more engine doc updates

Author: topepo

man/details_bag_tree_rpart.Rd

@@ -107,8 +107,7 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 ## 
 ## Model fit template:
 ## ipred::bagging(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
-##     minsplit = integer(1))
+##     cp = double(1), maxdepth = integer(1), minsplit = integer(1))
 ```
 
 

man/details_boost_tree_mboost.Rd

@@ -42,7 +42,7 @@ boost_tree() %>%
 ## 
 ## Model fit template:
 ## censored::blackboost_train(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), family = mboost::CoxPH())
+##     family = mboost::CoxPH())
 ```
 
 `censored::blackboost_train()` is a wrapper around [mboost::blackboost()] (and other functions) that makes it easier to run this model. 

man/details_decision_tree_party.Rd

@@ -44,8 +44,8 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
 ## 
 ## Model fit template:
 ## censored::cond_inference_surv_ctree(formula = missing_arg(), 
-##     data = missing_arg(), weights = missing_arg(), maxdepth = integer(1), 
-##     minsplit = min_rows(0L, data))
+##     data = missing_arg(), maxdepth = integer(1), minsplit = min_rows(0L, 
+##         data))
 ```
 
 `censored::cond_inference_surv_ctree()` is a wrapper around [party::ctree()] (and other functions) that makes it easier to run this model. 

man/details_decision_tree_rpart.Rd

@@ -99,8 +99,8 @@ decision_tree(
 ## 
 ## Model fit template:
 ## pec::pecRpart(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
-##     minsplit = min_rows(0L, data))
+##     cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, 
+##         data))
 ```
 
 ## Preprocessing requirements

man/details_linear_reg_brulee.Rd

@@ -51,6 +51,11 @@ linear_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.