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‎.github/workflows/pytest.yml

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--ignore=pymc/tests/test_dist_math.py
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--ignore=pymc/tests/test_pickling.py
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--ignore=pymc/tests/test_plots.py
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--ignore=pymc/tests/test_updates.py
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pymc/tests/test_dist_math.py
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pymc/tests/test_minibatches.py
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pymc/tests/test_pickling.py
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pymc/tests/test_plots.py
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pymc/tests/test_updates.py
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pymc/tests/test_transforms.py
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‎.gitignore

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# Sphinx
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_build
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docs/jupyter_execute
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docs/**/generated/*
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# Merge tool
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*.orig

‎.pre-commit-config.yaml

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exclude: ^(docs/logos|pymc/tests/data)/
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.0.1
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rev: v4.1.0
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hooks:
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- id: check-merge-conflict
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- id: check-toml
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- id: isort
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name: isort
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- repo: https://github.com/asottile/pyupgrade
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rev: v2.29.1
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rev: v2.31.0
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hooks:
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- id: pyupgrade
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args: [--py37-plus]

‎CONTRIBUTING.md

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## Style guide
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We have configured a pre-commit hook that checks for `black`-compliant code style.
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We encourage you to configure the pre-commit hook as described in the [PyMC Python Code Style Wiki Page](https://github.com/pymc-devs/pymc/wiki/Python-Code-Style), because it will automatically enforce the code style on your commits.
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We encourage you to configure the pre-commit hook as described in the [PyMC Python Code Style Wiki Page](https://docs.pymc.io/en/latest/contributing/python_style.html), because it will automatically enforce the code style on your commits.
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Similarly, consult the [PyMC's Jupyter Notebook Style](https://github.com/pymc-devs/pymc/wiki/PyMC-Jupyter-Notebook-Style-Guide) guides for notebooks.
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Similarly, consult the [PyMC's Jupyter Notebook Style](https://docs.pymc.io/en/latest/contributing/jupyter_style.html) guides for notebooks.
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For documentation strings, we *prefer* [numpy style](https://numpydoc.readthedocs.io/en/latest/format.html) to comply with the style that predominates in our upstream dependencies.
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‎docs/source/Makefile renamed to ‎Makefile

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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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PAPER =
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BUILDDIR = _build
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SOURCEDIR = docs/source
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BUILDDIR = docs/_build
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# User-friendly check for sphinx-build
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ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
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clean:
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rm -rf $(BUILDDIR)/*
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rm -rf $(SOURCEDIR)/api/**/generated
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rm -rf docs/jupyter_execute
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html:
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$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
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$(SPHINXBUILD) $(SOURCEDIR) $(BUILDDIR) -b html
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@echo
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‎docs/source/404.md

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modified the base url in order to support multi version docs
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(you'll see the version switcher at the bottom right of the page).
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::::{grid} 3
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:::{grid-item}
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:::
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:::{grid-item}
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```{button-link} https://docs.pymc.io/en/stable/
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:color: primary
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:expand:
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Back to stable version documentation
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```
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:::
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:::{grid-item}
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:::
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::::
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Back to {{ '[documentation homepage](https://docs.pymc.io/en/{}/)'.format(version_slug) }}

‎docs/source/api.rst

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api/smc
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api/step_methods
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api/inference
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api/plots
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Indices and tables
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===================
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--------------
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API extensions
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--------------
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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Plots, stats and diagnostics
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----------------------------
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Plots, stats and diagnostics are delegated to the
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:doc:`ArviZ <arviz:index>`.
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library, a general purpose library for
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"exploratory analysis of Bayesian models".
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* Functions from the `arviz.plots` module are available through ``pymc.<function>`` or ``pymc.plots.<function>``,
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but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:plot_api>`.
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* Functions from the `arviz.stats` module are available through ``pymc.<function>`` or ``pymc.stats.<function>``,
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but for their API documentation please refer to the :ref:`ArviZ documentation <arviz:stats_api>`.
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ArviZ is a dependency of PyMC and so, in addition to the locations described above,
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importing ArviZ and using ``arviz.<function>`` will also work without any extra installation.
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Generalized Linear Models (GLMs)
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--------------------------------
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Generalized Linear Models are delegated to the
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`Bambi <https://bambinos.github.io/bambi>`_.
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library, a high-level Bayesian model-building
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interface built on top of PyMC.
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Bambi is not a dependency of PyMC and should be installed in addition to PyMC
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to use it to generate PyMC models via formula syntax.

‎docs/source/api/distributions/continuous.rst

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Continuous
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**********
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.. currentmodule:: pymc.distributions.continuous
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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‎docs/source/api/distributions/discrete.rst

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Discrete
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********
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‎docs/source/api/distributions/mixture.rst

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Mixture
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*******
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‎docs/source/api/distributions/multivariate.rst

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Multivariate
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************
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‎docs/source/api/distributions/simulator.rst

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Simulator
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**********
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‎docs/source/api/distributions/timeseries.rst

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Timeseries
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**********
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‎docs/source/api/distributions/transforms.rst

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****************************************************************
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Transformations of a random variable from one space to another
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****************************************************************
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Note that for convenience these entities can be addressed as
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``pm.transforms.``\ *X* for any name *X*, although they are actually
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logodds
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interval
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ordered
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log
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***************
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Transformations
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***************
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.. currentmodule:: pymc.transforms
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Transform Instances
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~~~~~~~~~~~~~~~~~~~
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.. glossary::
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``stick_breaking``
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``logodds``
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:toctree: generated
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simplex
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logodds
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interval
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log_exp_m1
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ordered
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log
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sum_to_1
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circular
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Transform Composition Classes
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:toctree: generated
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Chain
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CholeskyCovPacked
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.. autosummary::
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:toctree: generated
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LogExpM1
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Ordered
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SumTo1

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