Fix links, prep release

R/data.R

@@ -49,7 +49,6 @@
 
 #' Number of trees and aboveground biomass for Forest Inventory and Analysis plots in New York State
 #'
-#' This data is adapted from the Tidymodels "multi-scale assessment" lesson, available at <https://www.tidymodels.org/learn/work/multi-scale/>.
 #' The original data is derived from the Forest Inventory and Analysis program,
 #' implemented by the US Department of Agriculture's Forest Service.
 #'
@@ -61,9 +60,6 @@
 #' \item{agb}{The total aboveground biomass at the plot location, in pounds.}
 #' \item{geometry}{The centroid of the plot location.}
 #' }
-#'
-#' @source
-#' Tidymodels team, "Multi-scale Assessment". Retrieved 30 November 2022. Available at <https://www.tidymodels.org/learn/work/multi-scale/>
 "ny_trees"
 
 #' Simulated data based on WorldClim Bioclimatic variables

README.md

@@ -115,6 +115,14 @@ guerry_predicted <- guerry
 guerry_predicted$predictions <- predict(crime_model, guerry)
 
 ww_local_moran_i(guerry_predicted, Crm_prs, predictions)
+#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
+#> which was just loaded, will retire in October 2023.
+#> Please refer to R-spatial evolution reports for details, especially
+#> https://r-spatial.org/r/2023/05/15/evolution4.html.
+#> It may be desirable to make the sf package available;
+#> package maintainers should consider adding sf to Suggests:.
+#> The sp package is now running under evolution status 2
+#>      (status 2 uses the sf package in place of rgdal)
 #> # A tibble: 85 × 3
 #>    .metric       .estimator .estimate
 #>    <chr>         <chr>          <dbl>

cran-comments.md

@@ -1,7 +1,7 @@
-This is the fourth CRAN release of waywiser, adding methods for using
-raster data in multi-scale assessments, improving multi-scale assessment
-speed, and implementing a number of updates and fixes following evolution
-in the tidymodels ecosystem.
+This is the fifth CRAN release of waywiser. This patch release anticipates
+some breaking changes in new versions of the vip package, and updates some tests 
+and examples so that waywiser will still pass checks once the new version of vip 
+is released.
 
 ## R CMD check results
 

vignettes/multi-scale-assessment.Rmd

@@ -20,14 +20,9 @@ ggplot2::theme_set(ggplot2::theme_minimal())
 ```
 
 This vignette walks through how to use waywiser to assess model predictions at
-multiple spatial scales. The data and code here are adapted from the 
-[tidymodels multi-scale assessment tutorial.](https://www.tidymodels.org/learn/work/multi-scale/)
-However, that tutorial performs a cross-validation procedure at each scale of 
-aggregation, meaning that you're measuring how well your model does when both
-trained and evaluated at multiple scales of aggregation; this vignette focuses
-instead on evaluating a single model's predictions across multiple scales.
-
-We're going to use the `ny_trees` data in waywiser, adapted from that post. 
+multiple spatial scales, using the `ny_trees` data in waywiser, adapted from 
+that post. 
+
 First things first, we'll set up our environment, loading a few packages 
 and telling sf to download the coordinate reference system for our data,
 if needed: