Add fnd to Authors, add link to /find/parsnip to README

Author: juliasilge

DESCRIPTION

@@ -1,21 +1,12 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
 Version: 0.1.7.9001
-Authors@R: 
-    c(person(given = "Max",
-             family = "Kuhn",
-             role = c("aut", "cre"),
-             email = "[email protected]"),
-      person(given = "Davis",
-             family = "Vaughan",
-             role = "aut",
-             email = "[email protected]"),
-      person(given = "Emil",
-             family = "Hvitfeldt",
-             role = "ctb",
-             email = "[email protected]"),
-      person(given = "RStudio",
-             role = "cph"))
+Authors@R: c(
+    person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
+    person("Davis", "Vaughan", , "[email protected]", role = "aut"),
+    person("Emil", "Hvitfeldt", , "[email protected]", role = "ctb"),
+    person("RStudio", role = c("cph", "fnd"))
+  )
 Maintainer: Max Kuhn <[email protected]>
 Description: A common interface is provided to allow users to specify a
     model without having to remember the different argument names across
@@ -77,17 +68,17 @@ Config/Needs/website:
     glmnet, 
     keras, 
     kernlab, 
-    kknn, 
-    LiblineaR,
+    kknn,
+    LiblineaR, 
     mgcv, 
     nnet, 
     parsnip, 
     randomForest, 
     ranger, 
     rpart, 
-    rstanarm, 
-    tidymodels,
-    tidyverse/tidytemplate,
+    rstanarm,
+    tidymodels, 
+    tidyverse/tidytemplate, 
     xgboost
 Encoding: UTF-8
 LazyData: true

README.Rmd

@@ -91,7 +91,7 @@ The goals of parsnip are to:
 * 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 argument names (e.g. `n.trees`, `ntrees`, `trees`) so that users only need to 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:
+Using the example above, the parsnip approach would be:
 
 ```{r}
 library(parsnip)
@@ -119,9 +119,7 @@ rand_forest(mtry = 10, trees = 2000) %>%
   fit(mpg ~ ., data = mtcars)
 ```
 
-A list of all `parsnip` models across different CRAN packages can be found at
-[`tidymodels.org`](https://www.tidymodels.org/find/).
-
+A list of all parsnip models across different CRAN packages can be found at https://www.tidymodels.org/find/parsnip.
 
 ## Contributing
 

README.md

@@ -94,7 +94,7 @@ The goals of parsnip are to:
     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:
+Using the example above, the parsnip approach would be:
 
 ``` r
 library(parsnip)
@@ -140,7 +140,7 @@ rand_forest(mtry = 10, trees = 2000) %>%
   fit(mpg ~ ., data = mtcars)
 #> parsnip model object
 #> 
-#> Fit time:  316ms 
+#> Fit time:  45ms 
 #> Ranger result
 #> 
 #> Call:
@@ -154,12 +154,12 @@ rand_forest(mtry = 10, trees = 2000) %>%
 #> Target node size:                 5 
 #> Variable importance mode:         impurity 
 #> Splitrule:                        variance 
-#> OOB prediction error (MSE):       5.725636 
-#> R squared (OOB):                  0.8423737
+#> OOB prediction error (MSE):       5.976917 
+#> R squared (OOB):                  0.8354559
 ```
 
-A list of all `parsnip` models across different CRAN packages can be
-found at [`tidymodels.org`](https://www.tidymodels.org/find/).
+A list of all parsnip models across different CRAN packages can be found
+at <https://www.tidymodels.org/find/parsnip>.
 
 ## Contributing