model-based clustering and classification
wreath

Data Simulated from a 14-Component Mixture
randProj

Random projections of multidimensional data modeled by an MVN mixture.
chevron

Simulated minefield data
plot.MclustDR

Plotting method for dimension reduction for model-based clustering and classification
unmap

Indicator Variables given Classification
logLik.MclustDA

Log-Likelihood of a MclustDA object
partuniq

Classifies Data According to Unique Observations
plot.densityMclust

Plot for a mclustDensity object
banknote

Swiss banknotes data
imputeData

Missing Data Imputation via the mix package
mapClass

Correspondence between classifications.
mclustModelNames

MCLUST Model Names
estepE

E-step in the EM algorithm for a parameterized Gaussian mixture model.
me

EM algorithm starting with M-step for parameterized MVN mixture models.