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+---
+title: PeakPerformance documentation
+---
+
+# Welcome to the PeakPerformance documentation!
+
+[![](https://img.shields.io/pypi/v/peak-performance)](https://pypi.org/project/peak-performance)
+[![](https://img.shields.io/badge/code%20on-Github-lightgrey)](https://github.com/JuBiotech/peak-performance)
+[![](https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
+
+
+``peak_performance`` is a Python toolbox for Bayesian inference of peak areas.
+
+It defines PyMC models describing the intensity curves of chromatographic peaks.
+
+Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
+
+# Installation
+
+```bash
+pip install peak-performance
+```
+
+You can also download the latest version from [GitHub](https://github.com/JuBiotech/peak-performance).
+
+
+The documentation features various notebooks that demonstrate the usage.
+
+```{toctree}
+:caption: Tutorials
+:maxdepth: 1
+
+markdown/Installation
+markdown/Preparing_raw_data
+markdown/Peak_model_composition
+markdown/PeakPerformance_validation
+markdown/PeakPerformance_workflow
+markdown/Diagnostic_plots
+markdown/How_to_adapt_PeakPerformance_to_your_data
+```
+
+
+```{toctree}
+:caption: Examples
+:maxdepth: 1
+
+notebooks/Ex1_Simple_Pipeline.ipynb
+notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb
+notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb
+```
+
+
+In the following case studies we investigate certain aspects of peak modeling.
+
+```{toctree}
+:caption: Case Studies
+:maxdepth: 1
+
+notebooks/Investigation_doublepeak_separation.ipynb
+notebooks/Investigation_noise_sigma.ipynb
+```
+
+
+Below you can find documentation that was automatically generated from docstrings.
+
+```{toctree}
+:caption: API Reference
+:maxdepth: 1
+
+pp_models
+pp_pipeline
+pp_plots
+```
diff --git a/docs/source/index.rst b/docs/source/index.rst
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-Welcome to the PeakPerformance documentation!
-=============================================
-
-.. image:: https://img.shields.io/pypi/v/peak-performance
-   :target: https://pypi.org/project/peak-performance
-
-.. image:: https://img.shields.io/badge/code%20on-Github-lightgrey
-   :target: https://github.com/JuBiotech/peak-performance
-
-.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg
-   :target: https://zenodo.org/doi/10.5281/zenodo.10255543
-
-
-``peak_performance`` is a Python toolbox for Bayesian inference of peak areas.
-
-It defines PyMC models describing the intensity curves of chromatographic peaks.
-
-Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
-
-Installation
-============
-
-.. code-block:: bash
-
-   pip install peak-performance
-
-You can also download the latest version from `GitHub <https://github.com/JuBiotech/peak-performance>`_.
-
-Tutorials
-=========
-
-The documentation features various notebooks that demonstrate the usage and investigate certain aspects of peak modeling.
-
-.. toctree::
-   :maxdepth: 1
-
-   markdown/Installation
-   markdown/Preparing_raw_data
-   markdown/Peak_model_composition
-   markdown/PeakPerformance_validation
-   markdown/PeakPerformance_workflow
-   markdown/Diagnostic_plots
-   markdown/How_to_adapt_PeakPerformance_to_your_data
-   notebooks/Ex1_Simple_Pipeline.ipynb
-   notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb
-   notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb
-   notebooks/Investigation_doublepeak_separation.ipynb
-   notebooks/Investigation_noise_sigma.ipynb
-   notebooks/Processing_test_1_raw_data.ipynb
-   notebooks/Create_validation_plot_from_raw_data.ipynb
-
-
-API Reference
-=============
-
-.. toctree::
-   :maxdepth: 2
-
-   pp_models
-   pp_pipeline
-   pp_plots