RC 1.1.1 (#995)

* version bump

* Documented arguments not in \usage

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

* avoid tests on cran

* remove old checks; no longer supported in current python

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.1.0.9004
+Version: 1.1.1
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),
@@ -76,4 +76,4 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.2.3.9000

NEWS.md

@@ -1,14 +1,14 @@
-# parsnip (development version)
+# parsnip 1.1.1
 
-* Fixed bug where sparse data was being coerced to non-sparse format doing `predict()`. 
+* Fixed bug where prediction on rank deficient `lm()` models produced `.pred_res` instead of `.pred`. (#985)
 
-* `augment()` now works for censored regression models. 
+* Fixed bug where sparse data was being coerced to non-sparse format doing `predict()`. 
 
 * For BART models with the `dbarts` engine, `predict()` can now also return the standard error for confidence and prediction intervals (#976).
 
-* A few censored regression helper functions were exported: `.extract_surv_status()` and `.extract_surv_time()` (#973, #980).
+* `augment()` now works for censored regression models. 
 
-* Fixed bug where prediction on rank dificient `lm()` models produced `.pred_res` instead of `.pred`. (#985)
+* A few censored regression helper functions were exported: `.extract_surv_status()` and `.extract_surv_time()` (#973, #980).
 
 * Fixed bug where `boost_tree()` models couldn't be fit with 1 predictor if `validation` argument was used. (#994)
 

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)
@@ -473,7 +473,6 @@ check_interface_val <- function(x) {
 #'  `"rand_forest"`, etc).
 #' @param mode A single character string for the model mode (e.g. "regression").
 #' @param eng A single character string for the model engine.
-#' @param arg A single character string for the model argument name.
 #' @param has_submodel A single logical for whether the argument
 #'  can make predictions on multiple submodels at once.
 #' @param func A named character vector that describes how to call
@@ -483,10 +482,6 @@ check_interface_val <- function(x) {
 #'  used to invoke the usual linear regression function. In some
 #'  cases, it is helpful to use `c(fun = "predict")` when using a
 #'  package's `predict` method.
-#' @param fit_obj A list with elements `interface`, `protect`,
-#'  `func` and `defaults`. See the package vignette "Making a
-#'  `parsnip` model from scratch".
-#' @param pred_obj A list with elements `pre`, `post`, `func`, and `args`.
 #' @param type A single character value for the type of prediction. Possible
 #'  values are: `class`, `conf_int`, `numeric`, `pred_int`, `prob`, `quantile`,
 #'   and `raw`.
@@ -557,7 +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

@@ -1,7 +1,6 @@
 #' Knit engine-specific documentation
 #' @param pattern A regular expression to specify which files to knit. The
 #' default knits all engine documentation files.
-#' @param ... Options passed to [knitr::knit()].
 #' @return A tibble with column `file` for the file name and `result` (a
 #' character vector that echos the output file name or, when there is
 #' a failure, the error message).
@@ -138,7 +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)