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‎docs/source/conf.py

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# The short X.Y version.
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version = '0.2'
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# The full version, including alpha/beta/rc tags.
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release = '0.2.6'
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release = '0.2.7'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.

‎docs/source/index.rst

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News
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----
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October 2019:
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- v0.2.6 released. This release has one new feature (plotting of estimation plot inside any :py:mod:`matplotlib` :py:class:`Axes`; see the section on :ref:`inset_plot` in the :doc:`tutorial`). There are also two bug patches for the handling of bootstrap plotting, and of dependency installation. For more information, see the :doc:`release-notes`.
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- v0.2.7 released. A minor bugfix in the handling of wide datasets with unequal Ns in each group. For more information, see the :doc:`release-notes`.
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- v0.2.6 released. This release has one new feature (plotting of estimation plot inside any :py:mod:`matplotlib` :py:class:`Axes`; see the section on :ref:`inset_plot` in the :doc:`tutorial`). There are also two bug patches for the handling of bootstrap plotting, and of dependency installation.
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September 2019:
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- v0.2.5 released. This release addresses two feature requests, and also patches two bugs: one affecting the paired difference CIs, and one involving NaNs in unused/irrelevant columns.

‎docs/source/release-notes.rst

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Release Notes
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=============
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v0.2.6
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v0.2.7
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This release addresses a number of bugs and feature improvements. There are no known breaking changes to the API; hence **all users are strongly encouraged to upgrade to the latest version**.
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This release addresses a single bug. There are no known breaking changes to the API; hence **all users are strongly encouraged to upgrade to the latest version**.
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Bug-fixes:
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- Bug affecting display of Tufte gapped lines in Cumming plots if the supplied :py:mod:`pandas` :py:class:`DataFrame` was in 'wide' format, but did not have equal number of Ns in the groups. (`Issue #79 <https://github.com/ACCLAB/DABEST-python/issues/79>`_)
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v0.2.6
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------
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Feature additions:
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- It is now possible to specify a pre-determined :py:mod:`matplotlib` :py:class:`Axes` to create the estimation plot in. See the new section in the tutorial for more information. (`Pull request #73 <https://github.com/ACCLAB/DABEST-python/pull/73>`_; thanks to Adam Nekimken (`@anekimken <https://github.com/anekimken>`_).

‎docs/source/tutorial.rst

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We're using DABEST v0.2.6
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We're using DABEST v0.2.7
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Create dataset for demo
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DABEST v0.2.6
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DABEST v0.2.7
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=============
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Good afternoon!
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The current time is Thu Oct 3 17:38:00 2019.
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The current time is Mon Oct 21 11:29:00 2019.
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Effect size(s) with 95% confidence intervals will be computed for:
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DABEST v0.2.6
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DABEST v0.2.7
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=============
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Paired effect size(s) with 95% confidence intervals will be computed for:
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DABEST v0.2.6
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DABEST v0.2.7
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Effect size(s) with 90% confidence intervals will be computed for:
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DABEST v0.2.7
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=============
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The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
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The two-sided p-value of the Mann-Whitney test is 0.00163.
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You can access the results as a pandas DataFrame as well.
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You can access the results as a :py:mod:`pandas` :py:class:`DataFrame` as well.
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DABEST v0.2.7
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The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.317, 1.62].
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The current time is Thu Oct 3 17:38:25 2019.
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The current time is Mon Oct 21 11:29:25 2019.
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The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
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The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].
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