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mclust (version 5.4.1)

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

5.4.1

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

June 27th, 2018

Functions in mclust (5.4.1)

acidity

Acidity data
MclustDA

MclustDA discriminant analysis
MclustDR

Dimension reduction for model-based clustering and classification
estep

E-step for parameterized Gaussian mixture models.
chevron

Simulated minefield data
estepE

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

Pairwise Scatter Plots showing Classification
MclustBootstrap

Resampling-based Inference for Gaussian finite mixture models
Mclust

Model-Based Clustering
bic

BIC for Parameterized Gaussian Mixture Models
defaultPrior

Default conjugate prior for Gaussian mixtures.
cdens

Component Density for Parameterized MVN Mixture Models
dens

Density for Parameterized MVN Mixtures
cvMclustDA

MclustDA cross-validation
classError

Classification error
decomp2sigma

Convert mixture component covariances to matrix form.
mclust-internal

Internal MCLUST functions
cdensE

Component Density for a Parameterized MVN Mixture Model
mclustModelNames

MCLUST Model Names
cdfMclust

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
combiTree

Tree structure obtained from combining mixture components
mclust-deprecated

Deprecated Functions in mclust package
densityMclust

Density Estimation via Model-Based Clustering
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.
cross

Simulated Cross Data
clustCombi-internal

Internal clustCombi functions
diabetes

Diabetes data
densityMclust.diagnostic

Diagnostic plots for mclustDensity estimation
errorBars

Draw error bars on a plot
mclustVariance

Template for variance specification for parameterized Gaussian mixture models
em

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

Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
plot.mclustICL

ICL Plot for Model-Based Clustering
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
combMat

Combining Matrix
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
hcE

Model-based Hierarchical Clustering
hclass

Classifications from Hierarchical Agglomeration
hc

Model-based Hierarchical Clustering
hdrlevels

Highest Density Region (HDR) Levels
hypvol

Aproximate Hypervolume for Multivariate Data
icl

ICL for an estimated Gaussian Mixture Model
logLik.MclustDA

Log-Likelihood of a MclustDA object
imputeData

Missing data imputation via the mix package
majorityVote

Majority vote
emControl

Set control values for use with the EM algorithm.
mclust1Dplot

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

Univariate or Multivariate Normal Fit
plot.MclustDR

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

Number of Estimated Parameters in Gaussian Mixture Models
map

Classification given Probabilities
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
mapClass

Correspondence between classifications.
emE

EM algorithm starting with E-step for a parameterized Gaussian mixture model.
mclust2Dplot

Plot two-dimensional data modelled by an MVN mixture.
plot.clustCombi

Plot Combined Clusterings Results
meE

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

Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
mstep

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

Summarizing discriminant analysis based on Gaussian finite mixture modeling.
mclustBIC

BIC for Model-Based Clustering
logLik.Mclust

Log-Likelihood of a Mclust object
me

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

Plot of bootstrap distributions for mixture model parameters
mclustBootstrapLRT

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
plot.MclustDA

Plotting method for MclustDA discriminant analysis
partuniq

Classifies Data According to Unique Observations
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
me.weighted

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

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

Plot Model-Based Clustering Results
uncerPlot

Uncertainty Plot for Model-Based Clustering
priorControl

Conjugate Prior for Gaussian Mixtures.
plot.mclustBIC

BIC Plot for Model-Based Clustering
plot.densityMclust

Plots for Mixture-Based Density Estimate
unmap

Indicator Variables given Classification
mclust.options

Default values for use with MCLUST package
randProj

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

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

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

Random hierarchical structure
mclustICL

ICL Criterion for Model-Based Clustering
summary.mclustBIC

Summary function for model-based clustering via BIC
mclustModel

Best model based on BIC
simE

Simulate from a Parameterized MVN Mixture Model
mvn

Univariate or Multivariate Normal Fit
wreath

Data Simulated from a 14-Component Mixture
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
partconv

Numeric Encoding of a Partitioning
predict.MclustDA

Classify multivariate observations by Gaussian finite mixture modeling
predict.MclustDR

Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
sigma2decomp

Convert mixture component covariances to decomposition form.
sim

Simulate from Parameterized MVN Mixture Models
surfacePlot

Density or uncertainty surface for bivariate mixtures
thyroid

Thyroid gland data
Baudry_etal_2010_JCGS_examples

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

GvHD Dataset
MclustDRsubsel

Subset selection for GMMDR directions based on BIC.
banknote

Swiss banknotes data
adjustedRandIndex

Adjusted Rand Index
clustCombi

Combining Gaussian Mixture Components for Clustering
clustCombiOptim

Optimal number of clusters obtained by combining mixture components