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rebmix (version 2.17.0)

Finite Mixture Modeling, Clustering & Classification

Description

Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.

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Version

Install

install.packages('rebmix')

Monthly Downloads

583

Version

2.17.0

License

GPL (>= 2)

Maintainer

Marko Nagode

Last Published

November 6th, 2025

Functions in rebmix (2.17.0)

Total of Positive Relative Deviations

Extracts Chunk from Train and Test Datasets

Class "RCLSMIX"

Minimum Description Length

Partition Coefficient

RCLS.chunk-class

Class "RCLS.chunk"

RCLRMIX-methods

Predicts Cluster Membership Based Upon a Model Trained by REBMIX

Class "RCLRMIX"

Integrated Classification Likelihood Criterion

Approximate Integrated Classification Likelihood Criterion

REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation

Sum of Squares Error

Class "RNGMIX"

REBMIX.boot-class

Class "REBMIX.boot"

Class "REBMIX"

RCLSMIX-methods

Predicts Class Membership Based Upon a Model Trained by REBMIX

Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation

RNGMIX.Theta-class

Class "RNGMIX.Theta"

Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output

Random Univariate or Multivariate Finite Mixture Generation

Empirical Density Calculation

fhistogram-methods

Fast Histogram Calculation

Predictive Marginal Density Calculation

Bearings Faults Detection Data

Predictive Marginal Distribution Function Calculation

chistogram-methods

Compact Histogram Calculation

Binning of Data

rebmix-internal

Internal rebmix Functions, Methods and Classes

Empirical Distribution Function Calculation

Sequence of Bins or Nearest Neighbours Generation

mergelabels-methods

Merge Labels Based on Probability Adjacency Matrix

Weibull Dataset 8.1

optbins-methods

Optimal Numbers of Bins Calculation

mapclusters-methods

Wine Recognition Data

Splits Dataset into Train and Test Datasets

sensorlessdrive

Sensorless Drive Faults Detection Data

Weibull-normal Simulated Dataset

labelmoments-methods

Label Image Moments

Steel Plates Faults Recognition Data

Classification Likelihood Criterion

Akaike Information Criterion

Histogram-class

Class "Histogram"

EM Algorithm for Univariate or Multivariate Finite Mixture Estimation

Predicts Class Membership Based Upon the Best First Search Algorithm

Bayesian Information Criterion

Approximate Weight of Evidence Criterion

Hannan-Quinn Information Criterion

EMMIX.Theta-class

Class "EMMIX.Theta"

EM.Control-class

Class "EM.Control"