corrected links for ALEPlot::ALEPlot().

R/calc_ale.R

@@ -5,7 +5,7 @@
 #  Calculate ALE data
 #
 #  This function is not exported. It is uses tidyverse principles to rewrite
-#  `ALEPlot::ALEPlot`.
+#  [ALEPlot::ALEPlot()].
 #  This function is not usually called directly by the user. For details about
 #  arguments not documented here, see [ale()].
 #

R/calc_ale_ixn.R

@@ -4,16 +4,16 @@
 # Calculate ALE interaction data
 #
 # This function is not exported. It is copy-pasted (with some variable name changes)
-# from `ALEPlot::ALEPlot`.
+# from [ALEPlot::ALEPlot()].
 # This function is not usually called directly by the user. For details about
 # arguments not documented here, see [ale()].
 #
-#  @author Dan Apley (source of original calculation of ALE in `ALEPlot::ALEPlot`)
+#  @author Dan Apley (source of original calculation of ALE in [ALEPlot::ALEPlot()])
 #  @references Apley, Daniel W., and Jingyu Zhu.
 #  "Visualizing the effects of predictor variables in black box supervised learning models."
 #  Journal of the Royal Statistical Society Series B: Statistical Methodology
 #  82.4 (2020): 1059-1086.
-#  @author Chitu Okoli (rewrote the code based on `ALEPlot::ALEPlot` from while retaining ALE calculation)
+#  @author Chitu Okoli (rewrote the code based on [ALEPlot::ALEPlot()]` from while retaining ALE calculation)
 #
 # @param X dataframe. Data for which ALE is to be calculated. The y (outcome)
 # column is absent.

README.Rmd

@@ -21,7 +21,7 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/Tripartio/ale/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Tripartio/ale/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-Accumulated Local Effects (ALE) were initially developed as a [model-agnostic approach for global explanations of the results of black-box machine learning algorithms](https://www.doi.org/10.1111/rssb.12377 "Apley, Daniel W., and Jingyu Zhu. 'Visualizing the effects of predictor variables in black box supervised learning models.' Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086"). ALE has two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the `{ALEPlot}` package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference.
+Accumulated Local Effects (ALE) were initially developed as a [model-agnostic approach for global explanations of the results of black-box machine learning algorithms](https://www.doi.org/10.1111/rssb.12377 "Apley, Daniel W., and Jingyu Zhu. 'Visualizing the effects of predictor variables in black box supervised learning models.' Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086"). ALE has two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the [`{ALEPlot}` package](https://CRAN.r-project.org/package=ALEPlot) for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference.
 
 For more details, see Okoli, Chitu. 2023. "Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE)." arXiv. <https://doi.org/10.48550/arXiv.2310.09877>.
 

README.md

@@ -20,9 +20,10 @@ ALE has two primary advantages over other approaches like partial
 dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its
 values are not affected by the presence of interactions among variables
 in a model and its computation is relatively rapid. This package
-rewrites the original code from the `{ALEPlot}` package for calculating
-ALE data and it completely reimplements the plotting of ALE values. It
-also extends the original ALE concept to add bootstrap-based confidence
+rewrites the original code from the [`{ALEPlot}`
+package](https://CRAN.r-project.org/package=ALEPlot) for calculating ALE
+data and it completely reimplements the plotting of ALE values. It also
+extends the original ALE concept to add bootstrap-based confidence
 intervals and ALE-based statistics that can be used for statistical
 inference.
 

vignettes/ale-intro.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 
 ```
 
-Accumulated Local Effects (ALE) were initially developed as a [model-agnostic approach for global explanations of the results of black-box machine learning algorithms](https://www.doi.org/10.1111/rssb.12377 "Apley, Daniel W., and Jingyu Zhu. 'Visualizing the effects of predictor variables in black box supervised learning models.' Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086"). ALE has at least two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the 'ALEPlot' package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference.
+Accumulated Local Effects (ALE) were initially developed as a [model-agnostic approach for global explanations of the results of black-box machine learning algorithms](https://www.doi.org/10.1111/rssb.12377 "Apley, Daniel W., and Jingyu Zhu. 'Visualizing the effects of predictor variables in black box supervised learning models.' Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086"). ALE has at least two primary advantages over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values are not affected by the presence of interactions among variables in a model and its computation is relatively rapid. This package rewrites the original code from the [`{ALEPlot}` package](https://CRAN.r-project.org/package=ALEPlot) for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference.
 
 For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. <https://doi.org/10.48550/arXiv.2310.09877>.
 

vignettes/ale-x-datatypes.Rmd

@@ -101,7 +101,7 @@ cars_ale_ixn$plots |>
   })
 ```
 
-There are no interactions in this dataset. (To see what ALE interaction plots look like in the presence of interactions, see the [ALEPlot comparison vignette](ale-ALEPlot.html), which explains the interaction plots in more detail.)
+There are no interactions in this dataset. (To see what ALE interaction plots look like in the presence of interactions, see the [{ALEPlot} comparison vignette](ale-ALEPlot.html), which explains the interaction plots in more detail.)
 
 Finally, as explained in the vignette on modelling with [small datasets](ale-small-datasets.html "ale package for small datasets"), a more appropriate modelling workflow would require bootstrapping the entire model, not just the ALE data. So, let's do that now.