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mclust (version 5.4.6)

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

5.4.6

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

April 11th, 2020

Functions in mclust (5.4.6)

clPairs

Pairwise Scatter Plots showing Classification
classError

Classification error
cdfMclust

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
bic

BIC for Parameterized Gaussian Mixture Models
acidity

Acidity data
chevron

Simulated minefield data
clustCombi

Combining Gaussian Mixture Components for Clustering
clustCombi-internal

Internal clustCombi functions
classPriorProbs

Estimation of class prior probabilities by EM algorithm
cross

Simulated Cross Data
combMat

Combining Matrix
cdens

Component Density for Parameterized MVN Mixture Models
cvMclustDA

MclustDA cross-validation
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
cdensE

Component Density for a Parameterized MVN Mixture Model
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
adjustedRandIndex

Adjusted Rand Index
dens

Density for Parameterized MVN Mixtures
diabetes

Diabetes data
clustCombiOptim

Optimal number of clusters obtained by combining mixture components
banknote

Swiss banknotes data
dmvnorm

Density of multivariate Gaussian distribution
decomp2sigma

Convert mixture component covariances to matrix form
combiTree

Tree structure obtained from combining mixture components
hdrlevels

Highest Density Region (HDR) Levels
hcE

Model-based Hierarchical Clustering
mclust-deprecated

Deprecated Functions in mclust package
errorBars

Draw error bars on a plot
entPlot

Plot Entropy Plots
coordProj

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

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

Classifications from Hierarchical Agglomeration
em

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

Aproximate Hypervolume for Multivariate Data
defaultPrior

Default conjugate prior for Gaussian mixtures
densityMclust

Density Estimation via Model-Based Clustering
estepE

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

E-step for parameterized Gaussian mixture models.
hc

Model-based Agglomerative Hierarchical Clustering
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
mclustBIC

BIC for Model-Based Clustering
logLik.Mclust

Log-Likelihood of a Mclust object
emControl

Set control values for use with the EM algorithm
mclustBootstrapLRT

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
emE

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

Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
icl

ICL for an estimated Gaussian Mixture Model
mclustBICupdate

Update BIC values for parameterized Gaussian mixture models
mclust-internal

Internal MCLUST functions
imputeData

Missing data imputation via the mix package
meE

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

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

Classification given Probabilities
mapClass

Correspondence between classifications
mclust.options

Default values for use with MCLUST package
mstepE

M-step for a parameterized Gaussian mixture model
mclustLoglik

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

Univariate or Multivariate Normal Fit
mclustICL

ICL Criterion for Model-Based Clustering
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
logLik.MclustDA

Log-Likelihood of a MclustDA object
mclustModel

Best model based on BIC
mvnX

Univariate or Multivariate Normal Fit
partconv

Numeric Encoding of a Partitioning
nMclustParams

Number of Estimated Parameters in Gaussian Mixture Models
mclustModelNames

MCLUST Model Names
randomOrthogonalMatrix

Random orthogonal matrix
plot.MclustBootstrap

Plot of bootstrap distributions for mixture model parameters
plot.MclustDA

Plotting method for MclustDA discriminant analysis
mstep

M-step for parameterized Gaussian mixture models
sigma2decomp

Convert mixture component covariances to decomposition form.
plot.MclustDR

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

Template for variance specification for parameterized Gaussian mixture models
randomPairs

Random hierarchical structure
plot.densityMclust

Plots for Mixture-Based Density Estimate
majorityVote

Majority vote
randProj

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

Summarizing Gaussian Finite Mixture Model Fits
surfacePlot

Density or uncertainty surface for bivariate mixtures
plot.clustCombi

Plot Combined Clusterings Results
summary.MclustBootstrap

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

Summary function for model-based clustering via BIC
plot.mclustICL

ICL Plot for Model-Based Clustering
summary.MclustDR

Summarizing dimension reduction method for model-based clustering and classification
plot.mclustBIC

BIC Plot for Model-Based Clustering
predict.MclustDR

Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
mclust1Dplot

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

Indicator Variables given Classification
summary.MclustDA

Summarizing discriminant analysis based on Gaussian finite mixture modeling
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture
partuniq

Classifies Data According to Unique Observations
wdbc

Wisconsin diagnostic breast cancer (WDBC) data
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
simE

Simulate from a Parameterized MVN Mixture Model
me

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

Simulate from Parameterized MVN Mixture Models
thyroid

Thyroid gland data
me.weighted

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

Plotting method for Mclust model-based clustering
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
uncerPlot

Uncertainty Plot for Model-Based Clustering
wreath

Data Simulated from a 14-Component Mixture
priorControl

Conjugate Prior for Gaussian Mixtures.
Baudry_etal_2010_JCGS_examples

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

MclustDA discriminant analysis
Mclust

Model-Based Clustering
EuroUnemployment

Unemployment data for European countries in 2014
MclustDR

Dimension reduction for model-based clustering and classification
MclustBootstrap

Resampling-based Inference for Gaussian finite mixture models
MclustDRsubsel

Subset selection for GMMDR directions based on BIC
BrierScore

Brier score to assess the accuracy of probabilistic predictions
GvHD

GvHD Dataset