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EMCluster

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  • Author: Wei-Chen Chen and Ranjan Maitra

EMCluster is an R package providing EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

Installation

EMCluster requires

  • R version 3.0.0 or higher.
  • R package MASS, Matrix.

The package can be installed from the CRAN via the usual install.packages("EMCluster"), or via the devtools package:

library(devtools)
install_github("snoweye/EMCluster")

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Version

Install

install.packages('EMCluster')

Monthly Downloads

1,380

Version

0.2-18

License

Mozilla Public License 2.0

Issues

8

Pull Requests

0

Stars

17

Forks

0

Maintainer

Wei-Chen Chen

Last Published

July 28th, 2026

Functions in EMCluster (0.2-18)

EMCluster-package

EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
Plot Projection and Contour

Plot Contour
Conversion

Convert Matrices in Different Format
All Internal Functions

All Internal Functions of EMCluster
Initialization and EM

Initialization and EM Algorithm
Rand Index

Rand Index and Adjusted Rand Index
EM Control

EM Control Generator and Controller
Print and Summary

Functions for Printing or Summarizing Objects According to Classes
Dataset

Dataset for demonstrations
Likelihood Mixture Tests

Likelihood Mixture Tests
Single Step

Single E- and M-step
Information Criteria

Information Criteria for Model-Based Clustering
Recolor Classification IDs

Recolor Classification IDs
Other Initializations

Other Initializations
Jaccard Index

Jaccard Index
MVN

Density of (Mixture) Multivariate Normal Distribution
Assign Class

Assign Class Id
Plot Multivariate Data

Plot Multivariate Data
LMT Functions

Likelihood Mixture Test (LMT) Functions of EMCluster
Projection On 2D

Produce Projection on 2D
Post I Information Functions

Post I Information Functions of EMCluster
Plot EM Results

Plot Two Dimensional Data with clusters
EM Algorithm

EM Algorithm for model-based clustering