Merge pull request #8 from pymc-devs/autopypi

use github actions to release on pypi

Author: aloctavodia

.github/workflows/publish-to-test-pypi.yml

@@ -0,0 +1,40 @@
+name: Publish tagged releases to PyPI and TestPyPI
+
+on: push
+
+jobs:
+  build-n-publish:
+    name: Build and publish Python distributions to PyPI and TestPyPI
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: "3.9"
+
+    - name: Install pypa/build
+      run: >-
+        python -m
+        pip install
+        build
+        --user
+    - name: Build a binary wheel and a source tarball
+      run: >-
+        python -m
+        build
+        --sdist
+        --wheel
+        --outdir dist/
+
+    - name: Publish distribution to Test PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+        repository_url: https://test.pypi.org/legacy/
+  
+    - name: Publish distribution to PyPI
+      if: startsWith(github.ref, 'refs/tags')
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        password: ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -1,4 +1,4 @@
-# pymc_bart
+
 Bayesian Additive Regression Trees for Probabilistic programming with PyMC
 
 
@@ -16,16 +16,16 @@ If you use PyMC-BART and want to cite it please use [![arXiv](https://img.shield
 
 Here is the citation in BibTeX format
 
+```
 @misc{quiroga2022bart,
-  doi = {10.48550/ARXIV.2206.03619},
-  url = {https://arxiv.org/abs/2206.03619},
-  author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
-  keywords = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  title = {Bayesian additive regression trees for probabilistic programming},
-  publisher = {arXiv},
-  year = {2022},
-  copyright = {Creative Commons Attribution Share Alike 4.0 International}
+title={Bayesian additive regression trees for probabilistic programming},
+author={Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
+year={2022},
+doi={10.48550/ARXIV.2206.03619},
+archivePrefix={arXiv},
+primaryClass={stat.CO}
 }
+```
 
 ## Donations
 pymc_bart, as other pymc-devs projects, is a non-profit project under the NumFOCUS umbrella. If you want to support pymc_bart financially, you can donate [here](https://numfocus.org/donate-to-pymc).

pymc_bart/__init__.py

@@ -18,7 +18,7 @@
 from pymc_bart.utils import plot_dependence, plot_variable_importance, predict
 
 __all__ = ["BART", "PGBART"]
-__version__ = "0.0.1"
+__version__ = "0.0.2"
 
 import pymc as pm
 

setup.py

@@ -73,7 +73,7 @@ def get_version():
         license=LICENSE,
         url=URL,
         long_description=LONG_DESCRIPTION,
-        long_description_content_type="text/x-rst",
+        long_description_content_type="text/markdown",
         packages=find_packages(),
         # because of an upload-size limit by PyPI, we're temporarily removing docs from the tarball.
         # Also see MANIFEST.in