add citation for the local explanation paper

Author: Aggrathon

CITATIONS.bib

@@ -1,3 +1,14 @@
+@article{bjorklund2023explaining,
+  title   = {Explaining any black box model using real data},
+  author  = {Bj{\"o}rklund, Anton and Henelius, Andreas and Oikarinen, Emilia and Kallonen, Kimmo and Puolam{\"a}ki, Kai},
+  journal = {Frontiers in Computer Science},
+  volume  = {5},
+  year    = {2023},
+  url     = {https://www.frontiersin.org/articles/10.3389/fcomp.2023.1143904},
+  doi     = {10.3389/fcomp.2023.1143904},
+  issn    = {2624-9898}
+}
+
 @article{bjorklund2022robust,
   title   = {Robust regression via error tolerance},
   author  = {Bj{\"o}rklund, Anton and Henelius, Andreas and Oikarinen, Emilia and Kallonen, Kimmo and Puolam{\"a}ki, Kai},

README.md

@@ -8,7 +8,7 @@
 # SLISE - Sparse Linear Subset Explanations
 
 Python implementation of the SLISE algorithm. The SLISE algorithm can be used for both robust regression and to explain outcomes from black box models.
-For more details see [the original paper](https://rdcu.be/bVbda) or [the robust regression paper](https://rdcu.be/cFRHD).
+For more details see the [conference paper](https://rdcu.be/bVbda), the [robust regression paper](https://rdcu.be/cFRHD), or the [local explanation paper](https://doi.org/10.3389/fcomp.2023.1143904).
 Alternatively for a more informal overview see [the presentation](https://github.com/edahelsinki/slise/raw/master/vignettes/presentation.pdf), or [the poster](https://github.com/edahelsinki/slise/raw/master/vignettes/poster.pdf).
 Finally, for learning to use the python package there are several [examples](https://github.com/edahelsinki/pyslise/tree/master/examples/) and [the documentation](https://edahelsinki.github.io/pyslise/docs/slise/).
 
@@ -23,6 +23,11 @@ Finally, for learning to use the python package there are several [examples](htt
 > Data Mining and Knowledge Discovery.  
 > https://doi.org/10.1007/s10618-022-00819-2  
 
+> *Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.* (2023)  
+> **Explaining any black box model using real data.**  
+> Frontiers in Computer Science 5:1143904.  
+> https://doi.org/10.3389/fcomp.2023.1143904  
+
 ## The idea
 
 In robust regression we fit regression models that can handle data that contains outliers (see the example below for why outliers are problematic for normal regression). SLISE accomplishes this by fitting a model such that the largest possible subset of the data items have an error less than a given value. All items with an error larger than that are considered potential outliers and do not affect the resulting model.

mkdocs.yml

@@ -15,7 +15,7 @@ theme:
       primary: deep purple
       accent: deep orange
       toggle:
-        icon: material/lightbulb 
+        icon: material/lightbulb
         name: Switch to dark mode
     - media: "(prefers-color-scheme: dark)"
       scheme: slate
@@ -30,29 +30,30 @@ theme:
     - navigation.indexes
 
 nav:
-  - Home: 'index.md'
+  - Home: "index.md"
   - Documentation:
-    - slise: docs/slise.md
-    - slise.slise: docs/slise.slise.md
-    - slise.data: docs/slise.data.md
-    - slise.initialisation: docs/slise.initialisation.md
-    - slise.optimisation: docs/slise.optimisation.md
-    - slise.plot: docs/slise.plot.md
-    - slise.utils: docs/slise.utils.md
+      - slise: docs/slise.md
+      - slise.slise: docs/slise.slise.md
+      - slise.data: docs/slise.data.md
+      - slise.initialisation: docs/slise.initialisation.md
+      - slise.optimisation: docs/slise.optimisation.md
+      - slise.plot: docs/slise.plot.md
+      - slise.utils: docs/slise.utils.md
   - Links:
-    - GitHub: https://github.com/edahelsinki/pyslise
-    - PyPI: https://pypi.org/project/slise/
-    - Original paper: https://rdcu.be/bVbda
-    - Robust regression paper: https://rdcu.be/cFRHD
-    - Examples: https://github.com/edahelsinki/pyslise/tree/master/examples
+      - GitHub: https://github.com/edahelsinki/pyslise
+      - PyPI: https://pypi.org/project/slise/
+      - Original conference paper: https://rdcu.be/bVbda
+      - Robust regression paper: https://rdcu.be/cFRHD
+      - Local Explantions paper: https://doi.org/10.3389/fcomp.2023.1143904
+      - Examples: https://github.com/edahelsinki/pyslise/tree/master/examples
 
 plugins:
-  # - offline 
+  # - offline
   - search
   - mkdocstrings:
       handlers:
         python:
           options:
-              members_order: source
-              show_root_heading: True
-  - include-markdown
+            members_order: source
+            show_root_heading: True
+  - include-markdown