diff --git a/.circleci/config.yml b/.circleci/config.yml
index a59ed99d..ec6d40e9 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -22,7 +22,7 @@ jobs:
sudo apt-get install -y r-base-core cmake
- run:
command: |
- sudo apt-get install -y libxml2-dev
+ sudo apt-get install -y libxml2-dev libuv1-dev
- run:
command: |
echo "options(Ncpus=4)" >> ~/.Rprofile
diff --git a/DESCRIPTION b/DESCRIPTION
index 3f553102..a9573b74 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -74,11 +74,13 @@ Imports:
childsds,
purrr,
tibble,
- tidyselect
+ tidyselect,
+ stats,
+ lubridate
Suggests:
spelling,
testthat,
tidytable
-RoxygenNote: 7.3.3
+RoxygenNote: 8.0.0
Encoding: UTF-8
Language: en-GB
diff --git a/NAMESPACE b/NAMESPACE
index 9f7a5fbd..0474b2b1 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -36,6 +36,7 @@ export(dataFrameFillDS)
export(dataFrameSortDS)
export(dataFrameSubsetDS1)
export(dataFrameSubsetDS2)
+export(dateDS)
export(densityGridDS)
export(dimDS)
export(dmtC2SDS)
@@ -99,6 +100,7 @@ export(minMaxRandDS)
export(namesDS)
export(nsDS)
export(numNaDS)
+export(predictDS)
export(qlsplineDS)
export(quantileMeanDS)
export(rBinomDS)
@@ -118,8 +120,10 @@ export(recodeValuesDS)
export(repDS)
export(replaceNaDS)
export(rmDS)
+export(roundDS)
export(rowColCalcDS)
export(sampleDS)
+export(scaleDS)
export(scatterPlotDS)
export(seqDS)
export(setSeedDS)
diff --git a/R/dateDS.R b/R/dateDS.R
new file mode 100644
index 00000000..f6b442cd
--- /dev/null
+++ b/R/dateDS.R
@@ -0,0 +1,186 @@
+#'
+#' @title dateDS
+#' @description Takes an object that is either a data-frame column or a vector, and can do extraction of
+#' components of full date (\code{extractdate}), can combine date components to a full date (\code{makedate}),
+#' or can calculate the time between two dates (\code{timebetween}).
+#'
+#' @details
+#' If the input is a data-frame column, it must be provided in the \code{x} argument as data-frame$column.
+#' Inputs for \code{extractdate} and \code{timebetween} must be date objects.
+#' For \code{makedate}, three numeric vectors (year, month, day) must be provided in the correct order.
+#' The \code{add.column} argument determines whether the result is added as a new column in the existing
+#' data-frame (\code{TRUE}), or created as a new server-side object (\code{FALSE}).
+#' Note: \code{add.column = TRUE} is only valid for data-frame inputs.
+#'
+#'
+#' @param x Character vector specifying the server-side object(s). For data-frame columns, use the format \code{df$column}.
+#' @param type Character string specifying the operation: \code{"extractdate"}, \code{"makedate"}, or \code{"timebetween"}.
+#' @param newobj Character string for the name of the object that will be created on the server. Default is \code{"date.result"}.
+#' @param unit Character string specifying the unit for \code{extractdate} or \code{timebetween}: \code{"days"}, \code{"months"}, or \code{"years"}.
+#' @param add.column Logical. If \code{FALSE}, the result is created as a new server-side object;
+#' if \code{TRUE}, the result is added as a new column in the existing data-frame. Default is \code{FALSE}.
+#'
+#'
+#' @author Zulal Bekerecioglu
+#' @return the created numeric vector or date object, or the updated dataframe with the added column
+#' @export
+#'
+#'
+dateDS <- function(x=NULL, type=NULL,
+ newobj=NULL, unit=NULL, add.column=NULL) {
+
+ add.column <- as.logical(add.column)
+
+ # If argument not in c("extractdate", "makedate", "timebetween") throw an error
+ if (!(type %in% c("extractdate", "makedate", "timebetween"))) {
+ stop("Invalid argument. Must be one of: ", paste(c("extractdate", "makedate", "timebetween"), collapse=", "))
+ }
+
+ # Check if input(s) are valid.
+ error_message <- "Input object couldn't be found. Please provide the correct format.
+ For vectors, supply an existing object name; for columns, use the format dataframe$column,
+ where 'dataframe' is the name of the data frame and 'column' is the column name,
+ and ensure that both exist."
+
+
+ # If x is NULL, throw and error.
+ if (is.null(x)) {
+ stop(error_message, call. = FALSE)
+ }
+
+ # Each argument takes a unique number of elements, check if they match.
+ if (length(x) != 1 && type=="extractdate") {
+ stop(paste0("Invalid input length for argument '", type, "'. Expected ", 1,
+ " elements, but received ", length(x), ". Please provide exactly ", 1,
+ " object name(s) or column(s)."), call. = FALSE)
+ }
+
+ if (length(x) != 3 && type=="makedate") {
+ stop(paste0("Invalid input length for argument '", type, "'. Expected ", 3,
+ " elements, but received ", length(x), ". Please provide exactly ", 3,
+ " object name(s) or column(s)."), call. = FALSE)
+ }
+
+ if (length(x) != 2 && type=="timebetween") {
+ stop(paste0("Invalid input length for argument '", type, "'. Expected ", 2,
+ " elements, but received ", length(x), ". Please provide exactly ", 2,
+ " object name(s) or column(s)."), call. = FALSE)
+ }
+
+ inputs <- vector("list", length(x))
+
+ # When add.column = TRUE, the client function ensures that at least one input is a column,
+ # and if multiple columns are provided, they all come from the same data-frame.
+ # Therefore, it is safe to use any of these data-frames as common_df on the server.
+ common_df <- NULL
+
+ for (i in seq_along(x)) {
+ element <- x[i]
+
+ # Each element must be a single string
+ if (!is.character(element) || length(element) != 1) stop(error_message, call. = FALSE)
+
+ # Check if it is a df$col reference
+ if (grepl("\\$", element, perl = TRUE)) {
+ parts <- strsplit(element, "\\$", perl = TRUE)[[1]]
+
+ # Validate both parts
+ if (length(parts) != 2 || !nzchar(parts[1]) || !nzchar(parts[2])) stop(error_message, call. = FALSE)
+
+ # Try to get the dataframe
+ df <- tryCatch(get(parts[1]), error = function(e) NULL)
+ if (is.null(df)) stop(error_message, call. = FALSE)
+
+ # Check that column exists
+ if (!(parts[2] %in% names(df))) stop(error_message, call. = FALSE)
+
+ # Save the column values in the list
+ inputs[[i]] <- df[[parts[2]]]
+ common_df <- parts[1]
+
+ } else {
+ # It's a plain object, just get it
+ obj <- tryCatch(get(element), error = function(e) NULL)
+ if (is.null(obj)) stop(error_message, call. = FALSE)
+
+ inputs[[i]] <- obj
+ }
+ }
+
+
+ # extractdate
+ # x should be a column name or object with a date format, type should be provided,
+ # a new column or object with outputcolname will be created
+ if (type == "extractdate") {
+
+ # Only one input is expected
+ date_input <- inputs[[1]]
+
+ # Extract the requested component
+ result <- switch(unit,
+ days = lubridate::day(date_input),
+ months = lubridate::month(date_input),
+ years = lubridate::year(date_input),
+ stop("Invalid unit. Must be one of: days, months, years"))
+ }
+
+
+ # makedate
+ # inputcolname should be list of 3 strings: year-month-day
+ if (type == "makedate") {
+
+ # inputs[[1]] = year, inputs[[2]] = month, inputs[[3]] = day
+ year_vec <- as.numeric(inputs[[1]])
+ month_vec <- as.numeric(inputs[[2]])
+ day_vec <- as.numeric(inputs[[3]])
+
+ # Basic plausibility checks
+ if (length(unique(sapply(list(year_vec, month_vec, day_vec), length))) != 1) {
+ stop("Inputs for 'makedate' must be of equal length.", call. = FALSE)
+ }
+
+ if (any(year_vec < 1000 | year_vec > 3000, na.rm = TRUE)) {
+ stop("The 'year' input in 'makedate' must contain plausible 4-digit years (1000-3000).
+ Check that year, month, and day are given in the correct order (year, month, day).", call. = FALSE)
+ }
+ if (any(month_vec < 1 | month_vec > 12, na.rm = TRUE)) {
+ stop("The 'month' input in 'makedate' must contain values between 1 and 12.
+ Check that year, month, and day are given in the correct order (year, month, day).", call. = FALSE)
+ }
+ if (any(day_vec < 1 | day_vec > 31, na.rm = TRUE)) {
+ stop("The 'day' input in 'makedate' must contain values between 1 and 31.
+ Check that year, month, and day are given in the correct order (year, month, day).", call. = FALSE)
+ }
+
+
+ result <- lubridate::make_date(year = year_vec,
+ month = month_vec,
+ day = day_vec)
+ }
+
+
+ # timebetween
+ # inputcolname should be a list of 2 strings: start and end date
+ if (type == "timebetween") {
+
+ # inputs[[1]] = start date, inputs[[2]] = end date
+ units <- list(
+ years = lubridate::period(years = 1),
+ months = lubridate::period(months = 1),
+ days = lubridate::period(days = 1)
+ )
+
+ result <- lubridate::interval(inputs[[1]], inputs[[2]]) %/% units[[unit]]
+ }
+
+ # Save result based on add.column
+ if (!add.column) {
+ return(result)
+ } else {
+ # Assign to common_df as a new column
+ df <- get(common_df)
+ df[[newobj]] <- result
+ return(df)
+ }
+
+}
diff --git a/R/glmDS2.R b/R/glmDS2.R
index 9287075c..891aae71 100644
--- a/R/glmDS2.R
+++ b/R/glmDS2.R
@@ -382,7 +382,7 @@ glmDS2 <- function (formula, family, beta.vect, offset, weights, dataName) {
errorMessage.combined <- "No errors"
}
- return(list(family=f, info.matrix=info.matrix, score.vect=score.vect, numsubs=numsubs, dev=dev,
+ return(list(family=f$family, info.matrix=info.matrix, score.vect=score.vect, numsubs=numsubs, dev=dev,
Nvalid=Nvalid,Nmissing=Nmissing,Ntotal=Ntotal,disclosure.risk=disclosure.risk,
errorMessage2=errorMessage.combined))
diff --git a/R/glmSLMADS.assign.R b/R/glmSLMADS.assign.R
index f7b5fff4..68fe6929 100644
--- a/R/glmSLMADS.assign.R
+++ b/R/glmSLMADS.assign.R
@@ -21,21 +21,21 @@ glmSLMADS.assign <- function(formula, family, offsetName, weightsName, dataName)
# Convert transmitable text for special link variance combinations back to full representation
if(family=="quasigamma.link_log")
{family<-"quasi(link=log,variance=mu^2)"}
-
+
if(family=="Gamma.link_log")
{family<-"Gamma(link=log)"}
-
+
# Correctly name offset, weights and data objects in function call
# (to allow glmPredict to work correctly later)
calltext <- paste0("mg<-glm(formula,family=",family,",offset=",
offsetName,",weights=",weightsName,",data=", dataName,",x=TRUE)")
-
+
eval(parse(text=calltext))
-
+
# update the call object to include the actual formula
mg$call$formula <- formula
-
- return(mg)
+
+ return(mg)
}
# ASSIGN FUNCTION
diff --git a/R/predictDS.R b/R/predictDS.R
new file mode 100644
index 00000000..e3311f91
--- /dev/null
+++ b/R/predictDS.R
@@ -0,0 +1,137 @@
+#'
+#' @title predictDS
+#' @description Generates server-side predictions using the client-side output from \code{ds.glm}.
+#'
+#' @details
+#' This function uses the components supplied by the client-side function (coefficients, family, formula,
+#' and any categorical variables) to generate predictions on the server. To use the base R \code{predict()} function,
+#' a "dummy" glm object is created using the same model formula, family, and link function as the original model.
+#' The dummy model's coefficients are then replaced with the client-side coefficient estimates.
+#'
+#' To avoid mismatches between the factors used in the original glm and those in the dummy glm, the categorical
+#' variables saved by the client-side function are applied to the newdata.
+#'
+#' For intercept-only models, the function simply returns a vector of predicted values equal to the model intercept, with the appropriate length
+#' based on the row length of \code{newdataname}.
+#'
+#'
+#' @param newdataname A character string specifying the name of the new dataset to be used for predictions.
+#' @param traindataname A character string specifying the name of the dataset used for model training.
+#' @param type A character string specifying the type of prediction. Options are \code{"response"} or \code{"link"}.
+#' @param na.action A character string to specify the action to take if missing values are present. Default is \code{"na.pass"}.
+#'
+#'
+#' @author Zulal Bekerecioglu
+#' @return a numeric vector containing the predicted values
+#' @export
+#'
+#'
+predictDS <- function(newdataname, traindataname, type = c("response", "link"),
+ na.action = "na.pass") {
+
+ # Get the objects saved by the client function ds.predict
+ coefficients<-get("predictDS_coefficients")
+ model_formula <-get("predictDS_formula")
+ family <- get("predictDS_family")
+ categorical_variables <- get("predictDS_categorical_variables")
+
+
+
+ if(!is.null(traindataname))
+ {
+ traindata<-get(traindataname)
+
+ }else{
+ stop("'traindataname' couldn't be found, please provide a valid object name.", call.=FALSE)
+ }
+
+
+ if(!is.null(newdataname))
+ {
+ newdf<-get(newdataname)
+
+ }else{
+ stop("'newdataname' couldn't be found, please provide a valid object name.", call.=FALSE)
+ }
+
+ if (!is.character(type) || !(type %in% c("link","response"))) {
+ stop("Invalid argument. Must be one of: 'link','response'.", call. = FALSE)
+ }
+
+ # Convert the family object to it's corresponding function, i.e. poisson.link.log -> poisson(link= "log")
+ family_dist <- strsplit(family, "\\.")[[1]]
+
+ family_name <- family_dist[1] # "binomial"
+ link_name <- family_dist[3] # "logit"
+
+ family_func <- get(family_name) # gets the function binomial()
+ family_obj <- family_func(link = link_name)
+
+
+
+ # SPECIAL CASE HANDLING: y ~ 1, intercept only ######################
+ # A numeric vector will be created wit the mean, with the same length as the row number in newdataname
+ special_case <- length(attr(stats::terms(stats::formula(model_formula)), "term.labels")) == 0
+
+ if(special_case){
+ intercept <- coefficients
+
+ # If the input is just a numeric vector, get the length
+ if(all(c("numeric") %in% class(newdf))){
+ predictions.f <- rep(intercept, length(newdf))
+ } else if(all(c("data.frame") %in% class(newdf))){
+ predictions.f <- rep(intercept, nrow(newdf)) # Otherwise use the number of rows
+ } else {
+ stop("Invalid input: The object called 'newdataname' must be either a numeric vector or a data.frame.",, call. = FALSE)
+ }
+
+ if (type == "link") {
+ predictions.f <- predictions.f
+ } else if (family_name == "gaussian") { # if type is 'response'
+ # identity
+ predictions.f <- predictions.f
+ } else if (family_name == "poisson") {
+ # log
+ predictions.f <- exp(predictions.f)
+ } else if (family_name == "binomial") {
+ # logit
+ predictions.f <- stats::plogis(predictions.f)
+ } else {
+ stop("Unsupported family for intercept-only prediction: Family must be either Gaussian, Poisson, or Binomial.", call. = FALSE)
+ }
+
+ return(predictions.f)
+ }
+ # END OF SPECIAL CASE ######################
+
+
+
+ # Fix factor levels if any exists
+ for (var in categorical_variables) {
+
+ # First get all the factor levels from the train data
+ traindata[[var]] <- factor(traindata[[var]])
+
+ # Ensure new data variable is a factor with the SAME levels (this is needed if the newdf is missing some categories)
+ newdf[[var]] <- factor(newdf[[var]],
+ levels = levels(traindata[[var]]))
+ }
+
+ # Get the na.action argument
+ na.action.fun <- match.fun(na.action)
+
+ # Use a dummy glm object with the correct formula and family
+ dummy_fit <- stats::glm(model_formula,
+ data = traindata,
+ family = family_obj,
+ control = stats::glm.control(maxit = 1))
+
+ # Change its coefficients with the correct ones
+ names(coefficients) <- names(dummy_fit$coefficients)
+ dummy_fit$coefficients <- coefficients
+
+ # New predictions
+ prediction <- stats::predict(dummy_fit, newdata = newdf, type = type, na.action = na.action.fun)
+
+ return(prediction)
+}
diff --git a/R/roundDS.R b/R/roundDS.R
new file mode 100644
index 00000000..6e0d0c10
--- /dev/null
+++ b/R/roundDS.R
@@ -0,0 +1,77 @@
+#'
+#' @title roundDS
+#' @description Generates objects from a server-side object, which can be either a vector or
+#' a data-frame column. Supports five operations:
+#' 1. (\code{round})
+#' 2. (\code{ceiling})
+#' 3. (\code{floor})
+#' 4. (\code{trunc})
+#' 5. (\code{signif})
+#' where each function in baseR is applied on the server side to the specified object.
+#'
+#' @details
+#' Note: \code{add.column = TRUE} is only valid for data-frame inputs.
+#'
+#'
+#' @param x Character vector specifying the server-side object(s). For data-frame columns, use the format \code{df$column}.
+#' @param type Character string specifying the operation: \code{"round"}, \code{"ceiling"}, \code{"floor"},
+#' \code{trunc}, or \code{"signif"}.
+#' @param digits Number of digits to be used in arguments \code{"round"} and \code{"signif"}.
+#' @param add.column Logical. If \code{FALSE}, the result is created as a new server-side object;
+#' if \code{TRUE}, the result is added as a new column in the existing data-frame. Default is \code{FALSE}.
+#' @param newobj Character string for the name of the object that will be created on the server. Default is \code{"rounding.result"}.
+#'
+#'
+#' @author Zulal Bekerecioglu
+#' @return the created numeric vector or the updated dataframe with the added column
+#' @export
+#'
+#'
+roundDS <- function(x, type, digits, add.column, newobj) {
+
+ # If x is NULL, throw and error.
+ if (is.null(x)) {
+ stop("Input object couldn't be found, please provide an object for rounding in the correct format.
+ For vectors, supply an existing object name; for columns, use the format dataframe$column,
+ where 'dataframe' is the name of the data frame and 'column' is the column name,
+ and ensure that both exist.", call. = FALSE)
+ }
+
+ add.column <- as.logical(add.column)
+
+ # Check if object is a column (contains $), if it is then save the dataframe name for later use if necessary
+ if(grepl("\\$", x)) {
+ is_column <- TRUE
+ dataframe_name <- strsplit(x, "\\$")[[1]][1]
+ column_name <- strsplit(x, "\\$")[[1]][2]
+ } else {
+ is_column <- FALSE
+ }
+
+
+ if(!is.null(x)&&!(is_column))
+ {
+ object <- get(x)
+
+ } else if(!is.null(x)&&is_column){
+ df <- get(dataframe_name)
+ object <- df[[column_name]]
+ }
+
+ result <- switch(type,
+ round = round(object, digits = digits),
+ ceiling = ceiling(object),
+ floor = floor(object),
+ trunc = trunc(object),
+ signif = signif(object, digits = digits))
+
+ if(!(is_column)){ # if the object was a numerical vector, save the result in a new object
+ return(result)
+ } else if((is_column)&&!add.column){ # if the object was a column and add.column is FALSE, save the result in a new object
+ return(result)
+ } else if((is_column)&&add.column){ # if the object was a column and add.column is TRUE, save the result as a column
+ df[[newobj]] <- result
+ return(df)
+ }
+
+}
diff --git a/R/scaleDS.R b/R/scaleDS.R
new file mode 100644
index 00000000..96cbd103
--- /dev/null
+++ b/R/scaleDS.R
@@ -0,0 +1,61 @@
+#'
+#' @title scaleDS
+#' @description Generates scaled objects using a server-side object, which can be either a vector or
+#' a data-frame column.
+#'
+#' @details
+#' Note: \code{add.column = TRUE} is only valid for data-frame inputs.
+#'
+#'
+#' @param x Character string specifying the server-side vector For data-frame columns, use the format \code{df$column}.
+#' @param newobj Character string for the name of the object that will be created on the server. Default is \code{"scaled.data"}.
+#' @param add.column Logical. If \code{FALSE}, the result is created as a new server-side object;
+#' if \code{TRUE}, the result is added as a new column in the existing data-frame. Default is \code{FALSE}.
+#'
+#' @author Zulal Bekerecioglu
+#' @return the created numeric vector or the updated dataframe with the added column
+#' @export
+#'
+#'
+scaleDS <- function(x=NULL, newobj=NULL, add.column=NULL) {
+
+ add.column <- as.logical(add.column)
+
+ error_message <- "Input object couldn't found, please provide the correct format. For vectors, supply an existing object name;
+ for columns, use df$colname and ensure the dataframe and column exist."
+
+ # If x is NULL, throw and error.
+ if (is.null(x)) {
+ stop(error_message, call. = FALSE)
+ } else {
+ is_dataframe <- grepl("\\$", x)
+
+ if(is_dataframe) {
+ # Extract dataframe name
+ dataframe_name <- strsplit(x, "\\$")[[1]][1]
+ column_name <- strsplit(x, "\\$")[[1]][2]
+
+ df <-get(dataframe_name)
+ } else {
+
+ object <-get(x)
+ }
+ }
+
+
+ if(is_dataframe) {
+ result <- as.numeric(scale(df[[column_name]])) # scale the column
+ } else {
+ result <- as.numeric(scale(object)) # scale the vector
+ }
+
+ # Return the dataframe with the added column, or the new object.
+ if(is_dataframe&&add.column) {
+ df[[newobj]] <- result
+ return(df)
+ } else {
+ return(result)
+ }
+
+
+}
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index b79d39f0..da0d21aa 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -106,7 +106,7 @@ jobs:
sudo apt-get install -qq pkg-config -y
sudo apt-get install -qq libxml2-dev libcurl4-openssl-dev libssl-dev libgit2-dev libharfbuzz-dev libfribidi-dev libfontconfig1-dev -y
- sudo apt-get install -qq libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev -y
+ sudo apt-get install -qq libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev libuv1-dev -y
sudo apt-get install -qq r-base -y
sudo R -e "install.packages('devtools', dependencies=TRUE)"
sudo R -e "install.packages('RANN', dependencies=TRUE)"
@@ -257,7 +257,6 @@ jobs:
echo 'branch:'$(branchName) >> $(datetime).txt
echo 'os:'$(lsb_release -ds) >> $(datetime).txt
echo 'R:'$(R --version | head -n 1) >> $(datetime).txt
- echo 'opal:'$(opal system --opal localhost:8080 --user administrator --password "datashield_test&" --version) >> $(datetime).txt
workingDirectory: $(Pipeline.Workspace)/logs
displayName: 'Write versions to file'
diff --git a/docs/authors.html b/docs/authors.html
index dc54dd5e..3526a873 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -17,11 +17,7 @@
dsBase
-<<<<<<< HEAD
6.4.0.9000
-=======
- 6.3.5.9000
->>>>>>> origin/v6.3.5-dev
@@ -106,20 +102,12 @@
Citation
Burton P, Wilson R, Butters O, Ryser-Welch P, Westerberg A, Abarrategui L, Villegas-Diaz R, Avraam D, Marcon Y, Bishop T, Gaye A, Escribà-Montagut X, Wheater S (????).
dsBase: 'DataSHIELD' Server Side Base Functions.
-<<<<<<< HEAD
R package version 6.4.0.9000.
-=======
-R package version 6.3.5.9000.
->>>>>>> origin/v6.3.5-dev
@Manual{,
title = {dsBase: 'DataSHIELD' Server Side Base Functions},
author = {Paul Burton and Rebecca Wilson and Olly Butters and Patricia Ryser-Welch and Alex Westerberg and Leire Abarrategui and Roberto Villegas-Diaz and Demetris Avraam and Yannick Marcon and Tom Bishop and Amadou Gaye and Xavier Escribà-Montagut and Stuart Wheater},
-<<<<<<< HEAD
note = {R package version 6.4.0.9000},
-=======
- note = {R package version 6.3.5.9000},
->>>>>>> origin/v6.3.5-dev
}
Gaye A, Marcon Y, Isaeva J, LaFlamme P, Turner A, Jones E, Minion J, Boyd A, Newby C, Nuotio M, Wilson R, Butters O, Murtagh B, Demir I, Doiron D, Giepmans L, Wallace S, Budin-Ljøsne I, Schmidt C, Boffetta P, Boniol M, Bota M, Carter K, deKlerk N, Dibben C, Francis R, Hiekkalinna T, Hveem K, Kvaløy K, Millar S, Perry I, Peters A, Phillips C, Popham F, Raab G, Reischl E, Sheehan N, Waldenberger M, Perola M, van den Heuvel E, Macleod J, Knoppers B, Stolk R, Fortier I, Harris J, Woffenbuttel B, Murtagh M, Ferretti V, Burton P (2014).
“DataSHIELD: taking the analysis to the data, not the data to the analysis.”
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pkgdown: 2.2.0
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articles: {}
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asDataFrameDS a serverside assign function called by ds.asDataFrame — asDataMatrixDS • dsBaseasDataMatrixDS a serverside assign function called by ds.asDataMatrix — asDataMatrixDS • dsBase<
+head>asDataFrameDS a serverside assign function called by ds.asDataFrame — asDataMatrixDS • dsBasecheckPermissivePrivacyControlLevel — checkPermissivePrivacyControlLevel • dsBasecheckPermissivePrivacyControlLevel — checkPermissivePrivacyControlLevel • dsBase
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-dsBase: 'DataSHIELD' Server Side Base Functions — dsBase-package • dsBase
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Base 'DataSHIELD' functions for the server side. 'DataSHIELD' is a software package which allows you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see 'citation("dsBase")'.