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rebmix (version 2.8.0)

Finite Mixture Modeling, Clustering & Classification

Description

R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families.

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Version

Install

install.packages('rebmix')

Monthly Downloads

464

Version

2.8.0

License

GPL (>= 2)

Maintainer

Marko Nagode

Last Published

March 25th, 2016

Functions in rebmix (2.8.0)

BIC-methods

Bayesian Information Criterion
weibullnormal

Weibull-normal Simulated Dataset
adult

Adult Dataset
truck

Truck Dataset
ICL-methods

Integrated Classification Likelihood Criterion
REBMIX-methods

REBMIX Algorithm for Univariate or Multivariate Finite Mixture Estimation
plot-methods

Plots Univariate or Multivariate REBMIX Output
demix

Empirical Density Calculation
PC-methods

Partition Coefficient
kseq

Sequence of Bins or Nearest Neighbours Generation
REBMIX-class

Class "REBMIX"
RNGMIX-class

Class "RNGMIX"
HQC-methods

Hannan-Quinn Information Criterion
iris

Iris Data Set
wine

Wine Recognition Data
REBMIX.boot-class

Class "REBMIX.boot"
coef-methods

Prints Univariate or Multivariate REBMIX Coefficients
RCLSMIX-class

Class "RCLSMIX"
SSE-methods

Sum of Squares Error
pfmix

Predictive Distribution Function Calculation
RNGMIX-methods

Random Univariate or Multivariate Finite Mixture Generation
dfmix

Predictive Density Calculation
logL

Log Likelihood
boot-methods

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

Predicts Class Membership Based Upon a Model Trained by REBMIX
RCLRMIX-class

Class "RCLRMIX"
RCLRMIX-methods

Predicts Cluster Membership Based Upon a Model Trained by REBMIX
rebmix-internal

Internal rebmix Functions
galaxy

Galaxy Dataset
weibull

Weibull Dataset 8.1
AWE-methods

Approximate Weight of Evidence Criterion
CLC-methods

Classification Likelihood Criterion
ICLBIC-methods

Approximate Integrated Classification Likelihood Criterion
AIC-methods

Akaike Information Criterion
MDL-methods

Minimum Description Length
PRD-methods

Total of Positive Relative Deviations
pemix

Empirical Distribution Function Calculation