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Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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@@ -43,7 +43,7 @@ The preferred workflow for contributing to PyMC3 is to fork the [GitHub reposito
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$ git checkout -b my-feature
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```
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Always use a ``feature`` branch. It's good practice to never routinely work on the ``master`` branch of any repository.
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Always use a ``feature`` branch. It's good practice to never routinely work on the ``main`` branch of any repository.
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4. Project requirements are in ``requirements.txt``, and libraries used for development are in ``requirements-dev.txt``. The easiest (and recommended) way to set up a development environment is via [miniconda](https://docs.conda.io/en/latest/miniconda.html):
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After committing, it is a good idea to sync with the base repository in case there have been any changes:
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```bash
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$ git fetch upstream
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$ git rebase upstream/master
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$ git rebase upstream/main
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```
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Then push the changes to your GitHub account with:
page <https://github.com/pymc-devs/pymc3/graphs/contributors>`__. Also read our `Code of Conduct <https://github.com/pymc-devs/pymc3/blob/master/CODE_OF_CONDUCT.md>`__ guidelines for a better contributing experience.
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page <https://github.com/pymc-devs/pymc3/graphs/contributors>`__. Also read our `Code of Conduct <https://github.com/pymc-devs/pymc3/blob/main/CODE_OF_CONDUCT.md>`__ guidelines for a better contributing experience.
Copy file name to clipboardExpand all lines: docs/source/about.rst
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|NumFOCUS| |Quantopian| |ODSC|
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More details about sponsoring PyMC3 can be found `here <https://github.com/pymc-devs/pymc3/blob/master/GOVERNANCE.md#institutional-partners-and-funding>`_.
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More details about sponsoring PyMC3 can be found `here <https://github.com/pymc-devs/pymc3/blob/main/GOVERNANCE.md#institutional-partners-and-funding>`_.
Copy file name to clipboardExpand all lines: docs/source/api/bounds.rst
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* Bounds cannot be given to variables that are ``observed``. To model
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truncated data, use a :func:`~pymc3.model.Potential` in combination with a cumulative
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probability function. See `this example notebook <https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/weibull_aft.ipynb>`_.
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probability function. See `this example notebook <https://docs.pymc.io/pymc-examples/examples/survival_analysis/weibull_aft.html>`_.
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* The automatic transformation applied to continuous distributions results in
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an unnormalized probability distribution. This doesn't effect inference
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