diff --git a/NAMESPACE b/NAMESPACE
index 7865e4a5..8804b5c6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,24 +11,17 @@ export(create_custom_comparisons)
 export(create_custom_contrasts)
 export(eval_hist)
 export(fitModel)
-export(formatLongData)
 export(getModel)
 export(get_comparison_values)
-export(imputeData)
-export(inspectData)
 export(make_love_plot)
 export(perform_multiple_comparison_correction)
 export(trimWeights)
 importFrom(WeightIt,weightitMSM)
-importFrom(doRNG,"%dorng%")
 importFrom(dplyr,"%>%")
-importFrom(dplyr,arrange)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,filter)
 importFrom(dplyr,mutate)
 importFrom(dplyr,select)
-importFrom(foreach,getDoParName)
-importFrom(foreach,getDoParWorkers)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_rect)
@@ -50,17 +43,9 @@ importFrom(jtools,export_summs)
 importFrom(knitr,kable)
 importFrom(marginaleffects,avg_predictions)
 importFrom(marginaleffects,hypotheses)
-importFrom(mice,complete)
-importFrom(mice,ibind)
-importFrom(mice,mice)
 importFrom(mice,pool)
-importFrom(missMethods,delete_MAR_1_to_x)
-importFrom(parallel,detectCores)
-importFrom(purrr,map_dfr)
 importFrom(stargazer,stargazer)
 importFrom(stats,p.adjust)
 importFrom(stringr,str_count)
 importFrom(survey,svydesign)
 importFrom(survey,svyglm)
-importFrom(tibble,tibble)
-importFrom(tidyr,complete)
diff --git a/R/formatLongData.R b/R/formatLongData.R
deleted file mode 100644
index 2277ede2..00000000
--- a/R/formatLongData.R
+++ /dev/null
@@ -1,155 +0,0 @@
-
-#' Formats long data
-#'
-#' @param home_dir path to home directory
-#' @param data dataframe in long format
-#' @param exposure name of exposure variable
-#' @param exposure_time_pts list of integers at which weights will be
-#'   created/assessed that correspond to time points when exposure was measured
-#' @param outcome name of outcome variable with ".timepoint" suffix
-#' @param tv_confounders list of time-varying confounders with ".timepoint"
-#'   suffix
-#' @param time_var (optional) variable name in original dataset demarcating time
-#' @param id_var (optional) variable name in original dataset demarcating ID
-#' @param missing (optional) indicator for missing data in original dataset
-#' @param factor_confounders (optional) list of variable names that are factors
-#'   (default is numeric)
-#' @param save.out (optional) TRUE or FALSE indicator to save output and
-#'   intermediary output locally (default is TRUE)
-#' @return formatted long dataset
-#' @export
-#'
-#' @examples
-#' test <- data.frame(ID = 1:50,
-#'                    A.1 = rnorm(n = 50),
-#'                    A.2 = rnorm(n = 50),
-#'                    A.3 = rnorm(n = 50),
-#'                    B.1 = rnorm(n = 50),
-#'                    B.2 = rnorm(n = 50),
-#'                    B.3 = rnorm(n = 50),
-#'                    C = rnorm(n = 50),
-#'                    D.3 = rnorm(n = 50))
-#' test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-#'
-#' test_long <- stats::reshape(data = test,
-#'                             idvar = "ID", #'list ID variable
-#'                             varying = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'                             direction = "long")
-#'
-#' test_long_format <- formatLongData(data = test_long,
-#'                                    exposure = "A",
-#'                                    exposure_time_pts = c(1, 2, 3),
-#'                                    outcome = "D.3",
-#'                                    tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'                                    time_var = "time",
-#'                                    id_var = NA,
-#'                                    missing = NA,
-#'                                    factor_confounders = "C",
-#'                                    save.out = FALSE)
-
-
-formatLongData <- function(home_dir, data, exposure, exposure_time_pts, outcome, tv_confounders, time_var = NA, id_var = NA, missing = NA,
-                           factor_confounders = NULL, save.out = TRUE){
-
-  if(save.out){
-    if (missing(home_dir)){
-      stop("Please supply a home directory.", call. = FALSE)
-    }
-    else if (!dir.exists(home_dir)) {
-      stop('Please provide a valid home directory.', call. = FALSE)
-    }
-  }
-  if (missing(data)){
-    stop("Please supply data as either a dataframe with no missing data or imputed data in the form of a mids object or path to folder with imputed csv datasets.",
-         call. = FALSE)
-  }
-  if (missing(exposure)){
-    stop("Please supply a single exposure.", call. = FALSE)
-  }
-  if (missing(outcome)){
-    stop("Please supply a single outcome.", call. = FALSE)
-  }
-  if (missing(exposure_time_pts)){
-    stop("Please supply the exposure time points at which you wish to create weights.", call. = FALSE)
-  }
-  if (missing(tv_confounders)){
-    stop("Please supply a list of time-varying confounders.", call. = FALSE)
-  }
-
-  time_varying_covariates <- tv_confounders
-  options(readr.num_columns = 0)
-
-
-
-  # Reading and formatting LONG dataset
-  if (!is.na(time_var)){
-    colnames(data)[colnames(data) == time_var] <- "WAVE" # Assigning time variable
-  }
-
-  if(!is.na(id_var)){
-    colnames(data)[colnames(data) == id_var] <- "ID" # Assigning time variable
-  }
-
-  if(!is.na(missing)){
-    # data[data == missing] <- NA # Makes NA the missingness indicator
-    is.na(data[data == missing]) <- TRUE
-  }
-
-  if (which(colnames(data) == "ID") != 1){
-    data <- data[,which(colnames(data) == "ID"):ncol(data)]
-  }
-
-  # Exposure summary
-  exposure_summary <- data %>%
-    dplyr::filter(WAVE %in% exposure_time_pts) %>%
-    dplyr::group_by(WAVE) %>%
-    dplyr::summarize_at(dplyr::vars(all_of(exposure)),
-                        list(mean = mean, sd = sd, min = min, max = max), na.rm = TRUE)
-
-  cat(knitr::kable(exposure_summary, caption = paste0("Summary of ", exposure,
-                                                      " Exposure Information"), format = 'pipe'), sep = "\n")
-
-  if(save.out){
-    knitr::kable(exposure_summary, caption = paste0("Summary of ", exposure, " Exposure Information"), format = 'html') %>%
-      kableExtra::kable_styling() %>%
-      kableExtra::save_kable(file = file.path(home_dir, paste0(exposure, "_exposure_info.html")))
-
-    cat(paste0(exposure, " exposure descriptive statistics have now been saved in the home directory"), "\n")
-    cat("\n")
-  }
-
-
-  # Outcome summary
-  outcome_summary <- data[, !colnames(data) %in% "ID"]
-  outcome_summary <- outcome_summary %>% select(contains(sapply(strsplit(outcome, "\\."),
-                                                               "[", 1)))
-  outcome_summary <- psych::describe(outcome_summary, fast = TRUE)
-
-  cat(knitr::kable(outcome_summary, caption = paste0("Summary of Outcome ", outcome, " Information"),
-                   format = 'pipe'), sep = "\n")
-
-  if(save.out){
-    knitr::kable(outcome_summary, caption = paste0("Summary of Outcome ", outcome, " Information"), format = 'html') %>%
-      kableExtra::kable_styling() %>%
-      kableExtra::save_kable(file = file.path(home_dir, paste0(outcome, "_outcome_info.html")))
-
-    cat(paste0(outcome, " outcome descriptive statistics have now been saved in the home directory"), "\n")
-  }
-
-
-  data$ID <- as.factor(data$ID)
-
-  if(!is.null(factor_confounders)){
-    if (sum(factor_confounders %in% colnames(data)) < length(factor_confounders)) {
-      stop('Please provide factor covariates that correspond to columns in your data when creating the msm object',
-           call. = FALSE)
-    }
-    # Formatting factor covariates
-    data[, factor_confounders] <- lapply(data[, factor_confounders], as.factor)
-    # Formatting numeric covariates
-    numeric_vars <- colnames(data)[!colnames(data) %in% c(factor_confounders, "ID")]
-    data[, numeric_vars] <- lapply(data[, numeric_vars], as.numeric)
-  }
-
-  as.data.frame(data)
-}
diff --git a/R/imputeData.R b/R/imputeData.R
deleted file mode 100644
index 50903fbf..00000000
--- a/R/imputeData.R
+++ /dev/null
@@ -1,171 +0,0 @@
-#' Imputes dataset so there is no missing at each time point using parallel
-#' processing to speed up
-#'
-#' @export
-#' @importFrom mice mice
-#' @importFrom mice ibind
-#' @importFrom mice complete
-#' @importFrom tibble tibble
-#' @importFrom dplyr arrange
-#' @importFrom dplyr filter
-#' @importFrom dplyr mutate
-#' @importFrom tidyr complete
-#' @importFrom knitr kable
-#' @importFrom parallel detectCores
-#' @importFrom doRNG %dorng%
-#' @importFrom purrr map_dfr
-#' @importFrom foreach getDoParWorkers
-#' @importFrom foreach getDoParName
-#' @importFrom missMethods delete_MAR_1_to_x
-#' @seealso {[mice::mice()],
-#'   <https://cran.r-project.org/web/packages/mice/index.html>}
-#' @param data data in wide format
-#' @param m (optional) integer number of imputed datasets (default is 5)
-#' @param method (optional) character string of imputation method from mice()
-#'   (default is random forest "rf")
-#' @param home_dir path to home directory
-#' @param exposure name of exposure variable
-#' @param outcome name of outcome variable with ".timepoint" suffix
-#' @param para_proc (optional) TRUE/FALSE whether to do parallel processing
-#'   using multiple cores to speed up process (default = TRUE)
-#' @param save.out (optional) TRUE or FALSE indicator to save output and
-#'   intermediary output locally (default is TRUE)
-#' @param read_imps_from_file (optional) "yes" or "no" indicatorto read in weights
-#'   that have been previously run and saved locally (default is "no")
-#' @return mice object of m imputed datasets
-#' @examples
-#' test <- data.frame(ID = 1:50,
-#'                    A.1 = rnorm(n = 50),
-#'                    A.2 = rnorm(n = 50),
-#'                    A.3 = rnorm(n = 50),
-#'                    B.1 = rnorm(n = 50),
-#'                    B.2 = rnorm(n = 50),
-#'                    B.3 = rnorm(n = 50),
-#'                    C = rnorm(n = 50),
-#'                    D.3 = rnorm(n = 50))
-#' test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-#'
-#' test_miss <- missMethods::delete_MAR_1_to_x(as.data.frame(test), p = 0.20,
-#'                                             cols_mis = c("A.1", "B.2", "C"),
-#'                                             cols_ctrl = c("B.1", "B.1", "B.1"), 3)
-#' test_i <- imputeData(data = test_miss,
-#'                      m = 3,
-#'                      method = "rf",
-#'                      exposure = "A",
-#'                      outcome = "D.3",
-#'                      para_proc = TRUE,
-#'                      read_imps_from_file = "no",
-#'                      save.out = FALSE)
-
-
-imputeData <- function(data, m = 5, method = "rf", home_dir = NA, exposure, outcome, para_proc = TRUE,
-                       read_imps_from_file = "no", save.out = TRUE) {
-
-  if (save.out | read_imps_from_file == "yes"){
-    if (missing(home_dir)){
-      stop("Please supply a home directory.", call. = FALSE)
-    }
-    else if (!dir.exists(home_dir)) {
-      stop('Please provide a valid home directory.', call. = FALSE)
-    }
-  }
-  if (missing(data)){
-    stop("Please supply data as either a dataframe with no missing data or imputed data in the form of a mids object or path to folder with imputed csv datasets.",
-         call. = FALSE)
-  }
-  if (missing(exposure)){
-    stop("Please supply a single exposure.", call. = FALSE)
-  }
-  if (missing(outcome)){
-    stop("Please supply a single outcome.", call. = FALSE)
-  }
-
-
-  if(!is.character(method)){
-    stop("Please provide as a character a valid imputation method abbreviation.", call. = FALSE)
-  }
-  if(!is.numeric(m)){
-    stop("Please provide an integer value number of imputations.", call. = FALSE)
-  }
-
-  if (save.out | read_imps_from_file == "yes"){
-    imp_dir <- file.path(home_dir, "imputations")
-    if (!dir.exists(imp_dir)) {
-      dir.create(imp_dir)
-    }
-  }
-
-
-  if (read_imps_from_file == "yes") {
-    imputed_datasets <- list()
-
-    if (!file.exists(glue::glue("{home_dir}/imputations/{exposure}-{outcome}_all_imp.rds"))) {
-      stop("Imputations have not been created and saved locally. Please set 'read_imps_from_file' == 'no' and re-run.", call. = FALSE)
-    }
-
-    imp <- readRDS(glue::glue("{home_dir}/imputations/{exposure}-{outcome}_all_imp.rds"))
-    imputed_datasets <- imp
-
-    cat("\n")
-    cat(glue::glue("Reading in {imputed_datasets$m} imputations from the local folder."))
-    cat("\n")
-    return(imputed_datasets)
-
-  }
-  else {
-
-    if (sum(duplicated(data$"ID")) > 0){
-      stop("Please provide a wide dataset with a single row per ID.", call. = FALSE)
-    }
-
-    imp_method <- method
-    data_to_impute <- tibble::tibble(data)
-
-    cat(glue::glue("Creating {m} imputed datasets using the {imp_method} imputation method in mice. This may take some time to run."))
-    cat("\n")
-
-    if (para_proc){
-      # Configure parallelization
-      nCores <- min(parallel::detectCores(), 8)
-      options(mc.cores = nCores)
-      options(cores = nCores)
-      doParallel::registerDoParallel(cores = nCores)
-
-      cat("### Using", foreach::getDoParWorkers(), "cores\n")
-      cat("### Using", foreach::getDoParName(), "as the backend\n")
-
-      # Conducts imputations using parallelized execution cycling through m
-      imputed_datasets <- foreach::foreach(i = seq_len(m), .combine = mice::ibind) %dorng% {
-        cat("### Started iteration", i, "\n")
-        miceout <- mice::mice(data_to_impute, m = 1, method = imp_method, maxit = 0, #change maxit to default 5 after testing!!!
-                              print = F)
-        cat("### Completed iteration", i, "\n")
-        miceout
-      }
-    }
-    else{
-      imputed_datasets <- mice::mice(data_to_impute, m = m, method = imp_method, maxit = 0, #change maxit to default 5 after testing!!!
-                                     print = F)
-    }
-
-    if(save.out){
-      saveRDS(imputed_datasets, glue::glue("{home_dir}/imputations/{exposure}-{outcome}_all_imp.rds"))
-    }
-
-    # Print warnings
-    cat("USER ALERT: Please view any logged events from the imputation below:", "\n")
-    cat(knitr::kable(imputed_datasets$loggedEvents, caption = "Logged Events from mice", format = 'pipe'), sep = "\n")
-    cat("\n")
-
-    if(save.out){
-      # Save out individual imputed datasets
-      for (k in seq_len(m)) {
-        write.csv(mice::complete(imputed_datasets, k),
-                  file = glue::glue("{home_dir}/imputations/{exposure}-{outcome}_imp{k}.csv"))
-      }
-      cat("See the 'imputations/' folder for a .csv file of each imputed dataset and an .rds file of all imputed datasets", "\n")
-    }
-
-    imputed_datasets
-  }
-}
diff --git a/R/inspectData.R b/R/inspectData.R
deleted file mode 100644
index f33cc960..00000000
--- a/R/inspectData.R
+++ /dev/null
@@ -1,334 +0,0 @@
-
-#' Inspect long/wide/imputed data
-#'
-#' @param data data in wide format as: a data frame, list of imputed data
-#'   frames, or mids object
-#' @param home_dir (optional) path to home directory (required if save.out = TRUE)
-#' @param exposure name of exposure variable
-#' @param exposure_time_pts list of integers at which weights will be
-#'   created/assessed that correspond to time points when exposure was measured
-#' @param outcome name of outcome variable with ".timepoint" suffix
-#' @param tv_confounders list of time-varying confounders with ".timepoint"
-#'   suffix
-#' @param ti_confounders list of time invariant confounders
-#' @param epochs (optional) data frame of exposure epoch labels and values
-#' @param hi_lo_cut (optional) list of two numbers indicating quantile values
-#'   that reflect high and low values, respectively, for continuous exposure
-#'   (default is median split)
-#' @param reference (optional)string of "-"-separated "l" and "h" values
-#'   indicative of a reference exposure history to which to compare comparison,
-#'   required if comparison is specified
-#' @param comparison (optional)list of one or more strings of "-"-separated "l"
-#'   and "h" values indicative of comparison history/histories to compare to
-#'   reference, required if reference is specified
-#' @param verbose (optional) TRUE or FALSE indicator for user output (default is
-#'   TRUE)
-#' @param save.out (optional) TRUE or FALSE indicator to save output and
-#'   intermediary output locally (default is TRUE)
-#' @return none
-#' @export
-#' @examples
-#' test <- data.frame(ID = 1:50,
-#'                    A.1 = rnorm(n = 50),
-#'                    A.2 = rnorm(n = 50),
-#'                    A.3 = rnorm(n = 50),
-#'                    B.1 = rnorm(n = 50),
-#'                    B.2 = rnorm(n = 50),
-#'                    B.3 = rnorm(n = 50),
-#'                    C = rnorm(n = 50),
-#'                    D.3 = rnorm(n = 50))
-#' test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-#'
-#' inspectData(data = test,
-#'             exposure = "A",
-#'             exposure_time_pts = c(1, 2, 3),
-#'             outcome = "D.3",
-#'             tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'             ti_confounders = "C",
-#'             save.out = FALSE)
-#' inspectData(data = test,
-#'             exposure = "A",
-#'             exposure_time_pts = c(1, 2, 3),
-#'             outcome = "D.3",
-#'             tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'             ti_confounders = "C",
-#'             hi_lo_cut = c(0.8, 0.2),
-#'             save.out = FALSE)
-#' inspectData(data = test,
-#'             exposure = "A",
-#'             exposure_time_pts = c(1, 2, 3),
-#'             outcome = "D.3",
-#'             tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'             ti_confounders = "C",
-#'             hi_lo_cut = c(0.8, 0.2),
-#'             reference = "l-l-l",
-#'             comparison = "h-h-h",
-#'             save.out = FALSE)
-#' inspectData(data = test,
-#'             exposure = "A",
-#'             exposure_time_pts = c(1, 2, 3),
-#'             outcome = "D.3",
-#'             tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-#'             ti_confounders = "C",
-#'             epochs = data.frame(epochs = c("Infancy", "Toddlerhood"),
-#'                                 values = I(list(c(1, 2), c(3)))),
-#'             save.out = FALSE)
-
-inspectData <- function(data, home_dir, exposure, exposure_time_pts, outcome, tv_confounders, ti_confounders, epochs = NULL,
-                        hi_lo_cut = NULL, reference = NA, comparison = NULL, verbose = TRUE, save.out = TRUE){
-
-  if (save.out) {
-    if (missing(home_dir)) {
-      stop("Please supply a home directory.", call. = FALSE)
-    }
-    else if(!dir.exists(home_dir)) {
-      stop("Please provide a valid home directory path if you wish to save output locally.", call. = FALSE)
-    }
-  }
-  if (missing(data)){
-    stop("Please supply data as either a dataframe with no missing data or imputed data in the form of a mids object or path to folder with imputed csv datasets.",
-         call. = FALSE)
-  }
-  if (missing(exposure)){
-    stop("Please supply a single exposure.", call. = FALSE)
-  }
-  if (missing(outcome)){
-    stop("Please supply a single outcome.", call. = FALSE)
-  }
-  if (missing(exposure_time_pts)){
-    stop("Please supply the exposure time points at which you wish to create weights.", call. = FALSE)
-  }
-  if (missing(tv_confounders)){
-    stop("Please supply a list of time-varying confounders.", call. = FALSE)
-  }
-  if (missing(ti_confounders)){
-    stop("Please supply a list of time invariant confounders.", call. = FALSE)
-  }
-
-  if (!mice::is.mids(data) & !is.data.frame(data) & !inherits(data, "list")) {
-    stop("Please provide either a 'mids' object, a data frame, or a list of imputed csv files in the 'data' field.", call. = FALSE)
-  }
-
-
-
-  ID <- "ID"
-  time_invar_covars <- ti_confounders
-  time_var_covars <- tv_confounders
-  time_pts <- as.numeric(sapply(strsplit(tv_confounders[grepl(exposure, tv_confounders)] , "\\."), "[",2))
-
-  if (mice::is.mids(data)){
-    data <-as.data.frame(mice::complete(data,1))
-  }
-
-  else if (inherits(data, "list")) { #just inspects frist imputed dataset
-    data <- data[[1]]
-
-  }
-
-  # long format to wide
-  if("WAVE" %in% colnames(data)){
-    v <- sapply(strsplit(tv_confounders, "\\."), "[", 1)
-    v <- v[!duplicated(v)]
-    data_wide <- stats::reshape(data = data_long, idvar = "ID", v.names = v, timevar = "WAVE",
-                                direction = "wide")
-
-    #removing all NA cols (i.e., when data were not collected)
-    data_wide <- data_wide[,colSums(is.na(data_wide)) < nrow(data_wide)]
-    data <- data_wide
-  }
-
-
-  if(!inherits(data, "data.frame")){
-    warning(paste0("Your data is a ", class(data), ". Convert to data frame before running devMSMs."),
-            call. = FALSE)
-  }
-
-
-  exposure_type <- ifelse(inherits(data[, paste0(exposure, '.', exposure_time_pts[1])],
-                                   "numeric"), "continuous", "binary")
-
-
-  # Confounder summary
-  potential_covariates <- colnames(data)[!(colnames(data) %in% c(ID))]
-
-  if (sum(tv_confounders %in% potential_covariates) != length(tv_confounders)){
-    stop(paste(tv_confounders[!tv_confounders %in% potential_covariates]),
-         " time-varying confounders are not present in the dataset.", call. = FALSE)
-  }
-
-  if (sum(ti_confounders %in% potential_covariates) != length(ti_confounders)){
-    stop(paste(ti_confounders[!ti_confounders %in% potential_covariates]),
-         " time invariant confounders are not present in the dataset.", call. = FALSE)
-  }
-
-  all_potential_covariates <- c(time_invar_covars, time_var_covars)
-  all_potential_covariates <- all_potential_covariates[order(all_potential_covariates)]
-
-  # Format for table output to visualize available covariates by time point
-  covar_table <- data.frame(variable = sapply(strsplit(all_potential_covariates, "\\."), "[", 1),
-                            time_pt = sapply(strsplit(all_potential_covariates, "\\."), "[", 2)) %>%
-    dplyr::arrange(time_pt, variable) %>%
-    dplyr::group_by(time_pt) %>%
-    dplyr::summarize(variable = toString(variable))
-
-  if(save.out){
-    write.csv(covar_table, glue::glue("{home_dir}/{exposure}-{outcome}_covariates_considered_by_time_pt.csv"),
-              row.names = FALSE)
-  }
-
-  unique_vars <- length(unique(c(time_invar_covars, sapply(strsplit(all_potential_covariates, "\\."), "[", 1))))
-
-  test <- data.frame(matrix(nrow = length(time_pts), ncol = unique_vars))
-  colnames(test) <- unique(c(time_invar_covars, sapply(strsplit(all_potential_covariates, "\\."),
-                                                       "[", 1)))[order(unique(c(time_invar_covars,
-                                                                                sapply(strsplit(all_potential_covariates,
-                                                                                                "\\."), "[", 1))))]
-  rownames(test) <- time_pts
-
-  for (l in seq_len(nrow(test))) {
-    z = c(sapply(strsplit(all_potential_covariates[grepl(paste0(".", rownames(test)[l]),
-                                                         all_potential_covariates)], "\\."), "[", 1), time_invar_covars)
-    z = z[!duplicated(z)]
-    test[l, z ] <- 1
-  }
-
-  test <- test[, colnames(test)[!(colnames(test) %in% c(ID))]]
-  NumTimePts <- data.frame(NumTimePts = colSums(test, na.rm = TRUE))
-  test <- rbind(test, t(NumTimePts))
-  NumVars <- data.frame(NumVars = rowSums(test, na.rm = TRUE))
-  test[seq_len(nrow(test)), ncol(test) + 1] <- NumVars
-
-  if(save.out){
-    write.csv(test, glue::glue("{home_dir}/{exposure}-{outcome}_matrix_of_covariates_considered_by_time_pt.csv"),
-              row.names = TRUE)
-
-    if(verbose){
-      print(glue::glue("See the home directory for a table and matrix displaying all covariates confounders considered at each exposure time point for {exposure} and {outcome}."), "\n")
-
-      #-2 to exclude ID and WAVE
-      print(glue::glue("USER ALERT: Below are the {as.character(length(all_potential_covariates) - 2)} variables spanning {unique_vars - 2} unique domains that will be treated as confounding variables for the relation between {exposure} and {outcome}."),
-            "Please inspect this list carefully. It should include all time-varying covariates, time invariant covariates, as well as lagged levels of exposure and outcome variables if they were collected at time points earlier than the outcome time point.", "\n")
-      print(all_potential_covariates[!(all_potential_covariates %in% c(ID))])
-    }
-  }
-
-
-  # Data type
-  if(verbose){
-    cat("\n")
-    cat("The following variables are designated as numeric:", "\n")
-    print(paste(colnames(data)[sapply(data, class) == "numeric"], sep = ",", collapse = ", "))
-    cat("\n")
-
-    cat("The following variables are designated as factors:", "\n")
-    print(paste(colnames(data)[sapply(data, class) == "factor"], sep = ",", collapse = ", "))
-    cat("\n")
-
-    #temporary warning re: factor levels
-    cat("*temp: please inspect the levels of your factors below. at present, excluding ID, the code can only accept 2-level factors. set rest to numeric", "\n")
-    print(sapply(data[,colnames(data)[sapply(data, class) == "factor"]], nlevels))
-
-    oth <- data.frame(variable = names(sapply(data, class)) [!sapply(data, class) %in% c("numeric", "factor")],
-                      type = sapply(data, class) [!sapply(data, class) %in% c("numeric", "factor")])
-    if(nrow(oth) > 0 ){
-      cat(knitr::kable(oth, caption = "Other variable types",
-                       format = 'pipe'), sep = "\n")
-      cat("\n")
-    }
-
-    if(sum(sapply(data, is.character)) > 0){
-      warning(paste0(paste(names(data)[sapply(data, is.character)], sep = ", ", collapse = ", "),
-                     " are of class character.", " The package cannot accept character variables."), call. = FALSE)
-    }
-  }
-  #covariate correlations
-  covariates_to_include <- all_potential_covariates
-
-  # Creates final dataset with only relevant variables
-  covariates_to_include <- covariates_to_include[order(covariates_to_include)]
-  variables_to_include <- unique(c(ID,  outcome, covariates_to_include, time_var_covars))
-  data2 <- data %>%
-    select(all_of(variables_to_include))
-
-  # Makes correlation table
-  corr_matrix <- cor(as.data.frame(lapply(data2[, colnames(data2) != ID],
-                                          as.numeric)), use = "pairwise.complete.obs")
-
-  if(save.out){
-    ggcorrplot::ggcorrplot(corr_matrix,  type = "lower")+
-      ggplot2::theme(axis.text.x = element_text(size = 5, margin = ggplot2::margin(-2, 0, 0, 0)),  # Order: top, right, bottom, left
-                     axis.text.y = element_text(size = 5, margin = ggplot2::margin(0, -2, 0, 0))) +
-      ggplot2::geom_vline(xintercept = seq_len(ncol(mtcars)) - 0.5, colour="white", size = 2) +
-      ggplot2::geom_hline(yintercept = seq_len(ncol(mtcars)) - 0.5, colour="white", size = 2)
-
-    # Save correlation plot
-    pdf(file = paste0(home_dir, "/", exposure, "-", outcome, "_all_vars_corr_plot.pdf"))
-    print(ggplot2::last_plot())
-    dev.off()
-
-    if(verbose){
-      cat("\n")
-      cat("A correlation plot of all variables in the dataset has been saved in the home directory", "\n")
-      cat("\n")
-    }
-  }
-
-
-  # Exposure summary
-  exposure_summary <- data %>%
-    dplyr:: select(colnames(data)[grepl(exposure, colnames(data))])
-  exposure_summary <- sapply(exposure_summary, as.numeric)
-  exposure_summary <- psych::describe(exposure_summary, fast = TRUE)
-
-
-  if (save.out){
-    knitr::kable(exposure_summary, caption = paste0("Summary of ", exposure, " Exposure Information"),
-                 format = 'html') %>%
-      kableExtra::kable_styling() %>%
-      kableExtra::save_kable(file = file.path(home_dir, paste0("/", exposure, "_exposure_info.html")))
-    if(verbose){
-      cat(knitr::kable(exposure_summary, caption = paste0("Summary of ", exposure, " Exposure Information"),
-                       format = 'pipe'), sep = "\n")
-      cat(paste0(exposure, " exposure descriptive statistics have now been saved in the home directory"), "\n")
-      cat("\n")
-    }
-  }
-
-  eval_hist(data = data2, exposure, tv_confounders, epochs,
-            exposure_time_pts, hi_lo_cut, ref = reference, comps = comparison, verbose)
-
-  # Exposure history summary
-  if( is.null(epochs)){ #making epochs time pts if not specified by user
-    epochs <- data.frame(epochs = as.character(time_pts),
-                         values = time_pts)
-  }
-  else{
-    if( !is.data.frame(epochs) | ncol(epochs) != 2 | sum(colnames(epochs) == c("epochs", "values")) != ncol(epochs)){
-      stop("If you supply epochs, please provide a dataframe with two columns of epochs and values.",
-           call. = FALSE)
-    }
-    if(sum(is.na(epochs$values)) > 0){
-      stop("Please provide one or a list of several values for each epoch.", call. = FALSE)
-    }
-  }
-
-  # Outcome summary
-  outcome_summary <- data[, grepl(sapply(strsplit(outcome, "\\."),
-                                         "[", 1), colnames(data))]
-  outcome_summary <- psych::describe(outcome_summary, fast = TRUE)
-
-  if(save.out){
-    knitr::kable(outcome_summary, caption = paste0("Summary of Outcome ",
-                                                   sapply(strsplit(outcome, "\\."), "[", 1), " Information"), format = 'html') %>%
-      kableExtra::kable_styling() %>%
-      kableExtra::save_kable(file = file.path(home_dir, paste0("/", sapply(strsplit(outcome, "\\."), "[", 1), "_outcome_info.html")))
-
-    if (verbose){
-      cat(knitr::kable(outcome_summary, caption = paste0("Summary of Outcome ",
-                                                         sapply(strsplit(outcome, "\\."), "[", 1), " Information"),
-                       format = 'pipe'), sep = "\n")
-
-      cat(paste0(sapply(strsplit(outcome, "\\."), "[", 1), " outcome descriptive statistics have now been saved in the home directory"), "\n")
-    }
-  }
-}
diff --git a/man/formatLongData.Rd b/man/formatLongData.Rd
deleted file mode 100644
index be3c4545..00000000
--- a/man/formatLongData.Rd
+++ /dev/null
@@ -1,81 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/formatLongData.R
-\name{formatLongData}
-\alias{formatLongData}
-\title{Formats long data}
-\usage{
-formatLongData(
-  home_dir,
-  data,
-  exposure,
-  exposure_time_pts,
-  outcome,
-  tv_confounders,
-  time_var = NA,
-  id_var = NA,
-  missing = NA,
-  factor_confounders = NULL,
-  save.out = TRUE
-)
-}
-\arguments{
-\item{home_dir}{path to home directory}
-
-\item{data}{dataframe in long format}
-
-\item{exposure}{name of exposure variable}
-
-\item{exposure_time_pts}{list of integers at which weights will be
-created/assessed that correspond to time points when exposure was measured}
-
-\item{outcome}{name of outcome variable with ".timepoint" suffix}
-
-\item{tv_confounders}{list of time-varying confounders with ".timepoint"
-suffix}
-
-\item{time_var}{(optional) variable name in original dataset demarcating time}
-
-\item{id_var}{(optional) variable name in original dataset demarcating ID}
-
-\item{missing}{(optional) indicator for missing data in original dataset}
-
-\item{factor_confounders}{(optional) list of variable names that are factors
-(default is numeric)}
-
-\item{save.out}{(optional) TRUE or FALSE indicator to save output and
-intermediary output locally (default is TRUE)}
-}
-\value{
-formatted long dataset
-}
-\description{
-Formats long data
-}
-\examples{
-test <- data.frame(ID = 1:50,
-                   A.1 = rnorm(n = 50),
-                   A.2 = rnorm(n = 50),
-                   A.3 = rnorm(n = 50),
-                   B.1 = rnorm(n = 50),
-                   B.2 = rnorm(n = 50),
-                   B.3 = rnorm(n = 50),
-                   C = rnorm(n = 50),
-                   D.3 = rnorm(n = 50))
-test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-
-test_long <- stats::reshape(data = test,
-                            idvar = "ID", #'list ID variable
-                            varying = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-                            direction = "long")
-
-test_long_format <- formatLongData(data = test_long,
-                                   exposure = "A",
-                                   exposure_time_pts = c(1, 2, 3),
-                                   outcome = "D.3",
-                                   tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-                                   time_var = "time",
-                                   id_var = NA,
-                                   missing = NA,
-                                   factor_confounders = "C",
-                                   save.out = FALSE)
-}
diff --git a/man/imputeData.Rd b/man/imputeData.Rd
deleted file mode 100644
index 6124efcd..00000000
--- a/man/imputeData.Rd
+++ /dev/null
@@ -1,77 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/imputeData.R
-\name{imputeData}
-\alias{imputeData}
-\title{Imputes dataset so there is no missing at each time point using parallel
-processing to speed up}
-\usage{
-imputeData(
-  data,
-  m = 5,
-  method = "rf",
-  home_dir = NA,
-  exposure,
-  outcome,
-  para_proc = TRUE,
-  read_imps_from_file = "no",
-  save.out = TRUE
-)
-}
-\arguments{
-\item{data}{data in wide format}
-
-\item{m}{(optional) integer number of imputed datasets (default is 5)}
-
-\item{method}{(optional) character string of imputation method from mice()
-(default is random forest "rf")}
-
-\item{home_dir}{path to home directory}
-
-\item{exposure}{name of exposure variable}
-
-\item{outcome}{name of outcome variable with ".timepoint" suffix}
-
-\item{para_proc}{(optional) TRUE/FALSE whether to do parallel processing
-using multiple cores to speed up process (default = TRUE)}
-
-\item{read_imps_from_file}{(optional) "yes" or "no" indicatorto read in weights
-that have been previously run and saved locally (default is "no")}
-
-\item{save.out}{(optional) TRUE or FALSE indicator to save output and
-intermediary output locally (default is TRUE)}
-}
-\value{
-mice object of m imputed datasets
-}
-\description{
-Imputes dataset so there is no missing at each time point using parallel
-processing to speed up
-}
-\examples{
-test <- data.frame(ID = 1:50,
-                   A.1 = rnorm(n = 50),
-                   A.2 = rnorm(n = 50),
-                   A.3 = rnorm(n = 50),
-                   B.1 = rnorm(n = 50),
-                   B.2 = rnorm(n = 50),
-                   B.3 = rnorm(n = 50),
-                   C = rnorm(n = 50),
-                   D.3 = rnorm(n = 50))
-test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-
-test_miss <- missMethods::delete_MAR_1_to_x(as.data.frame(test), p = 0.20,
-                                            cols_mis = c("A.1", "B.2", "C"),
-                                            cols_ctrl = c("B.1", "B.1", "B.1"), 3)
-test_i <- imputeData(data = test_miss,
-                     m = 3,
-                     method = "rf",
-                     exposure = "A",
-                     outcome = "D.3",
-                     para_proc = TRUE,
-                     read_imps_from_file = "no",
-                     save.out = FALSE)
-}
-\seealso{
-{\code{\link[mice:mice]{mice::mice()}},
-\url{https://cran.r-project.org/web/packages/mice/index.html}}
-}
diff --git a/man/inspectData.Rd b/man/inspectData.Rd
deleted file mode 100644
index 4cbff80e..00000000
--- a/man/inspectData.Rd
+++ /dev/null
@@ -1,113 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/inspectData.R
-\name{inspectData}
-\alias{inspectData}
-\title{Inspect long/wide/imputed data}
-\usage{
-inspectData(
-  data,
-  home_dir,
-  exposure,
-  exposure_time_pts,
-  outcome,
-  tv_confounders,
-  ti_confounders,
-  epochs = NULL,
-  hi_lo_cut = NULL,
-  reference = NA,
-  comparison = NULL,
-  verbose = TRUE,
-  save.out = TRUE
-)
-}
-\arguments{
-\item{data}{data in wide format as: a data frame, list of imputed data
-frames, or mids object}
-
-\item{home_dir}{(optional) path to home directory (required if save.out = TRUE)}
-
-\item{exposure}{name of exposure variable}
-
-\item{exposure_time_pts}{list of integers at which weights will be
-created/assessed that correspond to time points when exposure was measured}
-
-\item{outcome}{name of outcome variable with ".timepoint" suffix}
-
-\item{tv_confounders}{list of time-varying confounders with ".timepoint"
-suffix}
-
-\item{ti_confounders}{list of time invariant confounders}
-
-\item{epochs}{(optional) data frame of exposure epoch labels and values}
-
-\item{hi_lo_cut}{(optional) list of two numbers indicating quantile values
-that reflect high and low values, respectively, for continuous exposure
-(default is median split)}
-
-\item{reference}{(optional)string of "-"-separated "l" and "h" values
-indicative of a reference exposure history to which to compare comparison,
-required if comparison is specified}
-
-\item{comparison}{(optional)list of one or more strings of "-"-separated "l"
-and "h" values indicative of comparison history/histories to compare to
-reference, required if reference is specified}
-
-\item{verbose}{(optional) TRUE or FALSE indicator for user output (default is
-TRUE)}
-
-\item{save.out}{(optional) TRUE or FALSE indicator to save output and
-intermediary output locally (default is TRUE)}
-}
-\value{
-none
-}
-\description{
-Inspect long/wide/imputed data
-}
-\examples{
-test <- data.frame(ID = 1:50,
-                   A.1 = rnorm(n = 50),
-                   A.2 = rnorm(n = 50),
-                   A.3 = rnorm(n = 50),
-                   B.1 = rnorm(n = 50),
-                   B.2 = rnorm(n = 50),
-                   B.3 = rnorm(n = 50),
-                   C = rnorm(n = 50),
-                   D.3 = rnorm(n = 50))
-test[, c("A.1", "A.2", "A.3")] <- lapply(test[, c("A.1", "A.2", "A.3")], as.numeric)
-
-inspectData(data = test,
-            exposure = "A",
-            exposure_time_pts = c(1, 2, 3),
-            outcome = "D.3",
-            tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-            ti_confounders = "C",
-            save.out = FALSE)
-inspectData(data = test,
-            exposure = "A",
-            exposure_time_pts = c(1, 2, 3),
-            outcome = "D.3",
-            tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-            ti_confounders = "C",
-            hi_lo_cut = c(0.8, 0.2),
-            save.out = FALSE)
-inspectData(data = test,
-            exposure = "A",
-            exposure_time_pts = c(1, 2, 3),
-            outcome = "D.3",
-            tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-            ti_confounders = "C",
-            hi_lo_cut = c(0.8, 0.2),
-            reference = "l-l-l",
-            comparison = "h-h-h",
-            save.out = FALSE)
-inspectData(data = test,
-            exposure = "A",
-            exposure_time_pts = c(1, 2, 3),
-            outcome = "D.3",
-            tv_confounders = c("A.1", "A.2", "A.3", "B.1", "B.2", "B.3"),
-            ti_confounders = "C",
-            epochs = data.frame(epochs = c("Infancy", "Toddlerhood"),
-                                values = I(list(c(1, 2), c(3)))),
-            save.out = FALSE)
-}