Learn R Programming

⚠️There's a newer version (2.16.0) of this package.Take me there.

rebmix (version 2.15.0)

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.

Copy Link

Version

Install

install.packages('rebmix')

Monthly Downloads

421

Version

2.15.0

License

GPL (>= 2)

Maintainer

Marko Nagode

Last Published

July 16th, 2023

Functions in rebmix (2.15.0)

Bayesian Information Criterion

EMMIX.Theta-class

Class "EMMIX.Theta"

Histogram-class

Class "Histogram"

Partition Coefficient

Class "RCLSMIX"

Extracts Chunk from Train and Test Datasets

Minimum Description Length

RCLRMIX-methods

Predicts Cluster Membership Based Upon a Model Trained by REBMIX

Integrated Classification Likelihood Criterion

RCLS.chunk-class

Class "RCLS.chunk"

Approximate Integrated Classification Likelihood Criterion

Class "RCLRMIX"

Total of Positive Relative Deviations

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

Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output

Class "RNGMIX"

RCLSMIX-methods

Predicts Class Membership Based Upon a Model Trained by REBMIX

Random Univariate or Multivariate Finite Mixture Generation

Class "REBMIX"

RNGMIX.Theta-class

Class "RNGMIX.Theta"

Sum of Squares Error

REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation

REBMIX.boot-class

Class "REBMIX.boot"

Sequence of Bins or Nearest Neighbours Generation

Binning of Data

fhistogram-methods

Fast Histogram Calculation

Bearings Faults Detection Data

chistogram-methods

Compact Histogram Calculation

labelmoments-methods

Label Image Moments

sensorlessdrive

Sensorless Drive Faults Detection Data

Weibull Dataset 8.1

optbins-methods

Optimal Numbers of Bins Calculation

mapclusters-methods

Empirical Density Calculation

Weibull-normal Simulated Dataset

Predictive Marginal Density Calculation

Empirical Distribution Function Calculation

Splits Dataset into Train and Test Datasets

Steel Plates Faults Recognition Data

Predictive Marginal Distribution Function Calculation

Wine Recognition Data

mergelabels-methods

Merge Labels Based on Probability Adjacency Matrix

rebmix-internal

Internal rebmix Functions, Methods and Classes

Akaike Information Criterion

Classification Likelihood Criterion

Predicts Class Membership Based Upon the Best First Search Algorithm

EM Algorithm for Univariate or Multivariate Finite Mixture Estimation

Hannan-Quinn Information Criterion

Approximate Weight of Evidence Criterion

EM.Control-class

Class "EM.Control"