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Update glm robust with outliers and add extra dependency template (#249)
* bundle of changes * black * black * fix references * some visual improvements to plots * activate codeautolink * fix typo * fix formatting of conclusions * try fixing build * update template and contributing * try reactivating codeautolink * improve extra installs template * address comments * remove GLM robust changes from PR
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:::{attention} | ||
This notebook uses libraries that are not PyMC dependencies | ||
and therefore need to be installed specifically to run this notebook. | ||
Open the dropdown below for extra guidance. | ||
::: | ||
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::::::{dropdown} Extra dependencies install instructions | ||
:icon: package-dependencies | ||
In order to run this notebook (either locally or on binder) you won't only | ||
need a working PyMC installation with all optional dependencies, | ||
but also to install some extra dependencies. | ||
For advise on installing PyMC itself, please refer to {ref}`pymc:installation` | ||
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You can install these dependencies with your preferred package manager, we provide | ||
as an example the pip and conda commands below. | ||
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> $ pip install {{ pip_dependencies }} | ||
Note that if you want (or need) to install the packages from inside the notebook instead | ||
of the command line, you can install | ||
the packages by running a variation of the pip command: | ||
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> import sys | ||
> | ||
> !{sys.executable} -m pip install {{ pip_dependencies }} | ||
You should not run `!pip install` as it might install the package in a different | ||
environment and not be available from the Jupyter notebook even if installed. | ||
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Another alternative is using conda instead: | ||
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> $ conda install {{ conda_dependencies }} | ||
when installing scientific python packages with conda, | ||
we recommend using [conda forge](https://conda-forge.org/index.html) | ||
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{{ extra_install_notes }} | ||
:::::: |
1,146 changes: 492 additions & 654 deletions
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examples/generalized_linear_models/GLM-robust-with-outlier-detection.ipynb
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