fixed some seealso typos

Author: topepo

R/linear_reg.R

@@ -105,7 +105,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()], [set_engine()]
+#' @seealso [fit()], [set_engine()]
 #' @examples
 #' linear_reg()
 #' # Parameters can be represented by a placeholder:

R/logistic_reg.R

@@ -104,7 +104,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' logistic_reg()
 #' # Parameters can be represented by a placeholder:

R/mars.R

@@ -62,7 +62,7 @@
 #'  attached.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' mars(mode = "regression", num_terms = 5)
 #' @export

R/mlp.R

@@ -84,7 +84,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' mlp(mode = "classification", penalty = 0.01)
 #' # Parameters can be represented by a placeholder:

R/multinom_reg.R

@@ -91,7 +91,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' multinom_reg()
 #' # Parameters can be represented by a placeholder:

R/nearest_neighbor.R

@@ -70,7 +70,7 @@
 #' on new data. This also means that a single value of that function's
 #' `kernel` argument (a.k.a `weight_func` here) can be supplied
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #'
 #' @examples
 #' nearest_neighbor(neighbors = 11)

R/rand_forest.R

@@ -94,7 +94,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' rand_forest(mode = "classification", trees = 2000)
 #' # Parameters can be represented by a placeholder:

R/surv_reg.R

@@ -64,7 +64,7 @@
 #'  predictions when there is a stratification variable and can be
 #'  overridden in other cases.
 #'
-#' @seealso [[fit()], [survival::Surv()]
+#' @seealso [fit()], [survival::Surv()]
 #' @references Jackson, C. (2016). `flexsurv`: A Platform for Parametric Survival
 #'  Modeling in R. _Journal of Statistical Software_, 70(8), 1 - 33.
 #' @examples

R/svm_poly.R

@@ -58,7 +58,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "regression"), "kernlab")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' svm_poly(mode = "classification", degree = 1.2)
 #' # Parameters can be represented by a placeholder: