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mclust (version 2.0-1)

Model-based cluster analysis

Description

Port of the 2002 MCLUST version by Fraley and Raftery

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Version

Install

install.packages('mclust')

Monthly Downloads

74,509

Version

2.0-1

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.

Homepage

http://www.stat.washington.edu/mclust

Maintainer

University of Washington R port by Ron Wehrens

Last Published

February 23rd, 2024

Functions in mclust (2.0-1)

Density for Parameterized MVN Mixtures

Select discriminant models using cross validation

BIC for Parameterized MVN Mixture Models

Component Density for Parameterized MVN Mixture Models

Model-based Hierarchical Clustering

Component Density for a Parameterized MVN Mixture Model

BIC for Model-Based Clustering

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

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

Multivariate Normal Fit

Plot confidence ellipses

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

Simulate from a Parameterized MVN Mixture Model

Aproximate Hypervolume for Multivariate Data

Multivariate Normal Fit

Plot two-dimensional data modelled by an MVN mixture.

Compare classifications having the same number of groups.

Plotting method for MclustDA discriminant analysis.

MclustDA Training

Generate grid points

Uncertainty Plot for Model-Based Clustering

MclustDA discriminant analysis.

Classifies Data According to Unique Observations

Very brief summary of an Mclust object.

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

MclustDA Testing

Indicator Variables given Classification

Plot one-dimensional data modelled by an MVN mixture.

E-step for parameterized MVN mixture models.

Model-based Hierarchical Clustering

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

Select models in discriminant analysis using BIC

Plot Model-Based Clustering Results

Density or uncertainty surface for two dimensional mixtures.

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

Norm of a vector

Pairwise Scatter Plots showing Classification

Classifications from Hierarchical Agglomeration

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

BIC for a Parameterized MVN Mixture Model

summary.mclustDAtest

Classification and posterior probability from mclustDAtest.

Convert mixture component covariances to matrix form.

Classification given Probabilities

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

summary.mclustDAtrain

Models and classifications from mclustDAtrain

Convert mixture component covariances to decomposition form.

summary.EMclustN

summary function for EMclustN

Set control values for use with MCLUST.

Simulate from Parameterized MVN Mixture Models

Defaults.Mclust

List of values controlling defaults for some MCLUST functions.

summary.EMclust

Summary function for EMclust

BIC for Model-Based Clustering with Poisson Noise

Convert partitioning into numerical vector.

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

Model-Based Clustering

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

Simulated minefield data

Maple trees in Lansing Woods