Update contribution documentation

README.md

@@ -32,7 +32,7 @@ Dodd](https://github.com/Daniel-Dodd).
 
 We would be delighted to receive contributions from interested individuals and
 groups. To learn how you can get involved, please read our [guide for
-contributing](https://github.com/JaxGaussianProcesses/GPJax/blob/master/CONTRIBUTING.md).
+contributing](https://github.com/JaxGaussianProcesses/GPJax/blob/main/docs/contributing.md).
 If you have any questions, we encourage you to [open an
 issue](https://github.com/JaxGaussianProcesses/GPJax/issues/new/choose). For
 broader conversations, such as best GP fitting practices or questions about the
@@ -67,7 +67,7 @@ process modelling.
 > - [**UCI regression**](https://docs.jaxgaussianprocesses.com/examples/yacht/)
 
 ## Conversion between `.ipynb` and `.py`
-Above examples are stored in [examples](examples) directory in the double
+Above examples are stored in [examples](docs/examples) directory in the double
 percent (`py:percent`) format. Checkout [jupytext
 using-cli](https://jupytext.readthedocs.io/en/latest/using-cli.html) for more
 info.

docs/contributing.md

@@ -72,13 +72,17 @@ you through every detail!
   Always use a `feature` branch. It's good practice to avoid
   work on the ``main`` branch of any repository.
 
-4.  Project requirements are in ``requirements.txt``. We suggest using a
-  [virtual environment](https://docs.python-guide.org/dev/virtualenvs/) for
-  development. Once the virtual environment is activated, run:
+4.  We use [Poetry](https://python-poetry.org/) for packaging and dependency management, and project requirements are in       ``pyproject.toml``. We suggest using a [virtual environment](https://docs.python-guide.org/dev/virtualenvs/) for
+development. For those using Apple Silicon chips, we advise using [Conda miniforge](https://github.com/conda-forge/miniforge). Once the virtual environment is activated, run:
 
   ```bash
-  $ pip install -e .
-  $ pip install -r requirements-dev.txt
+  $ poetry install
+  ```
+
+  At this point we recommend you check your installation passes the supplied unit tests:
+
+  ```bash
+  $ poetry run pytest
   ```
 
 5.  Install the pre-commit hooks.
@@ -91,7 +95,13 @@ you through every detail!
   successful, this will print the following output `pre-commit installed at
   .git/hooks/pre-commit`.
 
-1.  Add changed files using `git add` and then `git commit` files to record your
+6.  At this point you can manually run the pre-commit hooks with the following command:
+
+  ```bash
+  poetry run pre-commit run --all-files
+  ```
+
+7.  Add changed files using `git add` and then `git commit` files to record your
   changes locally:
 
   ```bash
@@ -112,7 +122,7 @@ you through every detail!
   $ git push -u origin my-feature
   ```
 
-7.  Go to the GitHub web page of your fork of the GPJax repo. Click the 'Pull
+8.  Go to the GitHub web page of your fork of the GPJax repo. Click the 'Pull
   request' button to send your changes to the project's maintainers for
   review.
 
@@ -143,13 +153,13 @@ request, we recommend you check the following:
 
   - Do all public methods have informative docstrings that describe their
   function, input(s) and output(s)?
+  - Do the pre-commit hooks pass?
   - Do the tests pass when everything is rebuilt from scratch?
   - Documentation and high-coverage tests are necessary for enhancements to be
   accepted. Test coverage can be checked with:
 
     ```bash
-    $ pip install -r requirements-dev.txt
-    $ pytest tests --cov=./ --cov-report=html
+    $ poetry run pytest tests --cov=./ --cov-report=html
     ```
 
   Navigate to the newly created folder `htmlcov` and open `index.html` to view

docs/examples/intro_to_gps.py

@@ -308,7 +308,7 @@
     )
 
 # %% [markdown]
-# Formmally, we can define this by letting $p(\mathbf{x}, \mathbf{y})$ be the
+# Formally, we can define this by letting $p(\mathbf{x}, \mathbf{y})$ be the
 # joint probability distribution defined over
 # $\mathbf{x}\sim\mathcal{N}(\boldsymbol{\mu}_{\mathbf{x}}, \boldsymbol{\Sigma}_{\mathbf{xx}})$ and
 # $\mathbf{y}\sim\mathcal{N}(\boldsymbol{\mu}_{\mathbf{y}}, \boldsymbol{\Sigma}_{\mathbf{yy}})$.

docs/examples/pytrees.md

@@ -9,7 +9,7 @@
   such as `grad`.
 
 We achieve this through providing a base `Module` abstraction to cleanly
-handles parameter trainability and optimising transformations for JAX models.
+handle parameter trainability and optimising transformations of JAX models.
 
 
 
@@ -106,7 +106,7 @@ compatible with JAX. To achieve this we must consider [JAX's _PyTree_](https://j
 abstraction.
 
 
-## PyTree’s
+## PyTrees
 
 JAX PyTrees are a powerful tool in the JAX library that enable users to work
 with complex data structures in a way that is efficient, flexible, and easy to
@@ -177,7 +177,7 @@ structure and handle each leaf individually. JAX PyTrees, therefore, are a
 powerful tool that can simplify many tasks in machine learning and scientific
 computing. As such, most JAX functions operate over _PyTrees of JAX arrays_.
 For instance, `jax.lax.scan`, accepts as input and produces as output a
-PyTrees of JAX arrays.
+PyTree of JAX arrays.
 
 Another key advantages of using JAX PyTrees is that they are designed to work
 efficiently with JAX's automatic differentiation and compilation features. For
@@ -231,7 +231,7 @@ For our RBF kernel we have two parameters; the lengthscale and the variance.
 Both of these have positive domains, and by default we want to train both of
 these parameters. To encode this we use a `param_field`, where we can define
 the domain of both parameters via a `Softplus` bijector (that restricts them
-to the positive domain), and define their trainble status to `True`.
+to the positive domain), and set their trainable status to `True`.
 
 ```python
 import tensorflow_probability.substrates.jax.bijectors as tfb