external functions are not imported directly

NAMESPACE

@@ -5,39 +5,4 @@ export(gos)
 export(gos_bestkappa)
 export(inverse_bcPower)
 export(sandwich)
-importFrom(dplyr,all_of)
-importFrom(dplyr,group_by)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,n)
-importFrom(dplyr,pick)
-importFrom(dplyr,select)
-importFrom(dplyr,summarise)
-importFrom(dplyr,ungroup)
-importFrom(ggplot2,aes)
-importFrom(ggplot2,geom_line)
-importFrom(ggplot2,geom_point)
-importFrom(ggplot2,ggplot)
-importFrom(ggplot2,scale_x_continuous)
-importFrom(ggplot2,scale_y_continuous)
-importFrom(ggplot2,theme_bw)
-importFrom(ggrepel,geom_label_repel)
 importFrom(magrittr,"%>%")
-importFrom(parallel,clusterExport)
-importFrom(parallel,makeCluster)
-importFrom(parallel,parLapply)
-importFrom(parallel,stopCluster)
-importFrom(purrr,map_dfr)
-importFrom(sf,st_area)
-importFrom(sf,st_as_sf)
-importFrom(sf,st_drop_geometry)
-importFrom(sf,st_geometry)
-importFrom(sf,st_intersection)
-importFrom(sf,st_join)
-importFrom(sf,st_union)
-importFrom(stats,as.formula)
-importFrom(stats,quantile)
-importFrom(stats,sd)
-importFrom(stats,var)
-importFrom(tibble,as_tibble)
-importFrom(tidyr,replace_na)

R/gos.R

@@ -21,12 +21,7 @@
 #' object.
 #'
 #' @return A tibble made up of predictions and uncertainties.
-#'
-#' @importFrom stats as.formula sd quantile
-#' @importFrom parallel makeCluster stopCluster parLapply
-#' @importFrom tibble as_tibble
-#' @importFrom purrr map_dfr
-#' @importFrom dplyr select
+#' @export
 #'
 #' @examples
 #' \dontrun{
@@ -36,9 +31,6 @@
 #' data = zn, newdata = grid, kappa = 0.08,cores = 6)
 #' g
 #' }
-
-#' @export
-
 gos = \(formula, data = NULL, newdata = NULL, kappa = 0.25, cores = 1){
   doclust = FALSE
   tau = 1 - kappa
@@ -74,22 +66,22 @@ gos = \(formula, data = NULL, newdata = NULL, kappa = 0.25, cores = 1){
   obs_explanatory = as.matrix(obs_explanatory)
   pred_explanatory = as.matrix(pred_explanatory)
   xall = rbind(obs_explanatory, pred_explanatory)
-  sdv = sapply(1:nv, \(x) sd(xall[,x]))
+  sdv = sapply(1:nv, \(x) stats::sd(xall[,x]))
 
   calculgos = \(u){
     xpredvalues = pred_explanatory[u,]
     ej1 = lapply(1:nv, \(x) Ej(obs_explanatory[,x], xpredvalues[x], np, sdv[x]))
     ej = do.call(pmin, ej1)
-    k = which(ej >= quantile(ej, tau))
+    k = which(ej >= stats::quantile(ej, tau))
     ej2 = ej[k]
 
     c(sum(response[k] * ej2) / sum(ej2),
-      1 - quantile(ej2, 0.9),
-      1 - quantile(ej2, 0.95),
-      1 - quantile(ej2, 0.99),
-      1 - quantile(ej2, 0.995),
-      1 - quantile(ej2, 0.999),
-      1 - quantile(ej2, 1)) -> pred_unce
+      1 - stats::quantile(ej2, 0.9),
+      1 - stats::quantile(ej2, 0.95),
+      1 - stats::quantile(ej2, 0.99),
+      1 - stats::quantile(ej2, 0.995),
+      1 - stats::quantile(ej2, 0.999),
+      1 - stats::quantile(ej2, 1)) -> pred_unce
     names(pred_unce) = c('pred',paste0('uncertainty',
                                        c(90, 95, 99, 99.5, 99.9, 100)))
     return(pred_unce)
@@ -133,13 +125,7 @@ gos = \(formula, data = NULL, newdata = NULL, kappa = 0.25, cores = 1){
 #' object.
 #'
 #' @return A list of the result of the best kappa and the computation process curve.
-#'
-#' @importFrom stats as.formula
-#' @importFrom dplyr summarise
-#' @importFrom parallel makeCluster stopCluster clusterExport parLapply
-#' @importFrom purrr map_dfr
-#' @importFrom ggplot2 ggplot aes geom_point geom_line scale_x_continuous scale_y_continuous theme_bw
-#' @importFrom ggrepel geom_label_repel
+#' @export
 #'
 #' @examples
 #' \dontrun{
@@ -156,7 +142,6 @@ gos = \(formula, data = NULL, newdata = NULL, kappa = 0.25, cores = 1){
 #' b1$bestkappa
 #' b1$plot
 #' }
-#' @export
 
 gos_bestkappa = \(formula, data = NULL, kappa = seq(0.05,1,0.05),
               nrepeat = 10,nsplit = 0.5,cores = 1){
@@ -216,10 +201,12 @@ gos_bestkappa = \(formula, data = NULL, kappa = seq(0.05,1,0.05),
   best_x = cv.out$kappa[k]
   best_y = cv.out$rmse[k]
 
-  p1 = ggplot2::ggplot(cv.out, aes(x = kappa, y = rmse))+
+  p1 = ggplot2::ggplot(cv.out,
+                       ggplot2::aes(x = kappa, y = rmse))+
     ggplot2::geom_point()+
     ggplot2::geom_line() +
-    ggrepel::geom_label_repel(data = data.frame(kappa=best_x, rmse=best_y),
+    ggrepel::geom_label_repel(data = data.frame(kappa = best_x,
+                                                rmse = best_y),
                               label=as.character(best_kappa)) +
     ggplot2::scale_x_continuous(limits = c(0,1), breaks = seq(0,1,0.2)) +
     ggplot2::scale_y_continuous(limits = c(min(cv.out$rmse) - l1,

R/sandwich.R

@@ -16,10 +16,6 @@
 #' Default is `area`.
 #'
 #' @return A `sf` object with estimated mean `sandwichest_mean` and standard error `sandwichest_standarderror`.
-#' @importFrom sf st_as_sf st_join st_drop_geometry st_intersection st_union
-#' @importFrom dplyr group_by pick group_by summarise n ungroup select left_join mutate all_of
-#' @importFrom tidyr replace_na
-#' @importFrom stats var
 #' @export
 #'
 #' @examples
@@ -34,8 +30,9 @@
 #'sandwich(sampling = sampling,stratification = ssh,reporting = reporting,
 #'         sampling_attr = 'Value',ssh_zone = 'X',reporting_id = 'Y',
 #'         weight_type = 'area')
-sandwich = \(sampling,stratification,reporting,sampling_attr,ssh_zone,
-             reporting_id,weight_type = 'area'){
+#'
+sandwich = \(sampling,stratification,reporting,sampling_attr,
+             ssh_zone, reporting_id, weight_type = 'area'){
   if (!inherits(sampling,"sf")){
     sampling = sf::st_as_sf(sampling)
   }

R/spEcula_helper.R

@@ -31,7 +31,6 @@ CalRMSE = \(yobse,ypred){
 }
 
 #' @title Rename sf object geometry name
-#' @importFrom sf st_geometry
 #' @noRd
 st_rename_geometry = \(g, name){
   current = attr(g, "sf_column")
@@ -41,7 +40,6 @@ st_rename_geometry = \(g, name){
 }
 
 #' @title Calculate the area of the polygon element using the specified unit
-#' @importFrom sf st_area
 #' @noRd
 st_geographical_area = \(g){
   garea = g %>%