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mclust (version 2.1-0)

Model-based cluster analysis

Description

Model-based cluster analysis: the 2002 version of MCLUST

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Install

install.packages('mclust')

Monthly Downloads

72,128

Version

2.1-0

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

February 23rd, 2024

Functions in mclust (2.1-0)

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

Density for Parameterized MVN Mixtures

Model-based Hierarchical Clustering

Classification error.

Compare classifications.

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

Kernel Density Estimation

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

Model-Based Clustering

Multivariate Normal Fit

Plot two-dimensional data modelled by an MVN mixture.

MclustDA discriminant analysis.

summary.EMclustN

summary function for EMclustN

Plotting method for MclustDA discriminant analysis.

E-step for parameterized MVN mixture models.

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

MclustDA Training

Aproximate Hypervolume for Multivariate Data

Select models in discriminant analysis using BIC

Convert partitioning into numerical vector.

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

Generate grid points

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

summary.mclustDAtest

Classification and posterior probability from mclustDAtest.

Multivariate Normal Fit

Convert mixture component covariances to decomposition form.

summary.EMclust

Summary function for EMclust

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

Plot one-dimensional data modelled by an MVN mixture.

Density or uncertainty surface for two dimensional mixtures.

Component Density for a Parameterized MVN Mixture Model

Indicator Variables given Classification

Very brief summary of an Mclust object.

Convert mixture component covariances to matrix form.

BIC for Model-Based Clustering

Simulate from a Parameterized MVN Mixture Model

Defaults.Mclust

List of values controlling defaults for some MCLUST functions.

BIC for a Parameterized MVN Mixture Model

summary.mclustDAtrain

Models and classifications from mclustDAtrain

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

BIC for Parameterized MVN Mixture Models

MclustDA Testing

Component Density for Parameterized MVN Mixture Models

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

Plot Model-Based Clustering Results

Simulate from Parameterized MVN Mixture Models

Set control values for use with MCLUST.

Classifies Data According to Unique Observations

BIC for Model-Based Clustering with Poisson Noise

Model-based Hierarchical Clustering

Correspondence between classifications.

Pairwise Scatter Plots showing Classification

Classification given Probabilities

mclust-internal

Internal MCLUST functions

Uncertainty Plot for Model-Based Clustering

Select discriminant models using cross validation

Classifications from Hierarchical Agglomeration

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

Simulated minefield data

Maple trees in Lansing Woods