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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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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)
Search all functions
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