minor fixes for submission

Author: topepo

NEWS.md

@@ -198,7 +198,7 @@ First CRAN release
 # parsnip 0.0.0.9003
 
 * `regularization` was changed to `penalty` in a few models to be consistent with [this change](https://tidymodels.github.io/model-implementation-principles/standardized-argument-names.html#tuning-parameters). 
-* If a mode is not chosen in the model specification, it is assigned at the time of fit. [51](https://github.com/tidyverse/parsnip/issues/51)
+* If a mode is not chosen in the model specification, it is assigned at the time of fit. [51](https://github.com/tidymodels/parsnip/issues/51)
 * The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some `predict` methods, the `earth` package will need to be attached to be fully operational.
 
 # parsnip 0.0.0.9002
@@ -212,7 +212,7 @@ First CRAN release
 
 # parsnip 0.0.0.9000
 
-* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit()` is the formula interface and [`fit_xy()` is for the x/y interface](https://github.com/tidyverse/parsnip/issues/33). 
+* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit()` is the formula interface and [`fit_xy()` is for the x/y interface](https://github.com/tidymodels/parsnip/issues/33). 
 * Added a `NEWS.md` file to track changes to the package.
-* `predict` methods were [overhauled](https://github.com/tidyverse/parsnip/issues/34) to be [consistent](https://github.com/tidyverse/parsnip/issues/41).
+* `predict` methods were [overhauled](https://github.com/tidymodels/parsnip/issues/34) to be [consistent](https://github.com/tidymodels/parsnip/issues/41).
 * MARS was added. 

R/aaa_models.R

@@ -45,8 +45,8 @@ pred_types <-
 #' @param name A single character value for a new symbol in the model environment.
 #' @param value A single value for a new value in the model environment.
 #' @keywords internal
-#' @references "Making a parsnip model from scratch"
-#'  \url{https://tidymodels.github.io/parsnip/articles/articles/Scratch.html}
+#' @references "How to build a parsnip model"
+#'  \url{https://www.tidymodels.org/learn/develop/models/}
 #' @examples
 #' # Access the model data:
 #' current_code <- get_model_env()
@@ -310,7 +310,6 @@ check_interface_val <- function(x) {
 #'  `func` and `defaults`. See the package vignette "Making a
 #'  `parsnip` model from scratch".
 #' @param pred_obj A list with elements `pre`, `post`, `func`, and `args`.
-#'  See the package vignette "Making a `parsnip` model from scratch".
 #' @param type A single character value for the type of prediction. Possible
 #'  values are: `class`, `conf_int`, `numeric`, `pred_int`, `prob`, `quantile`,
 #'   and `raw`.
@@ -374,8 +373,8 @@ check_interface_val <- function(x) {
 #'  _after_ `model.matrix()` is finished. This can be useful if the model
 #'  function (e.g. `lm()`) automatically generates an intercept.
 #'
-#' @references "Making a parsnip model from scratch"
-#'  \url{https://tidymodels.github.io/parsnip/articles/articles/Scratch.html}
+#' @references "How to build a parsnip model"
+#'  \url{https://www.tidymodels.org/learn/develop/models/}
 #' @examples
 #' # set_new_model("shallow_learning_model")
 #'