diff --git a/DESCRIPTION b/DESCRIPTION
index 423ed921..44ce6370 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: dsHelper
 Type: Package
 Title: Helper Functions for Use with 'DataSHIELD'
-Version: 0.4.19.9000
+Version: 0.4.19.9001
 Description: Often we need to automate things with 'DataSHIELD'. These functions help to do that.
 Authors@R: 
   c(person(given= "Tim", 
diff --git a/NAMESPACE b/NAMESPACE
index 59aad10a..abd1b145 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -3,6 +3,7 @@
 export(dh.anyData)
 export(dh.classDiscrepancy)
 export(dh.columnCast)
+export(dh.createTableOne)
 export(dh.defineCases)
 export(dh.dropCols)
 export(dh.findVarsIndex)
@@ -19,12 +20,16 @@ export(dh.makeStrata)
 export(dh.meanByAge)
 export(dh.meanByGroup)
 export(dh.metaManual)
+export(dh.metaSepModels)
+export(dh.multGLM)
 export(dh.predictLmer)
 export(dh.quartileSplit)
 export(dh.renameVars)
 export(dh.subjHasData)
 export(dh.tidyEnv)
 export(dh.trimPredData)
+export(dh.zByGroup)
+export(dt.makeExcludedDf)
 importFrom(DSI,datashield.aggregate)
 importFrom(DSI,datashield.assign)
 importFrom(DSI,datashield.connections_find)
@@ -75,6 +80,7 @@ importFrom(dsBaseClient,ds.dataFrame)
 importFrom(dsBaseClient,ds.dataFrameSort)
 importFrom(dsBaseClient,ds.dataFrameSubset)
 importFrom(dsBaseClient,ds.dim)
+importFrom(dsBaseClient,ds.glmSLMA)
 importFrom(dsBaseClient,ds.isNA)
 importFrom(dsBaseClient,ds.length)
 importFrom(dsBaseClient,ds.levels)
@@ -96,6 +102,7 @@ importFrom(dsBaseClient,ds.scatterPlot)
 importFrom(dsBaseClient,ds.table)
 importFrom(magrittr,"%<>%")
 importFrom(metafor,rma)
+importFrom(metafor,rma.uni)
 importFrom(purrr,cross2)
 importFrom(purrr,flatten_chr)
 importFrom(purrr,flatten_dbl)
@@ -115,6 +122,7 @@ importFrom(purrr,pmap_df)
 importFrom(purrr,set_names)
 importFrom(rlang,":=")
 importFrom(rlang,arg_match)
+importFrom(rlang,is_bool)
 importFrom(rlang,quo_name)
 importFrom(rlang,set_names)
 importFrom(rlang,sym)
@@ -129,8 +137,11 @@ importFrom(tibble,tibble)
 importFrom(tidyr,pivot_longer)
 importFrom(tidyr,pivot_wider)
 importFrom(tidyr,replace_na)
+importFrom(tidyr,separate)
 importFrom(tidyr,tibble)
 importFrom(utils,capture.output)
 importFrom(utils,combn)
 importFrom(utils,getFromNamespace)
 importFrom(utils,globalVariables)
+importFrom(utils,head)
+importFrom(utils,tail)
diff --git a/R/cast-cols.R b/R/cast-cols.R
index c47a911c..ccf51bfe 100644
--- a/R/cast-cols.R
+++ b/R/cast-cols.R
@@ -13,6 +13,7 @@
 #' 
 #' @importFrom dsBaseClient ds.asFactor ds.asCharacter ds.asNumeric ds.asInteger ds.colnames ds.dim ds.rep ds.dataFrameSubset ds.cbind
 #' @importFrom DSI datashield.connections_find
+#' @importFrom utils tail
 #'
 #' @return Tibble with a summary of the successful and failed casts
 #' @export
diff --git a/R/create-table-one.R b/R/create-table-one.R
new file mode 100644
index 00000000..0417a894
--- /dev/null
+++ b/R/create-table-one.R
@@ -0,0 +1,658 @@
+#' Creates tables in useful formats for including in manuscripts
+#'
+#' dh.getStats extracts key statistics and stores them in a clientside list. 
+#' dh.createTableOne builds on this by formatting the extract stats into a 
+#' table which can then be included in a manuscript. Flexible formatting options
+#' are included. 
+#'
+#' @param stats Exported object from dh.getStats.
+#' @param vars Variable to be included in table.
+#' @param var_labs Tibble with two columns: 'variable' containing the 
+#' names of the variables specified in `vars`, and 'var_label' containing the
+#' replacement labels for these variables.
+#' @param cat_labs Tibble with three columns: 'variable' containing the 
+#' names of the categorical variables specified in `vars`, 'category' 
+#' containing the categories of these variabels, and "cat_label" containing
+#' the replacement category labels for these variables.
+#' @param coh_labs Tibble with two columns: 'cohort' containing the names
+#' of all cohorts included in `stats`, and 'cohort_labs' containing the 
+#' replacement labels for these cohorts.
+#' @param type Character specifying which cohorts to include in the table. If
+#' "combined" then only combined stats will be returned, if "cohort" then only
+#' cohort-specific stats will be returned, if "both" then everything will be 
+#' returned.
+#' @param coh_direction Character specifying direction of data if `type` is 
+#' 'cohort' or 'both'. Use 'rows' to return cohorts as rows and variable as 
+#' columns, or use 'cols' to return cohorts as columns and variables as rows.
+#' Defauls is "col".
+#' @param cont_stats Character specifying which summary statistic to return
+#' for continuous stats. Use 'med_iqr' to return the median and interquartile
+#' range, use 'mean_sd' to return the mean and standard deviation. Default is
+#' "med_iqr".
+#' @param inc_missing Boolean specifying whether to return missing values in
+#' the output. Use TRUE for yes and FALSE for no.
+#' @param round_digits Optionally, the number of decimal places to round output 
+#' to. Default is 2.
+#' @param perc_denom The denominator for percentages. Either 'valid' for valid
+#' cases or 'total' for total cases.
+#'
+#' @return Tibble containing formatted summary statistics. If `coh_direction` is
+#' 'cols', the tibble will contain four columns: 'cohort', 'variable', 
+#' 'category' & value. If `coh_direction` is rows, the tibble will contain the
+#' column 'cohort' as well as as columns for all continuous variables and all
+#' categories of categorical variables.    
+#'
+#' @family descriptive functions
+#'
+#' @importFrom dplyr %>% left_join mutate select filter bind_rows
+#' @importFrom tidyr pivot_wider pivot_longer
+#' @importFrom rlang arg_match is_bool
+#'
+#' @export
+dh.createTableOne <- function(stats = NULL, vars = NULL, var_labs = NULL, 
+                              cat_labs = NULL, type = NULL, coh_labs = NULL, 
+                              coh_direction = "cols", cont_stats = "med_iqr", 
+                              inc_missing = NULL, round_digits = 2, 
+                              perc_denom = NULL){
+  
+  variable <- . <- cat_label <- var_label <- cohort <- value <- data_type <-
+    miss_n_perc <- category <- coh_label <- avail_stats <- vars_list <- 
+    stats_cat <- stats_cont <- old_var <- NULL
+  
+  if (is.null(stats)) {
+    stop("`stats` must not be NULL.", call. = FALSE)
+  }
+  
+  if (is.null(vars)) {
+    stop("`vars` must not be NULL.", call. = FALSE)
+  }
+
+  if (is.null(type)) {
+    stop("`type` must not be NULL.", call. = FALSE)
+  } 
+  
+  type <- arg_match(type, c("cohort", "combined", "both"))
+
+  if (is.null(coh_direction)) {
+    stop("`coh_direction` must not be NULL.", call. = FALSE)
+  } 
+  
+  coh_direction <- arg_match(coh_direction, c("rows", "cols"))
+
+  if (is.null(cont_stats)) {
+    stop("`cont_stats` must not be NULL.", call. = FALSE)
+  } 
+  
+  cont_stats <- arg_match(cont_stats, c("med_iqr", "mean_sd"))
+  
+  if (is.null(inc_missing)) {
+    stop("`inc_missing` must not be NULL.", call. = FALSE)
+  } 
+
+  if (is_bool(inc_missing) == FALSE){
+    stop("`inc_missing` must be either TRUE or FALSE")
+  }
+  
+  if (is.null(perc_denom)) {
+    stop("`perc_denom` must not be NULL.", call. = FALSE)
+  } 
+  
+  perc_denom <- arg_match(perc_denom, c("valid", "total"))
+  
+  stats_types <- .checkAvailStats(stats)
+  
+  avail_stats <- .splitStatsVars(stats, stats_types)
+  
+  .checkVarsAvail(unlist(avail_stats), vars)
+  
+  vars_list <- .splitTargetVars(avail_stats, vars)
+  
+  if(length(stats$categorical) > 0) {
+  
+    stats_cat <- .formatCatStats(stats, vars_list$categorical, inc_missing, 
+                               round_digits, perc_denom)
+  
+  }
+  
+  if(length(stats$continuous) > 0) {
+    
+  stats_cont <- .formatContStats(stats, vars_list$continuous, cont_stats, 
+                                 inc_missing, round_digits)
+  
+  }
+  
+  out <- bind_rows(stats_cat, stats_cont) %>%
+    mutate(variable = factor(variable, levels = vars, ordered = T)) %>%
+    arrange(variable)
+  
+  if(!is.null(cat_labs)){
+    
+    .checkLabType(cat_labs)
+    .checkLabCols(cat_labs, 3, c("variable", "category", "cat_label"))
+    .checkLabsMatchCats(stats_cat, cat_labs)
+    
+    out <- out %>%
+      left_join(., cat_labs, by = c("variable", "category")) %>%
+      mutate(category = cat_label) %>%
+      dplyr::select(-cat_label)
+    
+  }
+  
+  if(!is.null(var_labs)){
+    
+    .checkLabType(var_labs)
+    .checkLabCols(var_labs, 2, c("variable", "var_label"))
+    .checkLabsMatchVars(vars, var_labs)
+    
+    out <- out %>%
+      left_join(., 
+                var_labs %>% 
+                  dplyr::select(variable, var_label) %>%
+                  distinct, by = "variable") %>%
+      mutate(old_var = variable) %>%
+      mutate(variable = var_label) %>%
+      dplyr::select(-var_label)
+    
+  }
+  
+  if(!is.null(coh_labs)){
+    
+    .checkLabType(coh_labs)
+    .checkLabCols(coh_labs, 2, c("cohort", "coh_label"))
+    .checkLabsMatchCoh(stats, coh_labs, stats_types)
+    
+    out <- left_join(out, coh_labs, by = "cohort") %>%
+      dplyr::select(-cohort) %>%
+      dplyr::rename(cohort = coh_label) %>%
+      dplyr::select(cohort, everything())
+    
+  }
+  
+  if(type == "combined"){
+    
+    out <- out %>%
+      dplyr::filter(cohort == "combined") %>%
+      dplyr::select(-data_type, -old_var)
+    
+  } else if(type == "cohort"){
+    
+    out <- out %>%
+      dplyr::filter(cohort != "combined")
+    
+  }
+    
+    if(type %in% c("cohort", "both")){
+      
+      if(coh_direction == "cols"){
+
+        if(inc_missing == FALSE){
+          
+          out <- out %>%
+            pivot_wider(
+              names_from = cohort,
+              values_from = value)
+          
+        } else if(inc_missing == TRUE){
+          
+          out_cat <- out %>%
+            dplyr::filter(data_type == "cat") %>%
+            dplyr::select(-miss_n_perc) %>%
+            mutate(category = ifelse(is.na(category), "miss_n_perc", category)) %>%
+            pivot_wider(
+              names_from = cohort,
+              values_from = value)
+          
+          out_cont <- out %>%
+            dplyr::filter(data_type == "cont") %>%
+             pivot_wider(
+              names_from = cohort,
+              values_from = value)
+          
+          out <- bind_rows(out_cat, out_cont) 
+          
+        }
+        
+        out <- out %>%
+          dplyr::select(-data_type)
+        
+        } else if(coh_direction == "rows"){
+        
+        if(inc_missing == FALSE){
+          
+    out <- out %>%
+      dplyr::select(-data_type) %>%
+      pivot_wider(
+        names_from = c(variable, category),
+        values_from = value) 
+    
+        } else if(inc_missing == TRUE){
+          
+          out_cat <- out %>%
+            dplyr::filter(data_type == "cat") %>%
+            mutate(category = ifelse(is.na(category), "miss_n_perc", category)) %>%
+            dplyr::select(-miss_n_perc) 
+          
+          out_cont <- out %>%
+            dplyr::filter(data_type == "cont") %>%
+            dplyr::select(-category) %>%
+            pivot_longer(
+              cols = c(value, miss_n_perc), 
+              names_to = "category", 
+              values_to = "value")
+          
+          out <- bind_rows(out_cat, out_cont) %>%
+            dplyr::select(-data_type) %>%
+            mutate(old_var = factor(old_var, levels = vars, ordered = TRUE)) %>%
+            arrange(old_var)
+          
+        }
+          
+        }
+    
+    }
+      
+  avail_coh <- .availCoh(stats, stats_types)
+  
+  avail_coh <- c(avail_coh[!avail_coh == "combined"], "combined")
+  
+  if(coh_direction == "rows"){
+    
+  out <- out %>%
+    mutate(cohort = factor(cohort, levels = avail_coh, ordered = TRUE))
+  
+  } 
+  
+ # out <- out %>%
+#    dplyr::select(-old_var)
+  
+  return(out)
+  
+}
+
+#' Identifies whether there are both categorical and continuous stats
+#' 
+#' @param Exported object from dh.getStats
+#' 
+#' @return Character vector containing the names of available stats objects
+#' 
+#' @noRd
+.checkAvailStats <- function(stats){
+
+  var_types <- NULL
+
+if(length(stats$categorical) > 0 & length(stats$continuous) > 0 ){
+
+  var_types <- c("categorical", "continuous")  
+
+} else if (length(stats$categorical) > 0 & length(stats$continuous) == 0 ){
+  
+  var_types <- "categorical"
+  
+} else if (length(stats$categorical) == 0 & length(stats$continuous) > 0 ){
+  
+  var_types <- "continuous"
+  
+}
+  
+}
+
+
+#' Extracts variables names from `stats` object.
+#' 
+#' @param Exported object from dh.getStats
+#' 
+#' @return List with two elements named 'categorical' and 'continuous' 
+#' containing character vectors of respective variable names.
+#' 
+#' @importFrom dplyr %>% pull
+#' @importFrom purrr map set_names
+#' 
+#' @noRd
+.splitStatsVars <- function(stats, var_types){
+  
+  variable <- NULL
+  
+
+  
+  out <- var_types  %>%
+    map(function(x){
+      
+      stats[[x]] %>% pull(variable) %>% unique
+      
+    }) %>%
+    set_names(var_types)
+  
+  return(out)
+  
+}
+
+#' Checks that all the variable names provided to `vars` are available in the
+#' object provided to `stats`
+#' 
+#' @param source_vars Vector of variables names extracted from `stats`
+#' @param target_vars Vector of variable names provided to `vars`
+#' 
+#' @return Returns an error if all variables in `target_vars` are not included
+#' in `source_vars`, otherwise nothing is returned.
+#' 
+#' @noRd
+.checkVarsAvail <- function(source_vars, target_vars){
+  
+  avail <- target_vars[target_vars %in% source_vars]
+  not_avail <- target_vars[!target_vars %in% source_vars]
+  
+  if(length(not_avail > 0)){
+    
+    stop(
+      paste0(
+        "The following variables provided in `vars` are not present in the
+      statistics provided in `stats`\n\n", 
+        paste0(not_avail, collapse = ", "))
+    )
+    
+  }
+  
+}
+
+#' Divides variable names provided to `vars` into categorical and continuous 
+#' using the variable types in `stats`
+#' 
+#' @param stats_vars Output from .splitStatsVars
+#' @param target_vars Vector of variable names provided to `vars`
+#' 
+#' @return List with two elements named 'categorical' and 'continuous' 
+#' containing character vectors of respective variable names.
+#' 
+#' @importFrom dplyr %>%
+#' @importFrom purrr map set_names
+#' 
+#' @noRd
+.splitTargetVars <- function(stats_vars, target_vars){
+                             
+var_types <- c("categorical", "continuous")
+  
+  out <- var_types %>%
+    map(function(x){
+      
+      stats_vars[[x]][stats_vars[[x]] %in% target_vars]    
+      
+    }) %>%
+    set_names(var_types)
+  
+  return(out)
+  
+}
+
+#' Performs the initial formatting of categorical statistics
+#' 
+#' @param stats Exported object from dh.getStats
+#' @param vars Character vector of variable names
+#' @param inc_missing Boolean specifying whether to return missing values in
+#' the output. Use TRUE for yes and FALSE for no.
+#' 
+#' @return Tibble containing 5 columns: 'cohort', 'variable', 'category', 
+#' 'value' and 'data_type'
+#' 
+#' @importFrom dplyr %>% filter mutate select
+#' 
+#' @noRd
+#' 
+.formatCatStats <- function(stats, vars, inc_missing, round_digits, perc_denom){
+  
+  variable <- value <- perc_valid <- cohort <- category <- perc_total <- NULL
+  
+  out <- stats$categorical %>%
+    dplyr::filter(variable %in% vars) %>%
+    mutate(perc_valid = signif(perc_valid, round_digits))
+  
+  if(perc_denom == "total"){
+    
+    out <- out %>%
+    mutate(value = paste0(value, " (", perc_total, ")")) 
+    
+  } else if(perc_denom == "valid"){
+    
+    out <- out %>%
+      mutate(value = ifelse(
+        !is.na(category), 
+        paste0(value, " (", perc_valid, ")") ,
+        paste0(value, " (", perc_total, ")"))) 
+    
+  }
+    
+  if(inc_missing == FALSE){
+    
+    out <- out %>%
+     dplyr::filter(!is.na(category))
+    
+  } 
+  
+  out <- out  %>% 
+    dplyr::select(cohort, variable, category, value) %>%
+    mutate(data_type = "cat") %>%
+    mutate(category = ifelse(is.na(category), "missing", as.character(category)))
+  
+  return(out)
+  
+}
+
+#' Performs the initial formatting of continuous statistics
+#' 
+#' @param stats Exported object from dh.getStats
+#' @param vars Character vector of variable names
+#' @param cont_stats Character specifying which summary statistic to return
+#' for continuous stats. Use 'med_iqr' to return the median and interquartile
+#' range, use 'mean_sd' to return the mean and standard deviation.
+#' @param inc_missing Boolean specifying whether to return missing values in
+#' the output. Use TRUE for yes and FALSE for no.
+#' 
+#' @return Tibble containing 5 columns: 'cohort', 'variable', 'category', 
+#' 'value' and 'data_type'. If `inc_missing` is TRUE contains an sixth column
+#' 'miss_n_perc'
+#' 
+#' @importFrom dplyr %>% filter mutate select
+#' 
+#' @noRd
+.formatContStats <- function(stats, vars, cont_stats, inc_missing, round_digits){
+  
+  variable <- std.dev <- perc_50 <- perc_25 <- perc_75 <- perc_95 <- missing_n <-
+    missing_perc <- cohort <- category <- value <- miss_n_perc <- 
+    data_type <- valid_n <- NULL
+  
+  out <- stats$continuous %>%
+    dplyr::filter(variable %in% vars) %>%
+    mutate(across(c(mean:perc_95, missing_perc), ~signif(., round_digits))) %>%
+    mutate(data_type = "cont") %>%
+    pivot_longer(
+      cols = c(valid_n, missing_n),
+      names_to = "category", 
+      values_to = "missing") 
+  
+  if(cont_stats == "mean_sd"){
+    
+    out <- out %>%
+      mutate(value = paste0(mean, " \u00b1 ", std.dev)) 
+    
+  } else if(cont_stats == "med_iqr"){
+    
+    out <- out %>%
+    mutate(value = paste0(perc_50, " (", perc_25, ",", perc_75, ")")) 
+      
+  }
+  
+  if(inc_missing == TRUE){
+    
+    out <- out %>%
+      mutate(value = ifelse(category == "missing_n", 
+                            paste0(missing, " (", missing_perc, ")"), value)) %>%
+      dplyr::select(cohort, variable, category, value, miss_n_perc, data_type) %>%
+      mutate(category = case_when(
+        category == "valid_n" ~ "value",
+        category == "missing_n" ~ "missing"))
+    
+  } else if(inc_missing == FALSE){
+    
+    out <- out %>% 
+      dplyr::select(cohort, variable, category, value, data_type)
+    
+  }
+  
+  return(out)
+  
+}
+
+#' Checks whether the object provided is either a tibble or data frame.
+#' 
+#' @param labs Object provided either to var_labs, cat_labs or coh_labs
+#'  
+#' @return Returns an error of provided variables is not one of these types.
+#' Otherwise nothing is returned.
+#' 
+#' @noRd 
+.checkLabType <- function(labs){
+  
+  check_type <- class(labs)
+  
+  if(!any(check_type %in% c("tbl_df", "tbl", "data.frame") == TRUE) == TRUE){
+    
+    stop("Label object must be class tibble or data frame")
+    
+  }
+  
+}
+
+#' Checks that all variables provided to `vars` exist in `var_labs`.
+#' 
+#' @param vars Character vector of variable names
+#' @param var_labs Tibble with two columns: 'variable' containing the 
+#' names of the variables specified in `vars`, and 'var_label' containing the
+#' replacement labels for these variables.
+#' 
+#' @return Returns an error if all variables provided to `vars` do not exist in
+#' `var_labs`. Otherwise nothing is returned.
+#' 
+#' @noRd
+.checkLabsMatchVars <- function(vars, var_labs){
+  
+  missing_vars <- vars[!vars %in% var_labs$variable]
+  
+  if(length(missing_vars > 0)){
+    
+    stop(
+      cat("The following variables are specifed in `vars` but do not have a 
+         corresponding label provided in `var_labs`\n\n", missing_vars))
+    
+  }
+  
+}
+
+#' Checks that all categorical variables provided to `stats_cat` have 
+#' corresponding labels provided in `cat_labs`
+#' 
+#' @param stats_cat Object returned from .formatCatStats
+#' @param cat_labs Tibble with three columns: 'variable' containing the 
+#' names of the categorical variables specified in `vars`, 'category' 
+#' containing the categories of these variabels, and "cat_label" containing
+#' the replacement category labels for these variables.
+#' 
+#' @return Returns an error if labels have not been provided for all levels of
+#' all categorical variables. 
+#' 
+#' @importFrom dplyr left_join %>% filter 
+#' 
+#' @noRd
+.checkLabsMatchCats <- function(stats_cat, cat_labs){
+  
+  category <- cat_label <- NULL
+  
+  test_cats <- left_join(stats_cat, cat_labs, by = c("variable", "category"))
+  
+  missing_cats <- test_cats %>% 
+    dplyr::filter(!is.na(category) & is.na(cat_label))
+  
+  if(length(missing_cats > 0)){
+    
+    stop(
+      "The following categorical variables are included in 'vars' 
+      but do not have a corresponding labels for all categories provided in 
+      `cat_labs`\n\n")
+    
+    print(missing_cats)
+    
+  }
+  
+}
+
+#' Checks that the columns provided in labs contain all and only those provided
+#' in `required_cols`
+#' 
+#' @param labs Object provided either to var_labs, cat_labs or coh_labs
+#' @param n_col Integer specifying the required number of columns
+#' @param required_cols Character vector specifying the required column names
+#' 
+#' @return Returns an error if number and names of columns does not match that
+#' specified
+#' 
+#' noRd
+.checkLabCols <- function(labs, n_col, required_cols){
+  
+  check_cols <- all(required_cols %in% colnames(labs))
+  
+  if(check_cols == FALSE){
+    
+    stop(
+      paste0("Labels object must be a tibble containing ", n_col, 
+             " columns: ", paste0(required_cols, collapse = ", ")))
+    
+  }
+  
+}
+
+#' Makes character vector of cohorts present within `stats`
+#' 
+#' @param stats Exported object from dh.getStats
+#' 
+#' @return Character vector of cohort names
+#' 
+#' @importFrom dplyr %>% pull
+#' 
+#' @noRd
+.availCoh <- function(stats, stats_types){
+  
+  cohort <- NULL 
+  
+out <- stats_types %>%
+  map(function(x){stats[[x]] %>% pull(cohort) %>% unique}) %>%
+  unlist %>%
+  unique
+
+return(out)
+
+}
+
+#' Checks that all cohorts contained within `stats` exist in `coh_labs`.
+#' 
+#' @param stats Exported object from dh.getStats
+#' @param coh_labs Tibble with two columns: 'cohort' containing the 
+#' names of the cohorts present in `stats`, and 'coh_label' containing the
+#' replacement labels for these cohorts
+#' 
+#' @return Returns an error if all cohorts present in `stats` do not exist in
+#' `coh_labs`. Otherwise nothing is returned.
+#' 
+#' @noRd
+.checkLabsMatchCoh <- function(stats, coh_labs, stats_types){
+  
+  avail_coh <- .availCoh(stats, stats_types)
+  
+  missing_coh_labs <- avail_coh[!avail_coh %in% coh_labs$cohort]
+  
+  if(length(missing_coh_labs > 0)){
+    
+    stop(
+      cat("The following cohorts are contained in `stats` but do not have a 
+         corresponding label provided in `coh_labs`\n\n", missing_coh_labs))
+    
+  }
+  
+}
diff --git a/R/define-cases.R b/R/define-cases.R
index 9552cea9..648f0f6b 100644
--- a/R/define-cases.R
+++ b/R/define-cases.R
@@ -33,7 +33,7 @@
 #' @export
 dh.defineCases <- function(df = NULL, vars = NULL, type = NULL, new_obj = NULL,
                            conns = NULL, checks = FALSE, newobj = NULL) {
-  if (is.null(df)) {
+   if (is.null(df)) {
     stop("`df` must not be NULL.", call. = FALSE)
   }
 
diff --git a/R/drop-cols.R b/R/drop-cols.R
index 1aa84a91..8e82bb3c 100644
--- a/R/drop-cols.R
+++ b/R/drop-cols.R
@@ -27,13 +27,17 @@
 #' @importFrom stringr str_subset
 #'
 #' @export
-dh.dropCols <- function(df = NULL, vars = NULL, new_obj = df, type = NULL,
+dh.dropCols <- function(df = NULL, vars = NULL, new_obj = NULL, type = NULL,
                         conns = NULL, checks = TRUE, new_df_name = NULL) {
   . <- NULL
 
   if (is.null(conns)) {
     conns <- datashield.connections_find()
   }
+  
+  if (is.null(new_obj)) {
+    new_obj <- df
+  }
 
   if (checks == TRUE) {
     .isDefined(df = df, vars = vars, conns = conns)
diff --git a/R/get-stats.R b/R/get-stats.R
index b3c03e19..2bceee92 100644
--- a/R/get-stats.R
+++ b/R/get-stats.R
@@ -177,6 +177,7 @@ check with ds.class \n\n",
     ## ---- Check whether these levels are identical for all cohorts ---------------
     level_ref <- check_levels %>%
       map(function(x){x[!is.na(x)]}) %>%
+      map_depth(2, sort) %>%
       map(unique) %>%
       map(length) %>%
       bind_rows() %>%
@@ -205,14 +206,19 @@ check with ds.class \n\n",
   ################################################################################
   # 5. Get maximum ns for each cohort
   ################################################################################
-  cohort_ns <- ds.dim(df, type = "split", datasources = conns) %>%
+  cohort_ns_sep <- ds.dim(df, type = "split", datasources = conns) %>%
     map_df(~ .[1]) %>%
     set_names(names(conns)) %>%
     pivot_longer(
       cols = everything(),
       names_to = "cohort",
-      values_to = "cohort_n"
-    )
+      values_to = "cohort_n")
+  
+  cohort_n_comb <- tibble(
+    cohort = "combined",
+    cohort_n = sum(cohort_ns_sep$cohort_n))
+  
+  cohort_ns <- bind_rows(cohort_ns_sep, cohort_n_comb)
   
   ################################################################################
   # 6. Identify variable classes
@@ -482,7 +488,6 @@ check with ds.class \n\n",
       mutate(
         std.dev = sqrt(EstimatedVar),
         valid_n = replace_na(Nvalid, 0),
-        cohort_n = Ntotal,
         missing_n = cohort_n - valid_n,
         missing_perc = (missing_n / cohort_n) * 100
       ) %>%
diff --git a/R/lm-tab.R b/R/lm-tab.R
index ece2240c..6a24ffb9 100644
--- a/R/lm-tab.R
+++ b/R/lm-tab.R
@@ -153,8 +153,22 @@ dh.lmTab <- function(model = NULL, type = NULL, coh_names = NULL,
       map(~ as_tibble(x = ., rownames = "variable")) %>%
       bind_rows(.id = "cohort") %>%
       rename(est = Estimate) %>%
-      rename(se = "Std. Error") %>%
-      dplyr::select(cohort, variable, est, se) %>%
+      rename(se = "Std. Error")
+    
+    if(type == "glm_slma"){
+    
+    separate <- separate %>%
+      dplyr::select(cohort, variable, est, se, "Pr(>|z|)") %>%
+      dplyr::rename(pvalue = "Pr(>|z|)")
+    
+    } else if(type == "lmer_slma"){
+      
+      separate <- separate %>%
+        mutate(pvalue = NA)
+      
+    }
+    
+    separate <- separate %>%
       left_join(., ns, by = "cohort") %>%
       mutate(n_coh = 1)
     
@@ -236,7 +250,7 @@ dh.lmTab <- function(model = NULL, type = NULL, coh_names = NULL,
         values_from = value
       ) %>%
       mutate(est = paste0(est, " (", lowci, ",", uppci, ")")) %>%
-      dplyr::select(variable, se, est, pvalue)
+      dplyr::select(cohort, variable, se, est, pvalue)
   }
   
   if (type == "lmer_slma") {
diff --git a/R/lme-mult-poly.R b/R/lme-mult-poly.R
index af9ca728..05832a38 100644
--- a/R/lme-mult-poly.R
+++ b/R/lme-mult-poly.R
@@ -35,8 +35,10 @@
 #' @export
 dh.lmeMultPoly <- function(df = NULL, formulae = NULL, poly_names = NULL,
                            conns = NULL, checks = TRUE) {
-  sum_log <- NULL
 
+  loglik <- model <- study <- log_rank <- . <- av_rank <- loglik_study1 <-
+    loglik_study2 <- sum_log <- NULL
+  
   if (is.null(df)) {
     stop("`df` must not be NULL.", call. = FALSE)
   }
@@ -57,9 +59,6 @@ dh.lmeMultPoly <- function(df = NULL, formulae = NULL, poly_names = NULL,
     .isDefined(df = df, conns = conns)
   }
 
-  loglik <- model <- study <- log_rank <- . <- av_rank <- loglik_study1 <-
-    loglik_study2 <- NULL
-
   ## ---- Run the models ---------------------------------------------------------
   suppressWarnings(
     models <- formulae %>%
@@ -166,6 +165,9 @@ dh.lmeMultPoly <- function(df = NULL, formulae = NULL, poly_names = NULL,
       add_row(model = poly_names[fail_tmp == FALSE])
   }
 
+  ## Set names for the models
+  models <- models %>% set_names(poly_names)
+  
   out <- list(models = models, convergence = problems, fit = fit.tab)
 
   return(out)
diff --git a/R/make-age-polys.R b/R/make-age-polys.R
index 52cee04a..3761f891 100644
--- a/R/make-age-polys.R
+++ b/R/make-age-polys.R
@@ -31,6 +31,8 @@ dh.makeAgePolys <- function(df = NULL, age_var = NULL,
                             poly_form = c("^-2", "^-1", "^-0.5", "log", "^0.5", "^2", "^3"),
                             poly_names = c("_m_2", "_m_1", "_m_0_5", "log", "_0_5", "_2", "_3"),
                             conns = NULL, checks = TRUE, agevars = NULL) {
+  
+  df <- df
   if (is.null(df)) {
     stop("`df` must not be NULL.", call. = FALSE)
   }
@@ -63,12 +65,11 @@ dh.makeAgePolys <- function(df = NULL, age_var = NULL,
     poly_names <- poly_names[str_detect(poly_names, "log") == FALSE]
     poly_form <- poly_form[str_detect(poly_form, "log") == FALSE]
   }
-  df_age <- c(paste0(df, "$", age_var))
+  df_age <- paste0(df, "$", age_var)
 
   polys <- tibble(
     poly = cross2(age_var, poly_names) %>% map_chr(paste, sep = "", collapse = ""),
-    form = cross2(df_age, poly_form) %>% map_chr(paste, sep = "", collapse = "")
-  )
+    form = cross2(df_age, poly_form) %>% map_chr(paste, sep = "", collapse = ""))
 
   if (log_yn == TRUE) {
     polys <- add_row(
@@ -87,8 +88,12 @@ dh.makeAgePolys <- function(df = NULL, age_var = NULL,
       )
     })
 
-  ds.cbind(x = c(df, polys$poly), newobj = df)
+  ds.cbind(x = c(df, polys$poly), newobj = df, datasources = conns)
 
+  dh.tidyEnv(
+    obj = polys$poly, 
+    type = "remove")
+  
   cat("\nThe following transformations of age have been created in
     dataframe:", df, "\n\n", polys$poly)
 }
diff --git a/R/make-excluded-df.R b/R/make-excluded-df.R
new file mode 100644
index 00000000..5f28e060
--- /dev/null
+++ b/R/make-excluded-df.R
@@ -0,0 +1,91 @@
+#' Given df-A & df-B, creates a new df which is the rows in A but not in B
+#'
+#' When writing a paper often we need to exclude various participants for 
+#' various reasons. Then we will need a df with all of these excluded 
+#' participants. This is one way to do it.
+#'
+#' @param original_df Dataframe containing the full sample
+#' @param final_df Dataframe containing the included sample
+#' @param type Specifies type of dataframe in `original_df`. Either 'long' or
+#' 'wide'. NOTE NOT CURRENTLY FUNCTIONAL - ONLY WORKS FOR WIDE.
+#' @template new_obj
+#' @template conns
+#' 
+#' @return Creates a serverside dataframe containing the rows from `original_df`
+#' that are not contained in `final_df`   
+#' 
+#' @importFrom utils head
+#'
+#' @export
+dt.makeExcludedDf <- function(original_df, final_df, new_obj,
+                              type = "wide", conns = NULL){
+  
+  if (is.null(conns)) {
+    conns <- datashield.connections_find()
+  }
+  
+  dims <- ds.dim(final_df)
+  
+  length.ref <- tibble(
+    cohort = names(conns), 
+    length = dims %>% map(~.x[[1]]) %>% unlist() %>% head(-1) %>% as.character)
+  
+  length.ref %>%
+    pmap(function(cohort, length){
+      ds.rep(
+        x1 = 1,
+        times = length,
+        source.x1 = "clientside",
+        source.times = "c",
+        newobj = "final_ones", 
+        datasources = conns[cohort])
+    })
+  
+  ds.dataFrame(
+    x = c(paste0(final_df, "$child_id"), "final_ones"),
+    newobj = "final_tmp")
+  
+## ---- Now merge this vector with baseline_df ---------------------------------
+
+if(type == "wide"){
+  
+  ds.merge(
+  x.name = original_df,
+  y.name = "final_tmp",
+  by.x.names = "child_id",
+  by.y.names = "child_id",
+  all.x = TRUE,
+  all.y = FALSE, 
+  newobj = "orig_w_vec")
+  
+} else if(type == "long"){
+  
+  ds.merge(
+    x.name = original_df,
+    y.name = "final_tmp",
+    by.x.names = c("child_id", "age"),
+    by.y.names = c("child_id", "age"),
+    all.x = TRUE,
+    all.y = FALSE, 
+    newobj = "orig_w_vec")
+  
+}
+
+## ---- Transform case_def to binary vector ------------------------------------
+ds.Boole(
+  V1 = paste0("orig_w_vec$final_ones"), 
+  V2 = 1, 
+  Boolean.operator = "==", 
+  na.assign = "0", 
+  newobj = "exc_vec")
+  
+## ---- Create excluded subset based on this vector ----------------------------
+ds.dataFrameSubset(
+  df.name = original_df, 
+  V1.name = "exc_vec", 
+  V2.name = "0", 
+  Boolean.operator = "==", 
+  keep.NAs = FALSE, 
+  newobj = new_obj)
+
+}
diff --git a/R/make-lmer-form.R b/R/make-lmer-form.R
index 8233093f..dde3e7bb 100644
--- a/R/make-lmer-form.R
+++ b/R/make-lmer-form.R
@@ -32,7 +32,7 @@
 #'
 #' @export
 dh.makeLmerForm <- function(outcome = NULL, id_var = NULL, age_vars = NULL,
-                            random = NULL, fixed = NULL, age_interactions = FALSE) {
+                            random = NULL, fixed = NULL, age_interactions = NULL) {
   if (is.null(outcome)) {
     stop("`outcome` must not be NULL.", call. = FALSE)
   }
diff --git a/R/mean-by-group.R b/R/mean-by-group.R
index c1563488..2677ff93 100644
--- a/R/mean-by-group.R
+++ b/R/mean-by-group.R
@@ -10,7 +10,8 @@
 #' @importFrom dsBaseClient ds.meanSdGp
 #' @importFrom purrr map
 #' @importFrom tibble as_tibble
-#' @importFrom dplyr bind_rows %>% slice
+#' @importFrom dplyr bind_rows %>% slice 
+#' @importFrom tidyr separate
 #'
 #' @template conns
 #' @template df
@@ -30,7 +31,8 @@
 dh.meanByGroup <- function(df = NULL, outcome = NULL, group_var = NULL,
                            intervals = NULL, conns = NULL, checks = FALSE) {
   value <- op <- tmp <- varname <- new_df_name <- age <- group <- cohort <-
-    . <- enough_obs <- variable <- NULL
+    . <- enough_obs <- variable <- level <- std.dev <- nvalid <- ntotal <-
+    x <- nvalid_2 <- nvalid_1 <- mean_2 <- std.dev_2 <- nmissing <- NULL
 
   if (is.null(df)) {
     stop("`df` must not be NULL.", call. = FALSE)
@@ -184,21 +186,32 @@ paste0(warnings$issues$cohort, collapse = ", ")
           datasources = conns[cohort])
       })
     
+
     ## Now we take these values and put them into a neater table
     out <- obs_by_agecat_comb %>%
-      map(function(x) {
-        x$Mean_gp %>%
-          as_tibble(rownames = "group") %>%
-          slice(2) %>%
-      pivot_longer(
-        cols = -group,
-        names_to = "cohort", 
-        values_to = "mean")}) %>%
+      map(function(x){
+        
+        tibble(
+          group = dimnames(x$Mean_gp_study)[[1]],
+          mean = as.numeric(x$Mean_gp_study),
+          std.dev = as.numeric(x$StDev_gp_study), 
+          nvalid = as.numeric(x$Nvalid_gp_study),
+          cohort = colnames(x$Mean_gp_study)) 
+      }) %>%
       bind_rows() %>%
-      mutate(group = str_remove(group, "_[^_]+$")) %>%
+      separate(group, into = c("group", "level"), sep="_(?=[^_]+$)") %>%
+      pivot_wider(
+        names_from = "level", 
+        values_from = c("nvalid", "mean", "std.dev")) %>%
+      dplyr::rename(
+        "nvalid" = nvalid_2, 
+        "nmissing" = nvalid_1, 
+        "mean" = mean_2,
+        "std.dev" = std.dev_2) %>%
       mutate(group = str_remove(group, "grp_")) %>%
-      dplyr::select(cohort, group, mean)
-
+      dplyr::select(cohort, group, mean, std.dev, nvalid, nmissing)
+    
+        
     ## ---- Remove temporary objects -------------------------------------------
     dh.tidyEnv(
       obj = c(cats$new_df_name, assign_conditions$varname),
diff --git a/R/meta-sep-models.R b/R/meta-sep-models.R
new file mode 100644
index 00000000..d9f6954d
--- /dev/null
+++ b/R/meta-sep-models.R
@@ -0,0 +1,170 @@
+#' Function in progress to meta-analyse separate models.
+#' 
+#' @param ref Tibble, output from dh.multGlm. 
+#' @param exp Logical, whether to exponentiate coefficients after meta-analysis
+#' @param method Method of meta-analysing coefficients.
+#' @param output Character; "cohort" to return cohort coefficients only, "meta"
+#' to return meta-analysed coefficients only, "both" to return both.
+#' 
+#' @return A tibble
+#'
+#' @family model building
+#'
+#' @importFrom rlang arg_match
+#' @importFrom purrr pmap set_names map pmap_df map_int
+#' @importFrom dplyr %>% bind_rows group_by group_keys group_split mutate select
+#' left_join
+#' @importFrom metafor rma.uni
+#' @importFrom tibble tibble
+#'
+#' @export
+dh.metaSepModels <- function(ref = NULL, exp = NULL, method = NULL, 
+                             output = "both"){
+  
+  exposure <- variable <- cohort <- . <- est <- lowci <- uppci <- 
+    model_id <- n_obs <- se <- NULL
+  
+  method <- arg_match(
+    arg = method, 
+    values = c("DL", "HE", "HS", "HSk", "SJ", "ML", "REML", "EB", "PM", "GENQ"))
+  
+  output <- arg_match(
+    arg = output, 
+    values = c("meta", "cohort", "both"))
+  
+  if(output %in% c("meta", "both") == TRUE){
+    ## ---- Get coefficients -----------------------------------------------------
+    model_coefs <- ref %>%
+      pmap(function(cohort, fit, ...){
+        
+        dh.lmTab(
+          model = fit,
+          coh_names = cohort,
+          type = "glm_slma",
+          ci_format = "separate",
+          direction = "wide",
+          family = "binomial",
+          digits = 50)   %>%
+          dplyr::filter(cohort != "combined") 
+      }) %>%
+      set_names(ref$model_id) %>%
+      bind_rows(.id = "model_id")
+    
+    ## ---- Create tibble respecting grouping order ------------------------------
+    model_holder <- model_coefs %>%
+      group_by(model_id, variable) %>%
+      group_keys 
+    
+    ## ---- Meta-analyse ---------------------------------------------------------
+    ma.fit <- model_coefs %>%
+      group_by(model_id, variable) %>%
+      group_split() %>%
+      map(
+        ~rma.uni(
+          yi = .x$est, 
+          sei = .x$se, 
+          method = method, 
+          control=list(stepadj=0.5, maxiter=1000)))
+    
+    ## ---- Put back together ----------------------------------------------------
+    model_holder <- model_holder %>%
+      mutate(meta = ma.fit)
+    
+    ma.out <- model_holder %>%
+      pmap_df(function(model_id, variable, meta){
+        
+        tibble(
+          model_id = model_id,
+          variable = variable,
+          est = meta$beta[1],
+          se = meta$se,
+          lowci = meta$ci.lb,
+          uppci = meta$ci.ub,
+          i2 = round(meta$I2, 2), 
+          t2 = round(meta$tau2, 2), 
+          q = round(meta$QE, 2), 
+          q_p = round(meta$QEp, 2),
+          k = round(meta$k, 2),
+          p = round(meta$p, 2),
+          t2_se = round(meta$se.tau2, 2), 
+          n_coh = meta$k)
+        
+      }) %>%
+      mutate(metafor_obj = model_holder$meta)
+    
+  }
+  
+  ## ---- Calculate combined sample size ---------------------------------------
+    sample_n_comb <- model_coefs %>%
+      group_by(model_id, variable) %>%
+      group_split %>%
+      map(function(x){
+        
+        x %>% 
+          mutate(n_obs = sum(x$n_obs)) %>%
+          dplyr::select(model_id, variable, n_obs) %>%
+          slice(1)
+      }) %>%
+      bind_rows
+   
+    ma.out <- left_join(ma.out, sample_n_comb, by = c("model_id", "variable")) %>%
+      mutate(cohort = "combined")
+    
+  if(output %in% c("cohort", "both") == TRUE){
+  
+    coh.out <- ref %>%
+      pmap(function(model, cohort, fit, ...){
+        
+        dh.lmTab(
+          model = fit,
+          coh_names = cohort,
+          type = "glm_slma",
+          direction = "wide",
+          family = "binomial", 
+          ci_format = "separate", 
+          digits = 50) %>%
+          dplyr::filter(cohort != "combined")
+        
+      }) %>%
+      set_names(ref$model_id) %>%
+      bind_rows(.id = "model_id")
+    
+  }
+  
+  if(output == "both"){
+    
+    both.out <- coh.out %>%
+      mutate(
+        i2 = NA, 
+        metafor_obj = NA,
+        weight = 1/(se^2)) %>%
+      group_by(model_id, variable) %>%
+      group_split() %>%
+      map(~mutate(., weight_scaled = (weight / sum(weight)*100))) %>%
+      bind_rows %>%
+      ungroup 
+
+    out <- bind_rows(both.out, ma.out)
+    
+  } else if(output == "cohort"){
+    
+    out <- coh.out
+    
+  } else if(output == "meta"){
+    
+    out <- ma.out
+    
+  }
+  
+  if(exp == TRUE){
+    
+    out <- out %>%
+      mutate(across(c(est, lowci, uppci), ~exp(.)))
+    
+  }
+    
+    out <- left_join(out, ref, by = c("model_id", "cohort")) 
+  
+  return(out)
+  
+}
\ No newline at end of file
diff --git a/R/multGlm.R b/R/multGlm.R
new file mode 100644
index 00000000..9ed55e60
--- /dev/null
+++ b/R/multGlm.R
@@ -0,0 +1,101 @@
+#' Loop multiple GLM models and handle errors & non-convergence
+#'
+#' This function allows you to fit different glm models to different cohorts, 
+#' for example if you want to include different covariates for different 
+#' cohorts.
+#'
+#' @importFrom dplyr %>% bind_cols mutate filter
+#' @importFrom dsBaseClient ds.glmSLMA
+#' @importFrom purrr map pmap
+#' @importFrom stringr str_detect
+#'
+#' @template df
+#' @param ref reference tibble, output from dh.buildModels.
+#' @param vary_df Option to provide different df for different models. Default
+#' is FALSE.
+#' @param family Family to use in glm models. Default is "gaussian".
+#' @template checks
+#' @template conns
+#'
+#' @return Tibble containing five columns:
+#' * model = Description of model fit, taken from `model_name`. 
+#' * formula = Formula for this model, taken from `formulae`.
+#' * cohort = Cohort for which this model was fit, taken from `cohort`.
+#' * fit = Output from glm model. 
+#' * converged = Logical; whether or not the model converged/fit'.
+#'
+#' @family analysis functions
+#' @md
+#'
+#' @export
+dh.multGLM <- function(df = NULL, ref = NULL, checks = TRUE, conns = NULL, 
+                       vary_df = F, family = "gaussian"){
+  formulae <- model_names <- cohort <- converged <- NULL
+  
+  if (vary_df == F & is.null(df)) {
+    stop("`df` must not be NULL.", call. = FALSE)
+  }
+  
+  if (is.null(ref)) {
+    stop("`ref` must not be NULL.", call. = FALSE)
+  }
+  
+  if (is.null(conns)) {
+    conns <- datashield.connections_find()
+  }
+  
+  ## ---- Sort out df ----------------------------------------------------------
+  if(vary_df == F){
+    
+    ref <- ref %>% mutate(df = df)
+    
+  }
+  
+  ## ---- Run the models -------------------------------------------------------
+  suppressWarnings(
+    models <- ref %>%
+      pmap(function(formula, cohort, df, ...){
+        
+        tryCatch(
+          {
+            dsBaseClient::ds.glmSLMA(
+              formula = unlist(formula), 
+              dataName = df, 
+              family = family, 
+              combine.with.metafor = TRUE,
+              datasources = conns[cohort]
+            )
+          },
+          error = function(error_message) {
+            out <- list("failed", error_message)
+            return(out)
+          }
+        )
+      })
+  )
+  
+  ## ---- Identify models which failed completely ------------------------------
+  fail_tmp <- models %>%
+    map(~ .[[1]][[1]][[1]]) %>% 
+    str_detect("POTENTIALLY DISCLOSIVE|failed", negate = TRUE)
+  
+  out <- ref %>%
+    mutate(
+      fit = models, 
+      converged = fail_tmp)
+  
+  problems <- out %>%
+    dplyr::filter(converged == FALSE)
+    
+  ## ---- Summarise convergence info ---------------------------------------------
+  if (all(out$converged == FALSE)) {
+    warning("All models failed. Check 'convergence' table for more details")
+  }
+  
+  if (any(out$converged == TRUE) & any(out$converged == FALSE)) {
+    warning("Some models threw an error message. Check 'converged' column for
+      more details")
+  }
+  
+  return(out)
+}
diff --git a/R/predict-lmer.R b/R/predict-lmer.R
index ce0d7c84..65bf8ada 100644
--- a/R/predict-lmer.R
+++ b/R/predict-lmer.R
@@ -47,18 +47,21 @@ dh.predictLmer <- function(model = NULL, new_data = NULL, coh_names = NULL,
   }
 
   ## ---- First we add a column to the new data for the intercept ----------------
+  if("intercept" %in% colnames(new_data) == FALSE){
+
   new_data <- new_data %>%
     mutate(intercept = 1) %>%
     select(intercept, everything())
 
-  ## ---- First we extract coefficients ------------------------------------------
+  }
+  
+  ## ---- Then we extract coefficients ------------------------------------------
   coefs <- dh.lmTab(
     model = model,
     type = "lmer_slma",
     coh_names = coh_names,
     direction = "long",
-    ci_format = "separate"
-  )
+    ci_format = "separate")
 
   ## ---- Now we get the names of coefficients which aren't the intercept --------
   coef_names <- coefs$fixed %>%
@@ -67,10 +70,10 @@ dh.predictLmer <- function(model = NULL, new_data = NULL, coh_names = NULL,
 
   ## ---- Now we get the coefficients for each cohort ----------------------------
   coefs_by_cohort <- coefs$fixed %>%
+    dplyr::select(cohort, variable, coefficient, value) %>%
     pivot_wider(
       names_from = variable,
-      values_from = value
-    ) %>%
+      values_from = value) %>%
     dplyr::filter(coefficient == "est") %>%
     select(-coefficient) %>%
     group_by(cohort)
@@ -103,7 +106,6 @@ dh.predictLmer <- function(model = NULL, new_data = NULL, coh_names = NULL,
         mutate(predicted = rowSums(x))
     })
 
-
   ## ---- Now get the standard errors ------------------------------------------------
   nstudy <- seq(1, model$num.valid.studies, 1)
   study_ref <- paste0("study", nstudy)
diff --git a/R/utils.R b/R/utils.R
index 9482c289..310bb9c2 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -133,7 +133,7 @@ isDefined <- function(datasources = NULL, obj = NULL, error.message = TRUE) {
 #' Convert strings indicating boolean operators to symbols. Used in functions
 #' make-strata and quantile-split.
 #'
-#' @param boole_string
+#' @param boole_string Argument string to be converted to operators.
 #' @importFrom dplyr case_when
 #' @noRd
 .convertBooleText <- function(boole_string){
diff --git a/R/z-score-by-group.R b/R/z-score-by-group.R
new file mode 100644
index 00000000..c9975e80
--- /dev/null
+++ b/R/z-score-by-group.R
@@ -0,0 +1,69 @@
+#' Creates z-scores within specified bands
+#'
+#' Especially with mental health outcomes, we often want to transform raw scores
+#' into z-scores, but within certain age bands (e.g. measurement occasion or 
+#' per year). This function does this.
+#' 
+#' @template df 
+#' @param out_var Variable to make z-scores for
+#' @param age_var Age variable
+#' @param low_band Lower band for z score
+#' @param upp_band Upper band for z score
+#' @template new_obj
+#' @template conns
+#'
+#' @return Z score serverside within specified bands
+#'
+#' @importFrom tibble tibble
+#' @importFrom dplyr %>%
+#' @importFrom purrr pmap cross2 map_chr
+#' @importFrom dsBaseClient ds.cbind
+#' @importFrom DSI datashield.connections_find
+#'
+#' @family trajectory functions
+#' @family data manipulation functions
+#'
+#' @export
+dh.zByGroup <- function(df, out_var, age_var, low_band, upp_band, conns, new_obj){
+  
+  .BooleTwoConditions(
+    df = df, 
+    var = "age", 
+    value_1 = low_band,
+    value_2 = upp_band,
+    op_1 = ">=", 
+    op_2 = "<", 
+    newobj = "tmp_1", 
+    conns = conns)
+  
+  ds.recodeValues(
+    var.name = "tmp_1", 
+    values2replace.vector = 0,
+    new.values.vector = NA, 
+    newobj = "tmp_2", 
+    datasources = conns)
+  
+  ref <- dh.meanByGroup(
+    df = df, 
+    outcome = out_var, 
+    group_var = age_var, 
+    intervals = c(low_band, upp_band), 
+    checks = F,
+    conns = conns)
+  
+  assign_form <- paste0(
+    "((", 
+    paste0(df, "$", out_var), 
+    "-", ref$mean, ")", "/", ref$std.dev, ")")
+  
+  ds.assign(
+    toAssign = assign_form,
+    newobj = "tmp_3", 
+    datasources = conns)
+  
+  ds.assign(
+    toAssign = paste0("tmp_2*tmp_3"),
+    newobj = new_obj, 
+    datasources = conns)
+  
+}
\ No newline at end of file
diff --git a/man/dh.anyData.Rd b/man/dh.anyData.Rd
index 42f27d72..acdae9d6 100644
--- a/man/dh.anyData.Rd
+++ b/man/dh.anyData.Rd
@@ -30,6 +30,7 @@ generates a look-up table with this information.
 \seealso{
 Other descriptive functions: 
 \code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.defineCases}()},
 \code{\link{dh.getStats}()},
 \code{\link{dh.lmTab}()},
diff --git a/man/dh.classDiscrepancy.Rd b/man/dh.classDiscrepancy.Rd
index c436ec6d..7795ca24 100644
--- a/man/dh.classDiscrepancy.Rd
+++ b/man/dh.classDiscrepancy.Rd
@@ -33,6 +33,7 @@ the class of each variable.
 \seealso{
 Other descriptive functions: 
 \code{\link{dh.anyData}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.defineCases}()},
 \code{\link{dh.getStats}()},
 \code{\link{dh.lmTab}()},
diff --git a/man/dh.createTableOne.Rd b/man/dh.createTableOne.Rd
new file mode 100644
index 00000000..c6ca3597
--- /dev/null
+++ b/man/dh.createTableOne.Rd
@@ -0,0 +1,85 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/create-table-one.R
+\name{dh.createTableOne}
+\alias{dh.createTableOne}
+\title{Creates tables in useful formats for including in manuscripts}
+\usage{
+dh.createTableOne(
+  stats = NULL,
+  vars = NULL,
+  var_labs = NULL,
+  cat_labs = NULL,
+  type = NULL,
+  coh_labs = NULL,
+  coh_direction = "cols",
+  cont_stats = "med_iqr",
+  inc_missing = NULL,
+  round_digits = 2,
+  perc_denom = NULL
+)
+}
+\arguments{
+\item{stats}{Exported object from dh.getStats.}
+
+\item{vars}{Variable to be included in table.}
+
+\item{var_labs}{Tibble with two columns: 'variable' containing the 
+names of the variables specified in `vars`, and 'var_label' containing the
+replacement labels for these variables.}
+
+\item{cat_labs}{Tibble with three columns: 'variable' containing the 
+names of the categorical variables specified in `vars`, 'category' 
+containing the categories of these variabels, and "cat_label" containing
+the replacement category labels for these variables.}
+
+\item{type}{Character specifying which cohorts to include in the table. If
+"combined" then only combined stats will be returned, if "cohort" then only
+cohort-specific stats will be returned, if "both" then everything will be 
+returned.}
+
+\item{coh_labs}{Tibble with two columns: 'cohort' containing the names
+of all cohorts included in `stats`, and 'cohort_labs' containing the 
+replacement labels for these cohorts.}
+
+\item{coh_direction}{Character specifying direction of data if `type` is 
+'cohort' or 'both'. Use 'rows' to return cohorts as rows and variable as 
+columns, or use 'cols' to return cohorts as columns and variables as rows.
+Defauls is "col".}
+
+\item{cont_stats}{Character specifying which summary statistic to return
+for continuous stats. Use 'med_iqr' to return the median and interquartile
+range, use 'mean_sd' to return the mean and standard deviation. Default is
+"med_iqr".}
+
+\item{inc_missing}{Boolean specifying whether to return missing values in
+the output. Use TRUE for yes and FALSE for no.}
+
+\item{round_digits}{Optionally, the number of decimal places to round output 
+to. Default is 2.}
+
+\item{perc_denom}{The denominator for percentages. Either 'valid' for valid
+cases or 'total' for total cases.}
+}
+\value{
+Tibble containing formatted summary statistics. If `coh_direction` is
+'cols', the tibble will contain four columns: 'cohort', 'variable', 
+'category' & value. If `coh_direction` is rows, the tibble will contain the
+column 'cohort' as well as as columns for all continuous variables and all
+categories of categorical variables.
+}
+\description{
+dh.getStats extracts key statistics and stores them in a clientside list. 
+dh.createTableOne builds on this by formatting the extract stats into a 
+table which can then be included in a manuscript. Flexible formatting options
+are included.
+}
+\seealso{
+Other descriptive functions: 
+\code{\link{dh.anyData}()},
+\code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.defineCases}()},
+\code{\link{dh.getStats}()},
+\code{\link{dh.lmTab}()},
+\code{\link{dh.meanByGroup}()}
+}
+\concept{descriptive functions}
diff --git a/man/dh.defineCases.Rd b/man/dh.defineCases.Rd
index c5ef8945..e4613a3b 100644
--- a/man/dh.defineCases.Rd
+++ b/man/dh.defineCases.Rd
@@ -51,6 +51,7 @@ This function replaces the deprecated dh.subjHasData.
 Other descriptive functions: 
 \code{\link{dh.anyData}()},
 \code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.getStats}()},
 \code{\link{dh.lmTab}()},
 \code{\link{dh.meanByGroup}()}
diff --git a/man/dh.dropCols.Rd b/man/dh.dropCols.Rd
index 99c4e66c..70b55089 100644
--- a/man/dh.dropCols.Rd
+++ b/man/dh.dropCols.Rd
@@ -7,7 +7,7 @@
 dh.dropCols(
   df = NULL,
   vars = NULL,
-  new_obj = df,
+  new_obj = NULL,
   type = NULL,
   conns = NULL,
   checks = TRUE,
@@ -49,6 +49,7 @@ Other data manipulation functions:
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.quartileSplit}()},
 \code{\link{dh.renameVars}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.getStats.Rd b/man/dh.getStats.Rd
index 49c251a4..3ebf73cd 100644
--- a/man/dh.getStats.Rd
+++ b/man/dh.getStats.Rd
@@ -70,6 +70,7 @@ dh.getStats will return the variable for that cohort with all NAs. See
 Other descriptive functions: 
 \code{\link{dh.anyData}()},
 \code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.defineCases}()},
 \code{\link{dh.lmTab}()},
 \code{\link{dh.meanByGroup}()}
diff --git a/man/dh.lmTab.Rd b/man/dh.lmTab.Rd
index ca639920..96f0d34d 100644
--- a/man/dh.lmTab.Rd
+++ b/man/dh.lmTab.Rd
@@ -74,6 +74,7 @@ ds.glm, ds.glmSLMA and ds.lmerSLMA objects.
 Other descriptive functions: 
 \code{\link{dh.anyData}()},
 \code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.defineCases}()},
 \code{\link{dh.getStats}()},
 \code{\link{dh.meanByGroup}()}
diff --git a/man/dh.lmeMultPoly.Rd b/man/dh.lmeMultPoly.Rd
index 552c73a8..df7df7a3 100644
--- a/man/dh.lmeMultPoly.Rd
+++ b/man/dh.lmeMultPoly.Rd
@@ -46,6 +46,7 @@ Other trajectory functions:
 \code{\link{dh.makeAgePolys}()},
 \code{\link{dh.makeLmerForm}()},
 \code{\link{dh.predictLmer}()},
-\code{\link{dh.trimPredData}()}
+\code{\link{dh.trimPredData}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{trajectory functions}
diff --git a/man/dh.makeAgePolys.Rd b/man/dh.makeAgePolys.Rd
index 47470799..6623c8b1 100644
--- a/man/dh.makeAgePolys.Rd
+++ b/man/dh.makeAgePolys.Rd
@@ -46,7 +46,8 @@ Other trajectory functions:
 \code{\link{dh.lmeMultPoly}()},
 \code{\link{dh.makeLmerForm}()},
 \code{\link{dh.predictLmer}()},
-\code{\link{dh.trimPredData}()}
+\code{\link{dh.trimPredData}()},
+\code{\link{dh.zByGroup}()}
 
 Other data manipulation functions: 
 \code{\link{dh.dropCols}()},
@@ -54,7 +55,8 @@ Other data manipulation functions:
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.quartileSplit}()},
 \code{\link{dh.renameVars}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
 \concept{trajectory functions}
diff --git a/man/dh.makeIQR.Rd b/man/dh.makeIQR.Rd
index 502570af..254b8562 100644
--- a/man/dh.makeIQR.Rd
+++ b/man/dh.makeIQR.Rd
@@ -49,6 +49,7 @@ Other data manipulation functions:
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.quartileSplit}()},
 \code{\link{dh.renameVars}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.makeLmerForm.Rd b/man/dh.makeLmerForm.Rd
index 1b19898f..c74fbef5 100644
--- a/man/dh.makeLmerForm.Rd
+++ b/man/dh.makeLmerForm.Rd
@@ -10,7 +10,7 @@ dh.makeLmerForm(
   age_vars = NULL,
   random = NULL,
   fixed = NULL,
-  age_interactions = FALSE
+  age_interactions = NULL
 )
 }
 \arguments{
@@ -50,6 +50,7 @@ Other trajectory functions:
 \code{\link{dh.lmeMultPoly}()},
 \code{\link{dh.makeAgePolys}()},
 \code{\link{dh.predictLmer}()},
-\code{\link{dh.trimPredData}()}
+\code{\link{dh.trimPredData}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{trajectory functions}
diff --git a/man/dh.makeStrata.Rd b/man/dh.makeStrata.Rd
index 39ab414a..8ccee904 100644
--- a/man/dh.makeStrata.Rd
+++ b/man/dh.makeStrata.Rd
@@ -113,6 +113,7 @@ Other data manipulation functions:
 \code{\link{dh.makeIQR}()},
 \code{\link{dh.quartileSplit}()},
 \code{\link{dh.renameVars}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.meanByGroup.Rd b/man/dh.meanByGroup.Rd
index 5b38303f..8c856e31 100644
--- a/man/dh.meanByGroup.Rd
+++ b/man/dh.meanByGroup.Rd
@@ -44,6 +44,7 @@ variable, which may not always be what is required.
 Other descriptive functions: 
 \code{\link{dh.anyData}()},
 \code{\link{dh.classDiscrepancy}()},
+\code{\link{dh.createTableOne}()},
 \code{\link{dh.defineCases}()},
 \code{\link{dh.getStats}()},
 \code{\link{dh.lmTab}()}
diff --git a/man/dh.metaSepModels.Rd b/man/dh.metaSepModels.Rd
new file mode 100644
index 00000000..c8ae45dd
--- /dev/null
+++ b/man/dh.metaSepModels.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/meta-sep-models.R
+\name{dh.metaSepModels}
+\alias{dh.metaSepModels}
+\title{Function in progress to meta-analyse separate models.}
+\usage{
+dh.metaSepModels(ref = NULL, exp = NULL, method = NULL, output = "both")
+}
+\arguments{
+\item{ref}{Tibble, output from dh.multGlm.}
+
+\item{exp}{Logical, whether to exponentiate coefficients after meta-analysis}
+
+\item{method}{Method of meta-analysing coefficients.}
+
+\item{output}{Character; "cohort" to return cohort coefficients only, "meta"
+to return meta-analysed coefficients only, "both" to return both.}
+}
+\value{
+A tibble
+}
+\description{
+Function in progress to meta-analyse separate models.
+}
+\concept{model building}
diff --git a/man/dh.multGLM.Rd b/man/dh.multGLM.Rd
new file mode 100644
index 00000000..7b9d0e71
--- /dev/null
+++ b/man/dh.multGLM.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/multGlm.R
+\name{dh.multGLM}
+\alias{dh.multGLM}
+\title{Loop multiple GLM models and handle errors & non-convergence}
+\usage{
+dh.multGLM(
+  df = NULL,
+  ref = NULL,
+  checks = TRUE,
+  conns = NULL,
+  vary_df = F,
+  family = "gaussian"
+)
+}
+\arguments{
+\item{df}{Character specifying a server-side data frame.}
+
+\item{ref}{reference tibble, output from dh.buildModels.}
+
+\item{checks}{Logical; if TRUE checks are performed prior to running the 
+function. Default is TRUE.}
+
+\item{conns}{DataSHIELD connections object.}
+
+\item{vary_df}{Option to provide different df for different models. Default
+is FALSE.}
+
+\item{family}{Family to use in glm models. Default is "gaussian".}
+}
+\value{
+Tibble containing five columns:
+\itemize{
+\item model = Description of model fit, taken from \code{model_name}.
+\item formula = Formula for this model, taken from \code{formulae}.
+\item cohort = Cohort for which this model was fit, taken from \code{cohort}.
+\item fit = Output from glm model.
+\item converged = Logical; whether or not the model converged/fit'.
+}
+}
+\description{
+This function allows you to fit different glm models to different cohorts,
+for example if you want to include different covariates for different
+cohorts.
+}
+\concept{analysis functions}
diff --git a/man/dh.predictLmer.Rd b/man/dh.predictLmer.Rd
index efcf12b6..1368db9c 100644
--- a/man/dh.predictLmer.Rd
+++ b/man/dh.predictLmer.Rd
@@ -34,6 +34,7 @@ Other trajectory functions:
 \code{\link{dh.lmeMultPoly}()},
 \code{\link{dh.makeAgePolys}()},
 \code{\link{dh.makeLmerForm}()},
-\code{\link{dh.trimPredData}()}
+\code{\link{dh.trimPredData}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{trajectory functions}
diff --git a/man/dh.quartileSplit.Rd b/man/dh.quartileSplit.Rd
index 599bc625..c5535f8e 100644
--- a/man/dh.quartileSplit.Rd
+++ b/man/dh.quartileSplit.Rd
@@ -62,6 +62,7 @@ Other data manipulation functions:
 \code{\link{dh.makeIQR}()},
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.renameVars}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.renameVars.Rd b/man/dh.renameVars.Rd
index 67531fe3..79290106 100644
--- a/man/dh.renameVars.Rd
+++ b/man/dh.renameVars.Rd
@@ -40,6 +40,7 @@ Other data manipulation functions:
 \code{\link{dh.makeIQR}()},
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.quartileSplit}()},
-\code{\link{dh.tidyEnv}()}
+\code{\link{dh.tidyEnv}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.tidyEnv.Rd b/man/dh.tidyEnv.Rd
index bc4fd3c4..2c06f233 100644
--- a/man/dh.tidyEnv.Rd
+++ b/man/dh.tidyEnv.Rd
@@ -28,6 +28,7 @@ Other data manipulation functions:
 \code{\link{dh.makeIQR}()},
 \code{\link{dh.makeStrata}()},
 \code{\link{dh.quartileSplit}()},
-\code{\link{dh.renameVars}()}
+\code{\link{dh.renameVars}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{data manipulation functions}
diff --git a/man/dh.trimPredData.Rd b/man/dh.trimPredData.Rd
index 454882e7..7db043d3 100644
--- a/man/dh.trimPredData.Rd
+++ b/man/dh.trimPredData.Rd
@@ -42,6 +42,7 @@ Other trajectory functions:
 \code{\link{dh.lmeMultPoly}()},
 \code{\link{dh.makeAgePolys}()},
 \code{\link{dh.makeLmerForm}()},
-\code{\link{dh.predictLmer}()}
+\code{\link{dh.predictLmer}()},
+\code{\link{dh.zByGroup}()}
 }
 \concept{trajectory functions}
diff --git a/man/dh.zByGroup.Rd b/man/dh.zByGroup.Rd
new file mode 100644
index 00000000..dd456502
--- /dev/null
+++ b/man/dh.zByGroup.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/z-score-by-group.R
+\name{dh.zByGroup}
+\alias{dh.zByGroup}
+\title{Creates z-scores within specified bands}
+\usage{
+dh.zByGroup(df, out_var, age_var, low_band, upp_band, conns, new_obj)
+}
+\arguments{
+\item{df}{Character specifying a server-side data frame.}
+
+\item{out_var}{Variable to make z-scores for}
+
+\item{age_var}{Age variable}
+
+\item{low_band}{Lower band for z score}
+
+\item{upp_band}{Upper band for z score}
+
+\item{conns}{DataSHIELD connections object.}
+
+\item{new_obj}{Character specifying name for created serverside object.}
+}
+\value{
+Z score serverside within specified bands
+}
+\description{
+Especially with mental health outcomes, we often want to transform raw scores
+into z-scores, but within certain age bands (e.g. measurement occasion or 
+per year). This function does this.
+}
+\seealso{
+Other trajectory functions: 
+\code{\link{dh.lmeMultPoly}()},
+\code{\link{dh.makeAgePolys}()},
+\code{\link{dh.makeLmerForm}()},
+\code{\link{dh.predictLmer}()},
+\code{\link{dh.trimPredData}()}
+
+Other data manipulation functions: 
+\code{\link{dh.dropCols}()},
+\code{\link{dh.makeAgePolys}()},
+\code{\link{dh.makeIQR}()},
+\code{\link{dh.makeStrata}()},
+\code{\link{dh.quartileSplit}()},
+\code{\link{dh.renameVars}()},
+\code{\link{dh.tidyEnv}()}
+}
+\concept{data manipulation functions}
+\concept{trajectory functions}
diff --git a/man/dot-checkLabCols.Rd b/man/dot-checkLabCols.Rd
new file mode 100644
index 00000000..d55f6431
--- /dev/null
+++ b/man/dot-checkLabCols.Rd
@@ -0,0 +1,26 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/create-table-one.R
+\name{.checkLabCols}
+\alias{.checkLabCols}
+\title{Checks that the columns provided in labs contain all and only those provided
+in `required_cols`}
+\usage{
+.checkLabCols(labs, n_col, required_cols)
+}
+\arguments{
+\item{labs}{Object provided either to var_labs, cat_labs or coh_labs}
+
+\item{n_col}{Integer specifying the required number of columns}
+
+\item{required_cols}{Character vector specifying the required column names}
+}
+\value{
+Returns an error if number and names of columns does not match that
+specified
+
+noRd
+}
+\description{
+Checks that the columns provided in labs contain all and only those provided
+in `required_cols`
+}
diff --git a/man/dt.makeExcludedDf.Rd b/man/dt.makeExcludedDf.Rd
new file mode 100644
index 00000000..2a6bc13f
--- /dev/null
+++ b/man/dt.makeExcludedDf.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/make-excluded-df.R
+\name{dt.makeExcludedDf}
+\alias{dt.makeExcludedDf}
+\title{Given df-A & df-B, creates a new df which is the rows in A but not in B}
+\usage{
+dt.makeExcludedDf(original_df, final_df, new_obj, type = "wide", conns = NULL)
+}
+\arguments{
+\item{original_df}{Dataframe containing the full sample}
+
+\item{final_df}{Dataframe containing the included sample}
+
+\item{new_obj}{Character specifying name for created serverside object.}
+
+\item{type}{Specifies type of dataframe in `original_df`. Either 'long' or
+'wide'. NOTE NOT CURRENTLY FUNCTIONAL - ONLY WORKS FOR WIDE.}
+
+\item{conns}{DataSHIELD connections object.}
+}
+\value{
+Creates a serverside dataframe containing the rows from `original_df`
+that are not contained in `final_df`
+}
+\description{
+When writing a paper often we need to exclude various participants for 
+various reasons. Then we will need a df with all of these excluded 
+participants. This is one way to do it.
+}