Rerender README.Rmd

Author: jemus42

README.md

@@ -12,19 +12,44 @@ badge](https://plantedml.r-universe.dev/glex/badges/version)](https://plantedml.
 status](https://www.r-pkg.org/badges/version/glex)](https://CRAN.R-project.org/package=glex)
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-Global explanations for tree-based models by decomposing regression or  
-classification functions into the sum of main components and interaction
-components of arbitrary order. Calculates exact interventional SHAP
-values and q-interaction interventional SHAP (q being the highest order
-of interaction present in the model) for tree-based models such as
-XGBoost.
+# Overview
+
+`glex` implements **global, functional decompositions** of tree-based
+regression and classification models that decomposes them into **main
+effects** and **interaction effects** of arbitrary order. In particular,
+it can
+
+- compute **exact interventional SHAP values** (and more generally,
+  *q-interaction interventional SHAP*, where *q* is the maximal
+  interaction order present in the model),
+- extract **partial-dependence-functions** (e.g., one dimensional
+  partial dependence plots),
+- produce **variable‐importance scores** for each main term and
+  interaction term (of any order), and
+- supports **de-biasing** by removing components that include protected
+  features.
+
+Under the hood, `glex` relies on fast algorithms to compute all required
+partial dependence functions exactly.
+
+For a detailed description of the methodology, see:
 
 See the accompanying paper for more details and exact definitions:
 “Unifying local and global model explanations by functional
 decomposition of low dimensional structures”
 ([arxiv](https://arxiv.org/abs/2208.06151),
 [PMLR](https://proceedings.mlr.press/v206/hiabu23a.html)).
 
+> **Hiabu, Meyer & Wright (2023).**  
+> *Unifying local and global model explanations by functional
+> decomposition of low dimensional structures.*  
+> [arXiv](https://arxiv.org/abs/2208.06151) • [AISTATS 2023
+> Proceedings](https://proceedings.mlr.press/v206/hiabu23a.html)
+
+> **Liu, Steensgaard, Wright, Pfister, Hiabu (2023).**  
+> *Fast Estimation of Partial Dependence Functions using Trees.*  
+> [arXiv](https://arxiv.org/abs/2410.13448)
+
 ## Installation
 
 You can install the development version of glex from
@@ -41,6 +66,17 @@ or from [r-universe](https://plantedml.r-universe.dev/packages) with
 install.packages("glex", repos = "https://plantedml.r-universe.dev")
 ```
 
+# Supported Models
+
+Currently, `glex` supports:
+
+- **XGBoost** (via the `xgboost` package).
+
+- **Random Planted Forest** (via the `randomPlantedForest` package).
+
+More tree‐based frameworks may be added in future releases. If you have
+a suggestion, please open an issue on our GitHub repository.
+
 ## What’s Included
 
 Currently `glex` works with