sumkl

the location parameters of the first distribution

thetaNew.mu

the covariance matrix of the first distribution

thetaNew.sigma

the location parameter of the second distribution

thetaOld.mu

the covariance matrix of the second distribution

thetaOld.sigma

sumkl
The sum of K-L divergence measure between two successive iterations 
for each component of a mixture distribution,

A robust clustering algorithm (Model-Based) similar to Expectation
Maximization for finite mixture normal distributions is implemented,
its main advantage is that the estimator is resistant to outliers,
that means that results of parameter estimation are still correct when there
are atypical values in the sample
(see Gonzalez, Maronna, Yohai and Zamar (2021)
<arxiv:2102.06851>).

sumkl: sumkl The sum of K-L divergence measure between two successive iterations for each component of a mixture distribution,

Description

Usage

Arguments

Value