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mclust (version 2.0-3)

Model-based cluster analysis

Description

Model-based cluster analysis: the 2002 version of MCLUST

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Install

install.packages('mclust')

Monthly Downloads

59,433

Version

2.0-3

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 (2.0-3)

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

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

Generate grid points

Aproximate Hypervolume for Multivariate Data

Convert mixture component covariances to matrix form.

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

Model-Based Clustering

Convert partitioning into numerical vector.

BIC for a Parameterized MVN Mixture Model

Multivariate Normal Fit

Set control values for use with MCLUST.

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

MclustDA Testing

Component Density for Parameterized MVN Mixture Models

MclustDA Training

Model-based Hierarchical Clustering

Density for Parameterized MVN Mixtures

mclust-internal

Internal MCLUST functions

Compare classifications having the same number of groups.

Component Density for a Parameterized MVN Mixture Model

Classifications from Hierarchical Agglomeration

Defaults.Mclust

List of values controlling defaults for some MCLUST functions.

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

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

summary.mclustDAtest

Classification and posterior probability from mclustDAtest.

Plot one-dimensional data modelled by an MVN mixture.

Pairwise Scatter Plots showing Classification

Plot two-dimensional data modelled by an MVN mixture.

Uncertainty Plot for Model-Based Clustering

Indicator Variables given Classification

Plotting method for MclustDA discriminant analysis.

Convert mixture component covariances to decomposition form.

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

summary.EMclust

Summary function for EMclust

summary.EMclustN

summary function for EMclustN

E-step for parameterized MVN mixture models.

Simulate from a Parameterized MVN Mixture Model

BIC for Model-Based Clustering

Select discriminant models using cross validation

BIC for Parameterized MVN Mixture Models

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

Classifies Data According to Unique Observations

Select models in discriminant analysis using BIC

Kernel Density Estimation

Classification given Probabilities

BIC for Model-Based Clustering with Poisson Noise

MclustDA discriminant analysis.

MclustDA training with noise

Simulate from Parameterized MVN Mixture Models

Model-based Hierarchical Clustering

Plot Model-Based Clustering Results

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

Very brief summary of an Mclust object.

summary.mclustDAtrain

Models and classifications from mclustDAtrain

Multivariate Normal Fit

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

Density or uncertainty surface for two dimensional mixtures.

Maple trees in Lansing Woods

Simulated minefield data