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mclust (version 5.4.7)

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

74,004

Version

5.4.7

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

November 20th, 2020

Functions in mclust (5.4.7)

Baudry_etal_2010_JCGS_examples

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

MclustSSC semi-supervised classification
MclustDR

Dimension reduction for model-based clustering and classification
Mclust

Model-Based Clustering
GvHD

GvHD Dataset
MclustDRsubsel

Subset selection for GMMDR directions based on BIC
EuroUnemployment

Unemployment data for European countries in 2014
MclustDA

MclustDA discriminant analysis
BrierScore

Brier score to assess the accuracy of probabilistic predictions
MclustBootstrap

Resampling-based Inference for Gaussian finite mixture models
cdens

Component Density for Parameterized MVN Mixture Models
banknote

Swiss banknotes data
acidity

Acidity data
cdensE

Component Density for a Parameterized MVN Mixture Model
combMat

Combining Matrix
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
covw

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

Simulated minefield data
cross

Simulated Cross Data
cdfMclust

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
bic

BIC for Parameterized Gaussian Mixture Models
adjustedRandIndex

Adjusted Rand Index
combiTree

Tree structure obtained from combining mixture components
classPriorProbs

Estimation of class prior probabilities by EM algorithm
clustCombi-internal

Internal clustCombi functions
em

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

Set control values for use with the EM algorithm
hclass

Classifications from Hierarchical Agglomeration
diabetes

Diabetes data
errorBars

Draw error bars on a plot
dmvnorm

Density of multivariate Gaussian distribution
majorityVote

Majority vote
hcRandomPairs

Random hierarchical structure
logLik.MclustDA

Log-Likelihood of a MclustDA object
clPairs

Pairwise Scatter Plots showing Classification
emE

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

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

ICL for an estimated Gaussian Mixture Model
clustCombiOptim

Optimal number of clusters obtained by combining mixture components
classError

Classification error
densityMclust

Density Estimation via Model-Based Clustering
hcE

Model-based Hierarchical Clustering
hc

Model-based Agglomerative Hierarchical Clustering
clustCombi

Combining Gaussian Mixture Components for Clustering
estep

E-step for parameterized Gaussian mixture models.
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
imputeData

Missing data imputation via the mix package
entPlot

Plot Entropy Plots
mclustVariance

Template for variance specification for parameterized Gaussian mixture models
mclustModelNames

MCLUST Model Names
mclustICL

ICL Criterion for Model-Based Clustering
plot.MclustDR

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

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
map

Classification given Probabilities
decomp2sigma

Convert mixture component covariances to matrix form
cvMclustDA

MclustDA cross-validation
mvn

Univariate or Multivariate Normal Fit
mstepE

M-step for a parameterized Gaussian mixture model
defaultPrior

Default conjugate prior for Gaussian mixtures
partconv

Numeric Encoding of a Partitioning
mclustLoglik

Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
mclust-deprecated

Deprecated Functions in mclust package
mclustModel

Best model based on BIC
plot.MclustSSC

Plotting method for MclustSSC semi-supervised classification
mclust-internal

Internal MCLUST functions
dens

Density for Parameterized MVN Mixtures
sim

Simulate from Parameterized MVN Mixture Models
wdbc

Wisconsin diagnostic breast cancer (WDBC) data
simE

Simulate from a Parameterized MVN Mixture Model
wreath

Data Simulated from a 14-Component Mixture
mclustBIC

BIC for Model-Based Clustering
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
estepE

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

Update BIC values for parameterized Gaussian mixture models
logLik.Mclust

Log-Likelihood of a Mclust object
gmmhd

Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
hdrlevels

Highest Density Region (HDR) Levels
hypvol

Aproximate Hypervolume for Multivariate Data
plot.mclustBIC

BIC Plot for Model-Based Clustering
plot.hc

Dendrograms for Model-based Agglomerative Hierarchical Clustering
randomOrthogonalMatrix

Random orthogonal matrix
predict.MclustSSC

Classification of multivariate observations by semi-supervised Gaussian finite mixtures
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
mapClass

Correspondence between classifications
mclust1Dplot

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

Univariate or Multivariate Normal Fit
mclust-package

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

Conjugate Prior for Gaussian Mixtures.
randProj

Random projections of multidimensional data modeled by an MVN mixture
mclust.options

Default values for use with MCLUST package
me.weighted

EM algorithm with weights starting with M-step for parameterized MVN mixture models
sigma2decomp

Convert mixture component covariances to decomposition form.
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
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture
mstep

M-step for parameterized Gaussian mixture models
nMclustParams

Number of Estimated Parameters in Gaussian Mixture Models
plot.MclustBootstrap

Plot of bootstrap distributions for mixture model parameters
plot.Mclust

Plotting method for Mclust model-based clustering
partuniq

Classifies Data According to Unique Observations
plot.MclustDA

Plotting method for MclustDA discriminant analysis
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
plot.mclustICL

ICL Plot for Model-Based Clustering
plot.clustCombi

Plot Combined Clusterings Results
summary.MclustDR

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

Uncertainty Plot for Model-Based Clustering
unmap

Indicator Variables given Classification
surfacePlot

Density or uncertainty surface for bivariate mixtures
summary.MclustDA

Summarizing discriminant analysis based on Gaussian finite mixture modeling
plot.densityMclust

Plots for Mixture-Based Density Estimate
predict.MclustDR

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

Thyroid gland data
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
summary.MclustSSC

Summarizing semi-supervised classification model based on Gaussian finite mixtures
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
summary.MclustBootstrap

Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
summary.mclustBIC

Summary function for model-based clustering via BIC