re-wrote the glmnet notes

Author: topepo

R/linear_reg.R

@@ -69,14 +69,14 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
 #'
 #' When using `glmnet` models, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument.
-#'  This can have an effect on the model object results. When using
-#'  the `predict()` method in these cases, the return object type
-#'  depends on the value of `penalty`. If a single value is
-#'  given, the results will be a simple numeric vector. When
-#'  multiple values or no values for `penalty` are used in
-#'  `linear_reg()`, the `predict()` method will return a data frame with
-#'  columns `values` and `lambda`.
+#'  multiple values (or no values) to the `penalty` argument. This
+#'  can have an effect on the model object results. When using the
+#'  `predict()` method in these cases, the return value depends on
+#'  the value of `penalty`. When using `predict()`, only a single
+#'  value of the penalty can be used. When predicting on multiple
+#'  penalties, the `multi_predict()` function can be used. It
+#'  returns a tibble with a list column called `.pred` that contains
+#'  a tibble with all of the penalty results.
 #'
 #' For prediction, the `stan` engine can compute posterior
 #'  intervals analogous to confidence and prediction intervals. In
@@ -130,7 +130,7 @@ print.linear_reg <- function(x, ...) {
   cat("Linear Regression Model Specification (", x$mode, ")\n\n", sep = "")
   model_printer(x, ...)
 
-  if(!is.null(x$method$fit$args)) {
+  if (!is.null(x$method$fit$args)) {
     cat("Model fit template:\n")
     print(show_call(x))
   }

R/logistic_reg.R

@@ -67,14 +67,14 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::logistic_reg(), "keras")}
 #'
 #' When using `glmnet` models, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument.
-#'  This can have an effect on the model object results. When using
-#'  the `predict()` method in these cases, the return object type
-#'  depends on the value of `penalty`. If a single value is
-#'  given, the results will be a simple numeric vector. When
-#'  multiple values or no values for `penalty` are used in
-#'  `logistic_reg()`, the `predict()` method will return a data frame with
-#'  columns `values` and `lambda`.
+#'  multiple values (or no values) to the `penalty` argument. This
+#'  can have an effect on the model object results. When using the
+#'  `predict()` method in these cases, the return value depends on
+#'  the value of `penalty`. When using `predict()`, only a single
+#'  value of the penalty can be used. When predicting on multiple
+#'  penalties, the `multi_predict()` function can be used. It
+#'  returns a tibble with a list column called `.pred` that contains
+#'  a tibble with all of the penalty results.
 #'
 #' For prediction, the `stan` engine can compute posterior
 #'  intervals analogous to confidence and prediction intervals. In

R/multinom_reg.R

@@ -58,14 +58,14 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "keras")}
 #'
 #' When using `glmnet` models, there is the option to pass
-#'  multiple values (or no values) to the `penalty` argument.
-#'  This can have an effect on the model object results. When using
-#'  the `predict()` method in these cases, the return object type
-#'  depends on the value of `penalty`. If a single value is
-#'  given, the results will be a simple numeric vector. When
-#'  multiple values or no values for `penalty` are used in
-#'  `multinom_reg()`, the `predict()` method will return a data frame with
-#'  columns `values` and `lambda`.
+#'  multiple values (or no values) to the `penalty` argument. This
+#'  can have an effect on the model object results. When using the
+#'  `predict()` method in these cases, the return value depends on
+#'  the value of `penalty`. When using `predict()`, only a single
+#'  value of the penalty can be used. When predicting on multiple
+#'  penalties, the `multi_predict()` function can be used. It
+#'  returns a tibble with a list column called `.pred` that contains
+#'  a tibble with all of the penalty results.
 #'
 #' @note For models created using the spark engine, there are
 #'  several differences to consider. First, only the formula