Updated rd files

man/rmd/C5_rules_C5.0.md

@@ -20,7 +20,7 @@ Note that C5.0 has a tool for _early stopping_ during boosting where less iterat
 The **rules** extension package is required to fit this model.
 
 
-```r
+``` r
 library(rules)
 
 C5_rules(

man/rmd/auto_ml_h2o.md

@@ -20,7 +20,7 @@ Engine arguments of interest
 [agua::h2o_train_auto()] is a wrapper around [h2o::h2o.automl()]. 
 
 
-```r
+``` r
 auto_ml() %>%  
   set_engine("h2o") %>% 
   set_mode("regression") %>% 
@@ -41,7 +41,7 @@ auto_ml() %>%
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 auto_ml() %>%  
   set_engine("h2o") %>% 
   set_mode("classification") %>% 

man/rmd/bag_mars_earth.md

@@ -22,7 +22,7 @@ The default value of `num_terms` depends on the number of predictor columns. For
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% 
   set_engine("earth") %>% 
   set_mode("regression") %>% 
@@ -50,7 +50,7 @@ bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = charac
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_mars(

man/rmd/bag_mlp_nnet.md

@@ -22,7 +22,7 @@ These defaults are set by the `baguette` package and are different than those in
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% 
@@ -52,7 +52,7 @@ bag_mlp(penalty = double(1), hidden_units = integer(1)) %>%
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_mlp(penalty = double(1), hidden_units = integer(1)) %>% 

man/rmd/bag_tree_C5.0.md

@@ -16,7 +16,7 @@ This model has 1 tuning parameters:
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_tree(min_n = integer()) %>% 

man/rmd/bag_tree_rpart.md

@@ -25,7 +25,7 @@ For the `class_cost` parameter, the value can be a non-negative scalar for a cla
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% 
@@ -56,7 +56,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 The **baguette** extension package is required to fit this model.
 
 
-```r
+``` r
 library(baguette)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% 
@@ -86,7 +86,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 The **censored** extension package is required to fit this model.
 
 
-```r
+``` r
 library(censored)
 
 bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% 

man/rmd/boost_tree_C5.0.md

@@ -20,7 +20,7 @@ The implementation of C5.0 limits the number of trees to be between 1 and 100.
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 boost_tree(trees = integer(), min_n = integer(), sample_size = numeric()) %>% 
   set_engine("C5.0") %>% 
   set_mode("classification") %>% 

man/rmd/boost_tree_h2o.md

@@ -36,7 +36,7 @@ This model has 8 tuning parameters:
 The **agua** extension package is required to fit this model.
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), tree_depth = integer(), 
   learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer()
@@ -73,7 +73,7 @@ boost_tree(
 The **agua** extension package is required to fit this model.
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), tree_depth = integer(), 
   learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer()

man/rmd/boost_tree_mboost.md

@@ -26,7 +26,7 @@ The `mtry` parameter is related to the number of predictors. The default is to u
 The **censored** extension package is required to fit this model.
 
 
-```r
+``` r
 library(censored)
 
 boost_tree() %>% 

man/rmd/boost_tree_spark.md

@@ -28,7 +28,7 @@ The `mtry` parameter is related to the number of predictors. The default depends
 ## Translation from parsnip to the original package (regression)
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
   learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric()
@@ -63,7 +63,7 @@ boost_tree(
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
   learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric()

man/rmd/boost_tree_xgboost.md

@@ -30,7 +30,7 @@ For `mtry`, the default value of `NULL` translates to using all available column
 ## Translation from parsnip to the original package (regression)
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
   learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(),
@@ -67,7 +67,7 @@ boost_tree(
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(),
   learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric(),
@@ -128,7 +128,7 @@ This model can utilize sparse data during model fitting and prediction. Both spa
 The xgboost function that parsnip indirectly wraps, [xgboost::xgb.train()], takes most arguments via the `params` list argument. To supply engine-specific arguments that are documented in [xgboost::xgb.train()] as arguments to be passed via `params`, supply the list elements directly as named arguments to [set_engine()] rather than as elements in `params`. For example, pass a non-default evaluation metric like this:
 
 
-```r
+``` r
 # good
 boost_tree() %>%
   set_engine("xgboost", eval_metric = "mae")
@@ -146,7 +146,7 @@ boost_tree() %>%
 ...rather than this:
 
 
-```r
+``` r
 # bad
 boost_tree() %>%
   set_engine("xgboost", params = list(eval_metric = "mae"))

man/rmd/cubist_rules_Cubist.md

@@ -21,7 +21,7 @@ This model has 3 tuning parameters:
 The **rules** extension package is required to fit this model.
 
 
-```r
+``` r
 library(rules)
 
 cubist_rules(

man/rmd/decision_tree_C5.0.md

@@ -14,7 +14,7 @@ This model has 1 tuning parameters:
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 decision_tree(min_n = integer()) %>% 
   set_engine("C5.0") %>% 
   set_mode("classification") %>%