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mclust (version 5.3)

Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Description

Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

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Version

Install

install.packages('mclust')

Monthly Downloads

75,576

Version

5.3

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

May 21st, 2017

Functions in mclust (5.3)

MclustDA

MclustDA discriminant analysis
MclustDR

Dimension reduction for model-based clustering and classification
Mclust

Model-Based Clustering
MclustBootstrap

Resampling-based Inference for Gaussian finite mixture models
Baudry_etal_2010_JCGS_examples

Simulated Example Datasets From Baudry et al. (2010)
GvHD

GvHD Dataset
adjustedRandIndex

Adjusted Rand Index
banknote

Swiss banknotes data
MclustDRsubsel

Subset selection for GMMDR directions based on BIC.
acidity

Acidity data
classError

Classification error
clustCombi-internal

Internal clustCombi functions
bic

BIC for Parameterized Gaussian Mixture Models
cdens

Component Density for Parameterized MVN Mixture Models
combMat

Combining Matrix
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
cdensE

Component Density for a Parameterized MVN Mixture Model
cdfMclust

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
covw

Weighted means, covariance and scattering matrices conditioning on a weighted matrix.
cross

Simulated Cross Data
diabetes

Diabetes data
em

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

Missing data imputation via the mix package
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
clustCombi

Combining Gaussian Mixture Components for Clustering
clustCombiOptim

Optimal number of clusters obtained by combining mixture components
combiTree

Tree structure obtained from combining mixture components
coordProj

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

Simulated minefield data
clPairs

Pairwise Scatter Plots showing Classification
cvMclustDA

MclustDA cross-validation
decomp2sigma

Convert mixture component covariances to matrix form.
entPlot

Plot Entropy Plots
errorBars

Draw error bars on a plot
logLik.Mclust

Log-Likelihood of a Mclust object
hypvol

Aproximate Hypervolume for Multivariate Data
icl

ICL for an estimated Gaussian Mixture Model
majorityVote

Majority vote
map

Classification given Probabilities
emControl

Set control values for use with the EM algorithm.
emE

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

Correspondence between classifications.
mclust-deprecated

Deprecated Functions in mclust package
densityMclust

Density Estimation via Model-Based Clustering
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
hcE

Model-based Hierarchical Clustering
hclass

Classifications from Hierarchical Agglomeration
mclustBootstrapLRT

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
mclustICL

ICL Criterion for Model-Based Clustering
mvn

Univariate or Multivariate Normal Fit
mvnX

Univariate or Multivariate Normal Fit
mclust2Dplot

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

BIC for Model-Based Clustering
mclustVariance

Template for variance specification for parameterized Gaussian mixture models
me

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

Plot Model-Based Clustering Results
plot.MclustBootstrap

Plot of bootstrap distributions for mixture model parameters
predict.MclustDR

Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
mclustModel

Best model based on BIC
mclustModelNames

MCLUST Model Names
partconv

Numeric Encoding of a Partitioning
partuniq

Classifies Data According to Unique Observations
logLik.MclustDA

Log-Likelihood of a MclustDA object
mstep

M-step for parameterized Gaussian mixture models.
plot.MclustDA

Plotting method for MclustDA discriminant analysis
plot.MclustDR

Plotting method for dimension reduction for model-based clustering and classification
plot.clustCombi

Plot Combined Clusterings Results
plot.densityMclust

Plots for Mixture-Based Density Estimate
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
mstepE

M-step for a parameterized Gaussian mixture model.
summary.MclustBootstrap

Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
unmap

Indicator Variables given Classification
wreath

Data Simulated from a 14-Component Mixture
plot.mclustBIC

BIC Plot for Model-Based Clustering
plot.mclustICL

ICL Plot for Model-Based Clustering
summary.MclustDA

Summarizing discriminant analysis based on Gaussian finite mixture modeling.
sim

Simulate from Parameterized MVN Mixture Models
simE

Simulate from a Parameterized MVN Mixture Model
thyroid

Thyroid gland data
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
randomPairs

Random hierarchical structure
sigma2decomp

Convert mixture component covariances to decomposition form.
summary.MclustDR

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

Uncertainty Plot for Model-Based Clustering
defaultPrior

Default conjugate prior for Gaussian mixtures.
dens

Density for Parameterized MVN Mixtures
estep

E-step for parameterized Gaussian mixture models.
estepE

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

Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
hc

Model-based Hierarchical Clustering
mclust.options

Default values for use with MCLUST package
mclust1Dplot

Plot one-dimensional data modeled by an MVN mixture.
me.weighted

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

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

Internal MCLUST functions
mclust-package

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

Number of Estimated Parameters in Gaussian Mixture Models
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
priorControl

Conjugate Prior for Gaussian Mixtures.
randProj

Random projections of multidimensional data modeled by an MVN mixture.
summary.mclustBIC

Summary function for model-based clustering via BIC
surfacePlot

Density or uncertainty surface for bivariate mixtures.