<commit_before>===========================
Glimix-core's documentation
===========================

:Date: |today|
:Version: |version|

Fast inference for Generalised Linear Mixed Models.

.. toctree::
    :caption: Table of contents
    :name: mastertoc
    :maxdepth: 3

    install
    mean
    cov
    lik
    link
    lmm
    glmm
    gp
    ggp
    random

*****************
Comments and bugs
*****************

You can get the source code and open issues `on Github.`_

.. _on Github.: https://github.com/limix/glimix-core

.. rubric:: References

.. [Lip11] Lippert, <NAME>, Jennifer, Liu, Ying, Kadie, <NAME>,
           Davidson, <NAME> & <NAME> (2011). FaST linear mixed
           models for genome-wide association studies. Nature methods, 8,
           833-835.
.. [ME17]  Maximum likelihood estimation. (2017, September 9). In Wikipedia,
           The Free Encyclopedia. Retrieved 14:48, September 22, 2017, from
           https://en.wikipedia.org/w/index.php?title=Maximum_likelihood_estimation&oldid=799704694
.. [Min01] <NAME>. (2001, August). Expectation propagation for approximate
           Bayesian inference. In Proceedings of the Seventeenth conference on
           Uncertainty in artificial intelligence (pp. 362-369).
           Morgan Kaufmann Publishers Inc..<commit_msg>not refer ref<commit_after>===========================
Glimix-core's documentation
===========================

:Date: |today|
:Version: |version|

Fast inference for Generalised Linear Mixed Models.

.. toctree::
    :caption: Table of contents
    :name: mastertoc
    :maxdepth: 3

    install
    mean
    cov
    lik
    link
    lmm
    glmm
    gp
    ggp
    random

*****************
Comments and bugs
*****************

You can get the source code and open issues `on Github.`_

.. _on Github.: https://github.com/limix/glimix-core

.. rubric:: References

.. [Min01] <NAME>. (2001, August). Expectation propagation for approximate
           Bayesian inference. In Proceedings of the Seventeenth conference on
           Uncertainty in artificial intelligence (pp. 362-369).
           Morgan Kaufmann Publishers Inc..
{'fraction_non_alphanumeric': 0.16651031894934334, 'fraction_numerical': 0.03048780487804878, 'mean_word_length': 2.99438202247191, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2916027', 'n_tokens_mistral': 687, 'n_tokens_neox': 588, 'n_words': 202}