RC 1.3.2 (#1274)

* more changes for #1271

* Error: SSL peer certificate or SSH remote key was not OK [www.rulequest.com]

* version bump

* sorted and formatted

* avoid namespace/class issues

* no suggests for the survival package

Author: topepo

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.3.1.9000
+Version: 1.3.2
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),

NEWS.md

@@ -1,4 +1,10 @@
-# parsnip (development version)
+# parsnip 1.3.2
+
+* Switch to base R pipe
+
+* Requires changes for CRAN's "No Suggests" check.
+
+* Avoid issues with reading from package files. (#1271)
 
 # parsnip 1.3.1
 

R/aaa_archive.R

@@ -552,7 +552,7 @@ xgb_by_tree <- function(tree, object, new_data, type, ...) {
 #' @param weights An optional numeric vector of case weights. Note
 #'  that the data used for the case weights will not be used as a
 #'  splitting variable in the model (see
-#'  \url{https://www.rulequest.com/see5-info.html} for
+#'  `https://www.rulequest.com/see5-info.html` for
 #'  Quinlan's notes on case weights).
 #' @param minCases An integer for the smallest number of samples
 #'  that must be put in at least two of the splits.

R/boost_tree.R

@@ -208,7 +208,7 @@ make_engine_list <- function(mod) {
   }
 
   exts <-
-    utils::read.delim(system.file("models.tsv", package = "parsnip")) |>
+    model_info_table |>
     dplyr::filter(model == mod) |>
     dplyr::group_by(engine, mode) |>
     dplyr::summarize(extensions = sum(!is.na(pkg)), .groups = "drop") |>

R/engine_docs.R

@@ -32,7 +32,7 @@ check_pkg_for_docs(parsnip:::extensions())
 make_mode_list <- function(mod, eng) {
   modes <- c("regression", "classification", "censored regression")
   exts <-
-    utils::read.delim(system.file("models.tsv", package = "parsnip")) |>
+    model_info_table |>
     dplyr::filter(model == mod & engine == eng) |> 
     dplyr::mutate(mode = factor(mode, levels = modes)) |> 
     dplyr::arrange(mode)
@@ -128,7 +128,7 @@ descr_models <- function(mod, eng) {
 
 uses_extension <- function(mod, eng, mod_mode) {
   exts <-
-    utils::read.delim(system.file("models.tsv", package = "parsnip")) |>
+    model_info_table |>
     dplyr::filter(
       model == mod & 
         engine == eng & 

man/C5.0_train.Rd

@@ -56,31 +56,33 @@ Some relevant arguments that can be passed to `set_engine()`:
 
 ```{r}
 #| label: bart-cls
-bart(
+parsnip::bart(
   trees = integer(1),
   prior_terminal_node_coef = double(1),
   prior_terminal_node_expo = double(1),
   prior_outcome_range = double(1)
 ) |> 
   set_engine("dbarts") |> 
   set_mode("classification") |> 
-  translate()
+  translate() |> 
+  print_model_spec()
 ```
 
 
 ## Translation from parsnip to the original package (regression)
 
 ```{r}
 #| label: bart-reg
-bart(
+parsnip::bart(
   trees = integer(1),
   prior_terminal_node_coef = double(1),
   prior_terminal_node_expo = double(1),
   prior_outcome_range = double(1)
 ) |> 
   set_engine("dbarts") |> 
   set_mode("regression") |> 
-  translate()
+  translate()|> 
+  print_model_spec()
 ```
 
 ## Preprocessing requirements

man/details_bart_dbarts.Rd

@@ -41,43 +41,67 @@ Some relevant arguments that can be passed to `set_engine()`:
 
 
 ``` r
-bart(
+parsnip::bart(
   trees = integer(1),
   prior_terminal_node_coef = double(1),
   prior_terminal_node_expo = double(1),
   prior_outcome_range = double(1)
 ) |> 
   set_engine("dbarts") |> 
   set_mode("classification") |> 
-  translate()
+  translate() |> 
+  print_model_spec()
 ```
 
 ```
+## BART Model Specification (classification)
 ## 
-## Call:
-## NULL
+## Main Arguments:
+##   trees = integer(1)
+##   prior_terminal_node_coef = double(1)
+##   prior_terminal_node_expo = double(1)
+##   prior_outcome_range = double(1)
+## 
+## Computational engine: dbarts 
+## 
+## Model fit template:
+## dbarts::bart(x = missing_arg(), y = missing_arg(), ntree = integer(1), 
+##     base = double(1), power = double(1), k = double(1), verbose = FALSE, 
+##     keeptrees = TRUE, keepcall = FALSE)
 ```
 
 
 ## Translation from parsnip to the original package (regression)
 
 
 ``` r
-bart(
+parsnip::bart(
   trees = integer(1),
   prior_terminal_node_coef = double(1),
   prior_terminal_node_expo = double(1),
   prior_outcome_range = double(1)
 ) |> 
   set_engine("dbarts") |> 
   set_mode("regression") |> 
-  translate()
+  translate()|> 
+  print_model_spec()
 ```
 
 ```
+## BART Model Specification (regression)
+## 
+## Main Arguments:
+##   trees = integer(1)
+##   prior_terminal_node_coef = double(1)
+##   prior_terminal_node_expo = double(1)
+##   prior_outcome_range = double(1)
+## 
+## Computational engine: dbarts 
 ## 
-## Call:
-## NULL
+## Model fit template:
+## dbarts::bart(x = missing_arg(), y = missing_arg(), ntree = integer(1), 
+##     base = double(1), power = double(1), k = double(1), verbose = FALSE, 
+##     keeptrees = TRUE, keepcall = FALSE)
 ```
 
 ## Preprocessing requirements