Update main according to latest changes on dev

doc/conf.py

@@ -37,6 +37,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
     'sphinx.ext.coverage',
+    'sphinx.ext.doctest',
     'sphinx.ext.graphviz',
     'sphinx.ext.intersphinx',
     'sphinx.ext.mathjax',
@@ -109,7 +110,13 @@
         {
             "name": "PyPI",
             "url": "https://pypi.org/project/DoubleML/",
-            "icon": "fa-solid fa-cube",
+            "icon": "fa-brands fa-python",
+            "type": "fontawesome",
+        },
+        {
+            "name": "Discussions",
+            "url": "https://github.com/DoubleML/doubleml-for-py/discussions",
+            "icon": "fa-solid fa-comments",
             "type": "fontawesome",
         },
     ],
@@ -118,7 +125,7 @@
         "version_match": version,
     },
     "show_version_warning_banner": True,
-    "check_switcher": False,
+    "check_switcher": True,
     "announcement": "Interested to learn more? We offer <a href='https://trainings.doubleml.org/'>DoubleML Trainings!</a>",
 }
 
@@ -161,6 +168,7 @@
     'examples/py_double_ml_basic_iv': '_static/basic_iv_example_nb.png',
     'examples/R_double_ml_basic_iv': '_static/basic_iv_example_nb.png',
     'examples/py_double_ml_ssm': '_static/ssm_example_nb.svg',
+    'examples/R_double_ml_ssm': '_static/ssm_example_nb.svg',
     'examples/py_double_ml_sensitivity_booking': '_static/dag_usecase_revised.png',
 }
 

doc/examples/index.rst

@@ -21,6 +21,7 @@ General Examples
     py_double_ml_pension.ipynb
     py_double_ml_sensitivity.ipynb
     py_double_ml_apo.ipynb
+    py_double_ml_irm_vs_apo.ipynb
     py_double_ml_learner.ipynb
     py_double_ml_firststage.ipynb
     py_double_ml_multiway_cluster.ipynb
@@ -62,6 +63,7 @@ These are case studies with the R package :ref:`DoubleML <doubleml_package>`.
     R_double_ml_pension.ipynb
     R_double_ml_did.ipynb
     R_double_ml_multiway_cluster.ipynb
+    R_double_ml_ssm.ipynb
     R_double_ml_basic_iv.ipynb
 
 Sandbox

doc/examples/py_double_ml_cate.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)."
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)."
    ]
   },
   {
@@ -409,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.12.3"
   },
   "vscode": {
    "interpreter": {

doc/examples/py_double_ml_cate_plr.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators)."
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)."
    ]
   },
   {

doc/examples/py_double_ml_gate.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
     "\n",
     "$$\n",
     "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n",

doc/examples/py_double_ml_gate_plr.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "We define a data generating process to create synthetic data to compare the estimates to the true effect. The data generating process is based on the Monte Carlo simulation from [Oprescu et al. (2019)](http://proceedings.mlr.press/v97/oprescu19a.html).\n",
     "\n",
-    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/dev/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
+    "The documentation of the data generating process can be found [here](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators). In this example the true effect depends only the first covariate $X_0$ and takes the following form\n",
     "\n",
     "$$\n",
     "\\theta_0(X) = \\exp(2X_0) + 3\\sin(4X_0).\n",