More pkgdown (#438)

.github/workflows/pkgdown.yaml

@@ -41,11 +41,9 @@ jobs:
           extra-packages: any::pkgdown, local::.
           needs: website
 
-      - name: Render python README to assets folder
+      - name: Copy shaprpy readme to folder for automatic deployment
         run: |
-          rmarkdown::render(input = "python/README.md",
-                  output_format = "html_document",
-                  output_file = "../pkgdown/assets/README_py.html")
+          file.copy("python/README.md", ".github/shaprpy.md")
         shell: Rscript {0}
 
       - name: Build site

README.Rmd

@@ -15,13 +15,13 @@ knitr::opts_chunk$set(
 )
 ```
 
-# shapr <img src="man/figures/NR-logo_utvidet_r32g60b136_small.png" align="right" height="50px"/>
+# shapr <img src="man/figures/nrlogo_new.jpg" align="right" height="139"/>
 
 <!-- badges: start -->
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/shapr)](https://cran.r-project.org/package=shapr)
 [![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/grand-total/shapr)](https://cran.r-project.org/package=shapr)
 [![R build status](https://github.com/NorskRegnesentral/shapr/workflows/R-CMD-check/badge.svg)](https://github.com/NorskRegnesentral/shapr/actions?query=workflow%3AR-CMD-check)
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.02027/status.svg)](https://doi.org/10.21105/joss.02027)
 
@@ -48,7 +48,7 @@ and regression-based methods
 See the [NEWS](https://github.com/NorskRegnesentral/shapr/blob/master/NEWS.md) for a complete list.
 
 ### Coming from shapr < 1.0.0?
-`shapr` version > 1.0.0 comes with a number of breaking changes. 
+`shapr` version >= 1.0.0 comes with a number of breaking changes. 
 Most notably, we moved from using two function (`shapr()` and `explain()`) to 
 a single function (`explain()`). 
 In addition, custom models are now explained by passing the prediction function directly to `explain()`, 
@@ -62,7 +62,7 @@ README with old syntax (v0.2.2).
 We provide an (experimental) Python wrapper (`shaprpy`) which allows explaining Python models with the methodology 
 implemented in `shapr`, directly from Python. 
 The wrapper calls `R` internally, and therefore requires an installation of `R`. 
-See [here](https://github.com/NorskRegnesentral/shapr/tree/master/python) for installation instructions and examples.
+See [here](https://github.com/NorskRegnesentral/shapr/shaprpy.html) for installation instructions and examples.
 
 
 ## The package
@@ -175,8 +175,10 @@ plot(explanation)
 ```
 
 See the [general usage vignette](https://norskregnesentral.github.io/shapr/articles/general_usage.html) for further 
-basic usage examples.
-
+basic usage examples and brief introductions to the methodology. 
+For more thorough information about the underlying methodology, see 
+@aas2019explaining, @redelmeier2020explaining, @jullum2021efficient, @olsen2022using, @olsen2024comparative .
+See also @sellereite2019shapr for a brief paper about the previous (< 1.0.0) version of the package.
 
 ## Contribution
 

README.md

@@ -1,7 +1,7 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# shapr <img src="man/figures/NR-logo_utvidet_r32g60b136_small.png" align="right" height="50px"/>
+# shapr <img src="man/figures/nrlogo_new.jpg" align="right" height="139"/>
 
 <!-- badges: start -->
 
@@ -10,16 +10,15 @@
 [![R build
 status](https://github.com/NorskRegnesentral/shapr/workflows/R-CMD-check/badge.svg)](https://github.com/NorskRegnesentral/shapr/actions?query=workflow%3AR-CMD-check)
 [![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![License:
 MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.02027/status.svg)](https://doi.org/10.21105/joss.02027)
 
 See the pkgdown site at
 [norskregnesentral.github.io/shapr/](https://norskregnesentral.github.io/shapr/)
 for a complete introduction with examples and documentation of the
-package. 
-<!-- badges: end -->
+package. <!-- badges: end -->
 
 ## NEWS
 
@@ -46,7 +45,7 @@ for a complete list.
 
 ### Coming from shapr \< 1.0.0?
 
-`shapr` version \> 1.0.0 comes with a number of breaking changes. Most
+`shapr` version \>= 1.0.0 comes with a number of breaking changes. Most
 notably, we moved from using two function (`shapr()` and `explain()`) to
 a single function (`explain()`). In addition, custom models are now
 explained by passing the prediction function directly to `explain()`,
@@ -63,8 +62,8 @@ We provide an (experimental) Python wrapper (`shaprpy`) which allows
 explaining Python models with the methodology implemented in `shapr`,
 directly from Python. The wrapper calls `R` internally, and therefore
 requires an installation of `R`. See
-[here](https://github.com/NorskRegnesentral/shapr/tree/master/python)
-for installation instructions and examples.
+[here](https://github.com/NorskRegnesentral/shapr/shaprpy.html) for
+installation instructions and examples.
 
 ## The package
 
@@ -183,14 +182,14 @@ explanation <- explain(
 #> max_n_coalitions is NULL or larger than or 2^n_features = 16, 
 #> and is therefore set to 2^n_features = 16.
 #> 
-#> ── Starting `shapr::explain()` at 2025-01-21 13:30:06 ──────────────────────────
+#> ── Starting `shapr::explain()` at 2025-01-22 10:22:36 ──────────────────────────
 #> • Model class: <xgb.Booster>
 #> • Approach: empirical
 #> • Iterative estimation: FALSE
 #> • Number of feature-wise Shapley values: 4
 #> • Number of observations to explain: 6
 #> • Computations (temporary) saved at:
-#> '/tmp/Rtmpf5zleu/shapr_obj_3676de5b39f33b.rds'
+#> '/tmp/RtmpAnmtGl/shapr_obj_371a1a52bf4cce.rds'
 #> 
 #> ── Main computation started ──
 #> 
@@ -216,7 +215,12 @@ plot(explanation)
 
 See the [general usage
 vignette](https://norskregnesentral.github.io/shapr/articles/general_usage.html)
-for further basic usage examples.
+for further basic usage examples and brief introductions to the
+methodology. For more thorough information about the underlying
+methodology, see Aas, Jullum, and Løland (2021), Redelmeier, Jullum, and
+Aas (2020), Jullum, Redelmeier, and Aas (2021), Olsen et al. (2022),
+Olsen et al. (2024) . See also Sellereite and Jullum (2019) for a brief
+paper about the previous (\< 1.0.0) version of the package.
 
 ## Contribution
 
@@ -232,3 +236,59 @@ Conduct](https://norskregnesentral.github.io/shapr/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
 
 ## References
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+
+<div id="ref-aas2019explaining" class="csl-entry">
+
+Aas, Kjersti, Martin Jullum, and Anders Løland. 2021. “Explaining
+Individual Predictions When Features Are Dependent: More Accurate
+Approximations to Shapley Values.” *Artificial Intelligence* 298.
+
+</div>
+
+<div id="ref-jullum2021efficient" class="csl-entry">
+
+Jullum, Martin, Annabelle Redelmeier, and Kjersti Aas. 2021. “Efficient
+and Simple Prediction Explanations with groupShapley: A Practical
+Perspective.” In *Proceedings of the 2nd Italian Workshop on Explainable
+Artificial Intelligence*, 28–43. CEUR Workshop Proceedings.
+
+</div>
+
+<div id="ref-olsen2022using" class="csl-entry">
+
+Olsen, Lars Henry Berge, Ingrid Kristine Glad, Martin Jullum, and
+Kjersti Aas. 2022. “Using Shapley Values and Variational Autoencoders to
+Explain Predictive Models with Dependent Mixed Features.” *Journal of
+Machine Learning Research* 23 (213): 1–51.
+
+</div>
+
+<div id="ref-olsen2024comparative" class="csl-entry">
+
+———. 2024. “A Comparative Study of Methods for Estimating Model-Agnostic
+Shapley Value Explanations.” *Data Mining and Knowledge Discovery*,
+1–48.
+
+</div>
+
+<div id="ref-redelmeier2020explaining" class="csl-entry">
+
+Redelmeier, Annabelle, Martin Jullum, and Kjersti Aas. 2020. “Explaining
+Predictive Models with Mixed Features Using Shapley Values and
+Conditional Inference Trees.” In *International Cross-Domain Conference
+for Machine Learning and Knowledge Extraction*, 117–37. Springer.
+
+</div>
+
+<div id="ref-sellereite2019shapr" class="csl-entry">
+
+Sellereite, N., and M. Jullum. 2019. “Shapr: An r-Package for Explaining
+Machine Learning Models with Dependence-Aware Shapley Values.” *Journal
+of Open Source Software* 5 (46): 2027.
+<https://doi.org/10.21105/joss.02027>.
+
+</div>
+
+</div>

_pkgdown.yml

@@ -26,4 +26,4 @@ navbar:
       href: reference/index.html
     python:
       text: Python
-      href: README_py.html
+      href: shaprpy.html

inst/REFERENCES.bib

@@ -217,3 +217,15 @@ @inproceedings{covert2021improving
   organization={PMLR}
 }
 
+
+@article{sellereite2019shapr,
+  title        = {shapr: An R-package for explaining machine learning models with dependence-aware Shapley values},
+  author       = {Sellereite, N. and Jullum, M.},
+  journal      = {Journal of Open Source Software},
+  year         = {2019},
+  volume       = {5},
+  number       = {46},
+  pages        = {2027},
+  doi          = {10.21105/joss.02027}
+}
+

python/README.md

@@ -1,19 +1,15 @@
----
-output:
-  html_document: default
-  pdf_document: default
----
-## shaprpy
+
+# shaprpy
 
 Python wrapper for the R package [shapr](https://github.com/NorskRegnesentral/shapr).
 
-NOTE: This wrapper is in an EXPERIMENTAL state. Bugs and breaking changes are not unlikely to occur.
+NOTE: This wrapper is in an EXPERIMENTAL state. Bugs are not unlikely to occur.
 
 ### Install
 
 The below instructions assume you already have `pip` and `R` installed and exposed to the python environment where you want to run `shaprpy`. 
 Official instructions for installing `pip` can be found [here](https://pip.pypa.io/en/stable/installation/), and for `R` [here](https://cran.r-project.org/).
-R can also be installed with pip as follows:
+`R` can also be installed with pip as follows:
 ```
 pip install rbase
 ```