Release preparation

R/assessment_functions.R

@@ -2866,7 +2866,7 @@ assess_negativebinomial <- function(
     output$reference.values <- lapply(AC, function(i) {
       ctsm.lmm.refvalue(
         output, 
-        year = max(data$year), 
+        yearID = max(data$year), 
         refvalue = log(i),
         lower.tail = switch(good_status, low = TRUE, high = FALSE)
       )

R/graphics_functions.R

@@ -79,7 +79,7 @@ plot_assessment <- function(
     auxiliary = "default") {
 
   # silence non-standard evaluation warnings
-  seriesID <- NULL
+  .data <- NULL
 
   # graphics_functions.R
 
@@ -224,7 +224,7 @@ plot_assessment <- function(
 
   lapply(series_id, function(id) {
 
-    data <- dplyr::filter(assessment_obj$data, seriesID == id)
+    data <- dplyr::filter(assessment_obj$data, .data$seriesID == id)
 
     assessment <- assessment_obj$assessment[[id]]
 
@@ -1011,7 +1011,7 @@ plot_auxiliary <- function(
     xykey.cex = 1.0, ntick.x = 3, ntick.y = 3, newPage = TRUE, ...) {
 
   # silence non-standard evaluation warnings
-  info.imposex <- NULL
+  .data <- info.imposex <- NULL
 
   # auxiliary specifies the choice of 'auxiliary' variables to plot: 
   # default:

R/import_check_functions.R

@@ -454,7 +454,7 @@ ctsm.check.sex.biota <- function(data, info) {
   # extra checks for imposex determinands and EROD 
 
   # silence non-standard evaluation warnings
-  sex <- NULL
+  .data <- sex <- NULL
 
 
   # global check of ICES codes 

R/import_functions.R

@@ -2301,7 +2301,8 @@ create_timeseries <- function(
 
   # silence non-standard evaluation warnings
   .data <- .month <- .not_ok <- group <- value <- NULL
-  determinand <- uncertainty <- uncertainty_sd <- uncertainty_rel <- species_group <- NULL
+  determinand <- uncertainty <- uncertainty_sd <- uncertainty_rel <- NULL
+  concentration <- distribution <- species_group <- NULL
 
   # arguments
 
@@ -3926,6 +3927,11 @@ check_uncertainty <- function(data, info, type = c("reported", "calculated")) {
   # type = calculated is used to check whether implausible uncertainties have
   #   been created in e.g. the normalisation process
 
+  # silence non-standard evaluation warnings
+
+  .data <- reason <- NULL
+
+
   type <- match.arg(type)
 
 

R/information_functions.R

@@ -2649,14 +2649,15 @@ get.info.imposex <- function(
 #' 2024. The 2022 world health organization reevaluation of human and mammalian 
 #' toxic equivalency factors for polychlorinated dioxins, dibenzofurans and 
 #' biphenyls. Regulatory Toxicology and Pharmacology 146; 
-#' [https://doi.org/10.1016/j.yrtph.2023.105525]
+#' [https://doi.org/10.1016/j.yrtph.2023.105525](https://doi.org/10.1016/j.yrtph.2023.105525)
 #' 
 #' Van den Berg M, Birnbaum LS, Denison M, De Vito M, Farland W, Feeley M, 
 #' Fiedler H, Hakansson H, Hanberg A, Haws L, Rose M, Safe S, Schrenk D, 
 #' Tohyama C, Tritscher A, Tuomisto J, Tysklind M, Walker N, Peterson RE, 2006.
 #' The 2005 World Health Organization reevaluation of human and mammalian toxic 
 #' equivalency factors for dioxins and dioxin-like compounds. Toxicological 
-#' Sciences 93 223–241; [https://doi.org/10.1093/toxsci/kfl055]
+#' Sciences 93 223–241; 
+#' [https://doi.org/10.1093/toxsci/kfl055](https://doi.org/10.1093/toxsci/kfl055)
 #' 
 #' @export 
 info_TEF <- list(

R/reporting_functions.R

@@ -634,14 +634,14 @@ write_summary_table <- function(
     "shape", "colour"
   ) 
 
-  summary <- dplyr::relocate(summary, any_of(wk))
+  summary <- dplyr::relocate(summary, dplyr::any_of(wk))
 
   if ("dtrend_obs" %in% names(summary)) {
     wk <- c(
       "dtrend_obs", "dtrend_seq", "dtrend_ten", "nyear_seq", 
       "power_obs", "power_seq", "power_ten"
     )
-    summary <- dplyr::relocate(summary, all_of(wk), .after = "dtrend") 
+    summary <- dplyr::relocate(summary, dplyr::all_of(wk), .after = "dtrend") 
   }
 
   sortID <- intersect(

R/uncertainty_functions.R

@@ -48,7 +48,7 @@ ctsm_uncrt_workup <- function(harsat_obj) {
     paste0("LOIGN", id_aux)
   )
 
-  data <- dplyr::select(data, any_of(id))
+  data <- dplyr::select(data, dplyr::any_of(id))
 
 
 
@@ -198,8 +198,10 @@ ctsm_uncrt_estimate <- function(data) {
 
   # remove data that is way off
 
-  data <- data %>% 
-    dplyr::filter(dplyr::between(relative_u, 1, 100)) 
+  data <- dplyr::filter(
+    data, 
+    dplyr::between(.data$relative_u, 1, 100)
+  ) 
 
 
   # relative error
@@ -250,6 +252,9 @@ ctsm_uncrt_estimate <- function(data) {
 #' @export
 ctsm_uncrt_plot_estimates <- function(uncrt_obj, group_id) {
 
+  # silence non-standard evaluation warnings
+  .data <- NULL
+
   data <- dplyr::filter(uncrt_obj$data, .data$group %in% group_id)
 
   data <- dplyr::arrange(data, .data$determinand, .data$concentration)