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mclust (version 5.0.2)

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

Description

Normal Mixture Modelling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.

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Version

Install

install.packages('mclust')

Monthly Downloads

74,509

Version

5.0.2

License

GPL (>= 2)

Maintainer

Luca Scrucca

Last Published

July 8th, 2015

Functions in mclust (5.0.2)

banknote

Swiss banknotes data
decomp2sigma

Convert mixture component covariances to matrix form.
combMat

Combining Matrix
errorBars

Draw error bars on a plot
MclustBootstrap

Bootstrap Inference for Gaussian finite mixture models
mapClass

Correspondence between classifications.
mclust-internal

Internal MCLUST functions
dens

Density for Parameterized MVN Mixtures
MclustDR

Dimension reduction for model-based clustering and classification
acidity

Acidity data
map

Classification given Probabilities
clPairs

Pairwise Scatter Plots showing Classification
em

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

Diagnostic plots for mclustDensity estimation
randProj

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

Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
mclust2Dplot

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

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

E-step for parameterized Gaussian mixture models.
clustCombi

Combining Gaussian Mixture Components for Clustering
surfacePlot

Density or uncertainty surface for bivariate mixtures.
mclustBIC

BIC for Model-Based Clustering
cdens

Component Density for Parameterized MVN Mixture Models
MclustDA

MclustDA discriminant analysis
predict.Mclust

Cluster multivariate observations by Gaussian finite mixture modeling
icl

ICL for an estimated Gaussian Mixture Model
mclust-package

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

Aproximate Hypervolume for Multivariate Data
mclust1Dplot

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

ICL Criterion for Model-Based Clustering
mclust-deprecated

Deprecated Functions in mclust package
plot.MclustBootstrap

Plot of bootstrap distributions for mixture model parameters
Mclust

Model-Based Clustering
summary.MclustBootstrap

Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
mclustModel

Best model based on BIC
emControl

Set control values for use with the EM algorithm.
print.clustCombi

Displays Combined Clusterings Results
cvMclustDA

MclustDA cross-validation
cross

Simulated Cross Data
cdensE

Component Density for a Parameterized MVN Mixture Model
mstepE

M-step for a parameterized Gaussian mixture model.
adjustedRandIndex

Adjusted Rand Index
sigma2decomp

Convert mixture component covariances to decomposition form.
plot.mclustICL

ICL Plot for Model-Based Clustering
diabetes

Diabetes data
logLik.Mclust

Log-Likelihood of a Mclust object
bic

BIC for Parameterized Gaussian Mixture Models
clustCombi-internal

Internal clustCombi functions
coordProj

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

M-step for parameterized Gaussian mixture models.
defaultPrior

Default conjugate prior for Gaussian mixtures.
summary.mclustBIC

Summary function for model-based clustering via BIC
mclustBootstrapLRT

Bootstrap Likelihood Ratio Test for the Number of Mixture Components
mclustVariance

Template for variance specification for parameterized Gaussian mixture models
mvn

Univariate or Multivariate Normal Fit
predict.densityMclust

Density estimate of multivariate observations by Gaussian finite mixture modeling
plot.clustCombi

Plot Combined Clusterings Results
hc

Model-based Hierarchical Clustering
me

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

Classifies Data According to Unique Observations
plot.MclustDA

Plotting method for MclustDA discriminant analysis
sim

Simulate from Parameterized MVN Mixture Models
imputeData

Missing Data Imputation via the mix package
priorControl

Conjugate Prior for Gaussian Mixtures.
me.weighted

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

Simulate from a Parameterized MVN Mixture Model
partconv

Numeric Encoding of a Partitioning
estepE

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

Default values for use with MCLUST package
summary.MclustDR

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

Simulated minefield data
nMclustParams

Number of Estimated Parameters in Gaussian Mixture Models
densityMclust

Density Estimation via Model-Based Clustering
covw

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

Classifications from Hierarchical Agglomeration
logLik.MclustDA

Log-Likelihood of a MclustDA object
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
plot.Mclust

Plot Model-Based Clustering Results
plot.MclustDR

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

Uncertainty Plot for Model-Based Clustering
imputePairs

Pairwise Scatter Plots showing Missing Data Imputations
mclustModelNames

MCLUST Model Names
meE

EM algorithm starting with M-step for a parameterized Gaussian mixture model.
plot.mclustBIC

BIC Plot for Model-Based Clustering
mvnX

Univariate or Multivariate Normal Fit
plot.densityMclust

Plots for Mixture-Based Density Estimate
wreath

Data Simulated from a 14-Component Mixture
unmap

Indicator Variables given Classification
thyroid

Thyroid gland data
randomPairs

Random hierarchical structure
summary.Mclust

Summarizing Gaussian Finite Mixture Model Fits
combiPlot

Plot Classifications Corresponding to Successive Combined Solutions
entPlot

Plot Entropy Plots
Baudry_etal_2010_JCGS_examples

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

GvHD Dataset
classError

Classification error
hcE

Model-based Hierarchical Clustering
nVarParams

Number of Variance Parameters in Gaussian Mixture Models
summary.MclustDA

Summarizing discriminant analysis based on Gaussian finite mixture modeling.