add quarto script to create qmd from docstring, issue #132

CONTRIBUTING.md

@@ -72,6 +72,8 @@ We appreciate your contributions and look forward to reviewing your pull request
 
 * Follow the Numpydoc docstring style for the documentation, see [link](https://numpydoc.readthedocs.io/en/latest/format.html)
 
+* To generate .qmd files for package documentation, see tutorial in [_quarto.yml](_quarto.yml)
+
 ## Makefile
 
 The `marginaleffects` repository includes a `Makefile` to facilitate some common tasks.

_quarto.yml

@@ -0,0 +1,55 @@
+####### README #######
+# This is the HOWTO and the config file to build .qmd files
+#
+# First install quartodoc with `uv pip install quartodoc`
+#
+# To create the qmd files, run the following command in the terminal:
+# > quartodoc build
+# the result will be in ./qmd_files or (else specified in the dir field)
+# modify the current file to change the included functions and the layout
+# finally, run the following command to preview the website if wanted:
+# > quarto preview
+# 
+# NOTE: This references the marginaleffects in .venv first, to reference the local marginaleffects, delete or rename the folder marginaleffects in .venv/lib/
+
+
+project:
+  type: website
+
+# tell quarto to read the generated sidebar
+# metadata-files:
+#   - _sidebar.yml
+
+format:
+  html:
+    theme: cosmo
+    css: styles.css
+    toc: false
+
+quartodoc:
+  # the name used to import the package you want to create reference docs for
+  package: marginaleffects
+  style: "pkgdown"
+  # write sidebar data to this file
+  # sidebar: _sidebar.yml
+  dir: qmd_files
+
+  sections:
+    - title: Marginaleffects API reference guide
+      desc: Marginaleffects functions.
+      contents:
+        # the functions being documented in the package.
+        # you can refer to anything: class methods, modules, etc..
+        - predictions
+        - comparisons
+        - slopes
+        - hypotheses
+        - datagrid
+        - plot_predictions
+        - plot_comparisons
+        - plot_slopes
+        - model_statsmodels
+        - model_sklearn
+        - name: utils
+          members: 
+            - get_dataset

marginaleffects/comparisons.py

@@ -364,6 +364,9 @@ def avg_comparisons(
     transform=None,
     eps=1e-4,
 ):
+    """
+    docstring (TO DO)
+    """
     if callable(newdata):
         newdata = newdata(model)
 

marginaleffects/model_sklearn.py

@@ -81,6 +81,9 @@ def get_predict(self, params, newdata: pl.DataFrame):
 def fit_sklearn(
     formula: str, data: pl.DataFrame, engine, kwargs_engine={}, kwargs_fit={}
 ):
+    """
+    fit_sklearn docstring (TO DO)
+    """
     d = listwise_deletion(formula, data=data)
     y, X = model_matrices(formula, d)
     # formulaic returns a matrix when the response is character or categorical

marginaleffects/model_statsmodels.py

@@ -109,6 +109,9 @@ def get_df(self):
 def fit_statsmodels(
     formula: str, data: pl.DataFrame, engine, kwargs_engine={}, kwargs_fit={}
 ):
+    """
+    fit docstring (TO DO)
+    """
     d = fml.listwise_deletion(formula, data=data)
     y, X = fml.model_matrices(formula, d)
     mod = engine(endog=y, exog=X, **kwargs_engine)

marginaleffects/predictions.py

@@ -192,6 +192,9 @@ def avg_predictions(
     transform=None,
     wts=None,
 ):
+    """
+    Predict average outcomes (TO DO)
+    """
     if callable(newdata):
         newdata = newdata(model)
 

marginaleffects/slopes.py

@@ -15,6 +15,9 @@ def slopes(
     eps=1e-4,
     eps_vcov=None,
 ):
+    """
+    slopes docstring (TO DO)
+    """
     if callable(newdata):
         newdata = newdata(model)
 
@@ -54,6 +57,9 @@ def avg_slopes(
     eps=1e-4,
     eps_vcov=None,
 ):
+    """
+    avg slopes docstring (TO DO)
+    """
     if callable(newdata):
         newdata = newdata(model)