diff --git a/R/boost_tree.R b/R/boost_tree.R index 795c34210..034390d6b 100644 --- a/R/boost_tree.R +++ b/R/boost_tree.R @@ -114,12 +114,12 @@ boost_tree <- function(mode = "unknown", + ..., mtry = NULL, trees = NULL, min_n = NULL, tree_depth = NULL, learn_rate = NULL, loss_reduction = NULL, sample_size = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% boost_tree_modes)) diff --git a/R/linear_reg.R b/R/linear_reg.R index 853a761f0..d2aed4342 100644 --- a/R/linear_reg.R +++ b/R/linear_reg.R @@ -105,10 +105,10 @@ #' @importFrom purrr map_lgl linear_reg <- function(mode = "regression", + ..., penalty = NULL, mixture = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% linear_reg_modes)) stop( diff --git a/R/logistic_reg.R b/R/logistic_reg.R index 151d4e2a2..7051b46a6 100644 --- a/R/logistic_reg.R +++ b/R/logistic_reg.R @@ -103,10 +103,10 @@ #' @importFrom purrr map_lgl logistic_reg <- function(mode = "classification", + ..., penalty = NULL, mixture = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% logistic_reg_modes)) stop( diff --git a/R/mars.R b/R/mars.R index 0268615bd..dbaa8e381 100644 --- a/R/mars.R +++ b/R/mars.R @@ -71,9 +71,9 @@ mars <- function(mode = "unknown", + ..., num_terms = NULL, prod_degree = NULL, prune_method = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% mars_modes)) diff --git a/R/mlp.R b/R/mlp.R index 5f44fbfdf..e4c3df660 100644 --- a/R/mlp.R +++ b/R/mlp.R @@ -93,10 +93,10 @@ mlp <- function(mode = "unknown", + ..., hidden_units = NULL, penalty = NULL, dropout = NULL, epochs = NULL, activation = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) act_funs <- c("linear", "softmax", "relu", "elu") diff --git a/R/multinom_reg.R b/R/multinom_reg.R index be0045363..6f079f167 100644 --- a/R/multinom_reg.R +++ b/R/multinom_reg.R @@ -85,10 +85,10 @@ #' @importFrom purrr map_lgl multinom_reg <- function(mode = "classification", + ..., penalty = NULL, mixture = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% multinom_reg_modes)) stop( diff --git a/R/nearest_neighbor.R b/R/nearest_neighbor.R index 8bd53abca..499be4ea0 100644 --- a/R/nearest_neighbor.R +++ b/R/nearest_neighbor.R @@ -77,11 +77,11 @@ #' #' @export nearest_neighbor <- function(mode = "unknown", + ..., neighbors = NULL, weight_func = NULL, dist_power = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) diff --git a/R/rand_forest.R b/R/rand_forest.R index b6999b281..bfc7cc587 100644 --- a/R/rand_forest.R +++ b/R/rand_forest.R @@ -103,9 +103,9 @@ rand_forest <- function(mode = "unknown", + ..., mtry = NULL, trees = NULL, min_n = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) ## TODO: make a utility function here diff --git a/R/surv_reg.R b/R/surv_reg.R index 0d72fb83b..16ad84b70 100644 --- a/R/surv_reg.R +++ b/R/surv_reg.R @@ -20,7 +20,7 @@ #' Since survival models typically involve censoring (and require the use of #' [survival::Surv()] objects), the [fit()] function will require that the #' survival model be specified via the formula interface. -#' +#' #' Also, for the `flexsurv::flexsurvfit` engine, the typical #' `strata` function cannot be used. To achieve the same effect, #' the extra parameter roles can be used (as described above). @@ -51,9 +51,9 @@ #' @export surv_reg <- function(mode = "regression", + ..., dist = NULL, - others = list(), - ...) { + others = list()) { check_empty_ellipse(...) if (!(mode %in% surv_reg_modes)) stop( diff --git a/docs/articles/articles/Classification.html b/docs/articles/articles/Classification.html index f1f1b8a2f..9b3e6090c 100644 --- a/docs/articles/articles/Classification.html +++ b/docs/articles/articles/Classification.html @@ -162,17 +162,17 @@

Classification Example

#> # A tibble: 1 x 2 #> .metric .estimate #> <chr> <dbl> -#> 1 roc_auc 0.822 +#> 1 roc_auc 0.823 test_results %>% accuracy(truth = Status, estimate = `nnet class`) #> # A tibble: 1 x 2 #> .metric .estimate #> <chr> <dbl> -#> 1 accuracy 0.800 +#> 1 accuracy 0.801 test_results %>% conf_mat(truth = Status, estimate = `nnet class`) #> Truth #> Prediction bad good -#> bad 171 81 -#> good 142 719 +#> bad 174 82 +#> good 139 718 - 
linear_reg(mode = "regression", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+    
linear_reg(mode = "regression", ..., penalty = NULL, mixture = NULL,
+  others = list())
 
 # S3 method for linear_reg
 update(object, penalty = NULL, mixture = NULL,
@@ -131,6 +131,11 @@ 
Arg
       mode
       
A single character string for the type of model.
 The only possible value for this model is "regression".

+    
+    
+      ...
+      
Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       penalty
@@ -151,11 +156,6 @@ 
Arg
 rstanarm::stan_glm, etc.). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.

-    
-    
-      ...
-      
Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/logistic_reg.html b/docs/reference/logistic_reg.html
index c29d25122..c95b52713 100644
--- a/docs/reference/logistic_reg.html
+++ b/docs/reference/logistic_reg.html
@@ -117,8 +117,8 @@ 
General Interface for Logistic Regression Models

     
     
 
-    
logistic_reg(mode = "classification", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+    
logistic_reg(mode = "classification", ..., penalty = NULL,
+  mixture = NULL, others = list())
 
 # S3 method for logistic_reg
 update(object, penalty = NULL, mixture = NULL,
@@ -131,6 +131,11 @@ 
Arg
       mode
       
A single character string for the type of model.
 The only possible value for this model is "classification".

+    
+    
+      ...
+      
Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       penalty
@@ -151,11 +156,6 @@ 
Arg
 rstanarm::stan_glm, etc.). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.

-    
-    
-      ...
-      
Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/mars.html b/docs/reference/mars.html
index 4dab481f6..50857713b 100644
--- a/docs/reference/mars.html
+++ b/docs/reference/mars.html
@@ -120,8 +120,8 @@ 
General Interface for MARS

     
     
 
-    
mars(mode = "unknown", num_terms = NULL, prod_degree = NULL,
-  prune_method = NULL, others = list(), ...)
+    
mars(mode = "unknown", ..., num_terms = NULL, prod_degree = NULL,
+  prune_method = NULL, others = list())
 
 # S3 method for mars
 update(object, num_terms = NULL, prod_degree = NULL,
@@ -135,6 +135,11 @@ 
Arg
       
A single character string for the type of model.
 Possible values for this model are "unknown", "regression", or
 "classification".

+    
+    
+      ...
+      
Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       num_terms
@@ -156,11 +161,6 @@ 
Arg
 and mode = "classification", others can include the glm argument to
 earth::earth. If this argument is not passed, it will be added prior to
 the fitting occurs.

-    
-    
-      ...
-      
Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/mlp.html b/docs/reference/mlp.html
index 33dd68334..36106bbae 100644
--- a/docs/reference/mlp.html
+++ b/docs/reference/mlp.html
@@ -120,9 +120,9 @@ 
General Interface for Single Layer Neural Network

     
     
 
-    
mlp(mode = "unknown", hidden_units = NULL, penalty = NULL,
+    
mlp(mode = "unknown", ..., hidden_units = NULL, penalty = NULL,
   dropout = NULL, epochs = NULL, activation = NULL,
-  others = list(), ...)
+  others = list())
 
 # S3 method for mlp
 update(object, hidden_units = NULL, penalty = NULL,
@@ -137,6 +137,11 @@ 
Arg
       
A single character string for the type of model.
 Possible values for this model are "unknown", "regression", or
 "classification".

+    
+    
+      ...
+      
Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       hidden_units
@@ -169,11 +174,6 @@ 
Arg
       
A named list of arguments to be used by the
 underlying models (e.g., nnet::nnet,
 keras::fit, keras::compile, etc.). .

-    
-    
-      ...
-      
Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/multinom_reg.html b/docs/reference/multinom_reg.html
index df9b907ba..fb468e37d 100644
--- a/docs/reference/multinom_reg.html
+++ b/docs/reference/multinom_reg.html
@@ -117,8 +117,8 @@ 
General Interface for Multinomial Regression Models

     
     
 
-    
multinom_reg(mode = "classification", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+    
multinom_reg(mode = "classification", ..., penalty = NULL,
+  mixture = NULL, others = list())
 
 # S3 method for multinom_reg
 update(object, penalty = NULL, mixture = NULL,
@@ -131,6 +131,11 @@ 
Arg
       mode
       
A single character string for the type of model.
 The only possible value for this model is "classification".

+    
+    
+      ...
+      
Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       penalty
@@ -150,11 +155,6 @@ 
Arg
 underlying models (e.g., glmnet::glmnet etc.). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.

-    
-    
-      ...
-      
Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/nearest_neighbor.html b/docs/reference/nearest_neighbor.html
index 2448260ec..af2f3cf0d 100644
--- a/docs/reference/nearest_neighbor.html
+++ b/docs/reference/nearest_neighbor.html
@@ -120,8 +120,8 @@ 
General Interface for K-Nearest Neighbor Models

     
     
 
-    
nearest_neighbor(mode = "unknown", neighbors = NULL,
-  weight_func = NULL, dist_power = NULL, others = list(), ...)

+    
nearest_neighbor(mode = "unknown", ..., neighbors = NULL,
+  weight_func = NULL, dist_power = NULL, others = list())

     
     
Arguments

     
@@ -131,6 +131,11 @@ 
Arg
       

+    
+    
+      
+      
@@ -155,11 +160,6 @@ 
Arg
 underlying models (e.g., kknn::train.kknn). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.

-    

-    
-      
-      
A single character string for the type of model.
 Possible values for this model are "unknown", "regression", or
 "classification".
...
Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     

     

       neighbors
...
Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     

     

     
diff --git a/docs/reference/rand_forest.html b/docs/reference/rand_forest.html
index 70c1465db..c6f5d68c0 100644
--- a/docs/reference/rand_forest.html
+++ b/docs/reference/rand_forest.html
@@ -118,8 +118,8 @@ 
General Interface for Random Forest Models

     
     
 
-    
rand_forest(mode = "unknown", mtry = NULL, trees = NULL,
-  min_n = NULL, others = list(), ...)
+    
rand_forest(mode = "unknown", ..., mtry = NULL, trees = NULL,
+  min_n = NULL, others = list())
 
 # S3 method for rand_forest
 update(object, mtry = NULL, trees = NULL,
@@ -133,6 +133,11 @@ 
Arg
       
A single character string for the type of model.
 Possible values for this model are "unknown", "regression", or
 "classification".

+    
+    
+      ...
+      
Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       mtry
@@ -154,11 +159,6 @@ 
Arg
       
A named list of arguments to be used by the
 underlying models (e.g., ranger::ranger,
 randomForest::randomForest, etc.). .

-    
-    
-      ...
-      
Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/docs/reference/surv_reg.html b/docs/reference/surv_reg.html
index c375b04d9..2a3c77fef 100644
--- a/docs/reference/surv_reg.html
+++ b/docs/reference/surv_reg.html
@@ -115,7 +115,7 @@ 
General Interface for Parametric Survival Models

     
     
 
-    
surv_reg(mode = "regression", dist = NULL, others = list(), ...)
+    
surv_reg(mode = "regression", ..., dist = NULL, others = list())
 
 # S3 method for surv_reg
 update(object, dist = NULL, others = list(),
@@ -128,6 +128,11 @@ 
Arg
       mode
       
A single character string for the type of model.
 The only possible value for this model is "regression".

+    
+    
+      ...
+      
Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use others instead.

     
     
       dist
@@ -140,11 +145,6 @@ 
Arg
 underlying models (e.g., flexsurv::flexsurvreg). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.

-    
-    
-      ...
-      
Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use others instead.

     
     
       object
diff --git a/man/boost_tree.Rd b/man/boost_tree.Rd
index 00a66e5ca..a7520d251 100644
--- a/man/boost_tree.Rd
+++ b/man/boost_tree.Rd
@@ -5,9 +5,9 @@
 \alias{update.boost_tree}
 \title{General Interface for Boosted Trees}
 \usage{
-boost_tree(mode = "unknown", mtry = NULL, trees = NULL,
+boost_tree(mode = "unknown", ..., mtry = NULL, trees = NULL,
   min_n = NULL, tree_depth = NULL, learn_rate = NULL,
-  loss_reduction = NULL, sample_size = NULL, others = list(), ...)
+  loss_reduction = NULL, sample_size = NULL, others = list())
 
 \method{update}{boost_tree}(object, mtry = NULL, trees = NULL,
   min_n = NULL, tree_depth = NULL, learn_rate = NULL,
@@ -19,6 +19,9 @@ boost_tree(mode = "unknown", mtry = NULL, trees = NULL,
 Possible values for this model are "unknown", "regression", or
 "classification".}
 
+\item{...}{Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{mtry}{An number for the number (or proportion) of predictors that will
 be randomly sampled at each split when creating the tree models (\code{xgboost}
 only).}
@@ -45,9 +48,6 @@ each iteration while \code{C5.0} samples once during traning.}
 \item{others}{A named list of arguments to be used by the
 underlying models (e.g., \code{xgboost::xgb.train}, etc.). .}
 
-\item{...}{Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A boosted tree model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd
index 1b7c8bd4a..b108de728 100644
--- a/man/linear_reg.Rd
+++ b/man/linear_reg.Rd
@@ -5,8 +5,8 @@
 \alias{update.linear_reg}
 \title{General Interface for Linear Regression Models}
 \usage{
-linear_reg(mode = "regression", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+linear_reg(mode = "regression", ..., penalty = NULL, mixture = NULL,
+  others = list())
 
 \method{update}{linear_reg}(object, penalty = NULL, mixture = NULL,
   others = list(), fresh = FALSE, ...)
@@ -15,6 +15,9 @@ linear_reg(mode = "regression", penalty = NULL, mixture = NULL,
 \item{mode}{A single character string for the type of model.
 The only possible value for this model is "regression".}
 
+\item{...}{Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{penalty}{An non-negative number representing the
 total amount of regularization (\code{glmnet} and \code{spark} only).}
 
@@ -29,9 +32,6 @@ underlying models (e.g., \code{stats::lm},
 until the model is fit and will be substituted into the model
 fit expression.}
 
-\item{...}{Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A linear regression model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd
index 8694db90b..1d4fc0533 100644
--- a/man/logistic_reg.Rd
+++ b/man/logistic_reg.Rd
@@ -5,8 +5,8 @@
 \alias{update.logistic_reg}
 \title{General Interface for Logistic Regression Models}
 \usage{
-logistic_reg(mode = "classification", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+logistic_reg(mode = "classification", ..., penalty = NULL,
+  mixture = NULL, others = list())
 
 \method{update}{logistic_reg}(object, penalty = NULL, mixture = NULL,
   others = list(), fresh = FALSE, ...)
@@ -15,6 +15,9 @@ logistic_reg(mode = "classification", penalty = NULL, mixture = NULL,
 \item{mode}{A single character string for the type of model.
 The only possible value for this model is "classification".}
 
+\item{...}{Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{penalty}{An non-negative number representing the
 total amount of regularization (\code{glmnet} and \code{spark} only).}
 
@@ -29,9 +32,6 @@ underlying models (e.g., \code{stats::glm},
 until the model is fit and will be substituted into the model
 fit expression.}
 
-\item{...}{Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A logistic regression model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/mars.Rd b/man/mars.Rd
index 4d4bb280e..f19dbc139 100644
--- a/man/mars.Rd
+++ b/man/mars.Rd
@@ -5,8 +5,8 @@
 \alias{update.mars}
 \title{General Interface for MARS}
 \usage{
-mars(mode = "unknown", num_terms = NULL, prod_degree = NULL,
-  prune_method = NULL, others = list(), ...)
+mars(mode = "unknown", ..., num_terms = NULL, prod_degree = NULL,
+  prune_method = NULL, others = list())
 
 \method{update}{mars}(object, num_terms = NULL, prod_degree = NULL,
   prune_method = NULL, others = list(), fresh = FALSE, ...)
@@ -16,6 +16,9 @@ mars(mode = "unknown", num_terms = NULL, prod_degree = NULL,
 Possible values for this model are "unknown", "regression", or
 "classification".}
 
+\item{...}{Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{num_terms}{The number of features that will be retained in the
 final model, including the intercept.}
 
@@ -29,9 +32,6 @@ and \code{mode = "classification"}, \code{others} can include the \code{glm} arg
 \code{earth::earth}. If this argument is not passed, it will be added prior to
 the fitting occurs.}
 
-\item{...}{Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A MARS model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/mlp.Rd b/man/mlp.Rd
index 887cbdef1..437e93f79 100644
--- a/man/mlp.Rd
+++ b/man/mlp.Rd
@@ -5,9 +5,9 @@
 \alias{update.mlp}
 \title{General Interface for Single Layer Neural Network}
 \usage{
-mlp(mode = "unknown", hidden_units = NULL, penalty = NULL,
+mlp(mode = "unknown", ..., hidden_units = NULL, penalty = NULL,
   dropout = NULL, epochs = NULL, activation = NULL,
-  others = list(), ...)
+  others = list())
 
 \method{update}{mlp}(object, hidden_units = NULL, penalty = NULL,
   dropout = NULL, epochs = NULL, activation = NULL,
@@ -18,6 +18,9 @@ mlp(mode = "unknown", hidden_units = NULL, penalty = NULL,
 Possible values for this model are "unknown", "regression", or
 "classification".}
 
+\item{...}{Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{hidden_units}{An integer for the number of units in the hidden model.}
 
 \item{penalty}{A non-negative numeric value for the amount of weight
@@ -38,9 +41,6 @@ function between the hidden and output layers is automatically set to either
 underlying models (e.g., \code{nnet::nnet},
 \code{keras::fit}, \code{keras::compile}, etc.). .}
 
-\item{...}{Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A random forest model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd
index a0f71a868..91a650952 100644
--- a/man/multinom_reg.Rd
+++ b/man/multinom_reg.Rd
@@ -5,8 +5,8 @@
 \alias{update.multinom_reg}
 \title{General Interface for Multinomial Regression Models}
 \usage{
-multinom_reg(mode = "classification", penalty = NULL, mixture = NULL,
-  others = list(), ...)
+multinom_reg(mode = "classification", ..., penalty = NULL,
+  mixture = NULL, others = list())
 
 \method{update}{multinom_reg}(object, penalty = NULL, mixture = NULL,
   others = list(), fresh = FALSE, ...)
@@ -15,6 +15,9 @@ multinom_reg(mode = "classification", penalty = NULL, mixture = NULL,
 \item{mode}{A single character string for the type of model.
 The only possible value for this model is "classification".}
 
+\item{...}{Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{penalty}{An non-negative number representing the
 total amount of regularization.}
 
@@ -28,9 +31,6 @@ underlying models (e.g., \code{glmnet::glmnet} etc.). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.}
 
-\item{...}{Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A multinomial regression model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/nearest_neighbor.Rd b/man/nearest_neighbor.Rd
index 95ddc722b..33bf3d34c 100644
--- a/man/nearest_neighbor.Rd
+++ b/man/nearest_neighbor.Rd
@@ -4,14 +4,17 @@
 \alias{nearest_neighbor}
 \title{General Interface for K-Nearest Neighbor Models}
 \usage{
-nearest_neighbor(mode = "unknown", neighbors = NULL,
-  weight_func = NULL, dist_power = NULL, others = list(), ...)
+nearest_neighbor(mode = "unknown", ..., neighbors = NULL,
+  weight_func = NULL, dist_power = NULL, others = list())
 }
 \arguments{
 \item{mode}{A single character string for the type of model.
 Possible values for this model are \code{"unknown"}, \code{"regression"}, or
 \code{"classification"}.}
 
+\item{...}{Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{neighbors}{A single integer for the number of neighbors
 to consider (often called \code{k}).}
 
@@ -27,9 +30,6 @@ calculating Minkowski distance.}
 underlying models (e.g., \code{kknn::train.kknn}). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.}
-
-\item{...}{Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
 }
 \description{
 \code{nearest_neighbor()} is a way to generate a \emph{specification} of a model
diff --git a/man/rand_forest.Rd b/man/rand_forest.Rd
index b65ad5762..a7f23e074 100644
--- a/man/rand_forest.Rd
+++ b/man/rand_forest.Rd
@@ -5,8 +5,8 @@
 \alias{update.rand_forest}
 \title{General Interface for Random Forest Models}
 \usage{
-rand_forest(mode = "unknown", mtry = NULL, trees = NULL,
-  min_n = NULL, others = list(), ...)
+rand_forest(mode = "unknown", ..., mtry = NULL, trees = NULL,
+  min_n = NULL, others = list())
 
 \method{update}{rand_forest}(object, mtry = NULL, trees = NULL,
   min_n = NULL, others = list(), fresh = FALSE, ...)
@@ -16,6 +16,9 @@ rand_forest(mode = "unknown", mtry = NULL, trees = NULL,
 Possible values for this model are "unknown", "regression", or
 "classification".}
 
+\item{...}{Used for method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{mtry}{An integer for the number of predictors that will
 be randomly sampled at each split when creating the tree models.}
 
@@ -29,9 +32,6 @@ in a node that are required for the node to be split further.}
 underlying models (e.g., \code{ranger::ranger},
 \code{randomForest::randomForest}, etc.). .}
 
-\item{...}{Used for method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A random forest model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/man/surv_reg.Rd b/man/surv_reg.Rd
index 037f4504a..a9ee647a6 100644
--- a/man/surv_reg.Rd
+++ b/man/surv_reg.Rd
@@ -5,7 +5,7 @@
 \alias{update.surv_reg}
 \title{General Interface for Parametric Survival Models}
 \usage{
-surv_reg(mode = "regression", dist = NULL, others = list(), ...)
+surv_reg(mode = "regression", ..., dist = NULL, others = list())
 
 \method{update}{surv_reg}(object, dist = NULL, others = list(),
   fresh = FALSE, ...)
@@ -14,6 +14,9 @@ surv_reg(mode = "regression", dist = NULL, others = list(), ...)
 \item{mode}{A single character string for the type of model.
 The only possible value for this model is "regression".}
 
+\item{...}{Used for S3 method consistency. Any arguments passed to
+the ellipses will result in an error. Use \code{others} instead.}
+
 \item{dist}{A character string for the outcome distribution. "weibull" is
 the default.}
 
@@ -22,9 +25,6 @@ underlying models (e.g., \code{flexsurv::flexsurvreg}). These are not evaluated
 until the model is fit and will be substituted into the model
 fit expression.}
 
-\item{...}{Used for S3 method consistency. Any arguments passed to
-the ellipses will result in an error. Use \code{others} instead.}
-
 \item{object}{A survival regression model specification.}
 
 \item{fresh}{A logical for whether the arguments should be
diff --git a/vignettes/articles/Scratch.Rmd b/vignettes/articles/Scratch.Rmd
index 0f3df7728..e2920ef46 100644
--- a/vignettes/articles/Scratch.Rmd
+++ b/vignettes/articles/Scratch.Rmd
@@ -91,13 +91,13 @@ This is a fairly simple function that can follow a basic template. The main argu
  * The mode. If the model can do more than one mode, you might default this to "unknown". In our case, since it is only a classification model, it makes sense to default it to that mode. 
  * The argument names (`subclasses` here). These should be defaulted to `NULL`.
  * An argument, `others`, that can be used to pass in other arguments to the underlying model fit functions. 
- * `...`, although they are not currently used. 
+ * `...`, although they are not currently used. We encourage developers to move the `...` after mode so that users are encouraged to use named arguments to the model specification. 
 
 A basic version of the function is:
 
 ```{r model-fun}
 mixture_da <-
-  function(mode = "classification",  subclasses = NULL, others = list(), ...) {
+  function(mode = "classification", ...,  subclasses = NULL, others = list()) {
     
     # start with some basic error traps
     check_empty_ellipse(...)