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(Conditional) independence tests
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Fisher-z
^^^^^^^^^^^^

Perform an independence test using Fisher-z's test [fisherz_]. This test is optimal for linear-Gaussian data.

Parameters test
""""""""""""""""""""""""""""""""""""
**data**: data matrices.

**X, Y and condition_set**: data matrices of size number_of_samples * dimensionality.

**correlation_matrix**: correlation matrix; None means without the parameter of correlation matrix.

Returns
""""""""""""""""""""""""""""""""""""
**p**: the p-value of the test.

.. [fisherz] Fisher, Ronald A. "On the'probable error'of a coefficient of correlation deduced from a small sample." Metron 1 (1921): 1-32.


Missing-value Fisher-z test
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Perform a testwise-deletion Fisher-z independence test to data sets with missing values.
With testwise-deletion, the test makes use of all data points that do not have missing values for the variables involved in the test.

Parameters
""""""""""""""""""""""""""""""""""""
**mvdata**: data with missing values.

**X, Y and condition_set**: data matrices of size number_of_samples * dimensionality.

Returns
""""""""""""""""""""""""""""""""""""
**p**: the p-value of the test.


Chi-Square test
^^^^^^^^^^^^^^^^

Perform an independence test on discrete variables using Chi-Square test.

Parameters
""""""""""""""""""""""""""""""""""""
**data**: data matrices.

**X, Y and condition_set**: data matrices of size number_of_samples * dimensionality.

**G_sq**: True means using G-Square test;
       False means using Chi-Square test.

Returns
""""""""""""""""""""""""""""""""""""
**p**: the p-value of the test.



Kernel-based conditional independence (KCI) test and independence test
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Kernel-based conditional independence (KCI_) test and independence test.
To test if x and y are conditionally or unconditionally independent on Z. For unconditional independence tests,
Z is set to the empty set.

Parameters
""""""""""""""""""""""""""""""""""""
**X, Y and Z**: data matrices of size number_of_samples * dimensionality. Z could be None.

**KernelX/Y/Z**: ['GaussianKernel', 'LinearKernel', 'PolynomialKernel'].
(For 'PolynomialKernel', the default degree is 2. Currently, users can change it by setting the 'degree' of 'class PolynomialKernel()'.

Returns
""""""""""""""""""""""""""""""""""""
**p_val**: the p value.


.. [KCI] Zhang, Kun, et al. "Kernel-based conditional independence test and application in causal discovery." UAI. 2011.


G-Square test
^^^^^^^^^^^^^^^


Perform an independence test using G-Square test [gSquare]_. This test is based on the log likelihood ratio test.

Parameters
""""""""""""""""""""""""""""""""""""
**data**: data matrices.

**X, Y and condition_set**: data matrices of size number_of_samples * dimensionality.

**G_sq**: True means using G-Square test; False means using Chi-Square test.

Returns
""""""""""""""""""""""""""""""""""""
p: the p-value of the test

.. [gSquare] Tsamardinos, Ioannis, Laura E. Brown, and Constantin F. Aliferis. "The max-min hill-climbing Bayesian network structure learning algorithm." Machine learning 65.1 (2006): 31-78.

