Update documentation and pre-commit changes

Installation.md

@@ -8,11 +8,15 @@ Choose the latest installer at the top of the page, click on "show all assets",
 mamba create -c conda-forge -n name_of_environment pymc nutpie arviz jupyter matplotlib openpyxl "python=3.10"
 ```
 3. Install PeakPerformance:
-- __Recommended__: Clone the PeakPerformance repository, then open the command line, navigate to your local clone, activate a Python environment, and install PeakPerformance via
+- __Recommended__: Clone the PeakPerformance repository, then open the command line, navigate to your local clone, activate the Python environment created in the previous step, and install PeakPerformance via
 ```
 pip install -e .
 ```
-- __Alternatively__: Download the latest Python wheel, then open the command line, navigate to the directory containing the wheel, activate the Python environment created above, and install PeakPerformance via
+- __Alternative a__: Activate the Python environment created in the previous step and install PeakPerformance via PyPI using
+```
+pip install peak-performance
+```
+- __Alternative b__: Download the latest Python wheel, then open the command line, navigate to the directory containing the wheel, activate the Python environment created above, and install PeakPerformance via
 ```
 pip install name_of_wheel.whl
 ```

README.md

@@ -18,7 +18,7 @@ np.save(Path(r"example_path/time_series.npy"), time_series)
 The naming convention of raw data files is `<acquisition name>_<precursor ion m/z or experiment number>_<product ion m/z start>_<product ion m/z end>.npy`. There should be no underscores within the named sections such as `acquisition name`. Essentially, the raw data names include the acquisition and mass trace, thus yielding a recognizable and unique name for each isotopomer/fragment/metabolite/sample.
 
 ## Model selection
-When it comes to selecting models, PeakPerformance has a function performing an automated selection process by analyzing one acquisiton per mass trace with all implemented models. Subsequently, all models are ranked based on an information criterion (either pareto-smoothed importance sampling leave-one-out cross-validation or widely applicable information criterion). For this process to work as intended, you need to specify acquisitions with representative peaks for each mass trace (see example notebook 1). If e.g. most peaks of an analyte show a skewed shape, then select an acquisition where this is the case. For double peaks, select an acquision where the peaks are as distinct and comparable in height as possible.  
+When it comes to selecting models, PeakPerformance has a function performing an automated selection process by analyzing one acquisiton per mass trace with all implemented models. Subsequently, all models are ranked based on an information criterion (either pareto-smoothed importance sampling leave-one-out cross-validation or widely applicable information criterion). For this process to work as intended, you need to specify acquisitions with representative peaks for each mass trace (see example notebook 1). If e.g. most peaks of an analyte show a skewed shape, then select an acquisition where this is the case. For double peaks, select an acquision where the peaks are as distinct and comparable in height as possible.
 Since model selection is a computationally demanding and time consuming process, it is suggested to state the model type as the user (see example notebook 1) if possible.
 
 ## Troubleshooting