update citation and python version (#72)

README.md

@@ -11,12 +11,12 @@ Kullback-Leibler projections for Bayesian model selection in Python.
 ## Overview
 
 Kulprit _(Pronounced: kuːl.prɪt)_ is a package for variable selection for [Bambi](https://github.com/bambinos/bambi) models.
-Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an issue.
+Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an [issue](https://github.com/bambinos/kulprit/issues).
 
 
 ## Installation
 
-Kulprit requires a working Python interpreter (3.9+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.
+Kulprit requires a working Python interpreter (3.10+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using pip:
 

docs/index.rst

@@ -14,15 +14,15 @@ Kullback-Leibler projections for Bayesian model selection
 .. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
     :target: https://github.com/ambv/black
 
-Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an issue on GitHub.
+Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an `issue <https://github.com/bambinos/kulprit/issues>`_ on GitHub.
 
-Besides this documentation, we also recommend you to read `Robust and efficient projection predictive inference <https://arxiv.org/abs/2306.15581>`_. The paper is not about Kulprit, but introduces the theory behind Kulprit and also provides some practical advice. You may also find this `guide <https://avehtari.github.io/modelselection/CV-FAQ.html>`_
+Besides this documentation, we also recommend you to read `Advances in projection predictive inference <https://arxiv.org/abs/2306.15581>`_. The paper is not about Kulprit, but introduces the theory behind Kulprit and also provides some practical advice. You may also find this `guide <https://avehtari.github.io/modelselection/CV-FAQ.html>`_
 on Cross-Validation and model selection is useful.
 
 Installation
 ============
 
-Kulprit requires a working Python interpreter (3.9+). We recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
+Kulprit requires a working Python interpreter (3.10+). We recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using pip:
 
@@ -40,7 +40,7 @@ Alternatively, if you want the bleeding edge version of the package you can inst
 Dependencies
 ============
 
-Kulprit is tested on Python 3.9+. Dependencies are listed in `pyproject.toml` and should all be installed by the Kulprit installer; no further action should be required.
+Kulprit is tested on Python 3.10+. Dependencies are listed in `pyproject.toml` and should all be installed by the Kulprit installer; no further action should be required.
 
 
 Contributing
@@ -62,13 +62,14 @@ If you find Kulprit useful in your work, please cite the following paper:
 
 .. code-block:: latex
 
-    @misc{mclatchie2023,
-        title={Robust and efficient projection predictive inference}, 
+    @misc{mclatchie2024,
+        title={Advances in projection predictive inference}, 
         author={Yann McLatchie and Sölvi Rögnvaldsson and Frank Weber and Aki Vehtari},
-        year={2023},
+        year={2024},
         eprint={2306.15581},
         archivePrefix={arXiv},
-        primaryClass={stat.ME}
+        primaryClass={stat.ME},
+        url={https://arxiv.org/abs/2306.15581}, 
     }