fix links in R_double_ml_pipeline.ipynb

Author: SvenKlaassen

doc/examples/R_double_ml_pipeline.ipynb

@@ -197,7 +197,7 @@
    "source": [
     "To specify a learner for the nuisance part in a causal model, we can either use [mlr3](https://mlr3.mlr-org.com/)'s [LearnerRegr](https://mlr3.mlr-org.com/reference/LearnerRegr.html) for a model's nuisance part with a continuous dependent variable or a [LearnerClassif](https://mlr3.mlr-org.com/reference/LearnerClassif.html) if the corresponding outcome variable is binary. \n",
     "\n",
-    "Moreover, it's possible to create a learner based on a pipeline. For example, we could think of [ensemble learners](https://mlr3book.mlr-org.com/pipelines.html#pipe-model-ensembles) which combine several estimators."
+    "Moreover, it's possible to create a learner based on a pipeline. For example, we could think of [ensemble learners](https://mlr3book.mlr-org.com/pipelines.html#sec-pipelines-intro) which combine several estimators."
    ]
   },
   {
@@ -1094,7 +1094,7 @@
    "id": "e4ddb48a",
    "metadata": {},
    "source": [
-    "Alternatively, different learners could also be stacked. Here we simply repeat the example from the [Pipelines Chapter in the mlr3book](https://mlr3book.mlr-org.com/pipelines.html#pipe-model-ensembles-stacking) in our Bonus data example."
+    "Alternatively, different learners could also be stacked. Here we simply repeat the example from the [Pipelines Chapter in the mlr3book](https://mlr3book.mlr-org.com/pipelines.html#sec-pipelines-stack) in our Bonus data example."
    ]
   },
   {
@@ -1241,7 +1241,7 @@
    "source": [
     "## How to exploit more features of `mlr3pipelines` in `DoubleML`\n",
     "\n",
-    "[mlr3pipelines](https://mlr3pipelines.mlr-org.com/reference/Graph.html) can do much more. For example, we could use it to perform some [feature engineering](https://mlr3book.mlr-org.com/pipelines.html#pipe-modeling) and even perform pipeline-based [parameter tuning](https://mlr3book.mlr-org.com/pipelines.html#pipe-tuning). We just have to define the steps we want to have in our pipeline by using the PipeOps.\n",
+    "[mlr3pipelines](https://mlr3pipelines.mlr-org.com/reference/Graph.html) can do much more. For example, we could use it to perform some [feature engineering](https://mlr3book.mlr-org.com/pipelines.html#sec-pipelines-combined) and even perform pipeline-based [parameter tuning](https://mlr3book.mlr-org.com/pipelines.html#sec-pipelines-tuning). We just have to define the steps we want to have in our pipeline by using the PipeOps.\n",
     "\n",
     "Let's have a look at two more examples from the [Pipelines Chapter in the mlr3book](https://mlr3book.mlr-org.com/pipelines.html). In the first one, we will do some data manipulation. The second example illustrate how we could use [mlr3pipelines](https://mlr3pipelines.mlr-org.com/reference/Graph.html) for parameter tuning."
    ]