Doc/contributor update jgh

docs/contribute/modules/experimentalist.md

@@ -29,13 +29,15 @@ a `pandas` data frame, `numpy` array, iterator variable or other data format.
     We generally **recommend using pandas data frames as outputs** in which
 columns correspond to the independent variables of an experiment. 
 
-Once you've created your repository, you can implement your experimentalist by editing the `init.py` file in 
+Once you've created your repository, you can implement your experimentalist by editing the 
+`__init__.py` file in 
 ``src/autora/experimentalist/name_of_your_experimentalist/``. 
 You may also add additional files to this directory if needed. 
-It is important that the `init.py` file contains a function called `name_of_your_experimentalist` 
+It is important that the `__init__.py` file contains a function called 
+`name_of_your_experimentalist` 
 which returns a set of experimental conditions (e.g., as a numpy array).
 
-The following example ``init.py`` illustrates the implementation of a simple experimentalist
+The following example ``__init__.py`` illustrates the implementation of a simple experimentalist
 that uniformly samples without replacement from a pool of candidate conditions.
 
 ```python 

docs/contribute/modules/index.md

@@ -24,7 +24,8 @@ After setting up your repository and linking it to your GitHub account, you can
 
 ### Implement Your Code
 
-You may implement your code in the ``init.py`` located in the respective feature folder in ``src/autora``.
+You may implement your code in the ``__init__.py`` located in the respective feature folder in 
+``src/autora``.
 
 Please refer to the following guides on implementing
 * [theorists](theorist.md)

docs/contribute/modules/theorist.md

@@ -2,7 +2,8 @@
 
 AutoRA theorists are meant to return scientific models describing the relationship between experimental conditions
 and observations. Such models may take the form of a simple linear regression, non-linear equations, causal graphs, 
-a more complex neural network, or other models which 
+a more complex neural network, or other models which
+
 - can be identified based on data (and prior knowledge)
 - can be used to make novel predictions about observations given experimental conditions.
 
@@ -26,16 +27,19 @@ Make sure to select the `theorist` option when prompted. You can skip all other
 
 ## Implementation
 
-Once you've created your repository, you can implement your theorist by editing the `init.py` file in 
+Once you've created your repository, you can implement your theorist by editing the `__init__.py` 
+file in 
 ``src/autora/theorist/name_of_your_theorist/``. You may also add additional files to this directory if needed. 
-It is important that the `init.py` file contains a class called `NameOfYourTheorist` which inherits from  
+It is important that the `__init__.py` file contains a class called `NameOfYourTheorist` which 
+inherits from  
 `sklearn.base.BaseEstimator` and implements the following methods:
 
 - `fit(self, conditions, observations)`
 - `predict(self, conditions)`
 
 See the [sklearn documentation](https://scikit-learn.org/stable/developers/develop.html) for more information on 
-how to implement the methods. The following example ``init.py`` illustrates the implementation of a simple theorist
+how to implement the methods. The following example ``__init__.py`` illustrates the implementation 
+of a simple theorist
 that fits a polynomial function to the data:
 
 ```python