.. This file should at least contain the root `toctree` directive. ordinalcorr =========== `ordinalcorr` is a Python package for computing correlation coefficients designed for ordinal-scale data. Installation ------------ ordinalcorr is available at the `PyPI `_ .. code-block:: bash pip install ordinalcorr Requirements ~~~~~~~~~~~~ - Python 3.10 or later - Dependencies: - numpy >= 1.23.0 - scipy >= 1.8.0 Features -------- Correlation Coefficients ~~~~~~~~~~~~~~~~~~~~~~~~ This package provides several correlation coefficients (e.g. Polyserial and Polychoric) .. code-block:: python >>> from ordinalcorr import polychoric >>> x = [1, 1, 2, 2, 3, 3] >>> y = [0, 0, 0, 1, 1, 1] >>> polychoric(x, y) 0.9986287922233864 Details can be found in the :doc:`api_reference` and the :doc:`method_selection_guide`. Heterogeneous Correlation Matrix ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A function for computing the *heterogeneous correlation matrix*—a correlation matrix that includes both continuous and ordinal variables—is also provided. .. code-block:: python >>> from ordinalcorr import hetcor >>> import pandas as pd >>> data = pd.DataFrame({ ... "continuous": [0.1, 0.1, 0.2, 0.2, 0.3, 0.3], ... "dichotomous": [0, 0, 0, 1, 1, 1], ... "polytomous": [1, 1, 3, 3, 2, 2], ... }) >>> hetcor(data) continuous dichotomous polytomous continuous 1.000000 0.989335 0.514870 dichotomous 0.989335 1.000000 0.549231 polytomous 0.514870 0.549231 1.000000 Table of Contents ----------------- .. toctree:: :maxdepth: 2 :caption: Contents: user_guide api_reference