doc update

Author: topepo

man/details_boost_tree_lightgbm.Rd

@@ -133,6 +133,11 @@ To effectively enable bagging, the user would also need to set the `bagging_freq
 
 bonsai quiets much of the logging output from [lightgbm::lgb.train()] by default. With default settings, logged warnings and errors will still be passed on to the user. To print out all logs during training, set `quiet = TRUE`.
 
+## Sparse Data
+
+
+This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.
+
 ## Examples 
 
 The "Introduction to bonsai" article contains [examples](https://bonsai.tidymodels.org/articles/bonsai.html) of `boost_tree()` with the `"lightgbm"` engine.

man/details_decision_tree_partykit.Rd

@@ -1,18 +1,18 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
 
 
 This model has 2 tuning parameters:
 
-- `min_n`: Minimal Node Size (type: integer, default: 20L)
-
 - `tree_depth`: Tree Depth (type: integer, default: see below)
 
+- `min_n`: Minimal Node Size (type: integer, default: 20L)
+
 The `tree_depth` parameter defaults to `0` which means no restrictions are applied to tree depth.
 
 An engine-specific parameter for this model is: 

man/details_linear_reg_lme.Rd

@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: