diff --git a/DESCRIPTION b/DESCRIPTION
index d97a973..66156dc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: gDRtestData
 Title: gDRtestData - R data package with testing dose response data
-Version: 1.11.1
+Version: 1.11.2
 Date: 2026-04-29
 Description: R package with internal dose-response test data. Package provides functions to generate
   input testing data that can be used as the input for gDR pipeline. It also contains qs2 files
diff --git a/NEWS.md b/NEWS.md
index 824916c..ab95c01 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,6 @@
+## gDRtestData 1.11.2 - 2026-05-18
+* apply updated gDRstyle rules
+
 ## gDRtestData 1.11.1 - 2026-04-29
 * synchronize Bioconductor and GitHub versioning
 
@@ -135,4 +138,4 @@
 ## gDRtestData 0.99.0 - 2023-03-31
 * preparing package for Bioc submission
 * fix examples
-  
+  
\ No newline at end of file
diff --git a/R/generate_data.R b/R/generate_data.R
index c24f3dd..d6f9461 100644
--- a/R/generate_data.R
+++ b/R/generate_data.R
@@ -7,17 +7,17 @@
 #'
 #'
 #' @examples
-#' 
+#'
 #' cell_lines <- create_synthetic_cell_lines()
 #' add_data_replicates(cell_lines)
-#' 
+#'
 #' @return data.table with replicates
 #' @keywords generate_data
 #' @export
 #'
 add_data_replicates <- function(df_layout) {
   df_layout_duplicates <- Reduce(rbind, list(df_layout)[rep(1, times = 3)])
-  barcode <- do.call(c, lapply(paste0("plate_", seq_len(3)), function(x) rep(x, nrow(df_layout))))
+  barcode <- do.call(c, lapply(paste0("plate_", seq_len(3)), function(x) rep(x, NROW(df_layout))))
   cbind(Barcode = barcode, df_layout_duplicates)
 }
 
@@ -30,10 +30,10 @@ add_data_replicates <- function(df_layout) {
 #' @keywords generate_data
 #'
 #' @examples
-#' 
+#'
 #' cell_lines <- create_synthetic_cell_lines()
 #' add_concentration(cell_lines)
-#' 
+#'
 #' @return data.table with concentrations
 #' @export
 #'
@@ -55,41 +55,41 @@ add_concentration <- function(df_layout, concentrations = 10 ^ (seq(-3, 1, 0.5))
 #'
 #'
 #' @examples
-#' 
+#'
 #' cell_lines <- create_synthetic_cell_lines()
 #' drugs <- create_synthetic_drugs()
 #' df_layout <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ])
 #' generate_response_data(df_layout)
-#' 
+#'
 #'
 #' @return data.table with response data
 #' @export
 #'
 generate_response_data <- function(df_layout, noise_level = 0.1) {
-  
+
   drugs <- create_synthetic_drugs()
   cell_lines <- create_synthetic_cell_lines()
   hill_coef <- generate_hill_coef(drugs, cell_lines)
   ec50 <- generate_ec50(drugs, cell_lines)
   e_inf <- generate_e_inf(drugs, cell_lines)
-  
+
   df_layout$ReadoutValue <- round(100 * pmax(
     getReadoutCoef(df_layout, e_inf, ec50, hill_coef) +
-      (noise_level * stats::runif(nrow(df_layout)) - (noise_level / 2)),  # add some noise
-    0.01 * stats::runif(nrow(df_layout)) + 0.005), # avoid hard 0 values
+      (noise_level * stats::runif(NROW(df_layout)) - (noise_level / 2)),  # add some noise
+    0.01 * stats::runif(NROW(df_layout)) + 0.005), # avoid hard 0 values
     1)
   df_layout$BackgroundValue <- 0
   df_layout$Duration <- 72
   df_layout <- introduceVehicle(df_layout)
-  
+
   if ("Gnumber_2" %in% colnames(df_layout)) { # combo data
     df_layout <- introduceGNum(df_layout, e_inf, ec50, hill_coef, "_2")
   }
-  
+
   if ("Gnumber_3" %in% colnames(df_layout)) { # combo data
     df_layout <- introduceGNum(df_layout, e_inf, ec50, hill_coef, "_3")
   }
-  
+
   df_layout
 }
 
@@ -98,24 +98,24 @@ getReadoutCoef <- function(df, e_inf, ec50, hill_coef, suffix = "") {
   apply(df, 1, function(x) {
     clid <- x["clid"]
     gnum <- x[paste0("Gnumber", suffix)]
-    
+
     e_inf_val <- e_inf[gnum, clid]
     ec50_val <- ec50[gnum, clid]
     hill_val <- hill_coef[gnum, clid]
     concentration <- as.numeric(x["Concentration"])
-    
-    e_inf_val + (1 - e_inf_val) * (ec50_val ^ hill_val / (concentration ^ hill_val + ec50_val ^ hill_val)) 
+
+    e_inf_val + (1 - e_inf_val) * (ec50_val ^ hill_val / (concentration ^ hill_val + ec50_val ^ hill_val))
   })
 }
 
 #' @keywords internal
 introduceVehicle <- function(df, suffix = "") {
   zeroIdx <- df[[paste0("Concentration", suffix)]] == 0
-  
+
   df[zeroIdx, paste0("Gnumber", suffix)] <- "vehicle"
   df[zeroIdx, paste0("DrugName", suffix)] <- "vehicle"
   df[zeroIdx, paste0("drug_moa", suffix)] <- "vehicle"
-  
+
   df
 }
 
@@ -123,7 +123,7 @@ introduceVehicle <- function(df, suffix = "") {
 introduceGNum <- function(df, e_inf, ec50, hill_coef, suffix) {
   df$ReadoutValue <- df$ReadoutValue * getReadoutCoef(df, e_inf, ec50, hill_coef, suffix)
   df <- introduceVehicle(df, suffix)
-  
+
   df
 }
 
@@ -134,7 +134,7 @@ introduceGNum <- function(df, e_inf, ec50, hill_coef, suffix) {
 #' @keywords generate_data
 #'
 #' @examples
-#' 
+#'
 #' cell_lines <- create_synthetic_cell_lines()
 #' drugs <- create_synthetic_drugs()
 #' df_merged <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ])
@@ -152,14 +152,14 @@ add_day0_data <- function(df_merged, noise_level = 0.05) {
     TRUE
   }
   df_Day0 <- unique(df_merged[df_merged$Concentration == 0 & cond, ])
-  
+
   df_Day0$ReadoutValue <- df_Day0$ReadoutValue / 2 ^ (df_Day0$Duration / df_Day0$ReferenceDivisionTime)
-  coef <- (1 - noise_level / 2 + noise_level * stats::runif(nrow(df_Day0)))
+  coef <- (1 - noise_level / 2 + noise_level * stats::runif(NROW(df_Day0)))
   df_Day0$ReadoutValue <- round(df_Day0$ReadoutValue * coef, 1)
-  
+
   df_Day0$Duration <- 0
   df_Day0$Barcode <- "plate_0"
-  
+
   df_merged <- rbind(df_merged, df_Day0)
   df_merged
 }
diff --git a/R/get_test_datasets.R b/R/get_test_datasets.R
index 8fa40df..ede0283 100644
--- a/R/get_test_datasets.R
+++ b/R/get_test_datasets.R
@@ -1,5 +1,5 @@
 #' get_test_dataset_paths
-#' 
+#'
 #' Returns named vector of absolute paths to test datasets.
 #'
 #' @param datasets_dir path to directory with datasets (default \code{NULL}).
@@ -9,13 +9,13 @@
 #' @keywords generate_test_data
 #'
 #' @return named vector of absolute paths
-#' 
+#'
 #' @examples
-#' 
+#'
 #' get_test_dataset_paths()
 #' path <- system.file("testdata", package = "gDRtestData", mustWork = TRUE)
 #' get_test_dataset_paths(path)
-#' 
+#'
 #' @export
 #'
 #' @author Kamil Foltyński \email{kamil.foltynski@@contractors.roche.com}
@@ -30,12 +30,10 @@ get_test_dataset_paths <-
    }
    checkmate::assert_string(datasets_dir, min.chars = 1)
    checkmate::assert_directory_exists(datasets_dir)
-   
+
    checkmate::assert_string(pattern, min.chars = 1)
-   
+
    epaths <- list.files(datasets_dir, pattern = paste0(pattern, ".*\\.qs2$"), full.names = TRUE)
    enames <- gsub(pattern, "", gsub("\\.qs2$", "", basename(epaths)))
    structure(epaths, names = enames)
  }
-   
-
diff --git a/R/helper_functions.R b/R/helper_functions.R
index d380f81..11fd8f9 100644
--- a/R/helper_functions.R
+++ b/R/helper_functions.R
@@ -1,7 +1,7 @@
 # Helper functions
 
 #' prepareData
-#' 
+#'
 #' Create data.table with input data for testing purposes
 #'
 #' @param cell_lines data.table with cell line info
@@ -10,11 +10,11 @@
 #' @keywords generate_test_data
 #'
 #' @return data.table with input data for testing
-#' 
+#'
 #' @examples
-#' 
+#'
 #' prepareData(create_synthetic_cell_lines(), create_synthetic_drugs())
-#' 
+#'
 #' @export
 prepareData <- function(cell_lines, drugs, conc = 10 ^ (seq(-3, 1, 0.5))) {
   df_layout <- data.table::as.data.table(merge.data.frame(cell_lines, drugs, by = NULL))
@@ -23,8 +23,8 @@ prepareData <- function(cell_lines, drugs, conc = 10 ^ (seq(-3, 1, 0.5))) {
 }
 
 #' prepareMergedData
-#' 
-#' Create data.table with input data containing noise for testing purposes 
+#'
+#' Create data.table with input data containing noise for testing purposes
 #'
 #' @param cell_lines data.table with cell line info
 #' @param drugs data.table with drug info
@@ -32,11 +32,11 @@ prepareData <- function(cell_lines, drugs, conc = 10 ^ (seq(-3, 1, 0.5))) {
 #' @keywords generate_test_data
 #'
 #' @return data.table with input data for testing
-#' 
+#'
 #' @examples
-#' 
+#'
 #' prepareMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())
-#' 
+#'
 #' @export
 prepareMergedData <- function(cell_lines, drugs, noise = 0.1) {
   df <- prepareData(cell_lines, drugs)
@@ -44,8 +44,8 @@ prepareMergedData <- function(cell_lines, drugs, noise = 0.1) {
 }
 
 #' prepareComboMergedData
-#' 
-#' Create data.table with input combination data containing noise for testing purposes 
+#'
+#' Create data.table with input combination data containing noise for testing purposes
 #'
 #' @param cell_lines data.table with cell line info
 #' @param drugs data.table with drug info
@@ -58,35 +58,35 @@ prepareMergedData <- function(cell_lines, drugs, noise = 0.1) {
 #' @keywords generate_test_data
 #'
 #' @return data.table with input data for testing
-#' 
+#'
 #' @examples
-#' 
+#'
 #' prepareComboMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())
-#' 
+#'
 #' @export
-prepareComboMergedData <- function(cell_lines, 
-                                   drugs, 
+prepareComboMergedData <- function(cell_lines,
+                                   drugs,
                                    drugsIdx1 = 2:4,
-                                   drugsIdx2 = c(26, 26, 26), 
-                                   concentration = c(0, .2, 1), 
-                                   noise = 0.1, 
+                                   drugsIdx2 = c(26, 26, 26),
+                                   concentration = c(0, .2, 1),
+                                   noise = 0.1,
                                    modifyDf2 = FALSE) {
   df_layout <- prepareData(cell_lines, drugs[drugsIdx1, ])
-  
+
   df_2 <- cbind(drugs[drugsIdx2, ], Concentration = concentration)
   colnames(df_2) <- paste0(colnames(df_2), "_2")
-  
+
   df_layout_2 <- data.table::as.data.table(merge.data.frame(df_layout, df_2, by = NULL))
   if (modifyDf2) {
     df_layout_2 <- df_layout_2[!(df_layout_2$Concentration == 0 & df_layout_2$Concentration_2 > 0), ]
   }
-  
+
   generate_response_data(df_layout_2, noise)
 }
 
 #' prepareCodilutionData
-#' 
-#' Create data.table with input co-dilution data containing noise for testing purposes 
+#'
+#' Create data.table with input co-dilution data containing noise for testing purposes
 #'
 #' @param cell_lines data.table with cell line info
 #' @param drugs data.table with drug info
@@ -94,32 +94,32 @@ prepareComboMergedData <- function(cell_lines,
 #' @param conc vector of doses
 #' @param noise number indicating level of noise
 #' @keywords generate_test_data
-#' 
+#'
 #' @return data.table with input data for testing
-#' 
+#'
 #' @examples
-#' 
+#'
 #' prepareCodilutionData(create_synthetic_cell_lines()[seq_len(2), ],
 #'                       create_synthetic_drugs()[seq_len(4), ])
-#' 
+#'
 #' @export
 prepareCodilutionData <- function(cell_lines,
                                   drugs,
                                   drugsIdx2 = 1,
                                   conc = 10 ^ (seq(-3, 1, 0.5)),
                                   noise = 0.1) {
-  
+
   df_layout <- prepareData(cell_lines = cell_lines, drugs = drugs, conc = conc)
-  
+
   df_2 <- cbind(drugs[drugsIdx2, , drop = FALSE],
                 df_layout[, "Concentration", drop = FALSE])
   colnames(df_2) <- paste0(colnames(df_2), "_2")
-  
+
   df_layout_2 <- cbind(df_layout, df_2)
   df_layout_2 <- df_layout_2[df_layout_2$DrugName != df_layout_2$DrugName_2, ]
   rows <- which(df_layout_2$Concentration_2 > 0)
   cols <- c("Concentration", "Concentration_2")
   df_layout_2[rows, (cols) := lapply(.SD, function(x) x / 2), .SDcols = cols]
-  
+
   generate_response_data(df_layout_2, noise)
 }
diff --git a/R/package.R b/R/package.R
index d5699c7..a3fbb19 100644
--- a/R/package.R
+++ b/R/package.R
@@ -6,7 +6,7 @@
 #' @examples
 #' path <- system.file("annotation_data", "cell_lines.csv", package = "gDRtestData")
 #' data.table::fread(file = path)
-#' 
+#'
 #' @return data.table
 NULL
 
@@ -15,9 +15,9 @@ NULL
 #' @name drugs
 #' @docType data
 #' @keywords data internal
-#' @examples 
+#' @examples
 #' path <- system.file("annotation_data", "drugs.csv", package = "gDRtestData")
 #' data.table::fread(file = path)
-#' 
+#'
 #' @return data.table
 NULL
diff --git a/R/random_data.R b/R/random_data.R
index 13bbeb1..3bf90c1 100644
--- a/R/random_data.R
+++ b/R/random_data.R
@@ -5,7 +5,7 @@
 #'
 #' @examples
 #' create_synthetic_cell_lines()
-#' 
+#'
 #' @export
 create_synthetic_cell_lines <- function() {
   cell_lines <- data.table::data.table(
@@ -15,7 +15,7 @@ create_synthetic_cell_lines <- function() {
     ReferenceDivisionTime = seq(22, 80, 4)
   )
   cell_lines <- Reduce(rbind, list(cell_lines)[rep(1, times = 6)])
-  cell_lines$clid <- paste0("CL000", 9 + (seq_len(nrow(cell_lines))))
+  cell_lines$clid <- paste0("CL000", 9 + (seq_len(NROW(cell_lines))))
   cell_lines$CellLineName <- paste0(cell_lines$CellLineName, sort(array(LETTERS[seq_len(15)], 90)))
   cell_lines$Tissue[16:40] <- "tissue_w"
   cell_lines$Tissue[41:50] <- "tissue_v"
@@ -28,7 +28,7 @@ create_synthetic_cell_lines <- function() {
 #' @return data.table with synthetic drugs
 #' @examples
 #' create_synthetic_drugs()
-#' 
+#'
 #' @export
 create_synthetic_drugs <- function() {
   drugs <- data.table::data.table(
@@ -37,8 +37,8 @@ create_synthetic_drugs <- function() {
     drug_moa = sort(paste0("moa_", array(LETTERS[c(1, seq_len(6), 6)], 40)))
   )
   drugs <- Reduce(rbind, list(drugs)[rep(1, times = 6)])
-  drugs$Gnumber <- sprintf("G00%03i", seq_len(nrow(drugs)))
-  drugs$DrugName <- sprintf("drug_%03i", seq_len(nrow(drugs)))
+  drugs$Gnumber <- sprintf("G00%03i", seq_len(NROW(drugs)))
+  drugs$DrugName <- sprintf("drug_%03i", seq_len(NROW(drugs)))
   drugs$drug_moa[-seq_len(80)] <- sort(paste0("moa_", array(LETTERS[seq_len(24)], 160)))
   drugs
 }
@@ -52,11 +52,11 @@ create_synthetic_drugs <- function() {
 #'
 #' @return matrix with random hill coefficient
 #' @examples
-#' generate_hill_coef(create_synthetic_drugs(), create_synthetic_cell_lines()) 
-#' 
+#' generate_hill_coef(create_synthetic_drugs(), create_synthetic_cell_lines())
+#'
 #' @export
 generate_hill_coef <- function(drugs, cell_lines) {
-  hill_coef <- matrix(1.8 + stats::runif(nrow(drugs) * nrow(cell_lines)), nrow(drugs), nrow(cell_lines))
+  hill_coef <- matrix(1.8 + stats::runif(NROW(drugs) * NROW(cell_lines)), NROW(drugs), NROW(cell_lines))
   colnames(hill_coef) <- cell_lines$clid
   rownames(hill_coef) <- drugs$Gnumber
   hill_coef
@@ -71,14 +71,14 @@ generate_hill_coef <- function(drugs, cell_lines) {
 #' @return matrix with random EC50
 #' @examples
 #' generate_ec50(create_synthetic_drugs(), create_synthetic_cell_lines())
-#' 
+#'
 #' @export
 generate_ec50 <- function(drugs, cell_lines) {
   checkmate::assert_data_table(drugs)
   checkmate::assert_data_table(cell_lines)
-  
-  nDrugs <- nrow(drugs)
-  nCells <- nrow(cell_lines)
+
+  nDrugs <- NROW(drugs)
+  nCells <- NROW(cell_lines)
   ec50 <- matrix(stats::runif(nDrugs * nCells) - 0.5, nDrugs, nCells) +
     matrix(sort(rep(seq(-1.2, 0, 0.3), 8)), nDrugs, nCells) +
     t(matrix(seq(-0.4, 0, 0.1), nCells, nDrugs))
@@ -110,15 +110,15 @@ generate_ec50 <- function(drugs, cell_lines) {
 #' @return matrix with random E inf
 #' @examples
 #' generate_e_inf(create_synthetic_drugs(), create_synthetic_cell_lines())
-#' 
+#'
 #' @export
 generate_e_inf <- function(drugs, cell_lines) {
   checkmate::assert_data_table(drugs)
   checkmate::assert_data_table(cell_lines)
-  
-  nDrugs <- nrow(drugs)
-  nCells <- nrow(cell_lines)
-  
+
+  nDrugs <- NROW(drugs)
+  nCells <- NROW(cell_lines)
+
   e_inf <- matrix(0.5 * stats::runif(nDrugs * nCells), nDrugs, nCells) +
     t(matrix(seq(0, 0.2, 0.05), nCells, nDrugs))
 
@@ -135,9 +135,9 @@ generate_e_inf <- function(drugs, cell_lines) {
   e_inf[moa_BE, tissue_x] <- e_inf[moa_BE, tissue_x] - 0.5 * stats::runif(sum(tissue_x))
   e_inf[moa_F, ] <- e_inf[moa_F, ] + 0.3 + 0.2 * stats::runif(sum(moa_F))
   e_inf <- matrix(pmin(0.89, pmax(0.01, e_inf)) + stats::runif(nDrugs * nCells) * 0.1, nDrugs, nCells)
-  
+
   colnames(e_inf) <- cell_lines$clid
   rownames(e_inf) <- drugs$Gnumber
-  
+
   e_inf
 }
diff --git a/inst/scripts/generate_example_data.R b/inst/scripts/generate_example_data.R
index 9946eae..25611ee 100644
--- a/inst/scripts/generate_example_data.R
+++ b/inst/scripts/generate_example_data.R
@@ -3,18 +3,18 @@
 #  The data have no day0 information.
 #  dataset with * are to be imported as example for visualization
 #
-#  "small_no_noise"             10 drugs (3 different drug_moa) by 10 lines (3 tissues); 
+#  "small_no_noise"             10 drugs (3 different drug_moa) by 10 lines (3 tissues);
 #                               single agent - no noise in the data
 #  "small"                  *   10 drugs (3 different drug_moa) by 10 lines (3 tissues); single agent
 #  "wLigand"                *   3 drugs by 4 lines (3 tissues); "Ligand = 0.1" as reference; single agent
 #  "medium"                 *   40 drugs (6 different drug_moa) by 15 lines (3 tissues); single agent
 #  "many_lines"             *   150 drugs (6 different drug_moa) by 10 lines (3 tissues); single agent
 #  "many_drugs"             *   150 drugs (6 different drug_moa) by 10 lines (3 tissues); single agent
-#  "combo_2dose_nonoise"    *   3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines; 
+#  "combo_2dose_nonoise"    *   3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines;
 #                               co-treatment drug occurs also as a primary drug
-#  "combo_2dose_nonoise2"       3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines; 
+#  "combo_2dose_nonoise2"       3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines;
 #                               co-treatment drug occurs also as a primary drug
-#  "combo_2dose_nonoise3"       3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines; 
+#  "combo_2dose_nonoise3"       3 drugs x 2 co-treatment (1 drug at 2 doses) by 3 cell lines;
 #                               co-treatment drug does NOT have single agent response
 #  "combo_1dose_many_drugs" *   149 drugs x 1 drug (1 dose) by 3 lines;
 #  "combo_matrix_small"         3 x 2 drugs (matrix) for 2 cell lines; no noise
@@ -97,4 +97,3 @@ generateCodilutionSmall(cell_lines, drugs)
 # generate the data for the test set with combo (co-dilution)
 set.seed(2)
 generateCodilution(cell_lines, drugs)
-
diff --git a/vignettes/gDRtestData.Rmd b/vignettes/gDRtestData.Rmd
index 42b8d5a..97f5d82 100644
--- a/vignettes/gDRtestData.Rmd
+++ b/vignettes/gDRtestData.Rmd
@@ -73,12 +73,12 @@ In a further step, the user may generate a set of synthetic results:
 
 1. Hill coefficient
 ```{r}
-hill <- generate_hill_coef(cell_lines, drugs) 
+hill <- generate_hill_coef(cell_lines, drugs)
 ```
 
 2. EC50 metric
 ```{r}
-ec50_met <- generate_ec50(cell_lines, drugs) 
+ec50_met <- generate_ec50(cell_lines, drugs)
 ```
 
 3. E inf metric
@@ -107,16 +107,16 @@ head(response_data)
 
 ## Synthetic object of gDR data model
 
-The gDR data model is built on the MultiAssayExperiments (MAE) structure. A detailed description of the gDR data model can be found in `gDRcore` package vignette.  
+The gDR data model is built on the MultiAssayExperiments (MAE) structure. A detailed description of the gDR data model can be found in `gDRcore` package vignette.
 
-In `inst/testdata` the user may find a set of `qs2` files that are examples of gDR data model for different data types. In the file `synthetic_list.yml` one can find a list of these datasets. Currently available are: 
+In `inst/testdata` the user may find a set of `qs2` files that are examples of gDR data model for different data types. In the file `synthetic_list.yml` one can find a list of these datasets. Currently available are:
 ```{r echo=FALSE}
 yml_path <- system.file(package = "gDRtestData", "testdata", "synthetic_list.yml")
 cat(paste0("* ", names(yaml::read_yaml(yml_path)), collapse = ", \n"), ".")
 ```
 
 The script `generate_example_data.R` which shows how to generate and process above-mentioned datasets is in `inst/scripts` dir.
-All key functions can be found in package `gDRcore` in script `generate_wrappers.R`. 
+All key functions can be found in package `gDRcore` in script `generate_wrappers.R`.
 
 ## Annotation data