# Finite Mixture Modeling, Clustering & Classification

## Documentation for package ‘rebmix’ version 2.12.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Code demos. Use demo() to run them.
- Package NEWS.

## Help Pages

A B C D E G H I K L M O P R S T W

## -- A --

adult | Adult Dataset
AIC | Akaike Information Criterion
AIC-method | Akaike Information Criterion
AIC-methods | Akaike Information Criterion
AIC3 | Akaike Information Criterion
AIC3-method | Akaike Information Criterion
AIC3-methods | Akaike Information Criterion
AIC4 | Akaike Information Criterion
AIC4-method | Akaike Information Criterion
AIC4-methods | Akaike Information Criterion
AICc | Akaike Information Criterion
AICc-method | Akaike Information Criterion
AICc-methods | Akaike Information Criterion
AWE | Approximate Weight of Evidence Criterion
AWE-method | Approximate Weight of Evidence Criterion
AWE-methods | Approximate Weight of Evidence Criterion

## -- B --

BFSMIX | Predicts Class Membership Based Upon the Best First Search Algorithm
BFSMIX-method | Predicts Class Membership Based Upon the Best First Search Algorithm
BFSMIX-methods | Predicts Class Membership Based Upon the Best First Search Algorithm
BIC | Bayesian Information Criterion
BIC-method | Bayesian Information Criterion
BIC-methods | Bayesian Information Criterion
bins | Binning of Data
bins-method | Binning of Data
bins-methods | Binning of Data
boot | Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation
boot-method | Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation
boot-methods | Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation

## -- C --

CAIC | Akaike Information Criterion
CAIC-method | Akaike Information Criterion
CAIC-methods | Akaike Information Criterion
chunk | Extracts Chunk from Train and Test Datasets
chunk-method | Extracts Chunk from Train and Test Datasets
chunk-methods | Extracts Chunk from Train and Test Datasets
CLC | Classification Likelihood Criterion
CLC-method | Classification Likelihood Criterion
CLC-methods | Classification Likelihood Criterion

## -- D --

demix | Empirical Density Calculation
demix-method | Empirical Density Calculation
demix-methods | Empirical Density Calculation
dfmix | Predictive Marginal Density Calculation
dfmix-method | Predictive Marginal Density Calculation
dfmix-methods | Predictive Marginal Density Calculation

## -- E --

EM.Control-class | Class '"EM.Control"'

## -- G --

galaxy | Galaxy Dataset

## -- H --

HQC | Hannan-Quinn Information Criterion
HQC-method | Hannan-Quinn Information Criterion
HQC-methods | Hannan-Quinn Information Criterion

## -- I --

ICL | Integrated Classification Likelihood Criterion
ICL-method | Integrated Classification Likelihood Criterion
ICL-methods | Integrated Classification Likelihood Criterion
ICLBIC | Approximate Integrated Classification Likelihood Criterion
ICLBIC-method | Approximate Integrated Classification Likelihood Criterion
ICLBIC-methods | Approximate Integrated Classification Likelihood Criterion
iris | Iris Data Set

## -- K --

kseq | Sequence of Bins or Nearest Neighbours Generation

## -- L --

logL | Log Likelihood
logL-method | Log Likelihood
logL-methods | Log Likelihood

## -- M --

MDL2 | Minimum Description Length
MDL2-method | Minimum Description Length
MDL2-methods | Minimum Description Length
MDL5 | Minimum Description Length
MDL5-method | Minimum Description Length
MDL5-methods | Minimum Description Length

## -- O --

optbins | Optimal Numbers of Bins Calculation
optbins-method | Optimal Numbers of Bins Calculation
optbins-methods | Optimal Numbers of Bins Calculation

## -- P --

PC | Partition Coefficient
PC-method | Partition Coefficient
PC-methods | Partition Coefficient
pemix | Empirical Distribution Function Calculation
pemix-method | Empirical Distribution Function Calculation
pemix-methods | Empirical Distribution Function Calculation
pfmix | Predictive Marginal Distribution Function Calculation
pfmix-method | Predictive Marginal Distribution Function Calculation
pfmix-methods | Predictive Marginal Distribution Function Calculation
plot-method | Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output
plot-methods | Plots RNGMIX, REBMIX, RCLRMIX and RCLSMIX Output
PRD | Total of Positive Relative Deviations
PRD-method | Total of Positive Relative Deviations
PRD-methods | Total of Positive Relative Deviations

## -- R --

RCLRMIX | Predicts Cluster Membership Based Upon a Model Trained by REBMIX
RCLRMIX-class | Class '"RCLRMIX"'
RCLRMIX-method | Predicts Cluster Membership Based Upon a Model Trained by REBMIX
RCLRMIX-methods | Predicts Cluster Membership Based Upon a Model Trained by REBMIX
RCLRMVNORM-class | Class '"RCLRMIX"'
RCLS.chunk-class | Class '"RCLS.chunk"'
RCLSMIX | Predicts Class Membership Based Upon a Model Trained by REBMIX
RCLSMIX-class | Class '"RCLSMIX"'
RCLSMIX-method | Predicts Class Membership Based Upon a Model Trained by REBMIX
RCLSMIX-methods | Predicts Class Membership Based Upon a Model Trained by REBMIX
RCLSMVNORM-class | Class '"RCLSMIX"'
REBMIX | REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
REBMIX-class | Class '"REBMIX"'
REBMIX-method | REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
REBMIX-methods | REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
REBMIX.boot-class | Class '"REBMIX.boot"'
REBMVNORM-class | Class '"REBMIX"'
REBMVNORM.boot-class | Class '"REBMIX.boot"'
RNGMIX | Random Univariate or Multivariate Finite Mixture Generation
RNGMIX-class | Class '"RNGMIX"'
RNGMIX-method | Random Univariate or Multivariate Finite Mixture Generation
RNGMIX-methods | Random Univariate or Multivariate Finite Mixture Generation
RNGMIX.Theta-class | Class '"RNGMIX.Theta"'
RNGMVNORM-class | Class '"RNGMIX"'
RNGMVNORM.Theta-class | Class '"RNGMIX.Theta"'

## -- S --

show-method | Class '"EM.Control"'
show-method | Predicts Cluster Membership Based Upon a Model Trained by REBMIX
show-method | Extracts Chunk from Train and Test Datasets
show-method | Predicts Class Membership Based Upon a Model Trained by REBMIX
show-method | REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
show-method | Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation
show-method | Random Univariate or Multivariate Finite Mixture Generation
show-method | Class '"RNGMIX.Theta"'
split | Splits Dataset into Train and Test Datasets
split-method | Splits Dataset into Train and Test Datasets
split-methods | Splits Dataset into Train and Test Datasets
SSE | Sum of Squares Error
SSE-method | Sum of Squares Error
SSE-methods | Sum of Squares Error
summary-method | Predicts Cluster Membership Based Upon a Model Trained by REBMIX
summary-method | Predicts Class Membership Based Upon a Model Trained by REBMIX
summary-method | REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
summary-method | Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation

## -- T --

truck | Truck Dataset

## -- W --

weibull | Weibull Dataset 8.1
weibullnormal | Weibull-normal Simulated Dataset
wine | Wine Recognition Data