get_hierarchical_partition

dataframe of probabilities/weights (<code>tau</code> must be strictly positive)

post

String giving the function name used to build the hierarchy. Available 
functions are: entr, prop, dich

omega

String giving the function name used to build the hierarchy. Available 
functions are: entr, demp, demp.mod, coda, coda.norm, prop

lambda

function with two parameters (<code>v_tau</code>, <code>a</code>). Parameter 
<code>v_tau</code> is a vector of probabilities, parameter <code>a</code> is the a selected class.
<code>omega</code>(<code>v_tau</code>, <code>a</code>) gives the representativeness of element with
probabities <code>v_tau</code> to class <code>a</code>

f_omega

function with three parameters (<code>v_tau</code>, <code>a</code>, <code>b</code>).
Parameter <code>v_tau</code> is a vector of probabilities, parameters <code>a</code> and <code>b</code>
are classes to be combined.

f_lambda


This function applies the methodology described in [citar article]
to build a hierarchy of classes using the weights or probabilities 
that an element belongs to each class


A collection of tools implemented to facilitate the analysis of the components of a finite mixture distributions. The package has some functions to generate random samples coming from a finite mixture. The package provides a C++ implementation for the construction of a hierarchy over the components of a given finite mixture.

get_hierarchical_partition: Build a hierchical partition from posterior probabilities

Description

Usage

Arguments