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mclust (version 2.1-1)

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

59,433

Version

2.1-1

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 (2.1-1)

clPairs

Pairwise Scatter Plots showing Classification
mstep

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

Model-Based Clustering
hc

Model-based Hierarchical Clustering
bicEMtrain

Select models in discriminant analysis using BIC
cdens

Component Density for Parameterized MVN Mixture Models
summary.Mclust

Very brief summary of an Mclust object.
cv1EMtrain

Select discriminant models using cross validation
coordProj

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

Compare classifications.
randProj

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

E-step for parameterized MVN mixture models.
map

Classification given Probabilities
uncerPlot

Uncertainty Plot for Model-Based Clustering
partconv

Convert partitioning into numerical vector.
estepE

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

Aproximate Hypervolume for Multivariate Data
mclust1Dplot

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

MclustDA discriminant analysis.
mclust-internal

Internal MCLUST functions
mvnX

Multivariate Normal Fit
EMclust

BIC for Model-Based Clustering
meE

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

MclustDA Training
mclustDAtest

MclustDA Testing
grid1

Generate grid points
em

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

BIC for a Parameterized MVN Mixture Model
hcE

Model-based Hierarchical Clustering
EMclustN

BIC for Model-Based Clustering with Poisson Noise
sigma2decomp

Convert mixture component covariances to decomposition form.
classError

Classification error.
cdensE

Component Density for a Parameterized MVN Mixture Model
mvn

Multivariate Normal Fit
surfacePlot

Density or uncertainty surface for two dimensional mixtures.
Defaults.Mclust

List of values controlling defaults for some MCLUST functions.
unmap

Indicator Variables given Classification
summary.EMclust

Summary function for EMclust
dens

Density for Parameterized MVN Mixtures
emE

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

Plot Model-Based Clustering Results
mclust2Dplot

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

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

Classification and posterior probability from mclustDAtest.
density

Kernel Density Estimation
simE

Simulate from a Parameterized MVN Mixture Model
mapClass

Correspondence between classifications.
sim

Simulate from Parameterized MVN Mixture Models
summary.mclustDAtrain

Models and classifications from mclustDAtrain
partuniq

Classifies Data According to Unique Observations
decomp2sigma

Convert mixture component covariances to matrix form.
me

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

BIC for Parameterized MVN Mixture Models
mclustOptions

Set control values for use with MCLUST.
summary.EMclustN

summary function for EMclustN
spinProj

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

Plotting method for MclustDA discriminant analysis.
hclass

Classifications from Hierarchical Agglomeration
diabetes

Diabetes data
chevron

Simulated minefield data
lansing

Maple trees in Lansing Woods