From 39c5f876adfe9f273e83c6d5507891486b7fe677 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 15:44:03 +0200
Subject: [PATCH 01/10] Updating CRAN comments

---
 cran-comments.md | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/cran-comments.md b/cran-comments.md
index 90c84c6..156962c 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -17,20 +17,16 @@ Hello CRAN team
 > checking HTML version of manual ... NOTE
   Skipping checking HTML validation: no command 'tidy' found
 
-### check_rhub(): Some usual notes...
+### RHub
 
 * checking package dependencies ... NOTE
 Packages which this enhances but not available for checking:
   'fastshap', 'h2o', 'lightgbm'
 * checking HTML version of manual ... NOTE
+Skipping checking HTML validation: no command 'tidy' found
 Skipping checking math rendering: package 'V8' unavailable
-* checking for non-standard things in the check directory ... NOTE
-Found the following files/directories:
-  ''NULL''
-* checking for detritus in the temp directory ... NOTE
-Found the following files/directories:
-  'lastMiKTeXException'
-
-### check_win_devel()
+
+
+### Winbuilder()
 
 Status: OK

From a4602d909472cf01dc4772cb7e9653858a962708 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 15:47:53 +0200
Subject: [PATCH 02/10] CRAN submission

---
 CRAN-SUBMISSION | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 66ec04c..023333e 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
-Version: 0.8.0
-Date: 2023-05-09 17:34:27 UTC
-SHA: e2f9b361e5cb79eb0faa79faabceb98a4bd1726a
+Version: 0.9.0
+Date: 2023-06-07 13:47:09 UTC
+SHA: 39c5f876adfe9f273e83c6d5507891486b7fe677

From 2f8e03af92a7ced1b94139411f795dffa4f7c008 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 16:34:31 +0200
Subject: [PATCH 03/10] Fix two typos in vignette

---
 vignettes/geographic.Rmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/geographic.Rmd b/vignettes/geographic.Rmd
index 9c91c4d..6814b7c 100644
--- a/vignettes/geographic.Rmd
+++ b/vignettes/geographic.Rmd
@@ -23,7 +23,7 @@ knitr::opts_chunk$set(
 
 ## Setting
 
-In a model with geographic components, we want to express a functional $T$ (usually the expectation or a quantile) of a response $Y$ as a function $f$ of a set of geographic features (latitude/longitude and/or postal code and/or other features varying with location):
+In a model with geographic components, we want to express a functional $T$ (usually the expectation or a quantile) of a response $Y$ as a function $f$ of a set of geographic features (latitude/longitude and/or postal code and/or other features varying with location), and other features:
 
 $$
   T(Y \mid X^\textrm{geo}, X^\textrm{other}) \approx f(X^\textrm{geo}, X^\textrm{other})
@@ -34,7 +34,7 @@ That's where the additivity of SHAP values comes into play: The sum of SHAP valu
 
 ## A first example
 
-For illustration, we will use a beautiful house price dataset containing information on about 14'000 houses sold in 2016 in Miami-Date County. Some of the columns are as follows:
+For illustration, we will use a beautiful house price dataset containing information on about 14'000 houses sold in 2016 in Miami-Dade County. Some of the columns are as follows:
 
 - **SALE_PRC**: Sale price in USD: Its logarithm will be our model response.
 - *LATITUDE*, *LONGITUDE*: Coordinates

From 66dd44a476a91e0037b7cb0b47e4c603f6169f3c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 17:06:12 +0200
Subject: [PATCH 04/10] resubmission

---
 cran-comments.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/cran-comments.md b/cran-comments.md
index 156962c..8bd838e 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,3 +1,7 @@
+# Resubmission
+
+I assume the slow examples on Debian are just a hickup?
+
 # shapviz 0.9.0
 
 Hello CRAN team

From 3ea4b7f55659195650d1074058e8f10e35ccdf59 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 20:45:04 +0200
Subject: [PATCH 05/10] Reducing duration of examples

---
 CRAN-SUBMISSION        |  4 ++--
 R/methods.R            |  2 +-
 R/shapviz.R            | 44 ++++++++++++++++++++----------------------
 R/sv_dependence.R      | 14 +++++---------
 R/sv_dependence2D.R    |  6 +-----
 R/sv_force.R           |  4 +---
 R/sv_importance.R      |  7 +------
 R/sv_interaction.R     |  5 ++---
 R/sv_waterfall.R       | 11 +----------
 cran-comments.md       |  5 ++++-
 man/shapviz.Rd         | 44 ++++++++++++++++++++----------------------
 man/split.shapviz.Rd   |  2 +-
 man/sv_dependence.Rd   | 14 +++++---------
 man/sv_dependence2D.Rd |  6 +-----
 man/sv_force.Rd        |  4 +---
 man/sv_importance.Rd   |  7 +------
 man/sv_interaction.Rd  |  5 ++---
 man/sv_waterfall.Rd    | 11 +----------
 packaging.R            |  2 +-
 19 files changed, 73 insertions(+), 124 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 023333e..ae18b10 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.9.0
-Date: 2023-06-07 13:47:09 UTC
-SHA: 39c5f876adfe9f273e83c6d5507891486b7fe677
+Date: 2023-06-07 15:08:12 UTC
+SHA: 66dd44a476a91e0037b7cb0b47e4c603f6169f3c
diff --git a/R/methods.R b/R/methods.R
index f32f19a..7e07caa 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -272,7 +272,7 @@ c.shapviz <- function(...) {
 #' @returns A "mshapviz" object.
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' sv <- shapviz(fit, X_pred = dtrain, X = iris)
 #' mx <- split(sv, f = iris$Species)
 #' sv_dependence(mx, "Petal.Length")
diff --git a/R/shapviz.R b/R/shapviz.R
index 6ec0aab..da2ed88 100644
--- a/R/shapviz.R
+++ b/R/shapviz.R
@@ -112,9 +112,9 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' @examples
 #'
 #' # XGBoost models
-#' X_pred <- data.matrix(iris[, -1L])
-#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50L, nthread = 1L)
+#' X_pred <- data.matrix(iris[, -1])
+#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1])
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #'
 #' # Will use numeric matrix "X_pred" as feature matrix
 #' x <- shapviz(fit, X_pred = X_pred)
@@ -129,15 +129,13 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' x <- shapviz(fit, X_pred = dtrain, X = iris)
 #'
 #' # Multiclass setting
-#' params <- list(objective = "multi:softprob", num_class = 3L)
-#' X_pred <- data.matrix(iris[, -5L])
-#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 5L]) - 1L)
-#' fit <- xgboost::xgb.train(
-#'   params = params, data = dtrain, nrounds = 50L, nthread = 1L
-#' )
+#' params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
+#' X_pred <- data.matrix(iris[, -5])
+#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 5]) - 1)
+#' fit <- xgboost::xgb.train(params = params, data = dtrain, nrounds = 10)
 #'
 #' # Select specific class
-#' x <- shapviz(fit, X_pred = X_pred, which_class = 3L)
+#' x <- shapviz(fit, X_pred = X_pred, which_class = 3)
 #' x
 #'
 #' # Or combine all classes to "mshapviz" object
@@ -145,9 +143,9 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' x
 #'
 #' # What if we would have one-hot-encoded values and want to explain the original column?
-#' X_pred <- stats::model.matrix(~ . -1, iris[, -1L])
-#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 1L]))
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50L)
+#' X_pred <- stats::model.matrix(~ . -1, iris[, -1])
+#' dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 1]))
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(
 #'   fit,
 #'   X_pred = X_pred,
@@ -159,29 +157,29 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' # Similarly with LightGBM
 #' if (requireNamespace("lightgbm", quietly = TRUE)) {
 #'   fit <- lightgbm::lgb.train(
-#'     params = list(objective = "regression", num_thread = 1L),
-#'     data = lightgbm::lgb.Dataset(X_pred, label = iris[, 1L]),
-#'     nrounds = 50L,
-#'     verbose = -2L
+#'     params = list(objective = "regression", num_thread = 1),
+#'     data = lightgbm::lgb.Dataset(X_pred, label = iris[, 1]),
+#'     nrounds = 10,
+#'     verbose = -2
 #'   )
 #'
 #'   x <- shapviz(fit, X_pred = X_pred)
 #'   x
 #'
 #'   # Multiclass
-#'   params <- list(objective = "multiclass", num_class = 3L, num_thread = 1L)
-#'   X_pred <- data.matrix(iris[, -5L])
-#'   dtrain <- lightgbm::lgb.Dataset(X_pred, label = as.integer(iris[, 5L]) - 1L)
-#'   fit <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 50L)
+#'   params <- list(objective = "multiclass", num_class = 3, num_thread = 1)
+#'   X_pred <- data.matrix(iris[, -5])
+#'   dtrain <- lightgbm::lgb.Dataset(X_pred, label = as.integer(iris[, 5]) - 1)
+#'   fit <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 10)
 #'
 #'   # Select specific class
-#'   x <- shapviz(fit, X_pred = X_pred, which_class = 3L)
+#'   x <- shapviz(fit, X_pred = X_pred, which_class = 3)
 #'   x
 #'
 #'   # Or combine all classes to a "mshapviz" object
 #'   mx <- shapviz(fit, X_pred = X_pred)
 #'   mx
-#'   all.equal(mx[[3L]], x)
+#'   all.equal(mx[[3]], x)
 #' }
 shapviz.xgb.Booster = function(object, X_pred, X = X_pred, which_class = NULL,
                                collapse = NULL, interactions = FALSE, ...) {
diff --git a/R/sv_dependence.R b/R/sv_dependence.R
index fb15f82..93bc2af 100644
--- a/R/sv_dependence.R
+++ b/R/sv_dependence.R
@@ -35,7 +35,7 @@
 #' @returns An object of class "ggplot" (or "patchwork") representing a dependence plot.
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris)
 #' sv_dependence(x, "Petal.Length")
 #' sv_dependence(x, "Petal.Length", color_var = "Species")
@@ -43,17 +43,13 @@
 #' sv_dependence(x, c("Species", "Petal.Length"))
 #' sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 #'
-#' # SHAP interaction values
+#' # SHAP interaction values/main effects
 #' x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_dependence(x2, "Petal.Length", interactions = TRUE)
-#' sv_dependence(x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE)
-#'
-#' # Show main effect of "Petal.Length" for setosa and virginica separately
-#' mx <- c(
-#'   setosa = x2[x2$X$Species == "setosa"],
-#'   virginica = x2[x2$X$Species == "virginica"]
+#' sv_dependence(
+#'   x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE
 #' )
-#' sv_dependence(mx, "Petal.Length", color_var = NULL, interactions = TRUE)
+#'
 #' @export
 #' @seealso [potential_interactions()]
 sv_dependence <- function(object, ...) {
diff --git a/R/sv_dependence2D.R b/R/sv_dependence2D.R
index d7d6167..0e88fc6 100644
--- a/R/sv_dependence2D.R
+++ b/R/sv_dependence2D.R
@@ -29,14 +29,10 @@
 #' @returns An object of class "ggplot" (or "patchwork") representing a dependence plot.
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' sv <- shapviz(fit, X_pred = dtrain, X = iris)
 #' sv_dependence2D(sv, x = "Petal.Length", y = "Species")
-#' sv_dependence2D(sv, x = "Petal.Length", y = "Sepal.Width")
 #' sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
-#' sv_dependence2D(
-#'   sv, x = c("Petal.Length", "Species"), y = c("Petal.Width", "Sepal.Width")
-#' )
 #'
 #' # SHAP interaction values
 #' sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
diff --git a/R/sv_force.R b/R/sv_force.R
index 6d99b11..27bf1c8 100644
--- a/R/sv_force.R
+++ b/R/sv_force.R
@@ -12,7 +12,7 @@
 #' @returns An object of class "ggplot" (or "patchwork") representing a force plot.
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 #' sv_force(x)
 #' sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
@@ -20,8 +20,6 @@
 #' # Aggregate over all observations with Petal.Length == 1.4
 #' sv_force(x, row_id = x$X$Petal.Length == 1.4)
 #'
-#' # Combine two force plots via {patchwork}
-#' sv_force(c(Obs1 = x[1], Obs2 = x[2]))
 #' @export
 #' @seealso [sv_waterfall()]
 sv_force <- function(object, ...) {
diff --git a/R/sv_importance.R b/R/sv_importance.R
index 1e9f2fe..50452a8 100644
--- a/R/sv_importance.R
+++ b/R/sv_importance.R
@@ -44,17 +44,12 @@
 #' @examples
 #' X_train <- data.matrix(iris[, -1])
 #' dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = X_train)
 #' sv_importance(x)
 #' sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 #' sv_importance(x, kind = "no")
 #'
-#' X <- data.frame(matrix(rnorm(1000), ncol = 20))
-#' S <- as.matrix(X)
-#' x2 <- shapviz(S, X)
-#' sv_importance(x2)
-#' sv_importance(x2, max_display = 5)
 #' @seealso \code{\link{sv_interaction}}
 #' @export
 sv_importance <- function(object, ...) {
diff --git a/R/sv_interaction.R b/R/sv_interaction.R
index 8f2f489..d77301c 100644
--- a/R/sv_interaction.R
+++ b/R/sv_interaction.R
@@ -19,10 +19,9 @@
 #'   absolute SHAP values (or a list of such matrices in case of "mshapviz" object).
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
-#' sv_interaction(x)
-#' sv_interaction(x, max_display = 2, size = 3, alpha = 0.1)
+#' sv_interaction(x, max_display = 2, size = 3)
 #' sv_interaction(x, kind = "no")
 #' @seealso [sv_importance()]
 #' @export
diff --git a/R/sv_waterfall.R b/R/sv_waterfall.R
index 0222053..0eeb560 100644
--- a/R/sv_waterfall.R
+++ b/R/sv_waterfall.R
@@ -36,7 +36,7 @@
 #' @returns An object of class "ggplot" (or "patchwork") representing a waterfall plot.
 #' @examples
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+#' fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 #' sv_waterfall(x)
 #' sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
@@ -49,15 +49,6 @@
 #' # Aggregate over all observations with Petal.Length == 1.4
 #' sv_waterfall(x, row_id = x$X$Petal.Length == 1.4)
 #'
-#' # More features
-#' X <- as.data.frame(matrix(1:100, nrow = 10))
-#' S <- as.matrix(X)
-#' shp <- shapviz(S, X)
-#' sv_waterfall(shp)
-#'
-#' # Combine two waterfall plots via {patchwork}
-#' sv_waterfall(c(Obs1 = x[1], Obs2 = x[2])) +
-#'   patchwork::plot_layout(ncol = 1)
 #' @export
 #' @seealso [sv_force()]
 sv_waterfall <- function(object, ...) {
diff --git a/cran-comments.md b/cran-comments.md
index 8bd838e..9151258 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,6 +1,9 @@
 # Resubmission
 
-I assume the slow examples on Debian are just a hickup?
+Examples taking too long on Linux. 
+
+- I have now reduced the number of examples.
+- And the number of boosting rounds.
 
 # shapviz 0.9.0
 
diff --git a/man/shapviz.Rd b/man/shapviz.Rd
index da5c9b6..679c291 100644
--- a/man/shapviz.Rd
+++ b/man/shapviz.Rd
@@ -170,9 +170,9 @@ X <- data.frame(x = c("a", "b"), y = c(100, 10))
 shapviz(S, X, baseline = 4)
 
 # XGBoost models
-X_pred <- data.matrix(iris[, -1L])
-dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1L])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50L, nthread = 1L)
+X_pred <- data.matrix(iris[, -1])
+dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1])
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 
 # Will use numeric matrix "X_pred" as feature matrix
 x <- shapviz(fit, X_pred = X_pred)
@@ -187,15 +187,13 @@ sv_dependence(x, "Species")
 x <- shapviz(fit, X_pred = dtrain, X = iris)
 
 # Multiclass setting
-params <- list(objective = "multi:softprob", num_class = 3L)
-X_pred <- data.matrix(iris[, -5L])
-dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 5L]) - 1L)
-fit <- xgboost::xgb.train(
-  params = params, data = dtrain, nrounds = 50L, nthread = 1L
-)
+params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
+X_pred <- data.matrix(iris[, -5])
+dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 5]) - 1)
+fit <- xgboost::xgb.train(params = params, data = dtrain, nrounds = 10)
 
 # Select specific class
-x <- shapviz(fit, X_pred = X_pred, which_class = 3L)
+x <- shapviz(fit, X_pred = X_pred, which_class = 3)
 x
 
 # Or combine all classes to "mshapviz" object
@@ -203,9 +201,9 @@ x <- shapviz(fit, X_pred = X_pred)
 x
 
 # What if we would have one-hot-encoded values and want to explain the original column?
-X_pred <- stats::model.matrix(~ . -1, iris[, -1L])
-dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 1L]))
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50L)
+X_pred <- stats::model.matrix(~ . -1, iris[, -1])
+dtrain <- xgboost::xgb.DMatrix(X_pred, label = as.integer(iris[, 1]))
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(
   fit,
   X_pred = X_pred,
@@ -217,29 +215,29 @@ summary(x)
 # Similarly with LightGBM
 if (requireNamespace("lightgbm", quietly = TRUE)) {
   fit <- lightgbm::lgb.train(
-    params = list(objective = "regression", num_thread = 1L),
-    data = lightgbm::lgb.Dataset(X_pred, label = iris[, 1L]),
-    nrounds = 50L,
-    verbose = -2L
+    params = list(objective = "regression", num_thread = 1),
+    data = lightgbm::lgb.Dataset(X_pred, label = iris[, 1]),
+    nrounds = 10,
+    verbose = -2
   )
 
   x <- shapviz(fit, X_pred = X_pred)
   x
 
   # Multiclass
-  params <- list(objective = "multiclass", num_class = 3L, num_thread = 1L)
-  X_pred <- data.matrix(iris[, -5L])
-  dtrain <- lightgbm::lgb.Dataset(X_pred, label = as.integer(iris[, 5L]) - 1L)
-  fit <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 50L)
+  params <- list(objective = "multiclass", num_class = 3, num_thread = 1)
+  X_pred <- data.matrix(iris[, -5])
+  dtrain <- lightgbm::lgb.Dataset(X_pred, label = as.integer(iris[, 5]) - 1)
+  fit <- lightgbm::lgb.train(params = params, data = dtrain, nrounds = 10)
 
   # Select specific class
-  x <- shapviz(fit, X_pred = X_pred, which_class = 3L)
+  x <- shapviz(fit, X_pred = X_pred, which_class = 3)
   x
 
   # Or combine all classes to a "mshapviz" object
   mx <- shapviz(fit, X_pred = X_pred)
   mx
-  all.equal(mx[[3L]], x)
+  all.equal(mx[[3]], x)
 }
 }
 \seealso{
diff --git a/man/split.shapviz.Rd b/man/split.shapviz.Rd
index eb0e527..55a0cb2 100644
--- a/man/split.shapviz.Rd
+++ b/man/split.shapviz.Rd
@@ -21,7 +21,7 @@ Splits "shapviz" object along a vector \code{f} into an object of class "mshapvi
 }
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 sv <- shapviz(fit, X_pred = dtrain, X = iris)
 mx <- split(sv, f = iris$Species)
 sv_dependence(mx, "Petal.Length")
diff --git a/man/sv_dependence.Rd b/man/sv_dependence.Rd
index 283c1b1..6b40d8d 100644
--- a/man/sv_dependence.Rd
+++ b/man/sv_dependence.Rd
@@ -87,7 +87,7 @@ to focus on pure interaction effects (multiplied by two) or on pure main effects
 }}
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris)
 sv_dependence(x, "Petal.Length")
 sv_dependence(x, "Petal.Length", color_var = "Species")
@@ -95,17 +95,13 @@ sv_dependence(x, "Petal.Length", color_var = NULL)
 sv_dependence(x, c("Species", "Petal.Length"))
 sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 
-# SHAP interaction values
+# SHAP interaction values/main effects
 x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_dependence(x2, "Petal.Length", interactions = TRUE)
-sv_dependence(x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE)
-
-# Show main effect of "Petal.Length" for setosa and virginica separately
-mx <- c(
-  setosa = x2[x2$X$Species == "setosa"],
-  virginica = x2[x2$X$Species == "virginica"]
+sv_dependence(
+  x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE
 )
-sv_dependence(mx, "Petal.Length", color_var = NULL, interactions = TRUE)
+
 }
 \seealso{
 \code{\link[=potential_interactions]{potential_interactions()}}
diff --git a/man/sv_dependence2D.Rd b/man/sv_dependence2D.Rd
index e8170ce..b11d4b8 100644
--- a/man/sv_dependence2D.Rd
+++ b/man/sv_dependence2D.Rd
@@ -94,14 +94,10 @@ to focus on pure interaction effects (multiplied by two).
 }}
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 sv <- shapviz(fit, X_pred = dtrain, X = iris)
 sv_dependence2D(sv, x = "Petal.Length", y = "Species")
-sv_dependence2D(sv, x = "Petal.Length", y = "Sepal.Width")
 sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
-sv_dependence2D(
-  sv, x = c("Petal.Length", "Species"), y = c("Petal.Width", "Sepal.Width")
-)
 
 # SHAP interaction values
 sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
diff --git a/man/sv_force.Rd b/man/sv_force.Rd
index bc1079c..4a2fb84 100644
--- a/man/sv_force.Rd
+++ b/man/sv_force.Rd
@@ -97,7 +97,7 @@ baseline SHAP value.
 }}
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_force(x)
 sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
@@ -105,8 +105,6 @@ sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
 # Aggregate over all observations with Petal.Length == 1.4
 sv_force(x, row_id = x$X$Petal.Length == 1.4)
 
-# Combine two force plots via {patchwork}
-sv_force(c(Obs1 = x[1], Obs2 = x[2]))
 }
 \seealso{
 \code{\link[=sv_waterfall]{sv_waterfall()}}
diff --git a/man/sv_importance.Rd b/man/sv_importance.Rd
index cbb026d..05512a1 100644
--- a/man/sv_importance.Rd
+++ b/man/sv_importance.Rd
@@ -113,17 +113,12 @@ are sorted in decreasing order of importance.
 \examples{
 X_train <- data.matrix(iris[, -1])
 dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = X_train)
 sv_importance(x)
 sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 sv_importance(x, kind = "no")
 
-X <- data.frame(matrix(rnorm(1000), ncol = 20))
-S <- as.matrix(X)
-x2 <- shapviz(S, X)
-sv_importance(x2)
-sv_importance(x2, max_display = 5)
 }
 \seealso{
 \code{\link{sv_interaction}}
diff --git a/man/sv_interaction.Rd b/man/sv_interaction.Rd
index 37660aa..bfc3ff5 100644
--- a/man/sv_interaction.Rd
+++ b/man/sv_interaction.Rd
@@ -88,10 +88,9 @@ The features are sorted in decreasing order of usual SHAP importance.
 }}
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
-sv_interaction(x)
-sv_interaction(x, max_display = 2, size = 3, alpha = 0.1)
+sv_interaction(x, max_display = 2, size = 3)
 sv_interaction(x, kind = "no")
 }
 \seealso{
diff --git a/man/sv_waterfall.Rd b/man/sv_waterfall.Rd
index 48b87f1..6238e3b 100644
--- a/man/sv_waterfall.Rd
+++ b/man/sv_waterfall.Rd
@@ -103,7 +103,7 @@ baseline SHAP value.
 }}
 \examples{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
-fit <- xgboost::xgb.train(data = dtrain, nrounds = 50, nthread = 1)
+fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_waterfall(x)
 sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
@@ -116,15 +116,6 @@ sv_waterfall(
 # Aggregate over all observations with Petal.Length == 1.4
 sv_waterfall(x, row_id = x$X$Petal.Length == 1.4)
 
-# More features
-X <- as.data.frame(matrix(1:100, nrow = 10))
-S <- as.matrix(X)
-shp <- shapviz(S, X)
-sv_waterfall(shp)
-
-# Combine two waterfall plots via {patchwork}
-sv_waterfall(c(Obs1 = x[1], Obs2 = x[2])) +
-  patchwork::plot_layout(ncol = 1)
 }
 \seealso{
 \code{\link[=sv_force]{sv_force()}}
diff --git a/packaging.R b/packaging.R
index 1a5681f..8ce1bdb 100644
--- a/packaging.R
+++ b/packaging.R
@@ -101,7 +101,7 @@ library(devtools)
 
 document()
 test()
-check(manual = TRUE, cran = TRUE, vignettes = TRUE)
+check(manual = TRUE, cran = TRUE, vignettes = FALSE)
 build()
 # build(binary = TRUE)
 install(upgrade = FALSE)

From 4a16a8993ddfa8b52ef72d3e0056901c2702fa37 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Jun 2023 20:49:12 +0200
Subject: [PATCH 06/10] Resubmission

---
 CRAN-SUBMISSION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index ae18b10..07e0368 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.9.0
-Date: 2023-06-07 15:08:12 UTC
-SHA: 66dd44a476a91e0037b7cb0b47e4c603f6169f3c
+Date: 2023-06-07 18:48:23 UTC
+SHA: 3ea4b7f55659195650d1074058e8f10e35ccdf59

From 11506767b515bf872e0a1fbee5426bd6cba1aea5 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Thu, 8 Jun 2023 14:43:22 +0200
Subject: [PATCH 07/10] Check for Linux debian

---
 packaging.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/packaging.R b/packaging.R
index 8ce1bdb..4f3ddec 100644
--- a/packaging.R
+++ b/packaging.R
@@ -109,7 +109,7 @@ install(upgrade = FALSE)
 # Run only if package is public(!) and should go to CRAN
 if (FALSE) {
   check_win_devel()
-  check_rhub()
+  check_rhub(platforms = "debian-gcc-devel")
 
   # Wait until above checks are passed without relevant notes/warnings
   # then submit to CRAN

From 32c5b1b9f2576d24d1522daf2f62df6cb41ad6a2 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Thu, 8 Jun 2023 20:18:19 +0200
Subject: [PATCH 08/10] Adding dont runs

---
 CRAN-SUBMISSION        | 4 ++--
 R/shapviz.R            | 2 ++
 R/sv_dependence.R      | 3 ++-
 R/sv_dependence2D.R    | 2 ++
 R/sv_force.R           | 4 +++-
 R/sv_importance.R      | 5 ++++-
 R/sv_interaction.R     | 5 ++++-
 R/sv_waterfall.R       | 3 ++-
 cran-comments.md       | 4 ++++
 man/shapviz.Rd         | 2 ++
 man/sv_dependence.Rd   | 3 ++-
 man/sv_dependence2D.Rd | 2 ++
 man/sv_force.Rd        | 4 +++-
 man/sv_importance.Rd   | 5 ++++-
 man/sv_interaction.Rd  | 5 ++++-
 man/sv_waterfall.Rd    | 3 ++-
 16 files changed, 44 insertions(+), 12 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 07e0368..7fbbb37 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.9.0
-Date: 2023-06-07 18:48:23 UTC
-SHA: 3ea4b7f55659195650d1074058e8f10e35ccdf59
+Date: 2023-06-08 12:47:01 UTC
+SHA: 11506767b515bf872e0a1fbee5426bd6cba1aea5
diff --git a/R/shapviz.R b/R/shapviz.R
index da2ed88..0cc686a 100644
--- a/R/shapviz.R
+++ b/R/shapviz.R
@@ -128,6 +128,7 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' # "X_pred" can also be passed as xgb.DMatrix, but only if X is passed as well!
 #' x <- shapviz(fit, X_pred = dtrain, X = iris)
 #'
+#' \dontrun{
 #' # Multiclass setting
 #' params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
 #' X_pred <- data.matrix(iris[, -5])
@@ -153,6 +154,7 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #'   collapse = list(Species = c("Speciessetosa", "Speciesversicolor", "Speciesvirginica"))
 #' )
 #' summary(x)
+#' }
 #'
 #' # Similarly with LightGBM
 #' if (requireNamespace("lightgbm", quietly = TRUE)) {
diff --git a/R/sv_dependence.R b/R/sv_dependence.R
index 93bc2af..850e9a4 100644
--- a/R/sv_dependence.R
+++ b/R/sv_dependence.R
@@ -43,13 +43,14 @@
 #' sv_dependence(x, c("Species", "Petal.Length"))
 #' sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 #'
+#' \dontrun{
 #' # SHAP interaction values/main effects
 #' x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_dependence(x2, "Petal.Length", interactions = TRUE)
 #' sv_dependence(
 #'   x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE
 #' )
-#'
+#' }
 #' @export
 #' @seealso [potential_interactions()]
 sv_dependence <- function(object, ...) {
diff --git a/R/sv_dependence2D.R b/R/sv_dependence2D.R
index 0e88fc6..83997bb 100644
--- a/R/sv_dependence2D.R
+++ b/R/sv_dependence2D.R
@@ -34,6 +34,7 @@
 #' sv_dependence2D(sv, x = "Petal.Length", y = "Species")
 #' sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
 #'
+#' \dontrun{
 #' # SHAP interaction values
 #' sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_dependence2D(sv2, x = "Petal.Length", y = "Species", interactions = TRUE)
@@ -44,6 +45,7 @@
 #' # mshapviz object
 #' mx <- split(sv, f = iris$Species)
 #' sv_dependence2D(mx, x = "Petal.Length", y = "Sepal.Width")
+#' }
 #' @export
 #' @seealso [sv_dependence()]
 sv_dependence2D <- function(object, ...) {
diff --git a/R/sv_force.R b/R/sv_force.R
index 27bf1c8..9f12805 100644
--- a/R/sv_force.R
+++ b/R/sv_force.R
@@ -15,11 +15,13 @@
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 #' sv_force(x)
+#'
+#' \dontrun{
 #' sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
 #'
 #' # Aggregate over all observations with Petal.Length == 1.4
 #' sv_force(x, row_id = x$X$Petal.Length == 1.4)
-#'
+#' }
 #' @export
 #' @seealso [sv_waterfall()]
 sv_force <- function(object, ...) {
diff --git a/R/sv_importance.R b/R/sv_importance.R
index 50452a8..54a3bd8 100644
--- a/R/sv_importance.R
+++ b/R/sv_importance.R
@@ -47,9 +47,12 @@
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = X_train)
 #' sv_importance(x)
-#' sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 #' sv_importance(x, kind = "no")
 #'
+#' \dontrun{
+#' sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
+#' }
+#'
 #' @seealso \code{\link{sv_interaction}}
 #' @export
 sv_importance <- function(object, ...) {
diff --git a/R/sv_interaction.R b/R/sv_interaction.R
index d77301c..5fc94c0 100644
--- a/R/sv_interaction.R
+++ b/R/sv_interaction.R
@@ -21,8 +21,11 @@
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
-#' sv_interaction(x, max_display = 2, size = 3)
 #' sv_interaction(x, kind = "no")
+#'
+#' \dontrun{
+#' sv_interaction(x, max_display = 2, size = 3)
+#' }
 #' @seealso [sv_importance()]
 #' @export
 sv_interaction <- function(object, ...) {
diff --git a/R/sv_waterfall.R b/R/sv_waterfall.R
index 0eeb560..4eae34e 100644
--- a/R/sv_waterfall.R
+++ b/R/sv_waterfall.R
@@ -41,6 +41,7 @@
 #' sv_waterfall(x)
 #' sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
 #'
+#' \dontrun{
 #' # Ordered by colnames(x), combined with max_display
 #' sv_waterfall(
 #'   x[, sort(colnames(x))], order_fun = function(s) length(s):1, max_display = 3
@@ -48,7 +49,7 @@
 #'
 #' # Aggregate over all observations with Petal.Length == 1.4
 #' sv_waterfall(x, row_id = x$X$Petal.Length == 1.4)
-#'
+#' }
 #' @export
 #' @seealso [sv_force()]
 sv_waterfall <- function(object, ...) {
diff --git a/cran-comments.md b/cran-comments.md
index 9151258..67fe31f 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,3 +1,7 @@
+# Re-resubmission
+
+Examples still taking too long on Linux. No idea how to fix this except using dontruns...
+
 # Resubmission
 
 Examples taking too long on Linux. 
diff --git a/man/shapviz.Rd b/man/shapviz.Rd
index 679c291..73a564f 100644
--- a/man/shapviz.Rd
+++ b/man/shapviz.Rd
@@ -186,6 +186,7 @@ sv_dependence(x, "Species")
 # "X_pred" can also be passed as xgb.DMatrix, but only if X is passed as well!
 x <- shapviz(fit, X_pred = dtrain, X = iris)
 
+\dontrun{
 # Multiclass setting
 params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
 X_pred <- data.matrix(iris[, -5])
@@ -211,6 +212,7 @@ x <- shapviz(
   collapse = list(Species = c("Speciessetosa", "Speciesversicolor", "Speciesvirginica"))
 )
 summary(x)
+}
 
 # Similarly with LightGBM
 if (requireNamespace("lightgbm", quietly = TRUE)) {
diff --git a/man/sv_dependence.Rd b/man/sv_dependence.Rd
index 6b40d8d..44487e5 100644
--- a/man/sv_dependence.Rd
+++ b/man/sv_dependence.Rd
@@ -95,13 +95,14 @@ sv_dependence(x, "Petal.Length", color_var = NULL)
 sv_dependence(x, c("Species", "Petal.Length"))
 sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 
+\dontrun{
 # SHAP interaction values/main effects
 x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_dependence(x2, "Petal.Length", interactions = TRUE)
 sv_dependence(
   x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE
 )
-
+}
 }
 \seealso{
 \code{\link[=potential_interactions]{potential_interactions()}}
diff --git a/man/sv_dependence2D.Rd b/man/sv_dependence2D.Rd
index b11d4b8..bc62eff 100644
--- a/man/sv_dependence2D.Rd
+++ b/man/sv_dependence2D.Rd
@@ -99,6 +99,7 @@ sv <- shapviz(fit, X_pred = dtrain, X = iris)
 sv_dependence2D(sv, x = "Petal.Length", y = "Species")
 sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
 
+\dontrun{
 # SHAP interaction values
 sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_dependence2D(sv2, x = "Petal.Length", y = "Species", interactions = TRUE)
@@ -110,6 +111,7 @@ sv_dependence2D(
 mx <- split(sv, f = iris$Species)
 sv_dependence2D(mx, x = "Petal.Length", y = "Sepal.Width")
 }
+}
 \seealso{
 \code{\link[=sv_dependence]{sv_dependence()}}
 }
diff --git a/man/sv_force.Rd b/man/sv_force.Rd
index 4a2fb84..1881cf3 100644
--- a/man/sv_force.Rd
+++ b/man/sv_force.Rd
@@ -100,11 +100,13 @@ dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_force(x)
+
+\dontrun{
 sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
 
 # Aggregate over all observations with Petal.Length == 1.4
 sv_force(x, row_id = x$X$Petal.Length == 1.4)
-
+}
 }
 \seealso{
 \code{\link[=sv_waterfall]{sv_waterfall()}}
diff --git a/man/sv_importance.Rd b/man/sv_importance.Rd
index 05512a1..402b148 100644
--- a/man/sv_importance.Rd
+++ b/man/sv_importance.Rd
@@ -116,9 +116,12 @@ dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = X_train)
 sv_importance(x)
-sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 sv_importance(x, kind = "no")
 
+\dontrun{
+sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
+}
+
 }
 \seealso{
 \code{\link{sv_interaction}}
diff --git a/man/sv_interaction.Rd b/man/sv_interaction.Rd
index bfc3ff5..b889818 100644
--- a/man/sv_interaction.Rd
+++ b/man/sv_interaction.Rd
@@ -90,8 +90,11 @@ The features are sorted in decreasing order of usual SHAP importance.
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
-sv_interaction(x, max_display = 2, size = 3)
 sv_interaction(x, kind = "no")
+
+\dontrun{
+sv_interaction(x, max_display = 2, size = 3)
+}
 }
 \seealso{
 \code{\link[=sv_importance]{sv_importance()}}
diff --git a/man/sv_waterfall.Rd b/man/sv_waterfall.Rd
index 6238e3b..987af0a 100644
--- a/man/sv_waterfall.Rd
+++ b/man/sv_waterfall.Rd
@@ -108,6 +108,7 @@ x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_waterfall(x)
 sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
 
+\dontrun{
 # Ordered by colnames(x), combined with max_display
 sv_waterfall(
   x[, sort(colnames(x))], order_fun = function(s) length(s):1, max_display = 3
@@ -115,7 +116,7 @@ sv_waterfall(
 
 # Aggregate over all observations with Petal.Length == 1.4
 sv_waterfall(x, row_id = x$X$Petal.Length == 1.4)
-
+}
 }
 \seealso{
 \code{\link[=sv_force]{sv_force()}}

From 3f6bd1f781851c169a080adb317142b133f58147 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Jun 2023 16:14:01 +0200
Subject: [PATCH 09/10] more dontruns

---
 CRAN-SUBMISSION        |  4 ++--
 R/methods.R            |  2 ++
 R/shapviz.R            |  7 +++----
 R/sv_dependence.R      |  2 +-
 R/sv_dependence2D.R    |  2 +-
 R/sv_force.R           |  3 +--
 R/sv_importance.R      |  3 +--
 R/sv_interaction.R     |  3 +--
 R/sv_waterfall.R       |  2 +-
 cran-comments.md       | 10 +++++-----
 man/shapviz.Rd         |  7 +++----
 man/split.shapviz.Rd   |  2 ++
 man/sv_dependence.Rd   |  2 +-
 man/sv_dependence2D.Rd |  2 +-
 man/sv_force.Rd        |  3 +--
 man/sv_importance.Rd   |  3 +--
 man/sv_interaction.Rd  |  3 +--
 man/sv_waterfall.Rd    |  2 +-
 18 files changed, 29 insertions(+), 33 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 7fbbb37..cebaeb2 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.9.0
-Date: 2023-06-08 12:47:01 UTC
-SHA: 11506767b515bf872e0a1fbee5426bd6cba1aea5
+Date: 2023-06-08 18:22:09 UTC
+SHA: 32c5b1b9f2576d24d1522daf2f62df6cb41ad6a2
diff --git a/R/methods.R b/R/methods.R
index 7e07caa..8b778e6 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -271,11 +271,13 @@ c.shapviz <- function(...) {
 #' @param ... Arguments passed to `split()`.
 #' @returns A "mshapviz" object.
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' sv <- shapviz(fit, X_pred = dtrain, X = iris)
 #' mx <- split(sv, f = iris$Species)
 #' sv_dependence(mx, "Petal.Length")
+#' }
 #' @export
 #' @seealso [shapviz()], [rbind.shapviz()]
 split.shapviz <- function(x, f, ...) {
diff --git a/R/shapviz.R b/R/shapviz.R
index 0cc686a..e1f61a2 100644
--- a/R/shapviz.R
+++ b/R/shapviz.R
@@ -63,7 +63,7 @@
 #'     scale of the SHAP values.
 #'   - `S_inter`: Numeric array of SHAP interaction values (or `NULL`).
 #' @seealso
-#'   [sv_importance()], [sv_dependence()], [sv_interaction()],
+#'   [sv_importance()], [sv_dependence()], [sv_dependence2D()], [sv_interaction()],
 #'   [sv_waterfall()], [sv_force()], [collapse_shap()]
 #' @examples
 #' S <- matrix(c(1, -1, -1, 1), ncol = 2, dimnames = list(NULL, c("x", "y")))
@@ -110,7 +110,7 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #'   Creates a "shapviz" object from an XGBoost model.
 #' @export
 #' @examples
-#'
+#' \dontrun{
 #' # XGBoost models
 #' X_pred <- data.matrix(iris[, -1])
 #' dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1])
@@ -128,7 +128,6 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #' # "X_pred" can also be passed as xgb.DMatrix, but only if X is passed as well!
 #' x <- shapviz(fit, X_pred = dtrain, X = iris)
 #'
-#' \dontrun{
 #' # Multiclass setting
 #' params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
 #' X_pred <- data.matrix(iris[, -5])
@@ -154,7 +153,6 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #'   collapse = list(Species = c("Speciessetosa", "Speciesversicolor", "Speciesvirginica"))
 #' )
 #' summary(x)
-#' }
 #'
 #' # Similarly with LightGBM
 #' if (requireNamespace("lightgbm", quietly = TRUE)) {
@@ -183,6 +181,7 @@ shapviz.matrix = function(object, X, baseline = 0, collapse = NULL,
 #'   mx
 #'   all.equal(mx[[3]], x)
 #' }
+#' }
 shapviz.xgb.Booster = function(object, X_pred, X = X_pred, which_class = NULL,
                                collapse = NULL, interactions = FALSE, ...) {
   stopifnot(
diff --git a/R/sv_dependence.R b/R/sv_dependence.R
index 850e9a4..20e7c2d 100644
--- a/R/sv_dependence.R
+++ b/R/sv_dependence.R
@@ -34,6 +34,7 @@
 #' @param ... Arguments passed to [ggplot2::geom_jitter()].
 #' @returns An object of class "ggplot" (or "patchwork") representing a dependence plot.
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris)
@@ -43,7 +44,6 @@
 #' sv_dependence(x, c("Species", "Petal.Length"))
 #' sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 #'
-#' \dontrun{
 #' # SHAP interaction values/main effects
 #' x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_dependence(x2, "Petal.Length", interactions = TRUE)
diff --git a/R/sv_dependence2D.R b/R/sv_dependence2D.R
index 83997bb..6f1e6f6 100644
--- a/R/sv_dependence2D.R
+++ b/R/sv_dependence2D.R
@@ -28,13 +28,13 @@
 #' @param ... Arguments passed to [ggplot2::geom_jitter()].
 #' @returns An object of class "ggplot" (or "patchwork") representing a dependence plot.
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' sv <- shapviz(fit, X_pred = dtrain, X = iris)
 #' sv_dependence2D(sv, x = "Petal.Length", y = "Species")
 #' sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
 #'
-#' \dontrun{
 #' # SHAP interaction values
 #' sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_dependence2D(sv2, x = "Petal.Length", y = "Species", interactions = TRUE)
diff --git a/R/sv_force.R b/R/sv_force.R
index 9f12805..1d8a463 100644
--- a/R/sv_force.R
+++ b/R/sv_force.R
@@ -11,12 +11,11 @@
 #'   (via [ggrepel::geom_text_repel()]).
 #' @returns An object of class "ggplot" (or "patchwork") representing a force plot.
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 #' sv_force(x)
-#'
-#' \dontrun{
 #' sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
 #'
 #' # Aggregate over all observations with Petal.Length == 1.4
diff --git a/R/sv_importance.R b/R/sv_importance.R
index 54a3bd8..fd53c65 100644
--- a/R/sv_importance.R
+++ b/R/sv_importance.R
@@ -42,14 +42,13 @@
 #'   `kind = "no"` - a named numeric vector of sorted SHAP feature importances
 #'   (or a list of such vectors in case of an object of class "mshapviz").
 #' @examples
+#' \dontrun{
 #' X_train <- data.matrix(iris[, -1])
 #' dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = X_train)
 #' sv_importance(x)
 #' sv_importance(x, kind = "no")
-#'
-#' \dontrun{
 #' sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 #' }
 #'
diff --git a/R/sv_interaction.R b/R/sv_interaction.R
index 5fc94c0..36c30d4 100644
--- a/R/sv_interaction.R
+++ b/R/sv_interaction.R
@@ -18,12 +18,11 @@
 #'   numeric matrix of average absolute SHAP interactions sorted by the average
 #'   absolute SHAP values (or a list of such matrices in case of "mshapviz" object).
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 #' sv_interaction(x, kind = "no")
-#'
-#' \dontrun{
 #' sv_interaction(x, max_display = 2, size = 3)
 #' }
 #' @seealso [sv_importance()]
diff --git a/R/sv_waterfall.R b/R/sv_waterfall.R
index 4eae34e..21e8783 100644
--- a/R/sv_waterfall.R
+++ b/R/sv_waterfall.R
@@ -35,13 +35,13 @@
 #'   will altogether suppress adding text to the bars.
 #' @returns An object of class "ggplot" (or "patchwork") representing a waterfall plot.
 #' @examples
+#' \dontrun{
 #' dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 #' fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 #' x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 #' sv_waterfall(x)
 #' sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
 #'
-#' \dontrun{
 #' # Ordered by colnames(x), combined with max_display
 #' sv_waterfall(
 #'   x[, sort(colnames(x))], order_fun = function(s) length(s):1, max_display = 3
diff --git a/cran-comments.md b/cran-comments.md
index 67fe31f..c1cd7fe 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,5 +1,9 @@
 # Re-resubmission
 
+Examples that take 0.09 seconds on my Windows laptop take 6 seconds on Linux. I don't know how to fix this, so I am resorting to dontruns around all examples involving XGBoost.
+
+# Re-resubmission
+
 Examples still taking too long on Linux. No idea how to fix this except using dontruns...
 
 # Resubmission
@@ -19,11 +23,7 @@ Hello CRAN team
 
 ## Checks look good
 
-### check(manual = TRUE, cran = TRUE) 
-
-> checking data for ASCII and uncompressed saves ... OK
-   WARNING
-  'qpdf' is needed for checks on size reduction of PDFs
+### check(manual = TRUE, cran = TRUE)
 
 > checking HTML version of manual ... NOTE
   Skipping checking HTML validation: no command 'tidy' found
diff --git a/man/shapviz.Rd b/man/shapviz.Rd
index 73a564f..3ef9642 100644
--- a/man/shapviz.Rd
+++ b/man/shapviz.Rd
@@ -168,7 +168,7 @@ return a "mshapviz" object, containing a "shapviz" object per output.
 S <- matrix(c(1, -1, -1, 1), ncol = 2, dimnames = list(NULL, c("x", "y")))
 X <- data.frame(x = c("a", "b"), y = c(100, 10))
 shapviz(S, X, baseline = 4)
-
+\dontrun{
 # XGBoost models
 X_pred <- data.matrix(iris[, -1])
 dtrain <- xgboost::xgb.DMatrix(X_pred, label = iris[, 1])
@@ -186,7 +186,6 @@ sv_dependence(x, "Species")
 # "X_pred" can also be passed as xgb.DMatrix, but only if X is passed as well!
 x <- shapviz(fit, X_pred = dtrain, X = iris)
 
-\dontrun{
 # Multiclass setting
 params <- list(objective = "multi:softprob", num_class = 3, nthread = 1)
 X_pred <- data.matrix(iris[, -5])
@@ -212,7 +211,6 @@ x <- shapviz(
   collapse = list(Species = c("Speciessetosa", "Speciesversicolor", "Speciesvirginica"))
 )
 summary(x)
-}
 
 # Similarly with LightGBM
 if (requireNamespace("lightgbm", quietly = TRUE)) {
@@ -242,7 +240,8 @@ if (requireNamespace("lightgbm", quietly = TRUE)) {
   all.equal(mx[[3]], x)
 }
 }
+}
 \seealso{
-\code{\link[=sv_importance]{sv_importance()}}, \code{\link[=sv_dependence]{sv_dependence()}}, \code{\link[=sv_interaction]{sv_interaction()}},
+\code{\link[=sv_importance]{sv_importance()}}, \code{\link[=sv_dependence]{sv_dependence()}}, \code{\link[=sv_dependence2D]{sv_dependence2D()}}, \code{\link[=sv_interaction]{sv_interaction()}},
 \code{\link[=sv_waterfall]{sv_waterfall()}}, \code{\link[=sv_force]{sv_force()}}, \code{\link[=collapse_shap]{collapse_shap()}}
 }
diff --git a/man/split.shapviz.Rd b/man/split.shapviz.Rd
index 55a0cb2..35227f2 100644
--- a/man/split.shapviz.Rd
+++ b/man/split.shapviz.Rd
@@ -20,12 +20,14 @@ A "mshapviz" object.
 Splits "shapviz" object along a vector \code{f} into an object of class "mshapviz".
 }
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 sv <- shapviz(fit, X_pred = dtrain, X = iris)
 mx <- split(sv, f = iris$Species)
 sv_dependence(mx, "Petal.Length")
 }
+}
 \seealso{
 \code{\link[=shapviz]{shapviz()}}, \code{\link[=rbind.shapviz]{rbind.shapviz()}}
 }
diff --git a/man/sv_dependence.Rd b/man/sv_dependence.Rd
index 44487e5..b486032 100644
--- a/man/sv_dependence.Rd
+++ b/man/sv_dependence.Rd
@@ -86,6 +86,7 @@ to focus on pure interaction effects (multiplied by two) or on pure main effects
 
 }}
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris)
@@ -95,7 +96,6 @@ sv_dependence(x, "Petal.Length", color_var = NULL)
 sv_dependence(x, c("Species", "Petal.Length"))
 sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))
 
-\dontrun{
 # SHAP interaction values/main effects
 x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_dependence(x2, "Petal.Length", interactions = TRUE)
diff --git a/man/sv_dependence2D.Rd b/man/sv_dependence2D.Rd
index bc62eff..7a685ee 100644
--- a/man/sv_dependence2D.Rd
+++ b/man/sv_dependence2D.Rd
@@ -93,13 +93,13 @@ to focus on pure interaction effects (multiplied by two).
 
 }}
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 sv <- shapviz(fit, X_pred = dtrain, X = iris)
 sv_dependence2D(sv, x = "Petal.Length", y = "Species")
 sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")
 
-\dontrun{
 # SHAP interaction values
 sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_dependence2D(sv2, x = "Petal.Length", y = "Species", interactions = TRUE)
diff --git a/man/sv_force.Rd b/man/sv_force.Rd
index 1881cf3..3621732 100644
--- a/man/sv_force.Rd
+++ b/man/sv_force.Rd
@@ -96,12 +96,11 @@ baseline SHAP value.
 
 }}
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_force(x)
-
-\dontrun{
 sv_force(x, row_id = 65, max_display = 3, size = 9, fill_colors = 4:5)
 
 # Aggregate over all observations with Petal.Length == 1.4
diff --git a/man/sv_importance.Rd b/man/sv_importance.Rd
index 402b148..596ec13 100644
--- a/man/sv_importance.Rd
+++ b/man/sv_importance.Rd
@@ -111,14 +111,13 @@ are sorted in decreasing order of importance.
 
 }}
 \examples{
+\dontrun{
 X_train <- data.matrix(iris[, -1])
 dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = X_train)
 sv_importance(x)
 sv_importance(x, kind = "no")
-
-\dontrun{
 sv_importance(x, kind = "beeswarm", show_numbers = TRUE)
 }
 
diff --git a/man/sv_interaction.Rd b/man/sv_interaction.Rd
index b889818..a8390fa 100644
--- a/man/sv_interaction.Rd
+++ b/man/sv_interaction.Rd
@@ -87,12 +87,11 @@ The features are sorted in decreasing order of usual SHAP importance.
 
 }}
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
 sv_interaction(x, kind = "no")
-
-\dontrun{
 sv_interaction(x, max_display = 2, size = 3)
 }
 }
diff --git a/man/sv_waterfall.Rd b/man/sv_waterfall.Rd
index 987af0a..35ac9ec 100644
--- a/man/sv_waterfall.Rd
+++ b/man/sv_waterfall.Rd
@@ -102,13 +102,13 @@ baseline SHAP value.
 
 }}
 \examples{
+\dontrun{
 dtrain <- xgboost::xgb.DMatrix(data.matrix(iris[, -1]), label = iris[, 1])
 fit <- xgboost::xgb.train(data = dtrain, nrounds = 20, nthread = 1)
 x <- shapviz(fit, X_pred = dtrain, X = iris[, -1])
 sv_waterfall(x)
 sv_waterfall(x, row_id = 123, max_display = 2, size = 9, fill_colors = 4:5)
 
-\dontrun{
 # Ordered by colnames(x), combined with max_display
 sv_waterfall(
   x[, sort(colnames(x))], order_fun = function(s) length(s):1, max_display = 3

From daf83f45b54e81d20413402a184d9897bbdcd8a2 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Jun 2023 17:13:27 +0200
Subject: [PATCH 10/10] Accepted

---
 CRAN-SUBMISSION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index cebaeb2..ab6e4fc 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.9.0
-Date: 2023-06-08 18:22:09 UTC
-SHA: 32c5b1b9f2576d24d1522daf2f62df6cb41ad6a2
+Date: 2023-06-09 14:22:29 UTC
+SHA: 3f6bd1f781851c169a080adb317142b133f58147