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mclust (version 6.1)

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

6.1

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

February 23rd, 2024

Functions in mclust (6.1)

acidity

Acidity data
cdens

Component Density for Parameterized MVN Mixture Models
clPairs

Pairwise Scatter Plots showing Classification
adjustedRandIndex

Adjusted Rand Index
banknote

Swiss banknotes data
chevron

Simulated minefield data
bic

BIC for Parameterized Gaussian Mixture Models
cdensE

Component Density for a Parameterized MVN Mixture Model
covw

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

Classification error
cdfMclust

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
crimcoords

Discriminant coordinates data projection
clustCombiOptim

Optimal number of clusters obtained by combining mixture components
clustCombi

Combining Gaussian Mixture Components for Clustering
dens

Density for Parameterized MVN Mixtures
coordProj

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

Default conjugate prior for Gaussian mixtures
cvMclustDA

MclustDA cross-validation
errorBars

Draw error bars on a plot
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
dmvnorm

Density of multivariate Gaussian distribution
cross

Simulated Cross Data
classPriorProbs

Estimation of class prior probabilities by EM algorithm
combMat

Combining Matrix
dupPartition

Partition the data by grouping together duplicated data
densityMclust

Density Estimation via Model-Based Clustering
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
estep

E-step for parameterized Gaussian mixture models.
em

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

Plot Entropy Plots
estepE

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

Diabetes Data (flawed)
emE

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

Log sum of exponentials
combiTree

Tree structure obtained from combining mixture components
hdrlevels

Highest Density Region (HDR) Levels
gmmhd

Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
decomp2sigma

Convert mixture component covariances to matrix form
emControl

Set control values for use with the EM algorithm
logLik.MclustDA

Log-Likelihood of a MclustDA object
clustCombi-internal

Internal clustCombi functions
imputeData

Missing data imputation via the mix package
mclustBIC

BIC for Model-Based Clustering
hypvol

Aproximate Hypervolume for Multivariate Data
icl

ICL for an estimated Gaussian Mixture Model
majorityVote

Majority vote
mapClass

Correspondence between classifications
hcE

Model-based Hierarchical Clustering
hc

Model-based Agglomerative Hierarchical Clustering
map

Classification given Probabilities
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture
mclust-deprecated

Deprecated Functions in mclust package
mclustICL

ICL Criterion for Model-Based Clustering
mclustLoglik

Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
me.weighted

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

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

Internal MCLUST functions
hcRandomPairs

Random hierarchical structure
logLik.Mclust

Log-Likelihood of a Mclust object
mstepE

M-step for a parameterized Gaussian mixture model
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
mclust-package

Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
mclust1Dplot

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

M-step for parameterized Gaussian mixture models
mclustBICupdate

Update BIC values for parameterized Gaussian mixture models
hclass

Classifications from Hierarchical Agglomeration
plot.Mclust

Plotting method for Mclust model-based clustering
mclust.options

Default values for use with MCLUST package
mclustBootstrapLRT

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
mclustVariance

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

Plotting method for MclustSSC semi-supervised classification
plot.mclustBIC

BIC Plot for Model-Based Clustering
plot.MclustBootstrap

Plot of bootstrap distributions for mixture model parameters
partconv

Numeric Encoding of a Partitioning
plot.clustCombi

Plot Combined Clusterings Results
plot.densityMclust

Plots for Mixture-Based Density Estimate
randomOrthogonalMatrix

Random orthogonal matrix
me

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

Simulate from Parameterized MVN Mixture Models
randProj

Random projections of multidimensional data modeled by an MVN mixture
sigma2decomp

Convert mixture component covariances to decomposition form.
plot.hc

Dendrograms for Model-based Agglomerative Hierarchical Clustering
partuniq

Classifies Data According to Unique Observations
mvn

Univariate or Multivariate Normal Fit
predict.MclustDR

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

Softmax function
summary.MclustDR

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

Simulate from a Parameterized MVN Mixture Model
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
summary.MclustDA

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

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

Univariate or Multivariate Normal Fit
predict.MclustSSC

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

Cluster multivariate observations by Gaussian finite mixture modeling
plot.MclustDA

Plotting method for MclustDA discriminant analysis
uncerPlot

Uncertainty Plot for Model-Based Clustering
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
mclustModel

Best model based on BIC
wdbc

UCI Wisconsin Diagnostic Breast Cancer Data
summary.MclustBootstrap

Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
mclustModelNames

MCLUST Model Names
nMclustParams

Number of Estimated Parameters in Gaussian Mixture Models
plot.mclustICL

ICL Plot for Model-Based Clustering
summary.MclustSSC

Summarizing semi-supervised classification model based on Gaussian finite mixtures
wreath

Data Simulated from a 14-Component Mixture
unmap

Indicator Variables given Classification
summary.mclustBIC

Summary function for model-based clustering via BIC
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
priorControl

Conjugate Prior for Gaussian Mixtures.
thyroid

UCI Thyroid Gland Data
surfacePlot

Density or uncertainty surface for bivariate mixtures
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
Baudry_etal_2010_JCGS_examples

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

MclustDA discriminant analysis
MclustDRsubsel

Subset selection for GMMDR directions based on BIC
Mclust

Model-Based Clustering
MclustDR

Dimension reduction for model-based clustering and classification
EuroUnemployment

Unemployment data for European countries in 2014
MclustSSC

MclustSSC semi-supervised classification
BrierScore

Brier score to assess the accuracy of probabilistic predictions
GvHD

GvHD Dataset
MclustBootstrap

Resampling-based Inference for Gaussian finite mixture models