Update Documentation to 0.9.1

.github/workflows/deploy_docu_dev.yml

@@ -9,7 +9,7 @@ on:
 jobs:
   build:
 
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
@@ -42,7 +42,7 @@ jobs:
       run: |
         sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
         sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
-        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
+        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/'
     - name: Install R
       run: |
         sudo apt-get update

.github/workflows/deploy_docu_stable.yml

@@ -9,7 +9,7 @@ on:
 jobs:
   build:
 
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
 
     steps:
     - name: Check out the repo containing the docu source
@@ -31,7 +31,7 @@ jobs:
       run: |
         sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
         sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
-        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
+        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/'
     - name: Install R
       run: |
         sudo apt-get update

.github/workflows/test_build_docu_dev.yml

@@ -27,7 +27,7 @@ on:
 jobs:
   build:
 
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
@@ -69,7 +69,7 @@ jobs:
       run: |
         sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
         sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
-        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
+        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/'
     - name: Install R
       run: |
         sudo apt-get update

.github/workflows/test_build_docu_released.yml

@@ -18,7 +18,7 @@ on:
 jobs:
   build:
 
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
 
     steps:
     - name: Check out the repo containing the docu source
@@ -40,7 +40,7 @@ jobs:
       run: |
         sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
         sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
-        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
+        sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/'
     - name: Install R
       run: |
         sudo apt-get update

doc/api/api.rst

@@ -38,6 +38,16 @@ Double machine learning models
     DoubleMLCVAR
     DoubleMLQTE
 
+Other models
+------------------------------
+.. currentmodule:: doubleml
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    rdd.RDFlex
+
 Datasets module
 ---------------
 
@@ -73,6 +83,7 @@ Dataset generators
    datasets.make_confounded_irm_data
    datasets.make_heterogeneous_data
    datasets.make_irm_data_discrete_treatments
+   rdd.datasets.make_simple_rdd_data
 
 Utility classes and functions
 -----------------------------

doc/conf.py

@@ -197,6 +197,12 @@
     'https://glmnet.stanford.edu/reference/cv.glmnet.html',
     # Valid URL (error not replicable), Causes 409 Client Error: Too Many Requests for url
     'http://dx.doi.org/10.2139/ssrn.3619201',
+    # Valid URL, Causes ConnectTimeoutError
+    'https://folia.unifr.ch/global/documents/306524',
+    # Valid DOI; Causes 403 Client Error: Forbidden for url:...
+    'https://doi.org/10.1146/annurev-economics-051520-021409',
+    # Valdi DOI; Causes 504 Server Error: Gateway Time-out for ...
+    'https://doi.org/10.1017/CBO9781139060035.008'
 ]
 
 # To execute R code via jupyter-execute one needs to install the R kernel for jupyter

doc/examples/index.rst

@@ -30,6 +30,8 @@ General Examples
     py_double_ml_did_pretest.ipynb
     py_double_ml_basic_iv.ipynb
     py_double_ml_plm_irm_hetfx.ipynb
+    py_double_ml_meets_flaml.ipynb
+    py_double_ml_rdflex.ipynb
 
 
 Effect Heterogeneity

doc/examples/py_double_ml_apo.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Python: Average Potential Outcome (APO) Models\n",
     "\n",
-    "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate average potential outcomes (APOs) in an interactive regression model (see [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm)).\n",
+    "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate average potential outcomes (APOs) in an interactive regression model (see [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#binary-interactive-regression-model-irm)).\n",
     "\n",
     "The goal is to estimate the average potential outcome\n",
     "\n",
@@ -172,8 +172,8 @@
    "source": [
     "## Single Average Potential Outcome Models (APO)\n",
     "\n",
-    "Further, we have to specify machine learning algorithms. As in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) model, we have to set ``ml_m`` as a classifier and ``ml_g`` as a regressor (since the outcome is continuous). As in the \n",
-    "[DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) model, the classifier ``ml_m`` is used to estimate the conditional probability of receiving treatment level $d$ given the covariates $X$\n",
+    "Further, we have to specify machine learning algorithms. As in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) model, we have to set ``ml_m`` as a classifier and ``ml_g`` as a regressor (since the outcome is continuous). As in the \n",
+    "[DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) model, the classifier ``ml_m`` is used to estimate the conditional probability of receiving treatment level $d$ given the covariates $X$\n",
     "\n",
     "$$m_{0,d}(X) = \\mathbb{E}[1\\{D=d\\}|X]$$\n",
     "\n",

doc/examples/py_double_ml_cate.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "# Python: Conditional Average Treatment Effects (CATEs) for IRM models\n",
     "\n",
-    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate conditional average treatment effects with B-splines for one or two-dimensional effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) model."
+    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate conditional average treatment effects with B-splines for one or two-dimensional effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) model."
    ]
   },
   {

doc/examples/py_double_ml_gate.ipynb

@@ -9,7 +9,7 @@
    "source": [
     "# Python: Group Average Treatment Effects (GATEs) for IRM models\n",
     "\n",
-    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate group average treatment effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) model."
+    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate group average treatment effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) model."
    ]
   },
   {
@@ -167,7 +167,7 @@
    "metadata": {},
    "source": [
     "## Interactive Regression Model (IRM)\n",
-    "The first step is to fit a [DoubleML IRM Model](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) to the data."
+    "The first step is to fit a [DoubleML IRM Model](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) to the data."
    ]
   },
   {

doc/examples/py_double_ml_gate_sensitivity.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Python: GATE Sensitivity Analysis\n",
     "\n",
-    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to perfrom a sensitivity analysis for group average treatment effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-model-irm) model.\n"
+    "In this simple example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to perfrom a sensitivity analysis for group average treatment effects in the [DoubleMLIRM](https://docs.doubleml.org/stable/guide/models.html#interactive-regression-models-irm) model.\n"
    ]
   },
   {