remove unneeded namespacing

R/container.R

@@ -17,7 +17,7 @@
 #' @export
 container <- function(mode = "unknown", type = "unknown", outcome = character(0),
                       estimate = character(0), probabilities = character(0),
-                      time = character(0), call = rlang::current_env()) {
+                      time = character(0), call = current_env()) {
   dat <-
     list(
       outcome = outcome,
@@ -37,13 +37,13 @@ container <- function(mode = "unknown", type = "unknown", outcome = character(0)
 }
 
 new_container <- function(mode, type, operations, columns, ptype, call) {
-  mode <- rlang::arg_match0(mode, c("unknown", "regression", "classification", "censored regression"))
+  mode <- arg_match0(mode, c("unknown", "regression", "classification", "censored regression"))
 
   if (mode == "regression") {
     type <- "regression"
   }
 
-  type <- rlang::arg_match0(type, c("unknown", "regression", "binary", "multiclass"))
+  type <- arg_match0(type, c("unknown", "regression", "binary", "multiclass"))
 
   if (!is.list(operations)) {
     cli::cli_abort("The {.arg operations} argument should be a list.", call = call)
@@ -88,23 +88,23 @@ print.container <- function(x, ...) {
 
 #' @export
 fit.container <- function(object, .data, outcome, estimate, probabilities = c(),
-                          time = c(), call = rlang::current_env(), ...) {
+                          time = c(), call = current_env(), ...) {
 
   # ------------------------------------------------------------------------------
   # set columns via tidyselect
 
   dat <- list()
-  dat$outcome <- names(tidyselect::eval_select(rlang::enquo(outcome), .data))
-  dat$estimate <- names(tidyselect::eval_select(rlang::enquo(estimate), .data))
+  dat$outcome <- names(tidyselect::eval_select(enquo(outcome), .data))
+  dat$estimate <- names(tidyselect::eval_select(enquo(estimate), .data))
 
-  probabilities <- tidyselect::eval_select(rlang::enquo(probabilities), .data)
+  probabilities <- tidyselect::eval_select(enquo(probabilities), .data)
   if (length(probabilities) > 0) {
     dat$probabilities <- names(probabilities)
   } else {
     dat$probabilities <- character(0)
   }
 
-  time <- tidyselect::eval_select(rlang::enquo(time), .data)
+  time <- tidyselect::eval_select(enquo(time), .data)
   if (length(time) > 0) {
     dat$time <- names(time)
   }    else {

R/equivocal_zone.R

@@ -47,7 +47,7 @@ adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }
 
@@ -81,7 +81,7 @@ predict.equivocal_zone <- function(object, new_data, parent, ...) {
   est_nm <- parent$columns$estimate
   prob_nm <- parent$columns$probabilities[1]
   lvls <- levels(new_data[[ est_nm ]])
-  col_syms <- rlang::syms(prob_nm[1])
+  col_syms <- syms(prob_nm[1])
   cls_pred <- probably::make_two_class_pred(new_data[[prob_nm]], levels = lvls,
                                             buffer = object$arguments$value,
                                             threshold = object$arguments$threshold)

R/numeric_calibration.R

@@ -26,7 +26,7 @@ adjust_numeric_calibration <- function(x, calibrator) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }
 

R/numeric_range.R

@@ -23,7 +23,7 @@ adjust_numeric_range <- function(x, lower_limit = -Inf, upper_limit = Inf) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }
 

R/operation.R

@@ -8,8 +8,8 @@ input_vals  <- c("numeric", "probability", "class", "everything")
 output_vals <- c("numeric", "probability_class", "class", "everything")
 
 new_operation <- function(cls, inputs, outputs, arguments, results = list(trained = FALSE), ...) {
-  inputs  <- rlang::arg_match0(inputs,  input_vals)
-  outputs <- rlang::arg_match0(outputs, output_vals)
+  inputs  <- arg_match0(inputs,  input_vals)
+  outputs <- arg_match0(outputs, output_vals)
 
   if (!any(names(results) == "trained")) {
     cli::cli_abort("The {.arg results} slot requires a logical variable called \\

R/predictions_custom.R

@@ -26,7 +26,7 @@
 #' @export
 adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
 
-  cmds <- rlang::enquos(...)
+  cmds <- enquos(...)
 
   op <-
     new_operation(
@@ -43,7 +43,7 @@ adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }
 

R/prob_threshold.R

@@ -43,7 +43,7 @@ adjust_probability_threshold <- function(x, threshold = 0.5) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }
 

R/probability_calibration.R

@@ -27,7 +27,7 @@ adjust_probability_calibration <- function(x, calibrator) {
     operations = c(x$operations, list(op)),
     columns = x$dat,
     ptype = x$ptype,
-    call = rlang::current_env()
+    call = current_env()
   )
 }