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mclust (version 4.0)
Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation
Description
Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.
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Install
install.packages('mclust')
Monthly Downloads
55,274
Version
4.0
License
GPL (>= 2)
Maintainer
Luca Scrucca
Last Published
August 9th, 2012
Functions in mclust (4.0)
Search all functions
clPairs
Pairwise Scatter Plots showing Classification
MclustDR
Dimension reduction for model-based clustering and classification
chevron
Simulated minefield data
imputeData
Missing Data Imputation via the mix package
GvHD
GvHD Dataset
hclass
Classifications from Hierarchical Agglomeration
me.weighted
EM algorithm with weights starting with M-step for parameterized MVN mixture models
mclust2Dplot
Plot two-dimensional data modelled by an MVN mixture.
bicEMtrain
Select models in discriminant analysis using BIC
cv1EMtrain
Select discriminant models using cross validation
cdens
Component Density for Parameterized MVN Mixture Models
mapClass
Correspondence between classifications.
cdfMclust
Cumulative density function from
mclustDensity
estimation
MclustDA
MclustDA discriminant analysis
meE
EM algorithm starting with M-step for a parameterized Gaussian mixture model.
plot.clustCombi
Plot Combined Clusterings Results
combMat
Combining Matrix
map
Classification given Probabilities
clustCombi
Combining Gaussian Mixture Components for Clustering
em
EM algorithm starting with E-step for parameterized Gaussian mixture models.
imputePairs
Pairwise Scatter Plots showing Missing Data Imputations
mclustBIC
BIC for Model-Based Clustering
predict.densityMclust
Density estimate of multivariate observations by Gaussian finite mixture modeling
cross
Simulated Cross Data
mclust-internal
Internal MCLUST functions
adjustedRandIndex
Adjusted Rand Index
hypvol
Aproximate Hypervolume for Multivariate Data
predict.Mclust
Cluster multivariate observations by Gaussian finite mixture modeling
Mclust
Model-Based Clustering
me
EM algorithm starting with M-step for parameterized MVN mixture models.
bic
BIC for Parameterized Gaussian Mixture Models
mvn
Univariate or Multivariate Normal Fit
partconv
Numeric Encoding of a Partitioning
summary.Mclust
Summarizing Gaussian Finite Mixture Model Fits
mclust.options
Default values for use with MCLUST package
hc
Model-based Hierarchical Clustering
mclust1Dplot
Plot one-dimensional data modeled by an MVN mixture.
summary.mclustBIC
Summary Function for model-based clustering.
unmap
Indicator Variables given Classification
coordProj
Coordinate projections of multidimensional data modeled by an MVN mixture.
dens
Density for Parameterized MVN Mixtures
estep
E-step for parameterized Gaussian mixture models.
defaultPrior
Default conjugate prior for Gaussian mixtures.
clustCombi-internal
Internal clustCombi functions
densityMclust.diagnostic
Diagnostic plots for
mclustDensity
estimation
mclustVariance
Template for variance specification for parameterized Gaussian mixture models
summary.MclustDR
Summarizing dimension reduction method for model-based clustering and classification
plot.Mclust
Plot Model-Based Clustering Results
partuniq
Classifies Data According to Unique Observations
classError
Classification error
banknote
Swiss banknotes data
entPlot
Plot Entropy Plots
emControl
Set control values for use with the EM algorithm.
emE
EM algorithm starting with E-step for a parameterized Gaussian mixture model.
priorControl
Conjugate Prior for Gaussian Mixtures.
logLik.MclustDA
Log-Likelihood of a
MclustDA
object
mstep
M-step for parameterized Gaussian mixture models.
nVarParams
Number of Variance Parameters in Gaussian Mixture Models
summary.MclustDA
Summarizing discriminant analysis based on Gaussian finite mixture modeling.
plot.mclustBIC
BIC Plot
surfacePlot
Density or uncertainty surface for bivariate mixtures.
plot.densityMclust
Plot for a
mclustDensity
object
mvnX
Univariate or Multivariate Normal Fit
simE
Simulate from a Parameterized MVN Mixture Model
predict.MclustDA
Classify multivariate observations by Gaussian finite mixture modeling
wreath
Data Simulated from a 14-Component Mixture
mclustModelNames
MCLUST Model Names
logLik.Mclust
Log-Likelihood of a
Mclust
object
plot.MclustDA
Plotting method for MclustDA discriminant analysis
sim
Simulate from Parameterized MVN Mixture Models
print.clustCombi
Displays Combined Clusterings Results
combiPlot
Plot Classifications Corresponding to Successive Combined Solutions
mstepE
M-step for a parameterized Gaussian mixture model.
uncerPlot
Uncertainty Plot for Model-Based Clustering
diabetes
Diabetes data
densityMclust
Density Estimation via Model-Based Clustering
sigma2decomp
Convert mixture component covariances to decomposition form.
plot.MclustDR
Plotting method for dimension reduction for model-based clustering and classification
Baudry_etal_2010_JCGS_examples
Simulated Example Datasets From Baudry et al. (2010)
decomp2sigma
Convert mixture component covariances to matrix form.
hcE
Model-based Hierarchical Clustering
cdensE
Component Density for a Parameterized MVN Mixture Model
estepE
E-step in the EM algorithm for a parameterized Gaussian mixture model.
randProj
Random projections of multidimensional data modeled by an MVN mixture.
cv.MclustDA
MclustDA cross-validation
mclustModel
Best model based on BIC