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rebmix (version 2.17.2)

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

447

Version

2.17.2

License

GPL (>= 2)

Maintainer

Marko Nagode

Last Published

August 20th, 2026

Functions in rebmix (2.17.2)

Class "RNGMIX"

RCLSMIX-methods

Predicts Class Membership Based Upon a Model Trained by REBMIX

RNGMIX.Theta-class

Class "RNGMIX.Theta"

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

Class "REBMIX"

Sum of Squares Error

REBMIX.boot-class

Class "REBMIX.boot"

REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation

Random Univariate or Multivariate Finite Mixture Generation

fhistogram-methods

Fast Histogram Calculation

Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output

Sequence of Bins or Nearest Neighbours Generation

Empirical Density Calculation

Predictive Marginal Density Calculation

Binning of Data

Bearings Faults Detection Data

chistogram-methods

Compact Histogram Calculation

mapclusters-methods

optbins-methods

Optimal Numbers of Bins Calculation

rebmix-internal

Internal rebmix Functions, Methods and Classes

sensorlessdrive

Sensorless Drive Faults Detection Data

Predictive Marginal Distribution Function Calculation

Splits Dataset into Train and Test Datasets

mergelabels-methods

Merge Labels Based on Probability Adjacency Matrix

Weibull-normal Simulated Dataset

Empirical Distribution Function Calculation

Weibull Dataset 8.1

labelmoments-methods

Label Image Moments

Wine Recognition Data

Steel Plates Faults Recognition Data

Predicts Class Membership Based Upon the Best First Search Algorithm

EM Algorithm for Univariate or Multivariate Finite Mixture Estimation

EM.Control-class

Class "EM.Control"

Classification Likelihood Criterion

Approximate Integrated Classification Likelihood Criterion

Integrated Classification Likelihood Criterion

EMMIX.Theta-class

Class "EMMIX.Theta"

Class "RCLSMIX"

Akaike Information Criterion

Bayesian Information Criterion

Histogram-class

Class "Histogram"

Extracts Chunk from Train and Test Datasets

Hannan-Quinn Information Criterion

Approximate Weight of Evidence Criterion

Class "RCLRMIX"

Total of Positive Relative Deviations

RCLRMIX-methods

Predicts Cluster Membership Based Upon a Model Trained by REBMIX

RCLS.chunk-class

Class "RCLS.chunk"

Minimum Description Length

Partition Coefficient