diff --git a/INSTALLATION.md b/INSTALLATION.md
index 6352fc3c..bb75569c 100644
--- a/INSTALLATION.md
+++ b/INSTALLATION.md
@@ -25,7 +25,7 @@ on how to do that. The newly created environment must be activated.
 
 The following command lines should do it
 ```
-conda create -n pylife-env python=3.11 --yes
+conda create -n pylife-env python=3.12 --yes
 conda activate pylife-env
 ```
 
@@ -41,7 +41,7 @@ That's not recommended. If you really want to do that, you probably know how to
 do it.
 
 
-### pip install
+### Install the pyLife package
 
 The simplest way to install pyLife is just using the pip package
 ```
@@ -75,7 +75,7 @@ Create an environment – usually a good idea to use a prefixed environment in
 your pyLife working directory and activate it.
 
 ```
-conda create -p .venv python=3.11 pip --yes
+conda create -p .venv python=3.12 pip --yes
 conda activate ./.venv
 ```
 
@@ -94,7 +94,7 @@ pytest
 
 If it creates an output ending like below, the installation was successful.
 ```
-================ 228 passed, 1 deselected, 13 warnings in 30.45s ===============
+================ 1171 passed, 7 skipped, 6 deselected, 3 warnings in 23.06s ===============
 ```
 
 There might be some `DeprecationWarning`s. Ignore them for now.
diff --git a/demos/load_collective.ipynb b/demos/load_collective.ipynb
index 8ece86da..963f061c 100644
--- a/demos/load_collective.ipynb
+++ b/demos/load_collective.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Load Collectives and Load Histograms\n",
     "\n",
-    "From the load (stress) side pyLife provides the classes `LoadCollective` and `LoadHistogram` to deal with load collectives.  `LoadCollective` contains individal hysteresis loops whereas `LoadHistogram` contains a 2D-histogram of classes of hysteresis loops and the number of cycles with which they occur."
+    "From the load (stress) side pyLife provides the classes [`LoadCollective`](https://pylife.readthedocs.io/en/stable/stress/load_collective.html) and [`LoadHistogram`](https://pylife.readthedocs.io/en/stable/stress/load_histogram.html) to deal with load collectives.  `LoadCollective` contains individal hysteresis loops whereas `LoadHistogram` contains a 2D-histogram of classes of hysteresis loops and the number of cycles with which they occur."
    ]
   },
   {
@@ -93,7 +93,7 @@
    "id": "31b400a3",
    "metadata": {},
    "source": [
-    "As you can see, the rainflow analysis found five histresis loops, three from 1.0 to -1.0 and two from -2.0 to 2.0. Alternatively you can ask the recorder for a load histogram:"
+    "As you can see, the rainflow analysis found five hystresis loops, three from 1.0 to -1.0 and two from -2.0 to 2.0. Alternatively you can ask the recorder for a load histogram:"
    ]
   },
   {
@@ -293,12 +293,36 @@
     "plt.pcolormesh(X, Y, numpy_hist)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf99a42b-3075-42e5-87c3-2d99ab55a681",
+   "metadata": {},
+   "source": [
+    "In order to use this load histogram for a damage calculation we can perform a mean stress transformation to transorm all the hysteresis loops to one given R-value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0d06dfd-2dd9-4689-b230-67c102220e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meanstress_sensitivity = pd.Series({\n",
+    "    'M': 0.3,\n",
+    "    'M2': 0.2\n",
+    "})\n",
+    "\n",
+    "transformed_histogram = histogram.meanstress_transform.fkm_goodman(meanstress_sensitivity, R_goal=-1)\n",
+    "transformed_histogram.to_pandas()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0f505f1a",
    "metadata": {},
    "source": [
-    "We can also plot the cumulated version of the histogram. Therefor we put the amplitude and the cycles into a dataframe."
+    "We can also plot the cumulated version of the histogram. Therefore we put the amplitude and the cycles into a dataframe."
    ]
   },
   {
@@ -309,8 +333,8 @@
    "outputs": [],
    "source": [
     "df = pd.DataFrame({\n",
-    "    'cycles': histogram.load_collective.cycles, \n",
-    "    'amplitude': histogram.load_collective.amplitude, \n",
+    "    'cycles': transformed_histogram.cycles, \n",
+    "    'amplitude': transformed_histogram.amplitude, \n",
     "}).sort_values('amplitude', ascending=False)"
    ]
   },
@@ -332,6 +356,14 @@
     "plt.plot(np.cumsum(df.cycles), df.amplitude)\n",
     "plt.loglog()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d3e056d-7579-4fca-bc40-5687f3779eb1",
+   "metadata": {},
+   "source": [
+    " "
+   ]
   }
  ],
  "metadata": {
@@ -349,7 +381,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,