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mclust (version 2.0-2)

Model-based cluster analysis

Description

Model-based cluster analysis: the 2002 version of MCLUST

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Install

install.packages('mclust')

Monthly Downloads

87,167

Version

2.0-2

License

copyright 1996, 1998, 2002 Department of Statistics, University of Washington funded by ONR contracts N00014-96-1-0192 and N00014-96-1-0330 and NIH Grant 1 R01 CA94212-01. Permission granted for unlimited redistribution for non-commercial use only. Commerical use requires a licensing agreement with the University of Washington.

Maintainer

University of Washington R port by Ron Wehrens

Last Published

October 31st, 2025

Functions in mclust (2.0-2)

Classification given Probabilities

Classifications from Hierarchical Agglomeration

BIC for Model-Based Clustering with Poisson Noise

Component Density for a Parameterized MVN Mixture Model

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

Select models in discriminant analysis using BIC

Set control values for use with MCLUST.

Component Density for Parameterized MVN Mixture Models

Plot two-dimensional data modelled by an MVN mixture.

Model-Based Clustering

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

BIC for a Parameterized MVN Mixture Model

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

summary.EMclustN

summary function for EMclustN

Convert partitioning into numerical vector.

Defaults.Mclust

List of values controlling defaults for some MCLUST functions.

Plot Model-Based Clustering Results

MclustDA Training

Aproximate Hypervolume for Multivariate Data

MclustDA training with noise

BIC for Parameterized MVN Mixture Models

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

Simulate from a Parameterized MVN Mixture Model

Convert mixture component covariances to matrix form.

summary.EMclust

Summary function for EMclust

Model-based Hierarchical Clustering

Very brief summary of an Mclust object.

Density for Parameterized MVN Mixtures

Kernel Density Estimation

E-step for parameterized MVN mixture models.

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

Compare classifications having the same number of groups.

Model-based Hierarchical Clustering

BIC for Model-Based Clustering

Multivariate Normal Fit

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

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

MclustDA Testing

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

Plot one-dimensional data modelled by an MVN mixture.

mclust-internal

Internal MCLUST functions

MclustDA discriminant analysis.

summary.mclustDAtrain

Models and classifications from mclustDAtrain

Density or uncertainty surface for two dimensional mixtures.

Select discriminant models using cross validation

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

Uncertainty Plot for Model-Based Clustering

Plotting method for MclustDA discriminant analysis.

Generate grid points

Simulate from Parameterized MVN Mixture Models

Multivariate Normal Fit

Convert mixture component covariances to decomposition form.

summary.mclustDAtest

Classification and posterior probability from mclustDAtest.

Pairwise Scatter Plots showing Classification

Classifies Data According to Unique Observations

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

Indicator Variables given Classification

Maple trees in Lansing Woods

Simulated minefield data