the check is already carried out via predict()

R/poisson_reg-glmnet.R

@@ -26,10 +26,6 @@
 #' @export
 predict._fishnet <-
   function(object, new_data, type = NULL, opts = list(), penalty = NULL, multi = FALSE, ...) {
-    if (any(names(enquos(...)) == "newdata")) {
-      rlang::abort("Did you mean to use `new_data` instead of `newdata`?")
-    }
-
     # See discussion in https://github.com/tidymodels/parsnip/issues/195
     if (is.null(penalty) & !is.null(object$spec$args$penalty)) {
       penalty <- object$spec$args$penalty
@@ -43,10 +39,6 @@ predict._fishnet <-
 
 #' @export
 predict_numeric._fishnet <- function(object, new_data, ...) {
-  if (any(names(enquos(...)) == "newdata")) {
-    rlang::abort("Did you mean to use `new_data` instead of `newdata`?")
-  }
-
   object$spec <- parsnip::eval_args(object$spec)
   parsnip::predict_numeric.model_fit(object, new_data = new_data, ...)
 }
@@ -65,10 +57,6 @@ predict_numeric._fishnet <- function(object, new_data, ...) {
 #' @export
 #' @keywords internal
 predict_raw._fishnet <- function(object, new_data, opts = list(), ...) {
-  if (any(names(enquos(...)) == "newdata")) {
-    rlang::abort("Did you mean to use `new_data` instead of `newdata`?")
-  }
-
   object$spec <- parsnip::eval_args(object$spec)
   opts$s <- object$spec$args$penalty
   parsnip::predict_raw.model_fit(object, new_data = new_data, opts = opts, ...)