rename internal helper functions to start with .

Author: hfrick

NAMESPACE

@@ -108,6 +108,8 @@ S3method(varying_args,model_spec)
 S3method(varying_args,recipe)
 S3method(varying_args,step)
 export("%>%")
+export(.check_glmnet_penalty_fit)
+export(.check_glmnet_penalty_predict)
 export(.cols)
 export(.dat)
 export(.facts)
@@ -123,8 +125,6 @@ export(augment)
 export(boost_tree)
 export(check_empty_ellipse)
 export(check_final_param)
-export(check_glmnet_penalty_fit)
-export(check_glmnet_penalty_predict)
 export(contr_one_hot)
 export(control_parsnip)
 export(convert_stan_interval)

R/linear_reg.R

@@ -107,7 +107,7 @@ translate.linear_reg <- function(x, engine = x$engine, ...) {
   x <- translate.default(x, engine, ...)
 
   if (engine == "glmnet") {
-    check_glmnet_penalty_fit(x)
+    .check_glmnet_penalty_fit(x)
     if (any(names(x$eng_args) == "path_values")) {
       # Since we decouple the parsnip `penalty` argument from being the same
       # as the glmnet `lambda` value, `path_values` allows users to set the
@@ -255,7 +255,7 @@ predict._elnet <-
       penalty <- object$spec$args$penalty
     }
 
-    object$spec$args$penalty <- check_glmnet_penalty_predict(penalty, object, multi)
+    object$spec$args$penalty <- .check_glmnet_penalty_predict(penalty, object, multi)
 
     object$spec <- eval_args(object$spec)
     predict.model_fit(object, new_data = new_data, type = type, opts = opts, ...)

R/logistic_reg.R

@@ -109,7 +109,7 @@ translate.logistic_reg <- function(x, engine = x$engine, ...) {
   arg_names <- names(arg_vals)
 
   if (engine == "glmnet") {
-    check_glmnet_penalty_fit(x)
+    .check_glmnet_penalty_fit(x)
     if (any(names(x$eng_args) == "path_values")) {
       # Since we decouple the parsnip `penalty` argument from being the same
       # as the glmnet `lambda` value, `path_values` allows users to set the
@@ -296,7 +296,7 @@ predict._lognet <- function(object, new_data, type = NULL, opts = list(), penalt
     penalty <- object$spec$args$penalty
   }
 
-  object$spec$args$penalty <- check_glmnet_penalty_predict(penalty, object, multi)
+  object$spec$args$penalty <- .check_glmnet_penalty_predict(penalty, object, multi)
 
   object$spec <- eval_args(object$spec)
   predict.model_fit(object, new_data = new_data, type = type, opts = opts, ...)

R/misc.R

@@ -329,18 +329,20 @@ stan_conf_int <- function(object, newdata) {
 #' Helper functions for checking the penalty of glmnet models
 #'
 #' @description
-#' `check_glmnet_penalty_fit()` checks that the model specification for fitting a
+#' These functions are for developer use.
+#'
+#' `.check_glmnet_penalty_fit()` checks that the model specification for fitting a
 #' glmnet model contains a single value.
 #'
-#' `check_glmnet_penalty_predict()` checks that the penalty value used for prediction is valid.
+#' `.check_glmnet_penalty_predict()` checks that the penalty value used for prediction is valid.
 #' If called by `predict()`, it needs to be a single value. Multiple values are
 #' allowed for `multi_predict()`.
 #'
 #' @param x An object of class `model_spec`.
 #' @rdname glmnet_helpers
 #' @keywords internal
 #' @export
-check_glmnet_penalty_fit <- function(x) {
+.check_glmnet_penalty_fit <- function(x) {
   pen <- rlang::eval_tidy(x$args$penalty)
 
   if (length(pen) != 1) {
@@ -360,7 +362,7 @@ check_glmnet_penalty_fit <- function(x) {
 #' @rdname glmnet_helpers
 #' @keywords internal
 #' @export
-check_glmnet_penalty_predict <- function(penalty = NULL, object, multi = FALSE) {
+.check_glmnet_penalty_predict <- function(penalty = NULL, object, multi = FALSE) {
 
   if (is.null(penalty)) {
     penalty <- object$fit$lambda

R/multinom_reg.R

@@ -222,7 +222,7 @@ predict._multnet <-
       penalty <- object$spec$args$penalty
     }
 
-    object$spec$args$penalty <- check_glmnet_penalty_predict(penalty, object, multi)
+    object$spec$args$penalty <- .check_glmnet_penalty_predict(penalty, object, multi)
 
     object$spec <- eval_args(object$spec)
     res <- predict.model_fit(