vinecopulas.marginals
=====================

.. py:module:: vinecopulas.marginals

.. autoapi-nested-parse::

   Created on Thu Feb 22 17:13:20 2024




Functions
---------

.. autoapisummary::

   vinecopulas.marginals.best_fit_distributiondiscrete
   vinecopulas.marginals.best_fit_distribution
   vinecopulas.marginals.pseudodata
   vinecopulas.marginals.pseudodiscr


Module Contents
---------------

.. py:function:: best_fit_distributiondiscrete(data, bound=False, criterion='BIC')

   Fits the best discrete distribution to data.

   Arguments:
       *data* : The data which has to be fit as a 1-d numpy array.

       *bounds* : whether the data is bounded

       *criterion* : Either BIC, AIC or ML is used to choose the best distribution

   Returns:
    *bestdist* : the best distribution and its parameters.


.. py:function:: best_fit_distribution(data, criterion='BIC', dists=[])

   Fits the best continuous distribution to data.

   Arguments:
       *data* : The data which has to be fit as a 1-d numpy array.

       *criterion* : Either BIC, AIC or ML is used to choose the best distribution
       
       *dists* : Specify specific distributions if only specific distributions need to be tested, provided as a list.

   Returns:
    *bestdist* : the best distribution and its parameters.


.. py:function:: pseudodata(data)

   Compute the pseudo-observations for the given data (transforms data to standard uniform margins)

   Arguments:
       *data* : The data which has to be converted into pseudo data, provided as a numpy array where each column contains a separate variable (eg. x1,x2,...,xn)


   Returns:
    *u* : Pseudo data, provided as a numpy array where each column contains a separate variable (eg. u1,u2,...,un)


.. py:function:: pseudodiscr(xcdf, xpmf)

   Compute the pseudo-observations for the given variable that is discrete.

   Arguments:
       *xcdf* : The cumulative distribution function of the variable, calculated based on the best fit discrete distribution, provided as a 1-d numpy array.

       *xpmf* : The probability mass function of the variable, calculated based on the best fit discrete distribution, provided as a 1-d numpy array.

   Returns:
    *ui* : Pseudo data of a given variable  provided as a 1-d numpy array.