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mclust (version 2.1-3)

Model-based cluster analysis

Description

Model-based cluster analysis: the 2002 version of MCLUST

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Version

Install

install.packages('mclust')

Monthly Downloads

55,830

Version

2.1-3

License

See http://www.stat.washington.edu/mclust/license.txt

Maintainer

University of Washington R port by Ron Wehrens

Last Published

February 23rd, 2024

Functions in mclust (2.1-3)

dens

Density for Parameterized MVN Mixtures
EMclustN

BIC for Model-Based Clustering with Poisson Noise
bicEMtrain

Select models in discriminant analysis using BIC
estep

E-step for parameterized MVN mixture models.
mstepE

M-step in the EM algorithm for a parameterized MVN mixture model.
compareClass

Compare classifications.
mclust2Dplot

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

BIC for Parameterized MVN Mixture Models
hclass

Classifications from Hierarchical Agglomeration
mclustDAtrain

MclustDA Training
grid1

Generate grid points
mclust-internal

Internal MCLUST functions
plot.Mclust

Plot Model-Based Clustering Results
unmap

Indicator Variables given Classification
mclustDA

MclustDA discriminant analysis.
cdensE

Component Density for a Parameterized MVN Mixture Model
EMclust

BIC for Model-Based Clustering
Defaults.Mclust

List of values controlling defaults for some MCLUST functions.
summary.EMclust

Summary function for EMclust
em

EM algorithm starting with E-step for parameterized MVN mixture models.
meE

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

Classifies Data According to Unique Observations
plot.mclustDA

Plotting method for MclustDA discriminant analysis.
bicE

BIC for a Parameterized MVN Mixture Model
partconv

Convert partitioning into numerical vector.
mclust1Dplot

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

M-step in the EM algorithm for parameterized MVN mixture models.
sigma2decomp

Convert mixture component covariances to decomposition form.
cdens

Component Density for Parameterized MVN Mixture Models
mvn

Multivariate Normal Fit
summary.Mclust

Very brief summary of an Mclust object.
summary.mclustDAtest

Classification and posterior probability from mclustDAtest.
hypvol

Aproximate Hypervolume for Multivariate Data
Mclust

Model-Based Clustering
mvnX

Multivariate Normal Fit
mclustOptions

Set control values for use with MCLUST.
density

Kernel Density Estimation
randProj

Random projections for data in more than two dimensions modelled by an MVN mixture.
coordProj

Coordinate projections of data in more than two dimensions modelled by an MVN mixture.
surfacePlot

Density or uncertainty surface for two dimensional mixtures.
me

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

MclustDA Testing
clPairs

Pairwise Scatter Plots showing Classification
mapClass

Correspondence between classifications.
classError

Classification error.
map

Classification given Probabilities
uncerPlot

Uncertainty Plot for Model-Based Clustering
decomp2sigma

Convert mixture component covariances to matrix form.
hc

Model-based Hierarchical Clustering
emE

EM algorithm starting with E-step for a parameterized MVN mixture model.
spinProj

Planar spin for random projections of data in more than two dimensions modelled by an MVN mixture.
summary.mclustDAtrain

Models and classifications from mclustDAtrain
summary.EMclustN

summary function for EMclustN
cv1EMtrain

Select discriminant models using cross validation
hcE

Model-based Hierarchical Clustering
simE

Simulate from a Parameterized MVN Mixture Model
sim

Simulate from Parameterized MVN Mixture Models
estepE

E-step in the EM algorithm for a parameterized MVN mixture model.
lansing

Maple trees in Lansing Woods
chevron

Simulated minefield data
diabetes

Diabetes data