avoiding "Possibly mis-spelled words in DESCRIPTION", r-hub suggestions and doc update

Author: topepo

DESCRIPTION

@@ -1,9 +1,10 @@
 Package: parsnip
 Version: 0.0.1
 Title: A Common API to Modeling and Analysis Functions
-Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. R, spark, stan, etc). 
+Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. R, Spark, Stan, etc). 
 Authors@R: c(
     person("Max", "Kuhn", , "max@rstudio.com", c("aut", "cre")),
+    person("Davis", "Vaughan", , "davis@rstudio.com", c("aut")),
     person("RStudio", role = "cph"))
 Maintainer: Max Kuhn <max@rstudio.com>
 URL: https://tidymodels.github.io/parsnip

NEWS.md

@@ -1,3 +1,7 @@
+# parsnip 0.0.1
+
+First CRAN release
+
 # parsnip 0.0.0.9005
 
 * The engine, and any associated arguments, are now specified using `set_engine`. There is no `engine` argument 

R/boost_tree.R

@@ -361,7 +361,7 @@ multi_predict._xgb.Booster <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))
       stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
-    
+
     if (is.null(trees))
       trees <- object$fit$nIter
     trees <- sort(trees)
@@ -409,7 +409,8 @@ xgb_by_tree <- function(tree, object, new_data, type, ...) {
 
 #' Boosted trees via C5.0
 #'
-#' `C5.0_train` is a wrapper for [C50::C5.0()] tree-based models
+#' `C5.0_train` is a wrapper for the `C5.0()` function in the
+#' \pkg{C50} package that fits tree-based models
 #'  where all of the model arguments are in the main function.
 #'
 #' @param x A data frame or matrix of predictors.
@@ -467,7 +468,7 @@ multi_predict._C5.0 <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))
       stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
-    
+
     if (is.null(trees))
       trees <- min(object$fit$trials)
     trees <- sort(trees)

README.md

@@ -32,14 +32,37 @@ rf_3 <- ml_random_forest(
 
 Note that the model syntax is very different and that the argument names (and formats) are also different. This is a pain if you go between implementations. 
 
-In this example, the **type** of model is "random forest" while the **mode** of the model is "classification" (as opposed to regression, survival analysis, etc). 
+In this example, 
+
+ * the **type** of model is "random forest" 
+ * the **mode** of the model is "classification" (as opposed to regression,  etc). 
+ * the computational **engine** is the name of the R package. 
 
 
 The idea of `parsnip` is to:
 
 * Separate the definition of a model from its evaluation.
 * Decouple the model specification from the implementation (whether the implementation is in R, spark, or something else). For example, the user would call `rand_forest` instead of `ranger::ranger` or other specific packages. 
 * Harmonize the argument names (e.g. `n.trees`, `ntrees`, `trees`) so that users can remember a single name. This will help _across_ model types too so that `trees` will be the same argument across random forest as well as boosting or bagging. 
+
+Using the example above, the `parsnip` approach would be
+
+```r
+rand_forest(mtry = 12, trees = 2000) %>%
+  set_engine("ranger", importance = 'impurity') %>%
+  fit(y ~ ., data = dat)
+```
+
+The engine can be easily changed and the mode can be determined when `fit` is called. To use Spark, the change is simple:
+
+```r
+rand_forest(mtry = 12, trees = 2000) %>%
+  set_engine("spark") %>%
+  fit(y ~ ., data = dat)
+```
+
+
+
 To install it, use:
 
 ```r

_pkgdown.yml

@@ -18,6 +18,7 @@ reference:
   - title: Models
     contents:
       - boost_tree
+      - decision_tree
       - linear_reg
       - logistic_reg
       - mars