diff --git a/DESCRIPTION b/DESCRIPTION
index 77257df..5571ec9 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -38,7 +38,7 @@ Suggests:
     ISLR2,
     knitr,
     patchwork,
-    randomPlantedForest,
+    randomPlantedForest (>= 0.3.0),
     ranger,
     rmarkdown,
     testthat (>= 3.0.0),
@@ -51,7 +51,7 @@ Remotes:
     PlantedML/randomPlantedForest
 Additional_repositories: https://plantedml.r-universe.dev
 Config/roxygen2/version: 8.0.0
+Config/roxygen2/markdown: TRUE
 Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
-Roxygen: list(markdown = TRUE)
diff --git a/NEWS.md b/NEWS.md
index 8bc516b..6f50144 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # glex 0.6.0.9000 (development version)
 
+* `randomPlantedForest (>= 0.3.0)` is now required (in `Suggests:`): it fixes an
+  out-of-bounds read in `purify_3()` that crashed R on Windows
+  (PlantedML/randomPlantedForest#61), so rpf tests and examples run on all platforms.
+
 * `$shap` is now a scalar `NA` (with a warning) when the decomposition is constrained via
   `max_interaction` or `features`: a constrained decomposition does not sum to the
   full model prediction, so SHAP values cannot be reconstructed from it without
diff --git a/R/autoplot.R b/R/autoplot.R
index b7924fe..eb7020c 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -35,7 +35,7 @@ autoplot.glex <- function(object, predictors, ...) {
 #' `"Remaining terms"` label.
 #' @param ... (Unused)
 #'
-#' @return A [`ggplot`][ggplot2::ggplot] object.
+#' @returns A [`ggplot`][ggplot2::ggplot] object.
 #' @export
 #' @seealso [glex_vi]
 #' @family Visualization functions
diff --git a/R/data_bike.R b/R/data_bike.R
index d78c0a6..5bafc82 100644
--- a/R/data_bike.R
+++ b/R/data_bike.R
@@ -3,7 +3,7 @@
 #' A reduced version of the `Bikeshare` data as included with `ISLR2`.
 #' The dataset has been converted to a [data.table::data.table()], with the following changes:
 #'
-#' - `hr` has been copnverted to a numeric
+#' - `hr` has been converted to a numeric
 #' - `workingday` was recoded to a binary `factor` with labels `c("No Workingday", "Workingday")`
 #' - `season` was recoded to a `factor` with labels `c("Winter", "Spring", "Summer", "Fall")`
 #' - Variables `atemp`, `day`, `registered` and `casual` were removed
diff --git a/R/glex-options.R b/R/glex-options.R
index 5be615d..0d768c6 100644
--- a/R/glex-options.R
+++ b/R/glex-options.R
@@ -3,28 +3,26 @@
 #' The color choices used across all `glex` visualization functions can be
 #' adjusted globally via [options()]:
 #'
-#' \describe{
-#'   \item{`glex.palette`}{(`NULL`) Diverging palette used to color continuous
-#'     interaction effects in [plot_twoway_effects()] and
-#'     [plot_threeway_effects()]. The default `NULL` uses a blue/red gradient
-#'     built from `glex.colors_sign`, matching the look of the Python `shap`
-#'     and `shapiq` packages. Set to the name of a diverging
-#'     [scico][scico::scico] palette (e.g. `"vik"`, `"roma"`) to use that
-#'     instead.}
-#'   \item{`glex.palette_discrete`}{(`"Dark2"`) Discrete palette used to color
-#'     categorical predictors in interaction plots. Accepts a vector of colors
-#'     (used via [ggplot2::scale_color_manual()]), the string `"okabe-ito"`
-#'     (the colorblind-safe Okabe-Ito palette via [grDevices::palette.colors()]),
-#'     the name of a [scico][scico::scico] palette, or the name of an
-#'     [RColorBrewer][ggplot2::scale_color_brewer] palette.}
-#'   \item{`glex.colors_sign`}{(`c("#008BFB", "#FF0051")`) Two colors for
-#'     negative and positive contributions in [glex_explain()], also used as
-#'     the endpoints of the default continuous gradient. The defaults
-#'     follow the blue/red convention familiar from the Python `shap` and
-#'     `shapiq` packages.}
-#'   \item{`glex.color_line`}{(`"#194155"`) Color for main effect lines and
-#'     columns drawn by `autoplot()` and [plot_pdp()].}
-#' }
+#' * `glex.palette` (`NULL`): Diverging palette used to color continuous
+#'   interaction effects in [plot_twoway_effects()] and
+#'   [plot_threeway_effects()]. The default `NULL` uses a blue/red gradient
+#'   built from `glex.colors_sign`, matching the look of the Python `shap`
+#'   and `shapiq` packages. Set to the name of a diverging
+#'   [scico][scico::scico] palette (e.g. `"vik"`, `"roma"`) to use that
+#'   instead.
+#' * `glex.palette_discrete` (`"Dark2"`): Discrete palette used to color
+#'   categorical predictors in interaction plots. Accepts a vector of colors
+#'   (used via [ggplot2::scale_color_manual()]), the string `"okabe-ito"`
+#'   (the colorblind-safe Okabe-Ito palette via [grDevices::palette.colors()]),
+#'   the name of a [scico][scico::scico] palette, or the name of an
+#'   [RColorBrewer][ggplot2::scale_color_brewer] palette.
+#' * `glex.colors_sign` (`c("#008BFB", "#FF0051")`): Two colors for
+#'   negative and positive contributions in [glex_explain()], also used as
+#'   the endpoints of the default continuous gradient. The defaults
+#'   follow the blue/red convention familiar from the Python `shap` and
+#'   `shapiq` packages.
+#' * `glex.color_line` (`"#194155"`): Color for main effect lines and
+#'   columns drawn by `autoplot()` and [plot_pdp()].
 #'
 #' @examples
 #' # Use a scico palette for continuous effects instead of the default gradient
diff --git a/R/glex.R b/R/glex.R
index 3e692ad..1e6146d 100644
--- a/R/glex.R
+++ b/R/glex.R
@@ -15,10 +15,10 @@
 #'  Defaults to using all possible interactions available in the model.\cr
 #'  For [`xgboost`][xgboost::xgb.train], this defaults to the `max_depth` parameter of the model fit.\cr
 #'  If not set in `xgboost`, the default value of `6` is assumed.
-#' @param features Vector of column names in x to calculate components for. Default is \code{NULL}, i.e. all features are used.
+#' @param features Vector of column names in `x` to calculate components for. Default is `NULL`, i.e. all features are used.
 #' @param ... Further arguments passed to methods.
 #'
-#' @return Decomposition of the regression or classification function.
+#' @returns Decomposition of the regression or classification function.
 #' A `list` with elements:
 #' * `shap`: SHAP values, derived from the functional decomposition as
 #'   \eqn{\phi_j = \sum_{S \ni j} m_S / |S|}. This reconstruction is only valid if the
@@ -71,17 +71,15 @@ glex.default <- function(object, ...) {
 
 #' @rdname glex
 #' @export
-#' @examples
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #'
 #' # Random Planted Forest -----
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 #' library(randomPlantedForest)
 #'
 #' rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)
 #'
 #' glex_rpf <- glex(rp, mtcars[27:32, ])
 #' str(glex_rpf, list.len = 5)
-#' }
 glex.rpf <- function(object, x, max_interaction = NULL, features = NULL, ...) {
   if (!requireNamespace("randomPlantedForest", quietly = TRUE)) {
     stop(paste0(
@@ -168,15 +166,15 @@ glex.rpf <- function(object, x, max_interaction = NULL, features = NULL, ...) {
 #'
 #' @param max_background_sample_size The maximum number of background samples used for the FastPD algorithm, only used when `weighting_method = "fastpd"`. Defaults to `nrow(x)`.
 #' @param weighting_method Use either "path-dependent", "fastpd" (default), or "empirical". See References for details.
-#' @examples
+#' @examplesIf requireNamespace("xgboost", quietly = TRUE)
 #' # xgboost -----
-#' if (requireNamespace("xgboost", quietly = TRUE)) {
 #' library(xgboost)
 #' x <- as.matrix(mtcars[, -1])
 #' y <- mtcars$mpg
 #' xg <- xgboost(x[1:26, ], y[1:26],
-#'               max_depth = 4, learning_rate = .1,
-#'               nrounds = 10, verbosity = 0, nthreads = 1)
+#'   max_depth = 4, learning_rate = .1,
+#'   nrounds = 10, verbosity = 0, nthreads = 1
+#' )
 #' glex(xg, x[27:32, ])
 #' glex(xg, mtcars[27:32, ])
 #'
@@ -185,7 +183,6 @@ glex.rpf <- function(object, x, max_interaction = NULL, features = NULL, ...) {
 #' doParallel::registerDoParallel()
 #' glex(xg, x[27:32, ])
 #' }
-#' }
 glex.xgb.Booster <- function(
   object,
   x,
@@ -365,20 +362,24 @@ get_xgb_base_score <- function(object) {
 #' @importFrom stats predict
 #' @importFrom utils combn
 #' @details
-#' The different weighting methods are described in detail in Liu et al. (2024). The default method is "fastpd" as it consistently estimates the correct partial dependence function.
+#' The different weighting methods are described in detail in Liu et al. (2025). The default
+#' method is `"fastpd"` as it consistently estimates the correct partial dependence function.
 #' @references
-#' Liu, J., Steensgaard, T., Wright, M. N., Pfister, N., & Hiabu, M. (2024).
-#' \emph{Fast Estimation of Partial Dependence Functions using Trees}.
-#' arXiv preprint \href{https://arxiv.org/abs/2410.13448}{arXiv:2410.13448}.
-#' @examples
+#' Liu, J., Steensgaard, T., Wright, M. N., Pfister, N., & Hiabu, M. (2025).
+#' *Fast Estimation of Partial Dependence Functions using Trees*.
+#' Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:39496-39534.
+#' [PMLR](https://proceedings.mlr.press/v267/liu25bm.html) |
+#' [arXiv:2410.13448](https://arxiv.org/abs/2410.13448)
+#' @examplesIf requireNamespace("ranger", quietly = TRUE)
 #' # ranger -----
-#' if (requireNamespace("ranger", quietly = TRUE)) {
 #' library(ranger)
 #' x <- as.matrix(mtcars[, -1])
 #' y <- mtcars$mpg
-#' rf <- ranger(x = x[1:26, ], y = y[1:26],
-#'              num.trees = 5, max.depth = 3,
-#'              node.stats = TRUE)
+#' rf <- ranger(
+#'   x = x[1:26, ], y = y[1:26],
+#'   num.trees = 5, max.depth = 3,
+#'   node.stats = TRUE
+#' )
 #' glex(rf, x[27:32, ])
 #' glex(rf, mtcars[27:32, ])
 #'
@@ -387,7 +388,6 @@ get_xgb_base_score <- function(object) {
 #' doParallel::registerDoParallel()
 #' glex(rf, x[27:32, ])
 #' }
-#' }
 glex.ranger <- function(
   object,
   x,
@@ -838,7 +838,7 @@ max_order <- function(trees) {
 #' @param trees data.table
 #' @param x observerations, matrix like data-structure
 #' @param all_S all combinations of interactions up to certain order
-#' @param weighting_method the weighting method that was supplied to \code{glex}
+#' @param weighting_method the weighting method that was supplied to `glex`
 #' @keywords internal
 #' @noRd
 tree_fun_wrapper <- function(
diff --git a/R/glex_explain.R b/R/glex_explain.R
index d3e7724..09392af 100644
--- a/R/glex_explain.R
+++ b/R/glex_explain.R
@@ -18,7 +18,7 @@
 #' Preferred value may depend on the number of vertical elements, hence it may be necessary to adjust
 #' this value as needed.
 #'
-#' @return A [ggplot][ggplot2::ggplot] object.
+#' @returns A [ggplot][ggplot2::ggplot] object.
 # Invisibly: A `list` with elements
 # * `components`: A [`data.table`] of the prediction components scaled by their degree of interaction,
 #   grouped by their associated reference term.
@@ -30,10 +30,9 @@
 #' @export
 #' @family Visualization functions
 #'
-#' @examples
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' set.seed(1)
 #' # Random Planted Forest -----
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 #' library(randomPlantedForest)
 #'
 #' rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)
@@ -41,7 +40,6 @@
 #' glex_rpf <- glex(rp, mtcars[27:32, ])
 #'
 #' glex_explain(glex_rpf, id = 3, predictors = "hp", threshold = 0.01)
-#' }
 glex_explain <- function(
   object,
   id,
diff --git a/R/glex_vi.R b/R/glex_vi.R
index eba5f11..72d13c0 100644
--- a/R/glex_vi.R
+++ b/R/glex_vi.R
@@ -3,7 +3,7 @@
 #' @param object Object of class `glex`.
 #' @param ... (Unused)
 #'
-#' @return A [data.table::data.table()] with columns:
+#' @returns A [data.table::data.table()] with columns:
 #' * `degree` (`integer`): Degree of interaction of the `term`, with `1` being main effects,
 #'    `2` being 2-degree interactions etc.
 #' * `term` (`character`): Model term, e.g. main effect `x1` or interaction term `x1:x2`, `x1:x3:x5` etc.
@@ -24,24 +24,23 @@
 #' \deqn{\mathtt{m\_rel} = \frac{\mathtt{m}}{m_0}}
 #'
 #' @seealso [autoplot.glex_vi]
-#' @examples
+#' @examplesIf requireNamespace("xgboost", quietly = TRUE)
 #' set.seed(1)
 #'
 #' # xgboost -----
-#' if (requireNamespace("xgboost", quietly = TRUE)) {
 #' library(xgboost)
 #' x <- as.matrix(mtcars[, -1])
 #' y <- mtcars$mpg
 #' xg <- xgboost(x[1:26, ], y[1:26],
-#'               max_depth = 4, learning_rate = .1,
-#'               nrounds = 10, verbosity = 0, nthreads = 1)
+#'   max_depth = 4, learning_rate = .1,
+#'   nrounds = 10, verbosity = 0, nthreads = 1
+#' )
 #' glex_xgb <- glex(xg, x[27:32, ])
 #' vi_xgb <- glex_vi(glex_xgb)
 #'
 #' library(ggplot2)
 #' autoplot(vi_xgb)
 #' autoplot(vi_xgb, by_degree = TRUE)
-#' }
 glex_vi <- function(object, ...) {
   checkmate::assert_class(object, classes = "glex")
 
diff --git a/R/plot_main_effect.R b/R/plot_main_effect.R
index 1d265e4..bb590f6 100644
--- a/R/plot_main_effect.R
+++ b/R/plot_main_effect.R
@@ -11,13 +11,12 @@
 #' Default is `"b"` for both sides. Set to `"none"` to disable rug plot.
 #' @param ... Used for future expansion.
 #'
-#' @return A `ggplot2` object.
+#' @returns A `ggplot2` object.
 #' @import ggplot2
 #' @export
 #' @seealso [plot_pdp()]
 #'
-#' @examples
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' library(randomPlantedForest)
 #'
 #' # introduce factor variables to show categorical feature handling
@@ -32,7 +31,6 @@
 #' # Main effects ----
 #' plot_main_effect(components, "wt")
 #' plot_main_effect(components, "cyl")
-#' }
 plot_main_effect <- function(object, predictor, rug_sides = "b", ...) {
   plot_main_effect_impl(object, predictor, pdp = FALSE, ...)
 }
@@ -46,13 +44,12 @@ plot_main_effect <- function(object, predictor, rug_sides = "b", ...) {
 #' @param predictor `(character(1))` predictor names, e.g. `"x1"` to plot
 #'   main effect of `x1`.
 #'
-#' @return A `ggplot2` object.
+#' @returns A `ggplot2` object.
 #' @import ggplot2
 #' @export
 #' @seealso [plot_main_effect()]
 #' @family Visualization functions
-#' @examples
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' library(randomPlantedForest)
 #'
 #' # introduce factor variables to show categorical feature handling
@@ -66,7 +63,6 @@ plot_main_effect <- function(object, predictor, rug_sides = "b", ...) {
 #'
 #' plot_pdp(components, "wt")
 #' plot_pdp(components, "cyl")
-#' }
 plot_pdp <- function(object, predictor, rug_sides = "b", ...) {
   plot_main_effect_impl(object, predictor, pdp = TRUE)
 }
diff --git a/R/plot_twoway_effects.R b/R/plot_twoway_effects.R
index 3781805..5fc1e8c 100644
--- a/R/plot_twoway_effects.R
+++ b/R/plot_twoway_effects.R
@@ -1,7 +1,6 @@
 #' @rdname plot_components
 #' @export
-#' @examples
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' library(randomPlantedForest)
 #'
 #' # 2-degree interaction effects ----
@@ -16,7 +15,6 @@
 #' # 2d categorical, heatmap of arbitrary orientation
 #' plot_twoway_effects(components, c("vs", "cyl"))
 #' plot_twoway_effects(components, c("cyl", "vs"))
-#' }
 plot_twoway_effects <- function(object, predictors, rug_sides = "b", ...) {
   checkmate::assert_class(object, "glex")
   checkmate::assert_character(predictors, len = 2, unique = TRUE)
diff --git a/R/print.R b/R/print.R
index d866226..dbc7463 100644
--- a/R/print.R
+++ b/R/print.R
@@ -8,14 +8,12 @@
 #'
 #' @export
 #' @importFrom utils str
-#' @examples
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' # Random Planted Forest -----
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 #' library(randomPlantedForest)
 #' rp <- rpf(mpg ~ hp + wt + drat, data = mtcars[1:26, ], max_interaction = 2)
 #'
 #' glex(rp, mtcars[27:32, ])
-#' }
 print.glex <- function(x, ...) {
   n <- nrow(x$x)
   n_m <- ncol(x$m)
diff --git a/R/theme_glex.R b/R/theme_glex.R
index f36e3e2..7f23b86 100644
--- a/R/theme_glex.R
+++ b/R/theme_glex.R
@@ -8,7 +8,7 @@
 #' @param grid_x (`TRUE`) Display horizontal grid lines?
 #' @param grid_y (`FALSE`) Display vertical grid lines?
 #'
-#' @return A `ggplot2` theme object
+#' @returns A `ggplot2` theme object
 #' @export
 #' @import ggplot2
 #' @examples
diff --git a/R/utils-components.R b/R/utils-components.R
index 920abe9..d798e92 100644
--- a/R/utils-components.R
+++ b/R/utils-components.R
@@ -3,14 +3,13 @@
 #' @param components An object of class `glex`.
 #' @param term (`character(1)`) A main term name to subset by, e.g. `"x1"`.
 #'
-#' @return
-#' - `subset_components`: An object of class `glex`.
-#' - `subset_component_names`: A character vector.
+#' @returns
+#' - `subset_components()`: An object of class `glex`.
+#' - `subset_component_names()`: A character vector.
 #'
 #' @rdname subset_components
 #' @export
-#' @examples
-#' if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+#' @examplesIf requireNamespace("randomPlantedForest", quietly = TRUE)
 #' library(randomPlantedForest)
 #'
 #' # introduce factor variables to show categorical feature handling
@@ -26,7 +25,6 @@
 #' subset_components(components, "hp")
 #'
 #' subset_component_names(components, "hp")
-#' }
 #'
 subset_components <- function(components, term) {
   checkmate::assert_string(term)
diff --git a/air.toml b/air.toml
index 2a2578a..829d478 100644
--- a/air.toml
+++ b/air.toml
@@ -6,3 +6,4 @@ line-ending = "lf"
 persistent-line-breaks = true # preserve existing line breaks when reformatting
 default-exclude = true        # use built-in default exclusions
 assignment-style = "arrow"
+exclude = ["attic/", ".git/"]
diff --git a/data-raw/bike_xg.R b/data-raw/bike_xg.R
index 0bae5cd..3d424bb 100644
--- a/data-raw/bike_xg.R
+++ b/data-raw/bike_xg.R
@@ -6,23 +6,25 @@ bike[, `:=`(mnth = as.integer(mnth), workingday = as.integer(workingday) - 1)]
 
 # Based on mlr3pipelines PipeOpEncode
 # https://github.com/mlr-org/mlr3pipelines/blob/master/R/PipeOpEncode.R
-onehot_encode = function(x) {
+onehot_encode <- function(x) {
   # Ensuring we have a data.table, even if it's a matrix for some reason
-  x = data.table::as.data.table(x)
-  x_names = colnames(x)
-  to_encode = x_names[vapply(
+  x <- data.table::as.data.table(x)
+  x_names <- colnames(x)
+  to_encode <- x_names[vapply(
     x_names,
     \(x) inherits(x[[x]], c("character", "factor")),
     logical(1)
   )]
 
   # If no categorical features are found, return input (as DT though)
-  if (length(to_encode) == 0) return(x)
+  if (length(to_encode) == 0) {
+    return(x)
+  }
 
-  contrast_list = sapply(
+  contrast_list <- sapply(
     to_encode,
     \(column_name) {
-      levels_in = unique(x[[column_name]])
+      levels_in <- unique(x[[column_name]])
 
       stats::contr.treatment(levels_in, contrasts = FALSE)
     },
@@ -30,18 +32,18 @@ onehot_encode = function(x) {
     USE.NAMES = TRUE
   )
 
-  cols_encoded = sapply(
+  cols_encoded <- sapply(
     to_encode,
     \(column_name) {
-      x = as.character(x[[column_name]])
-      current_contrasts = contrast_list[[column_name]]
+      x <- as.character(x[[column_name]])
+      current_contrasts <- contrast_list[[column_name]]
       current_contrasts[match(x, rownames(current_contrasts)), , drop = FALSE]
     },
     simplify = FALSE,
     USE.NAMES = TRUE
   )
 
-  cols_encoded = data.table::as.data.table(cols_encoded)
+  cols_encoded <- data.table::as.data.table(cols_encoded)
   data.table::setnames(
     cols_encoded,
     names(cols_encoded),
@@ -49,24 +51,26 @@ onehot_encode = function(x) {
   )
 
   # Column rekajiggering would fail if input was single-column
-  if (length(x_names) == 1) return(cols_encoded)
+  if (length(x_names) == 1) {
+    return(cols_encoded)
+  }
   # Bind original data sans recodable variable with newly created ones
   cbind(x[, setdiff(x_names, to_encode), with = FALSE], cols_encoded)
 }
 
 
-bmat = as.matrix(bike)[, "season", drop = FALSE]
+bmat <- as.matrix(bike)[, "season", drop = FALSE]
 
 as.data.table(bmat) |>
   onehot_encode()
 
-bike_enc = onehot_encode(bike)
+bike_enc <- onehot_encode(bike)
 setcolorder(
   bike_enc,
   neworder = c("bikers", setdiff(names(bike_enc), "bikers"))
 )
 
-bike_xgb = list(
+bike_xgb <- list(
   x = as.matrix(bike_enc[, -1]),
   label = bike_enc$bikers
 )
diff --git a/man/bike.Rd b/man/bike.Rd
index e03c5ef..e3986c9 100644
--- a/man/bike.Rd
+++ b/man/bike.Rd
@@ -19,7 +19,7 @@ The dataset has been converted to a \code{\link[data.table:data.table]{data.tabl
 }
 \details{
 \itemize{
-\item \code{hr} has been copnverted to a numeric
+\item \code{hr} has been converted to a numeric
 \item \code{workingday} was recoded to a binary \code{factor} with labels \code{c("No Workingday", "Workingday")}
 \item \code{season} was recoded to a \code{factor} with labels \code{c("Winter", "Spring", "Summer", "Fall")}
 \item Variables \code{atemp}, \code{day}, \code{registered} and \code{casual} were removed
diff --git a/man/glex.Rd b/man/glex.Rd
index 8ab2d05..e596fe2 100644
--- a/man/glex.Rd
+++ b/man/glex.Rd
@@ -45,7 +45,7 @@ Defaults to using all possible interactions available in the model.\cr
 For \code{\link[xgboost:xgb.train]{xgboost}}, this defaults to the \code{max_depth} parameter of the model fit.\cr
 If not set in \code{xgboost}, the default value of \code{6} is assumed.}
 
-\item{features}{Vector of column names in x to calculate components for. Default is \code{NULL}, i.e. all features are used.}
+\item{features}{Vector of column names in \code{x} to calculate components for. Default is \code{NULL}, i.e. all features are used.}
 
 \item{...}{Further arguments passed to methods.}
 
@@ -102,27 +102,29 @@ q-interaction SHAP for all values of q for tree-based models such as xgboost.
 For parallel execution using \code{xgboost} models, register a backend, e.g. with
 \code{doParallel::registerDoParallel()}.
 
-The different weighting methods are described in detail in Liu et al. (2024). The default method is "fastpd" as it consistently estimates the correct partial dependence function.
+The different weighting methods are described in detail in Liu et al. (2025). The default
+method is \code{"fastpd"} as it consistently estimates the correct partial dependence function.
 }
 \examples{
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 
 # Random Planted Forest -----
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 library(randomPlantedForest)
 
 rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)
 
 glex_rpf <- glex(rp, mtcars[27:32, ])
 str(glex_rpf, list.len = 5)
-}
+\dontshow{\}) # examplesIf}
+\dontshow{if (requireNamespace("xgboost", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 # xgboost -----
-if (requireNamespace("xgboost", quietly = TRUE)) {
 library(xgboost)
 x <- as.matrix(mtcars[, -1])
 y <- mtcars$mpg
 xg <- xgboost(x[1:26, ], y[1:26],
-              max_depth = 4, learning_rate = .1,
-              nrounds = 10, verbosity = 0, nthreads = 1)
+  max_depth = 4, learning_rate = .1,
+  nrounds = 10, verbosity = 0, nthreads = 1
+)
 glex(xg, x[27:32, ])
 glex(xg, mtcars[27:32, ])
 
@@ -131,15 +133,17 @@ glex(xg, mtcars[27:32, ])
 doParallel::registerDoParallel()
 glex(xg, x[27:32, ])
 }
-}
+\dontshow{\}) # examplesIf}
+\dontshow{if (requireNamespace("ranger", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 # ranger -----
-if (requireNamespace("ranger", quietly = TRUE)) {
 library(ranger)
 x <- as.matrix(mtcars[, -1])
 y <- mtcars$mpg
-rf <- ranger(x = x[1:26, ], y = y[1:26],
-             num.trees = 5, max.depth = 3,
-             node.stats = TRUE)
+rf <- ranger(
+  x = x[1:26, ], y = y[1:26],
+  num.trees = 5, max.depth = 3,
+  node.stats = TRUE
+)
 glex(rf, x[27:32, ])
 glex(rf, mtcars[27:32, ])
 
@@ -148,10 +152,12 @@ glex(rf, mtcars[27:32, ])
 doParallel::registerDoParallel()
 glex(rf, x[27:32, ])
 }
-}
+\dontshow{\}) # examplesIf}
 }
 \references{
-Liu, J., Steensgaard, T., Wright, M. N., Pfister, N., & Hiabu, M. (2024).
+Liu, J., Steensgaard, T., Wright, M. N., Pfister, N., & Hiabu, M. (2025).
 \emph{Fast Estimation of Partial Dependence Functions using Trees}.
-arXiv preprint \href{https://arxiv.org/abs/2410.13448}{arXiv:2410.13448}.
+Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:39496-39534.
+\href{https://proceedings.mlr.press/v267/liu25bm.html}{PMLR} |
+\href{https://arxiv.org/abs/2410.13448}{arXiv:2410.13448}
 }
diff --git a/man/glex_explain.Rd b/man/glex_explain.Rd
index 44c03a3..bcdf41e 100644
--- a/man/glex_explain.Rd
+++ b/man/glex_explain.Rd
@@ -44,9 +44,9 @@ recommended to use \code{predictors}, \code{max_interaction}, or \code{threshold
 elements in the plot.
 }
 \examples{
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 set.seed(1)
 # Random Planted Forest -----
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 library(randomPlantedForest)
 
 rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)
@@ -54,7 +54,7 @@ rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)
 glex_rpf <- glex(rp, mtcars[27:32, ])
 
 glex_explain(glex_rpf, id = 3, predictors = "hp", threshold = 0.01)
-}
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 Other Visualization functions:
diff --git a/man/glex_options.Rd b/man/glex_options.Rd
index 4c7fbcb..1aaba31 100644
--- a/man/glex_options.Rd
+++ b/man/glex_options.Rd
@@ -8,27 +8,27 @@ The color choices used across all \code{glex} visualization functions can be
 adjusted globally via \code{\link[=options]{options()}}:
 }
 \details{
-\describe{
-\item{\code{glex.palette}}{(\code{NULL}) Diverging palette used to color continuous
+\itemize{
+\item \code{glex.palette} (\code{NULL}): Diverging palette used to color continuous
 interaction effects in \code{\link[=plot_twoway_effects]{plot_twoway_effects()}} and
 \code{\link[=plot_threeway_effects]{plot_threeway_effects()}}. The default \code{NULL} uses a blue/red gradient
 built from \code{glex.colors_sign}, matching the look of the Python \code{shap}
 and \code{shapiq} packages. Set to the name of a diverging
 \link[scico:scico]{scico} palette (e.g. \code{"vik"}, \code{"roma"}) to use that
-instead.}
-\item{\code{glex.palette_discrete}}{(\code{"Dark2"}) Discrete palette used to color
+instead.
+\item \code{glex.palette_discrete} (\code{"Dark2"}): Discrete palette used to color
 categorical predictors in interaction plots. Accepts a vector of colors
 (used via \code{\link[ggplot2:scale_color_manual]{ggplot2::scale_color_manual()}}), the string \code{"okabe-ito"}
 (the colorblind-safe Okabe-Ito palette via \code{\link[grDevices:palette.colors]{grDevices::palette.colors()}}),
 the name of a \link[scico:scico]{scico} palette, or the name of an
-\link[ggplot2:scale_color_brewer]{RColorBrewer} palette.}
-\item{\code{glex.colors_sign}}{(\code{c("#008BFB", "#FF0051")}) Two colors for
+\link[ggplot2:scale_color_brewer]{RColorBrewer} palette.
+\item \code{glex.colors_sign} (\code{c("#008BFB", "#FF0051")}): Two colors for
 negative and positive contributions in \code{\link[=glex_explain]{glex_explain()}}, also used as
 the endpoints of the default continuous gradient. The defaults
 follow the blue/red convention familiar from the Python \code{shap} and
-\code{shapiq} packages.}
-\item{\code{glex.color_line}}{(\code{"#194155"}) Color for main effect lines and
-columns drawn by \code{autoplot()} and \code{\link[=plot_pdp]{plot_pdp()}}.}
+\code{shapiq} packages.
+\item \code{glex.color_line} (\code{"#194155"}): Color for main effect lines and
+columns drawn by \code{autoplot()} and \code{\link[=plot_pdp]{plot_pdp()}}.
 }
 }
 \examples{
diff --git a/man/glex_vi.Rd b/man/glex_vi.Rd
index a29adc7..139bd79 100644
--- a/man/glex_vi.Rd
+++ b/man/glex_vi.Rd
@@ -36,23 +36,24 @@ In turn, \code{m_rel} rescales \code{m} by the average prediction of the model (
 \deqn{\mathtt{m\_rel} = \frac{\mathtt{m}}{m_0}}
 }
 \examples{
+\dontshow{if (requireNamespace("xgboost", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 set.seed(1)
 
 # xgboost -----
-if (requireNamespace("xgboost", quietly = TRUE)) {
 library(xgboost)
 x <- as.matrix(mtcars[, -1])
 y <- mtcars$mpg
 xg <- xgboost(x[1:26, ], y[1:26],
-              max_depth = 4, learning_rate = .1,
-              nrounds = 10, verbosity = 0, nthreads = 1)
+  max_depth = 4, learning_rate = .1,
+  nrounds = 10, verbosity = 0, nthreads = 1
+)
 glex_xgb <- glex(xg, x[27:32, ])
 vi_xgb <- glex_vi(glex_xgb)
 
 library(ggplot2)
 autoplot(vi_xgb)
 autoplot(vi_xgb, by_degree = TRUE)
-}
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \link{autoplot.glex_vi}
diff --git a/man/plot_components.Rd b/man/plot_components.Rd
index 3bba03c..ec7988c 100644
--- a/man/plot_components.Rd
+++ b/man/plot_components.Rd
@@ -35,7 +35,7 @@ Plotting the main effects among the prediction components is effectively
 identical to a partial dependence plot, centered to 0.
 }
 \examples{
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 library(randomPlantedForest)
 
 # introduce factor variables to show categorical feature handling
@@ -50,9 +50,9 @@ components <- glex(rpfit, mtcars)
 # Main effects ----
 plot_main_effect(components, "wt")
 plot_main_effect(components, "cyl")
-}
+\dontshow{\}) # examplesIf}
 # plot_threeway_effects(components, c("hr", "temp", "workingday"))
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 library(randomPlantedForest)
 
 # 2-degree interaction effects ----
@@ -67,7 +67,7 @@ plot_twoway_effects(components, c("wt", "cyl"))
 # 2d categorical, heatmap of arbitrary orientation
 plot_twoway_effects(components, c("vs", "cyl"))
 plot_twoway_effects(components, c("cyl", "vs"))
-}
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \code{\link[=plot_pdp]{plot_pdp()}}
diff --git a/man/plot_pdp.Rd b/man/plot_pdp.Rd
index 83e6f03..5765760 100644
--- a/man/plot_pdp.Rd
+++ b/man/plot_pdp.Rd
@@ -25,7 +25,7 @@ A version of \code{\link{plot_main_effect}} with the intercept term (horizontal
 resulting in a partial dependence plot.
 }
 \examples{
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 library(randomPlantedForest)
 
 # introduce factor variables to show categorical feature handling
@@ -39,7 +39,7 @@ components <- glex(rpfit, mtcars)
 
 plot_pdp(components, "wt")
 plot_pdp(components, "cyl")
-}
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \code{\link[=plot_main_effect]{plot_main_effect()}}
diff --git a/man/print.glex.Rd b/man/print.glex.Rd
index 07a63f3..33d68a6 100644
--- a/man/print.glex.Rd
+++ b/man/print.glex.Rd
@@ -17,11 +17,11 @@ uses many terms, which leads to a large amount of column names of \verb{$m} bein
 This function wraps \code{\link[utils:str]{str()}} with a truncated output for a more compact representation.
 }
 \examples{
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 # Random Planted Forest -----
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
 library(randomPlantedForest)
 rp <- rpf(mpg ~ hp + wt + drat, data = mtcars[1:26, ], max_interaction = 2)
 
 glex(rp, mtcars[27:32, ])
-}
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/subset_components.Rd b/man/subset_components.Rd
index e95e734..d11223e 100644
--- a/man/subset_components.Rd
+++ b/man/subset_components.Rd
@@ -16,15 +16,15 @@ subset_component_names(components, term)
 }
 \value{
 \itemize{
-\item \code{subset_components}: An object of class \code{glex}.
-\item \code{subset_component_names}: A character vector.
+\item \code{subset_components()}: An object of class \code{glex}.
+\item \code{subset_component_names()}: A character vector.
 }
 }
 \description{
 Subset components
 }
 \examples{
-if (requireNamespace("randomPlantedForest", quietly = TRUE)) {
+\dontshow{if (requireNamespace("randomPlantedForest", quietly = TRUE)) withAutoprint(\{ # examplesIf}
 library(randomPlantedForest)
 
 # introduce factor variables to show categorical feature handling
@@ -40,6 +40,5 @@ components <- glex(rpfit, mtcars)
 subset_components(components, "hp")
 
 subset_component_names(components, "hp")
-}
-
+\dontshow{\}) # examplesIf}
 }
diff --git a/tests/testthat/test-constrained-remainder.R b/tests/testthat/test-constrained-remainder.R
index 2874719..e046c1a 100644
--- a/tests/testthat/test-constrained-remainder.R
+++ b/tests/testthat/test-constrained-remainder.R
@@ -149,8 +149,7 @@ test_that("ranger probability forest: remainder lives on the response scale", {
 # that default would fold the back-transformation into the remainder, and for binary
 # models compare against the wrong class entirely.
 test_that("rpf binary: remainder is on the raw score scale, for every loss", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purification OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   for (loss in c("L2", "logit", "exponential")) {
     set.seed(1)
@@ -185,8 +184,7 @@ test_that("rpf binary: remainder is on the raw score scale, for every loss", {
 })
 
 test_that("rpf binary: remainder does not target the response scale", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purification OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   set.seed(1)
   rp <- randomPlantedForest::rpf(
     y ~ x1 + x2 + x3,
@@ -233,8 +231,7 @@ test_that("ranger: remainder completes a constrained decomposition", {
 })
 
 test_that("rpf: remainder completes a constrained decomposition", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purification OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   set.seed(1)
   rp <- randomPlantedForest::rpf(
     mpg ~ cyl + hp + wt,
@@ -259,8 +256,7 @@ test_that("rpf: remainder completes a constrained decomposition", {
 })
 
 test_that("an inert constraint leaves no remainder", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purification OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   # A constant predictor cannot be split on, so every term involving it is exactly zero --
   # on every platform, unlike a high-order term that merely happens to come out zero for a
@@ -293,8 +289,7 @@ test_that("an inert constraint leaves no remainder", {
 })
 
 test_that("rpf multiclass: remainder is class-wise, mirroring m", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purification OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   set.seed(1)
   mt <- mtcars
   mt$cyl <- factor(mt$cyl)
diff --git a/tests/testthat/test-glex_explain.R b/tests/testthat/test-glex_explain.R
index 0e2d2fe..fedad43 100644
--- a/tests/testthat/test-glex_explain.R
+++ b/tests/testthat/test-glex_explain.R
@@ -4,8 +4,7 @@
 
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -15,8 +14,7 @@ test_that("regression rpf", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -27,8 +25,7 @@ test_that("binary rpf", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -39,8 +36,7 @@ test_that("multiclass rpf", {
 
 # SHAP values come from `$shap` -----------------------------------------------------------------------------------
 test_that("glex_explain plots the SHAP values stored in $shap", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -66,8 +62,7 @@ test_that("glex_explain plots the SHAP values stored in $shap", {
 })
 
 test_that("glex_explain omits the SHAP bar for constrained decompositions", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
 
   gl <- glex(rp, xdat)
@@ -85,8 +80,7 @@ test_that("glex_explain omits the SHAP bar for constrained decompositions", {
 })
 
 test_that("glex_explain works on objects without $shap", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
 
   # Objects from earlier glex versions (and predict_components() output) have no
diff --git a/tests/testthat/test-glex_vi.R b/tests/testthat/test-glex_vi.R
index eab8715..64e1f5a 100644
--- a/tests/testthat/test-glex_vi.R
+++ b/tests/testthat/test-glex_vi.R
@@ -1,7 +1,6 @@
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -14,8 +13,7 @@ test_that("regression rpf", {
 })
 
 test_that("regression rpf plot", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -37,8 +35,7 @@ test_that("regression rpf plot", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -51,8 +48,7 @@ test_that("binary rpf", {
 })
 
 test_that("binary rpf plot", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -73,8 +69,7 @@ test_that("binary rpf plot", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -87,8 +82,7 @@ test_that("multiclass rpf", {
 })
 
 test_that("multiclass rpf plot", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(
     yk ~ x1 + x2 + x3,
     data = xdat,
diff --git a/tests/testthat/test-plot_main_effect.R b/tests/testthat/test-plot_main_effect.R
index efb47a9..2228d9f 100644
--- a/tests/testthat/test-plot_main_effect.R
+++ b/tests/testthat/test-plot_main_effect.R
@@ -4,8 +4,7 @@
 
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -15,8 +14,7 @@ test_that("regression rpf", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -29,8 +27,7 @@ test_that("binary rpf", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
diff --git a/tests/testthat/test-plot_pdp.R b/tests/testthat/test-plot_pdp.R
index bafc037..779af0d 100644
--- a/tests/testthat/test-plot_pdp.R
+++ b/tests/testthat/test-plot_pdp.R
@@ -4,8 +4,7 @@
 
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -15,8 +14,7 @@ test_that("regression rpf", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -27,8 +25,7 @@ test_that("binary rpf", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
diff --git a/tests/testthat/test-plot_threeway_effects.R b/tests/testthat/test-plot_threeway_effects.R
index bd54bf5..d2a6235 100644
--- a/tests/testthat/test-plot_threeway_effects.R
+++ b/tests/testthat/test-plot_threeway_effects.R
@@ -4,8 +4,7 @@
 
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   mtcars$cyl <- factor(mtcars$cyl)
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
@@ -16,8 +15,7 @@ test_that("regression rpf", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3 + x4 + x5 + x6, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -34,8 +32,7 @@ test_that("binary rpf", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3 + x4 + x5 + x6, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
diff --git a/tests/testthat/test-plot_twoway_effects.R b/tests/testthat/test-plot_twoway_effects.R
index 9905091..db2f14a 100644
--- a/tests/testthat/test-plot_twoway_effects.R
+++ b/tests/testthat/test-plot_twoway_effects.R
@@ -4,8 +4,7 @@
 
 # Regression / rpf ------------------------------------------------------------------------------------------------
 test_that("regression rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(mpg ~ cyl + hp + wt, data = mtcars, max_interaction = 3)
   gl <- glex(rp, mtcars)
 
@@ -15,8 +14,7 @@ test_that("regression rpf", {
 
 # Binary / rpf ------------------------------------------------------------------------------------------------------
 test_that("binary rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(y ~ x1 + x2 + x3 + x4 + x5, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
@@ -31,8 +29,7 @@ test_that("binary rpf", {
 
 # Multiclass / rpf ------------------------------------------------------------------------------------------------
 test_that("multiclass rpf", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   rp <- rpf(yk ~ x1 + x2 + x3 + x4 + x5, data = xdat, max_interaction = 3)
   gl <- glex(rp, xdat)
 
diff --git a/tests/testthat/test-print-glex.R b/tests/testthat/test-print-glex.R
index e3c6120..9743ec2 100644
--- a/tests/testthat/test-print-glex.R
+++ b/tests/testthat/test-print-glex.R
@@ -1,6 +1,5 @@
 test_that("print.glex works", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see test-rpf-sum-identity.R
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
   set.seed(2)
   rp <- rpf(mpg ~ cyl + hp, data = mtcars)
   gl <- glex(rp, mtcars)
diff --git a/tests/testthat/test-rpf-sum-identity.R b/tests/testthat/test-rpf-sum-identity.R
index fda71ce..ea25c81 100644
--- a/tests/testthat/test-rpf-sum-identity.R
+++ b/tests/testthat/test-rpf-sum-identity.R
@@ -1,9 +1,3 @@
-# These tests crash R on Windows: randomPlantedForest's purify_3() has an
-# out-of-bounds read (grid sized lim_list[dim-1].size() instead of .size() - 1
-# in src/lib/rpf.cpp, read at gridPoint + 1) that these tests happen to trigger.
-# Fixed upstream in https://github.com/PlantedML/randomPlantedForest/pull/61 —
-# remove the skips once that (or a minimal fix) is merged.
-
 # For classification, rpf decomposes the *raw score*, which `predict(type = "numeric")`
 # returns. The default `type = "prob"` applies rpf's response function -- a clamp to
 # [0, 1] for `loss = "L2"`, the inverse link for `"logit"` / `"exponential"` -- which the
@@ -18,8 +12,7 @@
 # pass "fastpd" and "path-dependent" and assert the same thing twice.
 
 test_that("rpf binary: sum identity matches the predicted raw score", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   rp <- randomPlantedForest::rpf(
     y ~ x1 + x2 + x3,
@@ -36,8 +29,7 @@ test_that("rpf binary: sum identity matches the predicted raw score", {
 })
 
 test_that("rpf multiclass: classwise sum identity holds up to the class intercept", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   rp <- randomPlantedForest::rpf(
     yk ~ x1 + x2 + x3,
@@ -69,8 +61,7 @@ test_that("rpf multiclass: classwise sum identity holds up to the class intercep
 })
 
 test_that("rpf: shap is derived from components and satisfies efficiency", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   rp <- randomPlantedForest::rpf(
     mpg ~ cyl + hp + wt,
@@ -89,8 +80,7 @@ test_that("rpf: shap is derived from components and satisfies efficiency", {
 })
 
 test_that("rpf: constrained decompositions invalidate shap", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   rp <- randomPlantedForest::rpf(
     mpg ~ cyl + hp + wt,
@@ -116,8 +106,7 @@ test_that("rpf: constrained decompositions invalidate shap", {
 })
 
 test_that("rpf multiclass: shap mirrors the class-suffixed structure of m", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   mt <- mtcars
   mt$cyl <- factor(mt$cyl)
@@ -152,8 +141,7 @@ test_that("rpf multiclass: shap mirrors the class-suffixed structure of m", {
 })
 
 test_that("rpf: constraints that drop only zero terms keep shap valid", {
-  skip_if_not_installed("randomPlantedForest")
-  skip_on_os("windows") # rpf purify_3() OOB read, see comment at top of file
+  skip_if_not_installed("randomPlantedForest", minimum_version = "0.3.0")
 
   # The inert term has to be zero *by construction*, not by luck of the fit. An earlier
   # version of this test fit at the maximum order and assumed the top-order term came out