Merge pull request #16 from fraunhoferportugal/dev

Minor formatting erros in mkdocs documentation

docs/index.md

@@ -7,7 +7,7 @@ real and synthetic datasets across image, tabular, and time-series data modaliti
 developed to address gaps in existing evaluation frameworks that either lack metrics for
 specific data modalities or do not include certain state-of-the-art metrics. The library is designed to be modular, allowing users to easily extend it with new metrics.
 
-The source code is available on [GitHub](https://github.com/fraunhoferportugal/pymdma/tree/main) and the documentation can be found [here](dummy).
+The source code is available on [GitHub](https://github.com/fraunhoferportugal/pymdma/tree/main).
 
 ## Metric Categories
 Each metric class is organized based on the modality, validation type, metric group and goal. Following is a brief description of these categories:

docs/installation.md

@@ -6,12 +6,6 @@ PyPI is currently unavailable. To install the package, you can install it direct
 $ pip install "pymdma @ git+https://github.com/fraunhoferportugal/pymdma.git"
 ```
 
-<!-- It is recommended to install the package in a virtual environment. To install the package, run the following command:
-
-```bash
-$ pip install pymdma
-``` -->
-
 Depending on the data modality you are working with, you may need to install additional dependencies. We have three groups of denpendencies: `image`, `tabular` and `time_series`. As an example, to work with image data, you will need to run the following command:
 
 ```bash