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Install
install.packages('mclust')
Monthly Downloads
71,265
Version
5.4.9
License
GPL (>= 2)
Maintainer
Luca Scrucca
Last Published
December 17th, 2021
Functions in mclust (5.4.9)
Search functions
MclustDR
Dimension reduction for model-based clustering and classification
MclustSSC
MclustSSC semi-supervised classification
GvHD
GvHD Dataset
Baudry_etal_2010_JCGS_examples
Simulated Example Datasets From Baudry et al. (2010)
EuroUnemployment
Unemployment data for European countries in 2014
MclustDA
MclustDA discriminant analysis
MclustBootstrap
Resampling-based Inference for Gaussian finite mixture models
BrierScore
Brier score to assess the accuracy of probabilistic predictions
Mclust
Model-Based Clustering
MclustDRsubsel
Subset selection for GMMDR directions based on BIC
clPairs
Pairwise Scatter Plots showing Classification
cdens
Component Density for Parameterized MVN Mixture Models
bic
BIC for Parameterized Gaussian Mixture Models
banknote
Swiss banknotes data
classError
Classification error
combMat
Combining Matrix
combiPlot
Plot Classifications Corresponding to Successive Combined Solutions
chevron
Simulated minefield data
cdfMclust
Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution
dmvnorm
Density of multivariate Gaussian distribution
cross
Simulated Cross Data
coordProj
Coordinate projections of multidimensional data modeled by an MVN mixture.
classPriorProbs
Estimation of class prior probabilities by EM algorithm
clustCombi-internal
Internal clustCombi functions
cdensE
Component Density for a Parameterized MVN Mixture Model
covw
Weighted means, covariance and scattering matrices conditioning on a weighted matrix
acidity
Acidity data
combiTree
Tree structure obtained from combining mixture components
adjustedRandIndex
Adjusted Rand Index
crimcoords
Discriminant coordinates data projection
defaultPrior
Default conjugate prior for Gaussian mixtures
emControl
Set control values for use with the EM algorithm
decomp2sigma
Convert mixture component covariances to matrix form
em
EM algorithm starting with E-step for parameterized Gaussian mixture models
dupPartition
Partition the data by grouping together duplicated data
estep
E-step for parameterized Gaussian mixture models.
errorBars
Draw error bars on a plot
imputePairs
Pairwise Scatter Plots showing Missing Data Imputations
logLik.Mclust
Log-Likelihood of a
Mclust
object
dens
Density for Parameterized MVN Mixtures
hc
Model-based Agglomerative Hierarchical Clustering
diabetes
Diabetes data
clustCombiOptim
Optimal number of clusters obtained by combining mixture components
clustCombi
Combining Gaussian Mixture Components for Clustering
densityMclust.diagnostic
Diagnostic plots for
mclustDensity
estimation
emE
EM algorithm starting with E-step for a parameterized Gaussian mixture model
logLik.MclustDA
Log-Likelihood of a
MclustDA
object
entPlot
Plot Entropy Plots
cvMclustDA
MclustDA cross-validation
estepE
E-step in the EM algorithm for a parameterized Gaussian mixture model.
hdrlevels
Highest Density Region (HDR) Levels
mclust.options
Default values for use with MCLUST package
mclust-deprecated
Deprecated Functions in mclust package
mclust-internal
Internal MCLUST functions
majorityVote
Majority vote
densityMclust
Density Estimation via Model-Based Clustering
imputeData
Missing data imputation via the
mix
package
icl
ICL for an estimated Gaussian Mixture Model
mclust1Dplot
Plot one-dimensional data modeled by an MVN mixture.
mclustBIC
BIC for Model-Based Clustering
mclust-package
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
mclustLoglik
Log-likelihood from a table of BIC values for parameterized Gaussian mixture models
hypvol
Aproximate Hypervolume for Multivariate Data
mclustModel
Best model based on BIC
hcRandomPairs
Random hierarchical structure
mclustBootstrapLRT
Bootstrap Likelihood Ratio Test for the Number of Mixture Components
hclass
Classifications from Hierarchical Agglomeration
mclust2Dplot
Plot two-dimensional data modelled by an MVN mixture
gmmhd
Identifying Connected Components in Gaussian Finite Mixture Models for Clustering
mclustICL
ICL Criterion for Model-Based Clustering
mclustModelNames
MCLUST Model Names
mclustBICupdate
Update BIC values for parameterized Gaussian mixture models
mvnX
Univariate or Multivariate Normal Fit
nMclustParams
Number of Estimated Parameters in Gaussian Mixture Models
hcE
Model-based Hierarchical Clustering
plot.clustCombi
Plot Combined Clusterings Results
mclustVariance
Template for variance specification for parameterized Gaussian mixture models
map
Classification given Probabilities
mstepE
M-step for a parameterized Gaussian mixture model
mvn
Univariate or Multivariate Normal Fit
mapClass
Correspondence between classifications
partconv
Numeric Encoding of a Partitioning
predict.MclustSSC
Classification of multivariate observations by semi-supervised Gaussian finite mixtures
predict.densityMclust
Density estimate of multivariate observations by Gaussian finite mixture modeling
plot.densityMclust
Plots for Mixture-Based Density Estimate
partuniq
Classifies Data According to Unique Observations
plot.Mclust
Plotting method for Mclust model-based clustering
plot.mclustBIC
BIC Plot for Model-Based Clustering
plot.hc
Dendrograms for Model-based Agglomerative Hierarchical Clustering
summary.Mclust
Summarizing Gaussian Finite Mixture Model Fits
summary.MclustSSC
Summarizing semi-supervised classification model based on Gaussian finite mixtures
summary.MclustBootstrap
Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
me.weighted
EM algorithm with weights starting with M-step for parameterized MVN mixture models
surfacePlot
Density or uncertainty surface for bivariate mixtures
plot.mclustICL
ICL Plot for Model-Based Clustering
me
EM algorithm starting with M-step for parameterized MVN mixture models
predict.Mclust
Cluster multivariate observations by Gaussian finite mixture modeling
randProj
Random projections of multidimensional data modeled by an MVN mixture
priorControl
Conjugate Prior for Gaussian Mixtures.
summary.MclustDA
Summarizing discriminant analysis based on Gaussian finite mixture modeling
meE
EM algorithm starting with M-step for a parameterized Gaussian mixture model
plot.MclustBootstrap
Plot of bootstrap distributions for mixture model parameters
plot.MclustDR
Plotting method for dimension reduction for model-based clustering and classification
summary.mclustBIC
Summary function for model-based clustering via BIC
summary.MclustDR
Summarizing dimension reduction method for model-based clustering and classification
thyroid
Thyroid gland data
randomOrthogonalMatrix
Random orthogonal matrix
wdbc
Wisconsin diagnostic breast cancer (WDBC) data
wreath
Data Simulated from a 14-Component Mixture
sigma2decomp
Convert mixture component covariances to decomposition form.
predict.MclustDA
Classify multivariate observations by Gaussian finite mixture modeling
plot.MclustSSC
Plotting method for MclustSSC semi-supervised classification
plot.MclustDA
Plotting method for MclustDA discriminant analysis
mstep
M-step for parameterized Gaussian mixture models
nVarParams
Number of Variance Parameters in Gaussian Mixture Models
predict.MclustDR
Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling
uncerPlot
Uncertainty Plot for Model-Based Clustering
sim
Simulate from Parameterized MVN Mixture Models
unmap
Indicator Variables given Classification
simE
Simulate from a Parameterized MVN Mixture Model