diff --git a/DESCRIPTION b/DESCRIPTION
index dd838b8c2..9cb98d931 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -90,4 +90,4 @@ Suggests:
     DSLite
 Encoding: UTF-8
 Language: en-GB
-Config/roxygen2/version: 8.1.0
+Config/roxygen2/version: 8.1.1
diff --git a/R/ds.auc.R b/R/ds.auc.R
index ebfc5fc31..8b16502b1 100644
--- a/R/ds.auc.R
+++ b/R/ds.auc.R
@@ -11,19 +11,12 @@
 #' the default set of connections will be used: see \code{\link[DSI]{datashield.connections_default}}.
 #' @return returns the AUC and its standard error
 #' @author Demetris Avraam for DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @export
 #'
 ds.auc <- function(pred=NULL, y=NULL, datasources=NULL){
   
-  # look for DS connections
-  if(is.null(datasources)){
-    datasources <- DSI::datashield.connections_find()
-  }
-  
-  # ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
   
   # verify that 'pred' was set
   if(is.null(pred)){
@@ -35,10 +28,6 @@ ds.auc <- function(pred=NULL, y=NULL, datasources=NULL){
     stop("Please provide the name of the outcome variable", call.=FALSE)
   }
   
-  # check if the pred and y objects are defined in all the studies
-  defined.pred <- isDefined(datasources, pred)
-  defined.y <- isDefined(datasources, y)
-  
   cally <- call('aucDS', pred, y)
   output <- DSI::datashield.aggregate(datasources, cally)
   
diff --git a/R/ds.bp_standards.R b/R/ds.bp_standards.R
index ae76c54ca..fe9b69fa5 100644
--- a/R/ds.bp_standards.R
+++ b/R/ds.bp_standards.R
@@ -26,20 +26,13 @@
 #' blood pressure in children and adolescents:
 #' https://www.nhlbi.nih.gov/sites/default/files/media/docs/hbp_ped.pdf
 #' @author Demetris Avraam for DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @import data.table
 #' @export
 ds.bp_standards <- function(sex=NULL, age=NULL, height=NULL, bp=NULL, systolic=TRUE, 
                             newobj=NULL, datasources=NULL){
   
-  # look for DS connections
-  if(is.null(datasources)){
-    datasources <- DSI::datashield.connections_find()
-  }
-  
-  # ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
   
   # verify that 'sex' was set
   if(is.null(sex)){
@@ -61,12 +54,6 @@ ds.bp_standards <- function(sex=NULL, age=NULL, height=NULL, bp=NULL, systolic=T
     stop("Please provide the name of the blood pressure variable", call.=FALSE)
   }
   
-  # check if the input objects are defined in all the studies
-  defined.sex <- isDefined(datasources, sex)
-  defined.age <- isDefined(datasources, age)
-  defined.height <- isDefined(datasources, height)
-  defined.bp <- isDefined(datasources, bp)
-  
   # if no output object specified then provide a default name
   if(is.null(newobj)){
     newobj <- "bp.newobj"
diff --git a/R/ds.exists.R b/R/ds.exists.R
index 24c5bfa26..b7a378144 100644
--- a/R/ds.exists.R
+++ b/R/ds.exists.R
@@ -16,6 +16,7 @@
 #' @return \code{ds.exists} returns a logical object. 
 #' TRUE if the object is on the server-side and FALSE otherwise.
 #' @author DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @seealso \code{\link{ds.class}} to check the type of an object.
 #' @seealso \code{\link{ds.length}} to check the length of an object.
 #' @seealso \code{\link{ds.dim}} to check the dimension of an object.
@@ -61,15 +62,7 @@
 #'
 ds.exists <- function(x=NULL, datasources=NULL){
 
-  # look for DS connections
-  if(is.null(datasources)){
-    datasources <- datashield.connections_find()
-  }
-
-  # ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
 
   if(is.null(x)){
     stop("Please provide the name of the input object!", call.=FALSE)
diff --git a/R/ds.igb_standards.R b/R/ds.igb_standards.R
index 89498a0ca..dd4023045 100644
--- a/R/ds.igb_standards.R
+++ b/R/ds.igb_standards.R
@@ -36,20 +36,13 @@
 #' }
 #' @return assigns the converted measurement as a new object on the server-side
 #' @author Demetris Avraam for DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @export
 #' 
 ds.igb_standards <- function(gagebrth=NULL, z=0, p=50, val=NULL, var=NULL, sex=NULL, 
                              fun='igb_value2zscore', newobj=NULL, datasources=NULL){
   
-  # look for DS connections
-  if(is.null(datasources)){
-    datasources <- datashield.connections_find()
-  }
-  
-  # ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
   
   # check if user has provided the name of the input variables
   if(is.null(gagebrth)){
@@ -59,10 +52,6 @@ ds.igb_standards <- function(gagebrth=NULL, z=0, p=50, val=NULL, var=NULL, sex=N
     stop("Please provide the name of the sex variable", call.=FALSE)
   }
   
-  # check if the input objects are defined in all the studies
-  isDefined(datasources, gagebrth)
-  isDefined(datasources, sex)
-  
   # check if the provided fun is valid
   if(!(fun %in% c("igb_centile2value","igb_zscore2value","igb_value2zscore","igb_value2centile"))){
     stop("Please provide a valid fun", call.=FALSE)
@@ -72,8 +61,6 @@ ds.igb_standards <- function(gagebrth=NULL, z=0, p=50, val=NULL, var=NULL, sex=N
   if(fun %in% c("igb_value2zscore","igb_value2centile")){
     if(is.null(val)){
       stop("Please provide the name of the val variable", call.=FALSE)
-    }else{
-      isDefined(datasources, val)
     }
   }
   
diff --git a/R/ds.listDisclosureSettings.R b/R/ds.listDisclosureSettings.R
index 04851def6..f115bdb28 100644
--- a/R/ds.listDisclosureSettings.R
+++ b/R/ds.listDisclosureSettings.R
@@ -57,6 +57,7 @@
 #' @return \code{ds.listDisclosureSettings} returns a list containing the current settings of the
 #' \code{nfilters} in each study specified. 
 #' @author DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @export
 #' @examples
 #' \dontrun{
@@ -100,15 +101,7 @@
 #' }
 #'
 ds.listDisclosureSettings <- function(datasources=NULL){
-  # look for DS connections
-  if(is.null(datasources)){
-    datasources <- datashield.connections_find()
-  }
-
-  # ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
 
   # CALL THE MAIN SERVER SIDE FUNCTION
   calltext <- call("listDisclosureSettingsDS")
diff --git a/R/ds.mdPattern.R b/R/ds.mdPattern.R
index 37823071d..98fa8ec19 100644
--- a/R/ds.mdPattern.R
+++ b/R/ds.mdPattern.R
@@ -46,6 +46,7 @@
 #' @param datasources a list of \code{\link[DSI]{DSConnection-class}} objects obtained 
 #' after login. If the \code{datasources} argument is not specified, the default set of 
 #' connections will be used: see \code{\link[DSI]{datashield.connections_default}}.
+#' @template classConsistencyCheckTrue
 #' @return For type='split': A list with one element per study, each containing:
 #' \describe{
 #'   \item{pattern}{The missing data pattern matrix for that study}
@@ -60,6 +61,7 @@
 #'   \item{message}{A message describing the validity status}
 #' }
 #' @author Xavier Escribà montagut for DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @export
 #' @examples
 #' \dontrun{
@@ -111,17 +113,9 @@
 #'   datashield.logout(connections)
 #' }
 #'
-ds.mdPattern <- function(x = NULL, type = 'split', datasources = NULL){
+ds.mdPattern <- function(x = NULL, type = 'split', datasources = NULL, classConsistencyCheck = TRUE){
 
-  # Look for DS connections
-  if(is.null(datasources)){
-    datasources <- datashield.connections_find()
-  }
-
-  # Ensure datasources is a list of DSConnection-class
-  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-  }
+  datasources <- .set_datasources(datasources)
 
   if(is.null(x)){
     stop("Please provide the name of a data frame or matrix!", call.=FALSE)
@@ -134,10 +128,14 @@ ds.mdPattern <- function(x = NULL, type = 'split', datasources = NULL){
   cally <- call("mdPatternDS", x)
   results <- DSI::datashield.aggregate(datasources, cally)
 
+  if(classConsistencyCheck){
+    .checkClassConsistency(results)
+  }
+
   # Process results based on type
   if(type == "split"){
     # Return individual study results
-    return(results)
+    return(lapply(results, function(r) { r$class <- NULL; r }))
 
   } else if(type == "combine"){
     # Pool results across studies
@@ -176,7 +174,7 @@ ds.mdPattern <- function(x = NULL, type = 'split', datasources = NULL){
     }
 
     # Pool the patterns
-    pooled_pattern <- .pool_md_patterns(patterns_list, study_names)
+    pooled_pattern <- .poolMdPatterns(patterns_list, study_names)
 
     # Check validity of pooled results
     # Get threshold from first study's results or use a default check
@@ -217,8 +215,8 @@ ds.mdPattern <- function(x = NULL, type = 'split', datasources = NULL){
 #' @param patterns_list List of pattern matrices from each study
 #' @param study_names Names of the studies
 #' @return Pooled pattern matrix
-#' @keywords internal
-.pool_md_patterns <- function(patterns_list, study_names){
+#' @noRd
+.poolMdPatterns <- function(patterns_list, study_names){
 
   # Initialize with first study's pattern structure
   pooled <- patterns_list[[1]]
diff --git a/R/ds.rm.R b/R/ds.rm.R
index 652e9e58d..9e104228a 100644
--- a/R/ds.rm.R
+++ b/R/ds.rm.R
@@ -22,6 +22,7 @@
 #' is successful the message \code{"Object(s) '<x.names>' was deleted."} is returned
 #' to the client-side. 
 #' @author DataSHIELD Development Team
+#' @author Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 #' @examples
 #' \dontrun{
 #'
@@ -68,15 +69,7 @@
 
 ds.rm<-function(x.names=NULL, datasources=NULL){
     
-    # look for DS connections
-    if (is.null(datasources)) {
-        datasources <- datashield.connections_find()
-    }
-    
-    # ensure datasources is a list of DSConnection-class
-    if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
-        stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
-    }
+    datasources <- .set_datasources(datasources)
     
     if (is.null(x.names)) {
         stop("Please provide the names of the objects to be deleted (eg 'object.name') as the x.names argument", call.=FALSE)
diff --git a/dsBase_7.0.0-permissive.tar.gz b/dsBase_7.0.0-permissive.tar.gz
index 137855e31..b7e29c284 100644
Binary files a/dsBase_7.0.0-permissive.tar.gz and b/dsBase_7.0.0-permissive.tar.gz differ
diff --git a/man/dot-pool_md_patterns.Rd b/man/dot-pool_md_patterns.Rd
deleted file mode 100644
index baabf3e92..000000000
--- a/man/dot-pool_md_patterns.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/ds.mdPattern.R
-\name{.pool_md_patterns}
-\alias{.pool_md_patterns}
-\title{Pool missing data patterns across studies}
-\usage{
-.pool_md_patterns(patterns_list, study_names)
-}
-\arguments{
-\item{patterns_list}{List of pattern matrices from each study}
-
-\item{study_names}{Names of the studies}
-}
-\value{
-Pooled pattern matrix
-}
-\description{
-Internal function to pool md.pattern results from multiple studies
-}
-\keyword{internal}
diff --git a/man/ds.auc.Rd b/man/ds.auc.Rd
index 0ca1a5d37..133a904e2 100644
--- a/man/ds.auc.Rd
+++ b/man/ds.auc.Rd
@@ -28,4 +28,6 @@ The AUC determines the discriminative ability of a model.
 }
 \author{
 Demetris Avraam for DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.bp_standards.Rd b/man/ds.bp_standards.Rd
index edbdd06dd..ff7a31719 100644
--- a/man/ds.bp_standards.Rd
+++ b/man/ds.bp_standards.Rd
@@ -55,4 +55,6 @@ https://www.nhlbi.nih.gov/sites/default/files/media/docs/hbp_ped.pdf
 }
 \author{
 Demetris Avraam for DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.exists.Rd b/man/ds.exists.Rd
index eb9795a02..8569971e5 100644
--- a/man/ds.exists.Rd
+++ b/man/ds.exists.Rd
@@ -79,4 +79,6 @@ Server function called: \code{exists}
 }
 \author{
 DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.igb_standards.Rd b/man/ds.igb_standards.Rd
index e9ab20f51..686041a65 100644
--- a/man/ds.igb_standards.Rd
+++ b/man/ds.igb_standards.Rd
@@ -70,4 +70,6 @@ standard is used.
 }
 \author{
 Demetris Avraam for DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.listDisclosureSettings.Rd b/man/ds.listDisclosureSettings.Rd
index 5ac64a610..d86ac1326 100644
--- a/man/ds.listDisclosureSettings.Rd
+++ b/man/ds.listDisclosureSettings.Rd
@@ -114,4 +114,6 @@ Server function called: \code{listDisclosureSettingsDS}
 }
 \author{
 DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.mdPattern.Rd b/man/ds.mdPattern.Rd
index d52730497..05bb06f1a 100644
--- a/man/ds.mdPattern.Rd
+++ b/man/ds.mdPattern.Rd
@@ -4,7 +4,12 @@
 \alias{ds.mdPattern}
 \title{Display missing data patterns with disclosure control}
 \usage{
-ds.mdPattern(x = NULL, type = "split", datasources = NULL)
+ds.mdPattern(
+  x = NULL,
+  type = "split",
+  datasources = NULL,
+  classConsistencyCheck = TRUE
+)
 }
 \arguments{
 \item{x}{a character string specifying the name of a data frame or matrix on the 
@@ -17,6 +22,9 @@ across studies.}
 \item{datasources}{a list of \code{\link[DSI]{DSConnection-class}} objects obtained 
 after login. If the \code{datasources} argument is not specified, the default set of 
 connections will be used: see \code{\link[DSI]{datashield.connections_default}}.}
+
+\item{classConsistencyCheck}{logical. If TRUE, checks that the input object has the same
+class across all studies. Default TRUE.}
 }
 \value{
 For type='split': A list with one element per study, each containing:
@@ -127,4 +135,6 @@ for disclosure control:
 }
 \author{
 Xavier Escribà montagut for DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/man/ds.rm.Rd b/man/ds.rm.Rd
index 2988826a0..9db0eef5a 100644
--- a/man/ds.rm.Rd
+++ b/man/ds.rm.Rd
@@ -82,4 +82,6 @@ Server function called: \code{rmDS}
 }
 \author{
 DataSHIELD Development Team
+
+Tim Cadman, Genomics Coordination Centre, UMCG, Netherlands
 }
diff --git a/tests/testthat/perf_files/armadillo_azure-pipeline_perf-profile.csv b/tests/testthat/perf_files/armadillo_azure-pipeline_perf-profile.csv
index 3eb977f09..84d295b95 100644
--- a/tests/testthat/perf_files/armadillo_azure-pipeline_perf-profile.csv
+++ b/tests/testthat/perf_files/armadillo_azure-pipeline_perf-profile.csv
@@ -11,9 +11,11 @@
 "ds.asMatrix::perf::0","17.44","0.5","2"
 "ds.asNumeric::perf:0","16.79","0.5","2"
 "ds.assign::perf::0","16.75","0.5","2"
+"ds.auc::perf::0","20.20","0.5","2"
 "ds.Boole::perf::0","2.324","0.5","2"
 "ds.boxPlot::perf::0","5.837","0.5","2"
 "ds.boxPlotGG::perf::0","6.696","0.5","2"
+"ds.bp_standards::perf::0","13.64","0.5","2"
 "ds.c::perf::0","10.23","0.5","2"
 "ds.cbind::perf::0","3.814","0.5","2"
 "ds.changeRefGroup::perf::0","17.98","0.5","2"
@@ -41,12 +43,15 @@
 "ds.heatmapPlot::perf::0","4.519","0.5","2"
 "ds.hetcor::perf::0","0.2028","0.5","2"
 "ds.histogram::perf::0","9.261","0.5","2"
+"ds.igb_standards::perf::0","7.719","0.5","2"
 "ds.isNA::perf::combine:0","23.86","0.5","2"
+"ds.isValid::perf::0","22.52","0.5","2"
 "ds.kurtosis::perf::combine:0","8.377","0.5","2"
 "ds.length::perf::combine:0","24.04","0.5","2"
 "ds.levels::perf::combine:0","23.96","0.5","2"
 "ds.lexis::perf::0","3.760","0.5","2"
 "ds.list::perf::0","10.03","0.5","2"
+"ds.listDisclosureSettings::perf::0","22.08","0.5","2"
 "ds.log::perf::0","17.30","0.5","2"
 "ds.ls::perf::combine:0","24.35","0.5","2"
 "ds.lspline::perf::0","14.17","0.5","2"
@@ -59,6 +64,7 @@
 "ds.matrixInvert::perf::0","16.49","0.5","2"
 "ds.matrixMult::perf::0","16.65","0.5","2"
 "ds.matrixTranspose::perf::0","17.90","0.5","2"
+"ds.mdPattern::perf::0","6.701","0.5","2"
 "ds.mean::perf::combine:0","24.12","0.5","2"
 "ds.mean::perf::split:0","24.45","0.5","2"
 "ds.meanSdGp::perf::combine:0","19.84","0.5","2"
@@ -76,6 +82,7 @@
 "ds.rep::perf::0","17.44","0.5","2"
 "ds.replaceNA::perf::0","3.292","0.5","2"
 "ds.reShape::perf::0","17.31","0.5","2"
+"ds.rm::perf::0","19.85","0.5","2"
 "ds.rowColCalc::perf::0","16.70","0.5","2"
 "ds.rNorm::perf::0","8.597","0.5","2"
 "ds.rPois::perf::0","8.556","0.5","2"
diff --git a/tests/testthat/perf_files/armadillo_github-workflows_perf-profile.csv b/tests/testthat/perf_files/armadillo_github-workflows_perf-profile.csv
index d850131ae..e9eb1fa69 100644
--- a/tests/testthat/perf_files/armadillo_github-workflows_perf-profile.csv
+++ b/tests/testthat/perf_files/armadillo_github-workflows_perf-profile.csv
@@ -1,97 +1,103 @@
 "refer_name","rate","lower_tolerance","upper_tolerance"
-"conndisconn::perf::simple0","0.1581","0.5","2"
-"ds.abs::perf::0","17.27","0.5","2"
-"ds.asCharacter::perf::0","16.84","0.5","2"
-"ds.asDataMatrix::perf::0","17.44","0.5","2"
-"ds.asFactor::perf::0","10.22","0.5","2"
-"ds.asFactorSimple::perf::0","17.79","0.5","2"
-"ds.asInteger::perf:0","17.61","0.5","2"
-"ds.asList::perf:0","22.52","0.5","2"
-"ds.asLogical::perf::0","17.46","0.5","2"
-"ds.asMatrix::perf::0","17.44","0.5","2"
-"ds.asNumeric::perf:0","16.79","0.5","2"
-"ds.assign::perf::0","23.51","0.5","2"
-"ds.Boole::perf::0","5.479","0.5","2"
-"ds.boxPlot::perf::0","5.837","0.5","2"
-"ds.boxPlotGG::perf::0","6.696","0.5","2"
-"ds.c::perf::0","10.23","0.5","2"
-"ds.cbind::perf::0","3.814","0.5","2"
-"ds.changeRefGroup::perf::0","17.98","0.5","2"
-"ds.class::perf::combine:0","24.90","0.5","2"
-"ds.colnames::perf:0","24.83","0.5","2"
-"ds.completeCases::perf::combine:0","17.26","0.5","2"
-"ds.contourPlot::perf::0","4.593","0.5","2"
-"ds.cor::perf::combine:0","7.019","0.5","2"
-"ds.corTest::perf::combine:0","22.45","0.5","2"
-"ds.cov::perf::combine:0","7.010","0.5","2"
-"ds.dataFrame::perf::0","5.943","0.5","2"
-"ds.dataFrameFill::perf::0","2.749","0.5","2"
-"ds.dataFrameSort::perf::0","14.38","0.5","2"
-"ds.dataFrameSubset::perf::0","2.450","0.5","2"
-"ds.densityGrid::perf::0","4.653","0.5","2"
-"ds.dim::perf::0","23.82","0.5","2"
-"ds.dmtC2S::perf::0","16.61","0.5","2"
-"ds.elspline::perf::0","16.32","0.5","2"
-"ds.exists::perf::combine:0","24.21","0.5","2"
-"ds.exp::perf::0","17.18","0.5","2"
-"ds.gamlss::perf::0","21.11","0.5","2"
-"ds.getWGSR::perf::0","0.6753","0.5","2"
-"ds.glmPredict::perf::0","10.04","0.5","2"
-"ds.glmSummary::perf::0","10.10","0.5","2"
-"ds.heatmapPlot::perf::0","4.519","0.5","2"
-"ds.hetcor::perf::0","0.4028","0.5","2"
-"ds.histogram::perf::0","9.261","0.5","2"
-"ds.isNA::perf::combine:0","23.86","0.5","2"
-"ds.isValid::perf::0","33.16","0.5","2"
-"ds.kurtosis::perf::combine:0","8.377","0.5","2"
-"ds.length::perf::combine:0","24.04","0.5","2"
-"ds.levels::perf::combine:0","23.96","0.5","2"
-"ds.lexis::perf::0","3.760","0.5","2"
-"ds.list::perf::0","10.03","0.5","2"
-"ds.log::perf::0","17.30","0.5","2"
-"ds.ls::perf::combine:0","24.35","0.5","2"
-"ds.lspline::perf::0","14.17","0.5","2"
-"ds.make::perf::0","16.81","0.5","2"
-"ds.matrix::perf::0","16.140","0.5","2"
-"ds.matrixDet::perf::0","16.015","0.5","2"
-"ds.matrixDet.report::perf::0","24.038","0.5","2"
-"ds.matrixDiag::perf::0","16.109","0.5","2"
-"ds.matrixDimnames::perf::0","16.72","0.5","2"
-"ds.matrixInvert::perf::0","16.49","0.5","2"
-"ds.matrixMult::perf::0","16.65","0.5","2"
-"ds.matrixTranspose::perf::0","17.90","0.5","2"
-"ds.mean::perf::combine:0","24.12","0.5","2"
-"ds.mean::perf::split:0","24.45","0.5","2"
-"ds.meanSdGp::perf::combine:0","19.84","0.5","2"
-"ds.merge::perf::0","1.217","0.5","2"
-"ds.metadata::perf::0","24.58","0.5","2"
-"ds.mice::perf::0","3.896","0.5","2"
-"ds.names::perf::combine:0","24.22","0.5","2"
-"ds.ns::perf::0","28.41","0.5","2"
-"ds.numNA::perf::combine:0","29.58","0.5","2"
-"ds.qlspline::perf::0","29.40","0.5","2"
-"ds.quantileMean::perf::combine:0","8.354","0.5","2"
-"ds.rbind::perf::0","8.424","0.5","2"
-"ds.rBinom::perf::0","17.489","0.5","2"
-"ds.recodeValues::perf::0","16.842","0.5","2"
-"ds.rep::perf::0","17.44","0.5","2"
-"ds.replaceNA::perf::0","3.292","0.5","2"
-"ds.reShape::perf::0","17.31","0.5","2"
-"ds.rowColCalc::perf::0","23.48","0.5","2"
-"ds.rNorm::perf::0","17.97","0.5","2"
-"ds.rPois::perf::0","18.26","0.5","2"
-"ds.rUnif::perf::0","17.85","0.5","2"
-"ds.sample::perf::0","7.652","0.5","2"
-"ds.scatterPlot::perf::0","3.318","0.5","2"
-"ds.seq::perf::0","17.55","0.5","2"
-"ds.setSeed::perf::0","44.75","0.5","2"
-"ds.skewness::perf::combine:0","8.226","0.5","2"
-"ds.sqrt::perf::0","16.83","0.5","2"
-"ds.summary::perf::0","6.095","0.5","2"
-"ds.table::perf::0","22.03","0.5","2"
-"ds.tapply.assign::perf::0","30.61","0.5","2"
-"ds.tapply::perf::0","41.96","0.5","2"
-"ds.unique::perf::combine:0","22.04","0.5","2"
-"ds.unList::perf::0","16.70","0.5","2"
-"ds.var::perf::combine:0","25.02","0.5","2"
-"void::perf::void::0","52590","0.5","2"
+"conndisconn::perf::simple0","0.1581","0.333","3"
+"ds.abs::perf::0","17.27","0.333","3"
+"ds.asCharacter::perf::0","16.84","0.333","3"
+"ds.asDataMatrix::perf::0","17.44","0.333","3"
+"ds.asFactor::perf::0","10.22","0.333","3"
+"ds.asFactorSimple::perf::0","17.79","0.333","3"
+"ds.asInteger::perf:0","17.61","0.333","3"
+"ds.asList::perf:0","22.52","0.333","3"
+"ds.asLogical::perf::0","17.46","0.333","3"
+"ds.asMatrix::perf::0","17.44","0.333","3"
+"ds.asNumeric::perf:0","16.79","0.333","3"
+"ds.assign::perf::0","23.51","0.333","3"
+"ds.auc::perf::0","31.06","0.333","3"
+"ds.Boole::perf::0","5.479","0.333","3"
+"ds.boxPlot::perf::0","5.837","0.333","3"
+"ds.boxPlotGG::perf::0","6.696","0.333","3"
+"ds.bp_standards::perf::0","20.81","0.333","3"
+"ds.c::perf::0","10.23","0.333","3"
+"ds.cbind::perf::0","3.814","0.333","3"
+"ds.changeRefGroup::perf::0","17.98","0.333","3"
+"ds.class::perf::combine:0","24.90","0.333","3"
+"ds.colnames::perf:0","24.83","0.333","3"
+"ds.completeCases::perf::combine:0","17.26","0.333","3"
+"ds.contourPlot::perf::0","4.593","0.333","3"
+"ds.cor::perf::combine:0","7.019","0.333","3"
+"ds.corTest::perf::combine:0","22.45","0.333","3"
+"ds.cov::perf::combine:0","7.010","0.333","3"
+"ds.dataFrame::perf::0","4.043","0.333","3"
+"ds.dataFrameFill::perf::0","3.549","0.333","3"
+"ds.dataFrameSort::perf::0","24.38","0.333","3"
+"ds.dataFrameSubset::perf::0","2.450","0.333","3"
+"ds.densityGrid::perf::0","4.653","0.333","3"
+"ds.dim::perf::0","23.82","0.333","3"
+"ds.dmtC2S::perf::0","16.61","0.333","3"
+"ds.elspline::perf::0","16.32","0.333","3"
+"ds.exists::perf::combine:0","24.21","0.333","3"
+"ds.exp::perf::0","17.18","0.333","3"
+"ds.gamlss::perf::0","21.11","0.333","3"
+"ds.getWGSR::perf::0","0.6753","0.333","3"
+"ds.glmPredict::perf::0","10.04","0.333","3"
+"ds.glmSummary::perf::0","10.10","0.333","3"
+"ds.heatmapPlot::perf::0","4.519","0.333","3"
+"ds.hetcor::perf::0","0.4028","0.333","3"
+"ds.histogram::perf::0","9.261","0.333","3"
+"ds.igb_standards::perf::0","9.796","0.333","3"
+"ds.isNA::perf::combine:0","23.86","0.333","3"
+"ds.isValid::perf::0","33.16","0.333","3"
+"ds.kurtosis::perf::combine:0","8.377","0.333","3"
+"ds.length::perf::combine:0","24.04","0.333","3"
+"ds.levels::perf::combine:0","23.96","0.333","3"
+"ds.lexis::perf::0","3.760","0.333","3"
+"ds.list::perf::0","10.03","0.333","3"
+"ds.listDisclosureSettings::perf::0","25.17","0.333","3"
+"ds.log::perf::0","17.30","0.333","3"
+"ds.ls::perf::combine:0","24.35","0.333","3"
+"ds.lspline::perf::0","16.17","0.333","3"
+"ds.make::perf::0","16.81","0.333","3"
+"ds.matrix::perf::0","16.140","0.333","3"
+"ds.matrixDet::perf::0","18.015","0.333","3"
+"ds.matrixDet.report::perf::0","28.038","0.333","3"
+"ds.matrixDiag::perf::0","16.109","0.333","3"
+"ds.matrixDimnames::perf::0","16.72","0.333","3"
+"ds.matrixInvert::perf::0","16.49","0.333","3"
+"ds.matrixMult::perf::0","16.65","0.333","3"
+"ds.matrixTranspose::perf::0","17.90","0.333","3"
+"ds.mdPattern::perf::0","13.23","0.333","3"
+"ds.mean::perf::combine:0","24.12","0.333","3"
+"ds.mean::perf::split:0","24.45","0.333","3"
+"ds.meanSdGp::perf::combine:0","25.84","0.333","3"
+"ds.merge::perf::0","1.217","0.333","3"
+"ds.metadata::perf::0","24.58","0.333","3"
+"ds.mice::perf::0","3.896","0.333","3"
+"ds.names::perf::combine:0","24.22","0.333","3"
+"ds.ns::perf::0","14.04","0.333","3"
+"ds.numNA::perf::combine:0","29.58","0.333","3"
+"ds.qlspline::perf::0","14.37","0.333","3"
+"ds.quantileMean::perf::combine:0","8.354","0.333","3"
+"ds.rbind::perf::0","3.534","0.333","3"
+"ds.rBinom::perf::0","7.932","0.333","3"
+"ds.recodeValues::perf::0","16.842","0.333","3"
+"ds.rep::perf::0","17.44","0.333","3"
+"ds.replaceNA::perf::0","3.292","0.333","3"
+"ds.reShape::perf::0","17.31","0.333","3"
+"ds.rm::perf::0","30.72","0.333","3"
+"ds.rowColCalc::perf::0","23.48","0.333","3"
+"ds.rNorm::perf::0","8.623","0.333","3"
+"ds.rPois::perf::0","8.659","0.333","3"
+"ds.rUnif::perf::0","8.175","0.333","3"
+"ds.sample::perf::0","7.652","0.333","3"
+"ds.scatterPlot::perf::0","3.318","0.333","3"
+"ds.seq::perf::0","17.55","0.333","3"
+"ds.setSeed::perf::0","20.88","0.333","3"
+"ds.skewness::perf::combine:0","8.226","0.333","3"
+"ds.sqrt::perf::0","16.83","0.333","3"
+"ds.summary::perf::0","6.095","0.333","3"
+"ds.table::perf::0","10.91","0.333","3"
+"ds.tapply.assign::perf::0","30.61","0.333","3"
+"ds.tapply::perf::0","41.96","0.333","3"
+"ds.unique::perf::combine:0","22.04","0.333","3"
+"ds.unList::perf::0","16.70","0.333","3"
+"ds.var::perf::combine:0","25.02","0.333","3"
+"void::perf::void::0","52590","0.333","3"
diff --git a/tests/testthat/perf_files/opal_github-workflows_perf-profile.csv b/tests/testthat/perf_files/opal_github-workflows_perf-profile.csv
index b1329d947..693040be8 100644
--- a/tests/testthat/perf_files/opal_github-workflows_perf-profile.csv
+++ b/tests/testthat/perf_files/opal_github-workflows_perf-profile.csv
@@ -1,97 +1,103 @@
 "refer_name","rate","lower_tolerance","upper_tolerance"
-"conndisconn::perf::simple0","0.1654","0.5","2"
-"ds.abs::perf::0","12.94","0.5","2"
-"ds.asCharacter::perf::0","14.19","0.5","2"
-"ds.asDataMatrix::perf::0","14.88","0.5","2"
-"ds.asFactor::perf::0","7.273","0.5","2"
-"ds.asFactorSimple::perf::0","14.89","0.5","2"
-"ds.asInteger::perf:0","15.17","0.5","2"
-"ds.asList::perf:0","14.83","0.5","2"
-"ds.asLogical::perf::0","15.19","0.5","2"
-"ds.asMatrix::perf::0","15.25","0.5","2"
-"ds.asNumeric::perf:0","15.18","0.5","2"
-"ds.assign::perf::0","15.79","0.5","2"
-"ds.Boole::perf::0","5.393","0.5","2"
-"ds.boxPlot::perf::0","4.674","0.5","2"
-"ds.boxPlotGG::perf::0","4.526","0.5","2"
-"ds.c::perf::0","7.516","0.5","2"
-"ds.cbind::perf::0","2.756","0.5","2"
-"ds.changeRefGroup::perf::0","15.41","0.5","2"
-"ds.class::perf::combine:0","14.86","0.5","2"
-"ds.colnames::perf:0","15.85","0.5","2"
-"ds.completeCases::perf::combine:0","14.254","0.5","2"
-"ds.contourPlot::perf::0","4.599","0.5","2"
-"ds.cor::perf::combine:0","7.364","0.5","2"
-"ds.corTest::perf::combine:0","14.95","0.5","2"
-"ds.cov::perf::combine:0","13.18","0.5","2"
-"ds.dataFrame::perf::0","2.488","0.5","2"
-"ds.dataFrameFill::perf::0","1.098","0.5","2"
-"ds.dataFrameSort::perf::0","8.263","0.5","2"
-"ds.dataFrameSubset::perf::0","2.043","0.5","2"
-"ds.densityGrid::perf::0","2.365","0.5","2"
-"ds.dim::perf::0","8.687","0.5","2"
-"ds.dmtC2S::perf::0","8.161","0.5","2"
-"ds.elspline::perf::0","9.253","0.5","2"
-"ds.exists::perf::combine:0","10.02","0.5","2"
-"ds.exp::perf::0","8.644","0.5","2"
-"ds.gamlss::perf::0","6.853","0.5","2"
-"ds.getWGSR::perf::0","0.685","0.5","2"
-"ds.glmPredict::perf::0","5.006","0.5","2"
-"ds.glmSummary::perf::0","4.979","0.5","2"
-"ds.heatmapPlot::perf::0","2.409","0.5","2"
-"ds.hetcor::perf::0","0.3609","0.5","2"
-"ds.histogram::perf::0","3.939","0.5","2"
-"ds.isNA::perf::combine:0","8.677","0.5","2"
-"ds.isValid::perf::0","9.110","0.5","2"
-"ds.kurtosis::perf::combine:0","2.872","0.5","2"
-"ds.length::perf::combine:0","8.551","0.5","2"
-"ds.levels::perf::combine:0","8.594","0.5","2"
-"ds.lexis::perf::0","2.410","0.5","2"
-"ds.list::perf::0","4.415","0.5","2"
-"ds.log::perf::0","8.415","0.5","2"
-"ds.ls::perf::combine:0","8.689","0.5","2"
-"ds.lspline::perf::0","9.241","0.5","2"
-"ds.make::perf::0","10.92","0.5","2"
-"ds.matrix::perf::0","8.422","0.5","2"
-"ds.matrixDet::perf::0","9.613","0.5","2"
-"ds.matrixDet.report::perf::0","9.829","0.5","2"
-"ds.matrixDiag::perf::0","9.700","0.5","2"
-"ds.matrixDimnames::perf::0","9.754","0.5","2"
-"ds.matrixInvert::perf::0","9.728","0.5","2"
-"ds.matrixMult::perf::0","9.876","0.5","2"
-"ds.matrixTranspose::perf::0","9.746","0.5","2"
-"ds.mean::perf::combine:0","8.690","0.5","2"
-"ds.mean::perf::split:0","9.823","0.5","2"
-"ds.meanSdGp::perf::combine:0","8.285","0.5","2"
-"ds.merge::perf::0","1.207","0.5","2"
-"ds.metadata::perf::0","9.529","0.5","2"
-"ds.mice::perf::0","3.166","0.5","2"
-"ds.names::perf::combine:0","8.620","0.5","2"
-"ds.ns::perf::0","8.999","0.5","2"
-"ds.numNA::perf::combine:0","8.411","0.5","2"
-"ds.qlspline::perf::0","9.053","0.5","2"
-"ds.quantileMean::perf::combine:0","2.896","0.5","2"
-"ds.rbind::perf::0","1.726","0.5","2"
-"ds.rBinom::perf::0","3.925","0.5","2"
-"ds.recodeValues::perf::0","8.478","0.5","2"
-"ds.rep::perf::0","8.645","0.5","2"
-"ds.replaceNA::perf::0","1.280","0.5","2"
-"ds.reShape::perf::0","8.211","0.5","2"
-"ds.rNorm::perf::0","3.881","0.5","2"
-"ds.rowColCalc::perf::0","8.905","0.5","2"
-"ds.rPois::perf::0","3.848","0.5","2"
-"ds.rUnif::perf::0","3.864","0.5","2"
-"ds.sample::perf::0","3.764","0.5","2"
-"ds.seq::perf::0","8.522","0.5","2"
-"ds.setSeed::perf::0","8.990","0.5","2"
-"ds.skewness::perf::combine:0","2.889","0.5","2"
-"ds.sqrt::perf::0","8.666","0.5","2"
-"ds.summary::perf::0","2.149","0.5","2"
-"ds.scatterPlot::perf::0","2.163","0.5","2"
-"ds.table::perf::0","5.031","0.5","2"
-"ds.tapply.assign::perf::0","8.062","0.5","2"
-"ds.tapply::perf::0","8.194","0.5","2"
-"ds.unique::perf::combine:0","8.656","0.5","2"
-"ds.unList::perf::0","8.128","0.5","2"
-"ds.var::perf::combine:0","7.6420","0.5","2"
-"void::perf::void::0","46340","0.5","2"
+"conndisconn::perf::simple0","0.1654","0.333","3"
+"ds.abs::perf::0","12.94","0.333","3"
+"ds.asCharacter::perf::0","14.19","0.333","3"
+"ds.asDataMatrix::perf::0","14.88","0.333","3"
+"ds.asFactor::perf::0","7.273","0.333","3"
+"ds.asFactorSimple::perf::0","14.89","0.333","3"
+"ds.asInteger::perf:0","15.17","0.333","3"
+"ds.asList::perf:0","14.83","0.333","3"
+"ds.asLogical::perf::0","15.19","0.333","3"
+"ds.asMatrix::perf::0","15.25","0.333","3"
+"ds.asNumeric::perf:0","15.18","0.333","3"
+"ds.assign::perf::0","15.79","0.333","3"
+"ds.auc::perf::0","11.14","0.333","3"
+"ds.Boole::perf::0","5.393","0.333","3"
+"ds.boxPlot::perf::0","4.674","0.333","3"
+"ds.boxPlotGG::perf::0","4.526","0.333","3"
+"ds.bp_standard::perf::0","9.806","0.333","3"
+"ds.c::perf::0","7.516","0.333","3"
+"ds.cbind::perf::0","2.756","0.333","3"
+"ds.changeRefGroup::perf::0","15.41","0.333","3"
+"ds.class::perf::combine:0","14.86","0.333","3"
+"ds.colnames::perf:0","15.85","0.333","3"
+"ds.completeCases::perf::combine:0","14.254","0.333","3"
+"ds.contourPlot::perf::0","4.599","0.333","3"
+"ds.cor::perf::combine:0","7.364","0.333","3"
+"ds.corTest::perf::combine:0","14.95","0.333","3"
+"ds.cov::perf::combine:0","13.18","0.333","3"
+"ds.dataFrame::perf::0","2.488","0.333","3"
+"ds.dataFrameFill::perf::0","1.556","0.333","3"
+"ds.dataFrameSort::perf::0","13.83","0.333","3"
+"ds.dataFrameSubset::perf::0","1.559","0.333","3"
+"ds.densityGrid::perf::0","5.385","0.333","3"
+"ds.dim::perf::0","14.92","0.333","3"
+"ds.dmtC2S::perf::0","14.11","0.333","3"
+"ds.elspline::perf::0","14.55","0.333","3"
+"ds.exists::perf::combine:0","19.02","0.333","3"
+"ds.exp::perf::0","14.84","0.333","3"
+"ds.gamlss::perf::0","6.853","0.333","3"
+"ds.getWGSR::perf::0","0.785","0.333","3"
+"ds.glmPredict::perf::0","7.06","0.333","3"
+"ds.glmSummary::perf::0","8.19","0.333","3"
+"ds.heatmapPlot::perf::0","4.509","0.333","3"
+"ds.hetcor::perf::0","0.4609","0.333","3"
+"ds.histogram::perf::0","8.139","0.333","3"
+"ds.igb_standards::perf::0","17.639","0.333","3"
+"ds.isNA::perf::combine:0","18.677","0.333","3"
+"ds.isValid::perf::0","9.230","0.333","3"
+"ds.kurtosis::perf::combine:0","3.684","0.333","3"
+"ds.length::perf::combine:0","8.480","0.333","3"
+"ds.levels::perf::combine:0","10.724","0.333","3"
+"ds.lexis::perf::0","5.610","0.333","3"
+"ds.list::perf::0","9.915","0.333","3"
+"ds.listDisclosureSettings::perf::0","9.170","0.333","3"
+"ds.log::perf::0","19.415","0.333","3"
+"ds.ls::perf::combine:0","9.179","0.333","3"
+"ds.lspline::perf::0","9.741","0.333","3"
+"ds.make::perf::0","19.92","0.333","3"
+"ds.matrix::perf::0","18.422","0.333","3"
+"ds.matrixDet::perf::0","20.33","0.333","3"
+"ds.matrixDet.report::perf::0","20.829","0.333","3"
+"ds.matrixDiag::perf::0","10.40","0.333","3"
+"ds.matrixDimnames::perf::0","9.0954","0.333","3"
+"ds.matrixInvert::perf::0","9.728","0.333","3"
+"ds.matrixMult::perf::0","9.876","0.333","3"
+"ds.matrixTranspose::perf::0","9.446","0.333","3"
+"ds.mdPattern::perf::0","7.546","0.333","3"
+"ds.mean::perf::combine:0","8.690","0.333","3"
+"ds.mean::perf::split:0","9.823","0.333","3"
+"ds.meanSdGp::perf::combine:0","8.585","0.333","3"
+"ds.merge::perf::0","1.407","0.333","3"
+"ds.metadata::perf::0","9.529","0.333","3"
+"ds.mice::perf::0","2.596","0.333","3"
+"ds.names::perf::combine:0","8.620","0.333","3"
+"ds.ns::perf::0","8.999","0.333","3"
+"ds.numNA::perf::combine:0","17.65","0.333","3"
+"ds.qlspline::perf::0","9.053","0.333","3"
+"ds.quantileMean::perf::combine:0","2.896","0.333","3"
+"ds.rbind::perf::0","1.726","0.333","3"
+"ds.rBinom::perf::0","3.925","0.333","3"
+"ds.recodeValues::perf::0","17.57","0.333","3"
+"ds.rep::perf::0","10.66","0.333","3"
+"ds.replaceNA::perf::0","1.280","0.333","3"
+"ds.reShape::perf::0","18.45","0.333","3"
+"ds.rm::perf::0","19.74","0.333","3"
+"ds.rNorm::perf::0","6.568","0.333","3"
+"ds.rowColCalc::perf::0","18.31","0.333","3"
+"ds.rPois::perf::0","6.344","0.333","3"
+"ds.rUnif::perf::0","6.643","0.333","3"
+"ds.sample::perf::0","4.773","0.333","3"
+"ds.seq::perf::0","8.522","0.333","3"
+"ds.setSeed::perf::0","8.990","0.333","3"
+"ds.skewness::perf::combine:0","6.102","0.333","3"
+"ds.sqrt::perf::0","8.666","0.333","3"
+"ds.summary::perf::0","3.275","0.333","3"
+"ds.scatterPlot::perf::0","4.100","0.333","3"
+"ds.table::perf::0","5.031","0.333","3"
+"ds.tapply.assign::perf::0","8.062","0.333","3"
+"ds.tapply::perf::0","8.194","0.333","3"
+"ds.unique::perf::combine:0","18.12","0.333","3"
+"ds.unList::perf::0","16.49","0.333","3"
+"ds.var::perf::combine:0","16.39","0.333","3"
+"void::perf::void::0","85900","0.333","3"
diff --git a/tests/testthat/test-arg-ds.auc.R b/tests/testthat/test-arg-ds.auc.R
index ade352110..55978417a 100644
--- a/tests/testthat/test-arg-ds.auc.R
+++ b/tests/testthat/test-arg-ds.auc.R
@@ -15,6 +15,8 @@
 
 # context("ds.auc::arg::setup")
 
+connect.studies.dataset.cnsim(list("LAB_TSC"))
+
 #
 # Tests
 #
@@ -33,4 +35,5 @@ test_that("ds.auc errors for null pred or y", {
 #
 
 # context("ds.auc::arg::shutdown")
+disconnect.studies.dataset.cnsim()
 # context("ds.auc::arg::done")
diff --git a/tests/testthat/test-perf-ds.auc.R b/tests/testthat/test-perf-ds.auc.R
new file mode 100644
index 000000000..e961c8840
--- /dev/null
+++ b/tests/testthat/test-perf-ds.auc.R
@@ -0,0 +1,52 @@
+
+#
+# Set up
+#
+
+# context("ds.auc::perf::setup")
+connect.studies.dataset.cnsim(list("LAB_TRIG", "DIS_DIAB"))
+ds.glmSLMA('D$DIS_DIAB~D$LAB_TRIG', family = "binomial", newobj = "auc.glm")
+ds.glmPredict("auc.glm", output.type = "link", se.fit = FALSE, newobj = "auc.pred")
+
+#
+# Tests
+#
+
+# context("ds.auc::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.auc(pred = "auc.pred$fit", y = "auc.glm$y")
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.auc::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.auc::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.auc::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.auc::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.auc::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.auc::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.auc::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.auc::perf::shutdown")
+disconnect.studies.dataset.cnsim()
+# context("ds.auc::perf::done")
diff --git a/tests/testthat/test-perf-ds.bp_standards.R b/tests/testthat/test-perf-ds.bp_standards.R
new file mode 100644
index 000000000..c59cf0317
--- /dev/null
+++ b/tests/testthat/test-perf-ds.bp_standards.R
@@ -0,0 +1,52 @@
+
+#
+# Set up
+#
+
+# context("ds.bp_standards::perf::setup")
+connect.studies.dataset.anthro(list("age", "sex", "weight", "height", "muac"))
+ds.make("D$age/12", "age.years") # nolint: nonportable_path_linter. DataSHIELD expression, not a file path
+ds.make("D$muac*7", "bp.value")
+
+#
+# Tests
+#
+
+# context("ds.bp_standards::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.bp_standards(sex = "D$sex", age = "age.years", height = "D$height", bp = "bp.value", systolic = TRUE, newobj = "bp.out")
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.bp_standards::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.bp_standards::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.bp_standards::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.bp_standards::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.bp_standards::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.bp_standards::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.bp_standards::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.bp_standards::perf::shutdown")
+disconnect.studies.dataset.anthro()
+# context("ds.bp_standards::perf::done")
diff --git a/tests/testthat/test-perf-ds.igb_standards.R b/tests/testthat/test-perf-ds.igb_standards.R
new file mode 100644
index 000000000..12dd2e6be
--- /dev/null
+++ b/tests/testthat/test-perf-ds.igb_standards.R
@@ -0,0 +1,52 @@
+
+#
+# Set up
+#
+
+# context("ds.igb_standards::perf::setup")
+connect.studies.dataset.anthro(list("age", "sex", "weight", "height", "muac"))
+ds.make("(D$age*0)+280", "gagebrth")
+ds.recodeValues(var.name = "D$sex", values2replace.vector = c(1, 2), new.values.vector = c("Male", "Female"), newobj = "sex.label")
+
+#
+# Tests
+#
+
+# context("ds.igb_standards::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.igb_standards(gagebrth = "gagebrth", z = 0, p = 50, var = "lencm", sex = "sex.label", fun = "igb_centile2value", newobj = "igb.out")
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.igb_standards::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.igb_standards::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.igb_standards::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.igb_standards::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.igb_standards::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.igb_standards::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.igb_standards::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.igb_standards::perf::shutdown")
+disconnect.studies.dataset.anthro()
+# context("ds.igb_standards::perf::done")
diff --git a/tests/testthat/test-perf-ds.listDisclosureSettings.R b/tests/testthat/test-perf-ds.listDisclosureSettings.R
new file mode 100644
index 000000000..9de207f1a
--- /dev/null
+++ b/tests/testthat/test-perf-ds.listDisclosureSettings.R
@@ -0,0 +1,50 @@
+
+#
+# Set up
+#
+
+# context("ds.listDisclosureSettings::perf::setup")
+connect.studies.dataset.cnsim(list("LAB_TSC", "LAB_HDL"))
+
+#
+# Tests
+#
+
+# context("ds.listDisclosureSettings::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.listDisclosureSettings()
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.listDisclosureSettings::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.listDisclosureSettings::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.listDisclosureSettings::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.listDisclosureSettings::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.listDisclosureSettings::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.listDisclosureSettings::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.listDisclosureSettings::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.listDisclosureSettings::perf::shutdown")
+disconnect.studies.dataset.cnsim()
+# context("ds.listDisclosureSettings::perf::done")
diff --git a/tests/testthat/test-perf-ds.mdPattern.R b/tests/testthat/test-perf-ds.mdPattern.R
new file mode 100644
index 000000000..3c17fb465
--- /dev/null
+++ b/tests/testthat/test-perf-ds.mdPattern.R
@@ -0,0 +1,50 @@
+
+#
+# Set up
+#
+
+# context("ds.mdPattern::perf::setup")
+connect.studies.dataset.cnsim(list("LAB_TSC", "LAB_TRIG", "LAB_HDL"))
+
+#
+# Tests
+#
+
+# context("ds.mdPattern::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.mdPattern(x = "D", type = "split")
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.mdPattern::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.mdPattern::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.mdPattern::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.mdPattern::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.mdPattern::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.mdPattern::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.mdPattern::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.mdPattern::perf::shutdown")
+disconnect.studies.dataset.cnsim()
+# context("ds.mdPattern::perf::done")
diff --git a/tests/testthat/test-perf-ds.rm.R b/tests/testthat/test-perf-ds.rm.R
new file mode 100644
index 000000000..20d3765bc
--- /dev/null
+++ b/tests/testthat/test-perf-ds.rm.R
@@ -0,0 +1,50 @@
+
+#
+# Set up
+#
+
+# context("ds.rm::perf::setup")
+connect.studies.dataset.survival(list("survtime", "time.id", "female"))
+
+#
+# Tests
+#
+
+# context("ds.rm::perf::0")
+test_that("performance", {
+    .durationSec  <- perf.testduration()
+    .count        <- 0
+    .start.time   <- Sys.time()
+    .current.time <- .start.time
+
+    while (difftime(.current.time, .start.time, units = "secs")[[1]] < .durationSec) {
+        ds.rm("nonexistant_object")
+
+        .count <- .count + 1
+        .current.time <- Sys.time()
+    }
+
+    .current.rate   <- .count / (difftime(.current.time, .start.time, units = "secs")[[1]])
+    .reference.rate <- perf.reference.rate("ds.rm::perf::0")
+    if (any(length(.reference.rate) == 0) || any(is.null(.reference.rate))) {
+        print(paste("ds.rm::perf::0 ", .current.rate, 0.5, 2.0))
+        perf.reference.save("ds.rm::perf::0", .current.rate, 0.5, 2.0)
+    } else {
+        print(paste("ds.rm::perf::0 ", format(.current.rate, digits = 8), ", ", format(100.0 * .current.rate / .reference.rate, digits = 4), "%", sep = ''))
+    }
+
+    .reference.rate            <- perf.reference.rate("ds.rm::perf::0")
+    .reference.tolerance.lower <- perf.reference.tolerance.lower("ds.rm::perf::0")
+    .reference.tolerance.upper <- perf.reference.tolerance.upper("ds.rm::perf::0")
+
+    expect_gt(.current.rate, .reference.rate * .reference.tolerance.lower, label = "Observed rate", expected.label = "lower threshold on rate")
+    expect_lt(.current.rate, .reference.rate * .reference.tolerance.upper, label = "Observed rate", expected.label = "upper threshold on rate")
+})
+
+#
+# Done
+#
+
+# context("ds.rm::perf::shutdown")
+disconnect.studies.dataset.survival()
+# context("ds.rm::perf::done")
diff --git a/tests/testthat/test-smk-ds.auc.R b/tests/testthat/test-smk-ds.auc.R
new file mode 100644
index 000000000..4d99fe3e5
--- /dev/null
+++ b/tests/testthat/test-smk-ds.auc.R
@@ -0,0 +1,71 @@
+
+#
+# Set up
+#
+
+# context("ds.auc::smk::setup")
+
+connect.studies.dataset.cnsim(list("LAB_TRIG", "DIS_DIAB"))
+
+test_that("setup", {
+    ds_expect_variables(c("D"))
+})
+
+ds.glmSLMA('D$DIS_DIAB~D$LAB_TRIG', family = "binomial", newobj = "auc.glm")
+ds.glmPredict("auc.glm", output.type = "link", se.fit = FALSE, newobj = "auc.pred")
+
+#
+# Tests
+#
+
+# context("ds.auc::smk")
+test_that("simple auc", {
+    res <- ds.auc(pred = "auc.pred$fit", y = "auc.glm$y")
+
+    expect_length(res, 3)
+    for (study.res in res) {
+        expect_named(study.res, c("AUC", "se"))
+        expect_true(study.res$AUC > 0.5 && study.res$AUC < 1)
+        expect_true(study.res$se > 0)
+    }
+})
+
+test_that("fails if pred does not exist", {
+    expect_error(
+        ds.auc(pred = "nonexistent_object", y = "auc.glm$y"),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "The server-side object 'nonexistent_object' does not exist")
+    }
+})
+
+test_that("fails if y does not exist", {
+    expect_error(
+        ds.auc(pred = "auc.pred$fit", y = "nonexistent_object"),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "The server-side object 'nonexistent_object' does not exist")
+    }
+})
+
+#
+# Done
+#
+
+# context("ds.auc::smk::shutdown")
+
+test_that("shutdown", {
+    ds_expect_variables(c("D", "auc.glm", "auc.pred"))
+})
+
+disconnect.studies.dataset.cnsim()
+
+# context("ds.auc::smk::done")
diff --git a/tests/testthat/test-smk-ds.bp_standards.R b/tests/testthat/test-smk-ds.bp_standards.R
new file mode 100644
index 000000000..5e1c3c50c
--- /dev/null
+++ b/tests/testthat/test-smk-ds.bp_standards.R
@@ -0,0 +1,56 @@
+
+#
+# Set up
+#
+
+# context("ds.bp_standards::smk::setup")
+
+connect.studies.dataset.anthro(list("age", "sex", "weight", "height", "muac"))
+
+test_that("setup", {
+    ds_expect_variables(c("D"))
+})
+
+ds.make("D$age/12", "age.years") # nolint: nonportable_path_linter. DataSHIELD expression, not a file path
+ds.make("D$muac*7", "bp.value")
+
+#
+# Tests
+#
+
+# context("ds.bp_standards::smk")
+test_that("simple bp_standards", {
+    ds.bp_standards(sex = "D$sex", age = "age.years", height = "D$height", bp = "bp.value", systolic = TRUE, newobj = "bp.out")
+
+    res.class <- ds.class("bp.out")
+    for (study.class in res.class) {
+        expect_equal(study.class, "list")
+    }
+})
+
+test_that("fails if an input does not exist", {
+    expect_error(
+        ds.bp_standards(sex = "D$sex", age = "age.years", height = "D$height", bp = "nonexistent_object", systolic = TRUE),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "The server-side object 'nonexistent_object' does not exist")
+    }
+})
+
+#
+# Done
+#
+
+# context("ds.bp_standards::smk::shutdown")
+
+test_that("shutdown", {
+    ds_expect_variables(c("D", "age.years", "bp.value", "bp.out"))
+})
+
+disconnect.studies.dataset.anthro()
+
+# context("ds.bp_standards::smk::done")
diff --git a/tests/testthat/test-smk-ds.igb_standards.R b/tests/testthat/test-smk-ds.igb_standards.R
new file mode 100644
index 000000000..03d1454d3
--- /dev/null
+++ b/tests/testthat/test-smk-ds.igb_standards.R
@@ -0,0 +1,56 @@
+
+#
+# Set up
+#
+
+# context("ds.igb_standards::smk::setup")
+
+connect.studies.dataset.anthro(list("age", "sex", "weight", "height", "muac"))
+
+test_that("setup", {
+    ds_expect_variables(c("D"))
+})
+
+ds.make("(D$age*0)+280", "gagebrth")
+ds.recodeValues(var.name = "D$sex", values2replace.vector = c(1, 2), new.values.vector = c("Male", "Female"), newobj = "sex.label")
+
+#
+# Tests
+#
+
+# context("ds.igb_standards::smk")
+test_that("simple igb_standards", {
+    ds.igb_standards(gagebrth = "gagebrth", z = 0, p = 50, var = "lencm", sex = "sex.label", fun = "igb_centile2value", newobj = "igb.out")
+
+    res.class <- ds.class("igb.out")
+    for (study.class in res.class) {
+        expect_equal(study.class, "numeric")
+    }
+})
+
+test_that("fails if an input does not exist", {
+    expect_error(
+        ds.igb_standards(gagebrth = "nonexistent_object", z = 0, p = 50, var = "lencm", sex = "sex.label", fun = "igb_centile2value"),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "The server-side object 'nonexistent_object' does not exist")
+    }
+})
+
+#
+# Done
+#
+
+# context("ds.igb_standards::smk::shutdown")
+
+test_that("shutdown", {
+    ds_expect_variables(c("D", "gagebrth", "sex.label", "igb.out"))
+})
+
+disconnect.studies.dataset.anthro()
+
+# context("ds.igb_standards::smk::done")
diff --git a/tests/testthat/test-smk-ds.mdPattern.R b/tests/testthat/test-smk-ds.mdPattern.R
new file mode 100644
index 000000000..b2367f87e
--- /dev/null
+++ b/tests/testthat/test-smk-ds.mdPattern.R
@@ -0,0 +1,86 @@
+
+#
+# Set up
+#
+
+# context("ds.mdPattern::smk::setup")
+
+connect.studies.dataset.cnsim(list("LAB_TSC", "LAB_TRIG", "LAB_HDL"))
+
+test_that("setup", {
+    ds_expect_variables(c("D"))
+})
+
+#
+# Tests
+#
+
+# context("ds.mdPattern::smk::split")
+test_that("simple mdPattern, split", {
+    res <- ds.mdPattern(x = "D", type = "split")
+
+    expect_length(res, 3)
+    for (study.res in res) {
+        expect_named(study.res, c("pattern", "valid", "message"))
+        expect_true(is.matrix(study.res$pattern))
+    }
+})
+
+# context("ds.mdPattern::smk::combine")
+test_that("simple mdPattern, combine", {
+    caught_warnings <- capture_warnings(
+        res <- ds.mdPattern(x = "D", type = "combine")
+    )
+
+    expect_named(res, c("pattern", "valid", "message", "studies"))
+    expect_true(any(grepl("Disclosure control", caught_warnings)))
+    expect_true(is.matrix(res$pattern))
+})
+
+test_that("fails if the object does not exist", {
+    expect_error(
+        ds.mdPattern(x = "nonexistent_object"),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "The server-side object 'nonexistent_object' does not exist")
+    }
+})
+
+test_that("fails if the object is not a data.frame or matrix", {
+    expect_error(
+        ds.mdPattern(x = "D$LAB_TSC"),
+        "There are some DataSHIELD errors, list them with datashield.errors()",
+        fixed = TRUE
+    )
+
+    res.errors <- DSI::datashield.errors()
+    for (study.errors in res.errors) {
+        expect_match(study.errors, "must be of type data.frame or matrix")
+    }
+})
+
+test_that("class consistency check aborts when classes differ across studies", {
+    ds.asDataMatrix(x.name = "D", newobj = "mixed.class", datasources = ds.test_env$connections[1])
+    ds.assign(toAssign = "D", newobj = "mixed.class", datasources = ds.test_env$connections[2:3])
+
+    expect_error(ds.mdPattern(x = "mixed.class"), "not of the same class in all studies")
+    expect_no_error(ds.mdPattern(x = "mixed.class", classConsistencyCheck = FALSE))
+})
+
+#
+# Done
+#
+
+# context("ds.mdPattern::smk::shutdown")
+
+test_that("shutdown", {
+    ds_expect_variables(c("D", "mixed.class"))
+})
+
+disconnect.studies.dataset.cnsim()
+
+# context("ds.mdPattern::smk::done")