added more notes in engine docs

Author: topepo

man/rmd/bag_mars_earth.Rmd

@@ -57,6 +57,11 @@ bag_mars(
 ```{r child = "template-makes-dummies.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-uses-case-weights.Rmd"}
+```
+
 ## References
 
  - Breiman, L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/bag_mars_earth.md

@@ -84,6 +84,13 @@ bag_mars(
 
 Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
+## Case weights
+
+
+This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
+
+The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
+
 ## References
 
  - Breiman, L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/bag_tree_rpart.Rmd

@@ -72,6 +72,11 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 ```{r child = "template-tree-split-factors.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-uses-case-weights.Rmd"}
+```
+
 ## References
 
  - Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/bag_tree_rpart.md

@@ -116,6 +116,13 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1
 
 This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. `{a, c}` vs `{b, d}`) when splitting at a node. Dummy variables are not required for this model. 
 
+## Case weights
+
+
+This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
+
+The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
+
 ## References
 
  - Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/discrim_flexible_earth.Rmd

@@ -44,6 +44,10 @@ discrim_flexible(
 ```{r child = "template-makes-dummies.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-uses-case-weights.Rmd"}
+```
 
 ## References
 

man/rmd/discrim_flexible_earth.md

@@ -53,6 +53,12 @@ discrim_flexible(
 
 Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
 
+## Case weights
+
+
+This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
+
+The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
 
 ## References
 

man/rmd/discrim_linear_mda.Rmd

@@ -43,6 +43,11 @@ discrim_linear(penalty = numeric(0)) %>%
 ```{r child = "template-zv.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-uses-case-weights.Rmd"}
+```
+
 ## References
 
  - Hastie, Tibshirani & Buja (1994) Flexible Discriminant Analysis by Optimal 

man/rmd/discrim_linear_mda.md

@@ -48,6 +48,13 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 
 
 
+## Case weights
+
+
+This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
+
+The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
+
 ## References
 
  - Hastie, Tibshirani & Buja (1994) Flexible Discriminant Analysis by Optimal