changes for CRAN check; see Rdatatable/data.table#5658

R/aaa_models.R

@@ -54,7 +54,7 @@ model_info_table <-
 #' @keywords internal
 #' @references "How to build a parsnip model"
 #'  \url{https://www.tidymodels.org/learn/develop/models/}
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # Access the model data:
 #' current_code <- get_model_env()
 #' ls(envir = current_code)
@@ -552,7 +552,7 @@ check_interface_val <- function(x) {
 #'
 #' @references "How to build a parsnip model"
 #'  \url{https://www.tidymodels.org/learn/develop/models/}
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # set_new_model("shallow_learning_model")
 #'
 #' # Show the information about a model:

R/aaa_multi_predict.R

@@ -53,7 +53,7 @@ predict.model_spec <- function(object, ...) {
 #'  `multi_predict_args()` returns a character vector of argument names (or `NA`
 #'  if none exist).
 #' @keywords internal
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' lm_model_idea <- linear_reg() %>% set_engine("lm")
 #' has_multi_predict(lm_model_idea)
 #' lm_model_fit <- fit(lm_model_idea, mpg ~ ., data = mtcars)

R/adds.R

@@ -2,7 +2,7 @@
 #'
 #' @param x A data frame
 #' @return The same data frame with a column of 1-based integers named `.row`.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' mtcars %>% add_rowindex()
 #' @export
 add_rowindex <- function(x) {

R/arguments.R

@@ -40,7 +40,7 @@ check_eng_args <- function(args, obj, core_args) {
 #' @return An updated model object.
 #' @details `set_args()` will replace existing values of the arguments.
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' rand_forest()
 #'
 #' rand_forest() %>%
@@ -294,7 +294,7 @@ make_xy_call <- function(object, target, env) {
 #' @param offset A number subtracted off of the number of rows available in the
 #' data.
 #' @return An integer (and perhaps a warning).
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 
 #' nearest_neighbor(neighbors= 100) %>%
 #'   set_engine("kknn") %>%

R/augment.R

@@ -37,7 +37,7 @@
 #' @references
 #' \url{https://www.tidymodels.org/learn/statistics/survival-metrics/}
 #' @export
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' car_trn <- mtcars[11:32,]
 #' car_tst <- mtcars[ 1:10,]
 #'

R/bart.R

@@ -42,7 +42,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("bart")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("bart")
 #'
 #' bart(mode = "regression", trees = 5)

R/boost_tree.R

@@ -47,7 +47,7 @@
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("boost_tree")},
 #' [xgb_train()], [C5.0_train()]
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("boost_tree")
 #'
 #' boost_tree(mode = "classification", trees = 20)

R/c5_rules.R

@@ -39,7 +39,7 @@
 #' @seealso [C50::C5.0()], [C50::C5.0Control()],
 #' \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("C5_rules")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("C5_rules")
 #'
 #' C5_rules()
@@ -73,7 +73,7 @@ C5_rules <-
 #'
 #' @param object A `C5_rules` model specification.
 #' @inheritParams update.boost_tree
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' # ------------------------------------------------------------------------------
 #'

R/condense_control.R

@@ -13,7 +13,7 @@
 #' @keywords internal
 #' @export
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' ctrl <- control_parsnip(catch = TRUE)
 #' ctrl$allow_par <- TRUE
 #' str(ctrl)

R/control_parsnip.R

@@ -15,7 +15,7 @@
 #' @return An S3 object with class "control_parsnip" that is a named list
 #' with the results of the function call
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' control_parsnip(verbosity = 2L)
 #'
 #' @export

R/cubist_rules.R

@@ -95,7 +95,7 @@ cubist_rules <-
 # ------------------------------------------------------------------------------
 
 #' @param object A Cubist model specification.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' # ------------------------------------------------------------------------------
 #'

R/data.R

@@ -8,7 +8,7 @@
 #' @docType data
 #' @return \item{model_db}{a data frame}
 #' @keywords datasets internal
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' data(model_db)
 NULL
 

R/decision_tree.R

@@ -26,7 +26,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("decision_tree")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("decision_tree")
 #'
 #' decision_tree(mode = "classification", tree_depth = 5)

R/engine_docs.R

@@ -137,7 +137,7 @@ update_model_info_file <- function(path = "inst/models.tsv") {
 #' @name doc-tools
 #' @keywords internal
 #' @export
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # See this file for step-by-step instructions.
 #' system.file("README-DOCS.md", package = "parsnip")
 #'

R/engines.R

@@ -90,7 +90,7 @@ load_libs <- function(x, quiet, attach = FALSE) {
 #' @param ... Any optional arguments associated with the chosen computational
 #'  engine. These are captured as quosures and can be tuned with `tune()`.
 #' @return An updated model specification.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # First, set main arguments using the standardized names
 #' logistic_reg(penalty = 0.01, mixture = 1/3) %>%
 #'   # Now specify how you want to fit the model with another argument
@@ -160,7 +160,7 @@ set_engine.default <- function(object, engine, ...) {
 #' @param x The name of a `parsnip` model (e.g., "linear_reg", "mars", etc.)
 #' @return A tibble.
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("linear_reg")
 #' @export
 show_engines <- function(x) {

R/extract.R

@@ -41,7 +41,7 @@
 #' section.
 #'
 #' @name extract-parsnip
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' lm_spec <- linear_reg() %>% set_engine("lm")
 #' lm_fit <- fit(lm_spec, mpg ~ ., data = mtcars)
 #'

R/fit.R

@@ -55,7 +55,7 @@
 #' a "reverse Kaplan-Meier" curve that models the probability of censoring. This
 #' may be used later to compute inverse probability censoring weights for
 #' performance measures.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # Although `glm()` only has a formula interface, different
 #' # methods for specifying the model can be used
 #'

R/fit_control.R

@@ -18,7 +18,7 @@
 #' @return An S3 object with class "control_parsnip" that is a named list
 #' with the results of the function call
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' fit_control(verbosity = 2L)
 #'
 #' @details

R/gen_additive_mod.R

@@ -24,7 +24,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("gen_additive_mod")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("gen_additive_mod")
 #'
 #' gen_additive_mod()

R/grouped_binomial.R

@@ -21,7 +21,7 @@
 #' @param ... Options to pass to [stats::glm()]. If `family` is not set, it will
 #' automatically be assigned the basic binomial family.
 #' @return A object produced by [stats::glm()].
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' #----------------------------------------------------------------------------
 #' # The same data set formatted three ways
 #'

R/linear_reg.R

@@ -33,7 +33,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("linear_reg")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("linear_reg")
 #'
 #' linear_reg()

R/logistic_reg.R

@@ -40,7 +40,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("logistic_reg")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("logistic_reg")
 #'
 #' logistic_reg()

R/mars.R

@@ -25,7 +25,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("mars")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("mars")
 #'
 #' mars(mode = "regression", num_terms = 5)

R/misc.R

@@ -526,3 +526,20 @@ check_for_newdata <- function(..., call = rlang::caller_env()) {
     )
   }
 }
+
+
+# ------------------------------------------------------------------------------
+
+# driven by https://github.com/Rdatatable/data.table/issues/5658
+# nocov start
+# adapted from ps:::is_cran_check()
+is_cran_check <- function() {
+  if (identical(Sys.getenv("NOT_CRAN"), "true")) {
+    FALSE
+  }
+  else {
+    Sys.getenv("_R_CHECK_PACKAGE_NAME_", "") != ""
+  }
+}
+# nocov end
+

R/mlp.R

@@ -30,7 +30,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("mlp")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("mlp")
 #'
 #' mlp(mode = "classification", penalty = 0.01)

R/model_object_docs.R

@@ -175,7 +175,7 @@ NULL
 #'  stores model objects after seeing the data and applying a model.
 #' @rdname model_fit
 #' @name model_fit
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' # Keep the `x` matrix if the data are not too big.
 #' spec_obj <-

R/multinom_reg.R

@@ -40,7 +40,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("multinom_reg")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("multinom_reg")
 #'
 #' multinom_reg()

R/nearest_neighbor.R

@@ -29,7 +29,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("nearest_neighbor")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("nearest_neighbor")
 #'
 #' nearest_neighbor(neighbors = 11)

R/nullmodel.R

@@ -32,7 +32,7 @@
 #' `predict.nullmodel()` returns either a factor or numeric vector
 #' depending on the class of \code{y}. All predictions are always the same.
 #' @keywords models internal
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' outcome <- factor(sample(letters[1:2],
 #'                          size = 100,
@@ -140,7 +140,7 @@ predict.nullmodel <- function (object, new_data = NULL, type  = NULL, ...) {
 #' @includeRmd man/rmd/null-model.md details
 #'
 #' @seealso [fit.model_spec()]
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' null_model(mode = "regression")
 #' @export
 null_model <-
@@ -178,7 +178,7 @@ null_model <-
 #' @return A tibble with column `value`.
 #' @export
 #' @keywords internal
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' nullmodel(mtcars[,-1], mtcars$mpg) %>% tidy()
 

R/partykit.R

@@ -26,7 +26,7 @@
 #' @param ntree Number of trees to grow in a forest.
 #' @param ... Other options to pass to [partykit::ctree()] or [partykit::cforest()].
 #' @return An object of class `party` (for `ctree`) or `cforest`.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' if (rlang::is_installed(c("modeldata", "partykit"))) {
 #'   data(bivariate, package = "modeldata")
 #'   ctree_train(Class ~ ., data = bivariate_train)
@@ -184,7 +184,7 @@ cforest_train <-
 #' @param formula A model formula.
 #' @param data The training set (data frame).
 #' @return A value for `mtry`.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' # should be 9
 #' max_mtry_formula(200, cbind(wt, mpg) ~ ., data = mtcars)
 #' @export

R/pls.R

@@ -54,7 +54,7 @@ pls <-
 #' @param ... Not used for `update()`.
 #' @param fresh A logical for whether the arguments should be
 #'  modified in-place of or replaced wholesale.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' model <- pls(predictor_prop =  0.1)
 #' model
 #' update(model, predictor_prop = 1)

R/predict.R

@@ -114,7 +114,7 @@
 #'  multivariate models.
 #' @references
 #' \url{https://www.tidymodels.org/learn/statistics/survival-metrics/}
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' library(dplyr)
 #'
 #' lm_model <-

R/proportional_hazards.R

@@ -27,7 +27,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("proportional_hazards")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("proportional_hazards")
 #'
 #' proportional_hazards(mode = "censored regression")

R/rand_forest.R

@@ -27,7 +27,7 @@
 #'
 #' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("rand_forest")}
 #'
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' show_engines("rand_forest")
 #'
 #' rand_forest(mode = "classification", trees = 2000)

R/repair_call.R

@@ -13,7 +13,7 @@
 #'  is the data frame that was given to `parsnip` for the model fit (i.e., the
 #'  training set data). The name of this data object is inserted into the call.
 #' @return A modified `parsnip` fitted model.
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #'
 #' fitted_model <-
 #'   linear_reg() %>%

R/required_pkgs.R

@@ -5,7 +5,7 @@
 #' @param ... Not used.
 #' @return A character vector
 #' @name required_pkgs.model_spec
-#' @examples
+#' @examplesIf !parsnip:::is_cran_check()
 #' should_fail <- try(required_pkgs(linear_reg(engine = NULL)), silent = TRUE)
 #' should_fail
 #'