get_hierarchical_partition: Build a hierchical partition from posterior probabilities
In mcomas/mixpack: Tools to Work with Mixture Components

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

1 2	get_hierarchical_partition(post, omega, lambda, f_omega = NULL, f_lambda = NULL)

`post`	dataframe of probabilities/weights (`tau` must be strictly positive)
`omega`	String giving the function name used to build the hierarchy. Available functions are: entr, prop, dich
`lambda`	String giving the function name used to build the hierarchy. Available functions are: entr, demp, demp.mod, coda, coda.norm, prop
`f_omega`	function with two parameters (`v_tau`, `a`). Parameter `v_tau` is a vector of probabilities, parameter `a` is the a selected class. `omega`(`v_tau`, `a`) gives the representativeness of element with probabities `v_tau` to class `a`
`f_lambda`	function with three parameters (`v_tau`, `a`, `b`). Parameter `v_tau` is a vector of probabilities, parameters `a` and `b` are classes to be combined.