fixes #235

Author: christophM

manuscript/feature-importance.qmd

@@ -310,5 +310,5 @@ A good overview of various importance techniques is provided in the paper by Wei
 ## Code
 
 The `iml` R package was used for the examples.
-The R packages `DALEX` and `vip`, as well as the Python libraries `alibi`, `scikit-learn`, and `rfpimp`, also implement model-agnostic permutation feature importance.
+The R packages `DALEX` and `vip`, as well as the Python libraries `alibi`, `eli5`, `scikit-learn`, and `rfpimp`, also implement model-agnostic permutation feature importance.
 

manuscript/lime.qmd

@@ -656,6 +656,6 @@ But the method is still in development phase and many problems need to be solved
 ## Software
 
 The original Python implementation is in the [lime package](https://github.com/marcotcr/lime).
-Another Python implementation is in [PiML](https://selfexplainml.github.io/PiML-Toolbox/_build/html/index.html).
+Other Python implementations can be found in [PiML](https://selfexplainml.github.io/PiML-Toolbox/_build/html/index.html) and [eli5](https://eli5.readthedocs.io/en/latest/blackbox/lime.html).
 
 In R, you can use [iml](https://github.com/giuseppec/iml) or [DALEX](https://dalex.drwhy.ai/).