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rebmix (version 2.17.1)

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

2.17.1

License

GPL (>= 2)

Maintainer

Marko Nagode

Last Published

November 10th, 2025

Functions in rebmix (2.17.1)

RCLS.chunk-class

Class "RCLS.chunk"

Approximate Integrated Classification Likelihood Criterion

Class "RCLRMIX"

Total of Positive Relative Deviations

Minimum Description Length

Extracts Chunk from Train and Test Datasets

Integrated Classification Likelihood Criterion

Partition Coefficient

Class "RCLSMIX"

RCLRMIX-methods

Predicts Cluster Membership Based Upon a Model Trained by REBMIX

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

REBMIX.boot-class

Class "REBMIX.boot"

REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation

Class "REBMIX"

RNGMIX.Theta-class

Class "RNGMIX.Theta"

Class "RNGMIX"

Random Univariate or Multivariate Finite Mixture Generation

RCLSMIX-methods

Predicts Class Membership Based Upon a Model Trained by REBMIX

Sum of Squares Error

Bearings Faults Detection Data

Predictive Marginal Density Calculation

Empirical Density Calculation

chistogram-methods

Compact Histogram Calculation

Binning of Data

fhistogram-methods

Fast Histogram Calculation

Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output

sensorlessdrive

Sensorless Drive Faults Detection Data

Sequence of Bins or Nearest Neighbours Generation

mapclusters-methods

Predictive Marginal Distribution Function Calculation

labelmoments-methods

Label Image Moments

mergelabels-methods

Merge Labels Based on Probability Adjacency Matrix

Weibull Dataset 8.1

Weibull-normal Simulated Dataset

optbins-methods

Optimal Numbers of Bins Calculation

Empirical Distribution Function Calculation

Splits Dataset into Train and Test Datasets

rebmix-internal

Internal rebmix Functions, Methods and Classes

Wine Recognition Data

Steel Plates Faults Recognition Data

Histogram-class

Class "Histogram"

Classification Likelihood Criterion

Hannan-Quinn Information Criterion

Approximate Weight of Evidence Criterion

Bayesian Information Criterion

EM.Control-class

Class "EM.Control"

EMMIX.Theta-class

Class "EMMIX.Theta"

Akaike Information Criterion

Predicts Class Membership Based Upon the Best First Search Algorithm

EM Algorithm for Univariate or Multivariate Finite Mixture Estimation