diff --git a/R/iidda/NAMESPACE b/R/iidda/NAMESPACE
index 62af8b7..8e8cc59 100644
--- a/R/iidda/NAMESPACE
+++ b/R/iidda/NAMESPACE
@@ -32,14 +32,11 @@ export(extract_all_between_paren)
 export(extract_between_paren)
 export(extract_char_or_blank)
 export(extract_or_blank)
-export(factor_time_scale)
 export(failed_prep_script_outcomes)
 export(fill_and_wrap)
 export(fill_re_template)
 export(filter_dependencies)
-export(find_unaccounted_cases)
 export(fix_csv)
-export(flatten_disease_hierarchy)
 export(freq_to_by)
 export(freq_to_days)
 export(get_all_dependencies)
@@ -47,7 +44,6 @@ export(get_canmod_digitization_metadata)
 export(get_dataset_path)
 export(get_elements)
 export(get_firsts)
-export(get_implied_zeros)
 export(get_items)
 export(get_lookup_table)
 export(get_main_script)
@@ -62,7 +58,6 @@ export(identify_scales)
 export(iidda_data_dictionary)
 export(iidda_from_single_file)
 export(is_empty)
-export(is_leaf_disease)
 export(iso_3166_codes)
 export(iso_8601_dateranges)
 export(iso_8601_dates)
@@ -90,7 +85,6 @@ export(melt_tracking_table_keys)
 export(memoise_remove_age)
 export(nlist)
 export(normalize_diseases)
-export(normalize_time_scales)
 export(open_locally)
 export(or_pattern)
 export(pipeline_exploration_starter)
diff --git a/R/iidda/R/data_prep_tools.R b/R/iidda/R/data_prep_tools.R
index 2657b11..955fc31 100644
--- a/R/iidda/R/data_prep_tools.R
+++ b/R/iidda/R/data_prep_tools.R
@@ -701,108 +701,6 @@ add_basal_disease = function(data, lookup){
   with_basal
 }
 
-#' Is Leaf Disease
-#'
-#' Given a set of `disease`-`nesting_disease` pairs that all share the same
-#' \code{\link{basal_disease}},
-#'
-#' @param disease Disease name vector.
-#' @param nesting_disease Vector of the same length as \code{disease} giving
-#' the nesting diseases of element in \code{disease}.
-#'
-#' @return True if disease is never a nesting disease (it is a leaf disease),
-#' False if disease is a nesting disease.
-#'
-#' @export
-is_leaf_disease = function(disease, nesting_disease) !disease %in% unique(nesting_disease)
-
-#' Flatten Disease Hierarchy
-#'
-#' Take a tidy data set with a potentially complex disease hierarchy
-#' and flatten this hierarchy so that, at any particular time and location
-#' (or some other context), all diseases in the `disease` column have the
-#' same `nesting_disease`.
-#'
-#' @param data A tidy data set with the following minimal set of columns:
-#' `disease`, `nesting_disease`, `period_start_date`, `period_end_date`,
-#' and `location`. Note that the latter three can be modified with
-#' `grouping_columns`.
-#' @param disease_lookup A lookup table with `disease` and `nesting_disease`
-#' columns that describe a global disease hierarchy that will be applied
-#' locally to flatten disease hierarchy at each point in time and space
-#' in the tidy data set in the `data` argument.
-#' @param grouping_columns Character vector of column names to use when
-#' grouping to determine the context.
-#' @param basal_diseases_to_prune Character vector of `disease`s to
-#' remove from `data`.
-#' @param specials_pattern Optional regular expression to use to match
-#' `disease` names in `data` that should be added to the lookup table. This
-#' is useful for disease names that are not historical and produced for
-#' harmonization purposes. The most common example is `"_unaccounted$"`,
-#' which is the default. Setting this argument to `NULL` avoids adding
-#' any special disease names to the lookup table.
-#'
-#' @export
-flatten_disease_hierarchy = function(data
-   , disease_lookup
-   , grouping_columns = c("period_start_date", "period_end_date", "location")
-   , basal_diseases_to_prune = character()
-   , specials_pattern = "_unaccounted$"
-) {
-
-  # only need the lookup table to infer the hierarchy
-  disease_lookup = (disease_lookup
-   |> select(disease, nesting_disease)
-   |> distinct()
-  )
-
-  if (!is.null(specials_pattern)) {
-    specials = (data
-      |> filter(grepl(specials_pattern, disease))
-      |> select(disease, nesting_disease)
-      |> distinct()
-    )
-    disease_lookup = bind_rows(disease_lookup, specials)
-  }
-  pruned_lookup = (disease_lookup
-     |> filter(!disease %in% basal_diseases_to_prune)
-     |> mutate(nesting_disease = ifelse(
-            nesting_disease %in% basal_diseases_to_prune
-          , ''
-          , nesting_disease
-        )
-      )
-    )
-  (data
-
-    # getting basal disease for all diseases
-    |> rowwise()
-    |> mutate(basal_disease = basal_disease(disease, disease_lookup))
-    |> ungroup()
-
-    # prune basal_diseases
-    |> mutate(x = disease %in% basal_diseases_to_prune)
-    |> mutate(y = nesting_disease %in% basal_diseases_to_prune)
-    |> mutate(z = basal_disease %in% basal_diseases_to_prune)
-
-    |> filter(!x)
-    |> mutate(nesting_disease = ifelse(y, "", nesting_disease))
-    |> rowwise()
-    |> mutate(basal_disease = ifelse(z, basal_disease(disease, pruned_lookup), basal_disease))
-    |> ungroup()
-
-    # keeping only leaf diseases
-    |> group_by(across(c("basal_disease", all_of(grouping_columns)))) # period_start_date, period_end_date, location, basal_disease)
-    |> filter(is_leaf_disease(disease, nesting_disease))
-    |> ungroup()
-
-    # if there is only the basal disease (no sub-diseases), differentiate by adding '-only'
-    # mutate(disease = ifelse(disease == basal_disease, sprintf("%s-only", disease), disease))
-    # mutate(nesting_disease = basal_disease)
-    |> select(-x, -y, -z)
-
-  )
-}
 
 ## TODO: user-facing function to flatten the disease hierarchy. should probably
 ## be in iidda.analysis because it will make use of the api to get a
@@ -813,339 +711,3 @@ aggregate_disease_hierarchy = function(data, ...) {
    # group by nesting_disease etc ...
   )
 }
-
-time_scale_chooser = function(time_scale, which_fun) {
-  time_scale_order = c("wk", "2wk", "mo", "qr", "yr")
-  time_scale = as.character(time_scale)
-  bad_scale = !time_scale %in% time_scale_order
-  if (any(bad_scale)) {
-    these_bad_scales = paste0(time_scale[bad_scale], collapse = ", ")
-    stop(
-        "\nThese scales where found in the data but are not on the valid list:\n"
-      , these_bad_scales,
-      , "\nValid scales include these:\n"
-      , paste0(time_scale_order, collapse = ", ")
-    )
-  }
-  time_scale_factor = factor(time_scale, levels = time_scale_order)
-  r = time_scale[which_fun(as.numeric(time_scale_factor))]
-  if (length(r) != 1L) stop("Unable to choose a single time scale.")
-  r
-}
-
-#' Factor Time Scale
-#'
-#' @param data A tidy data set with a `time_scale` column.
-#'
-#' @return A data set with a factored time_scale column.
-#'
-#' @export
-factor_time_scale = function(data){
-  if (is.factor(data$time_scale)) {
-    return(data)
-  }
-  time_scale_map = c(wk = "wk", yr = "yr", mo = "mo", `2wk` = "2wk", mt = "mo", `two-wks` = "2wk", qrtr = "qr", qr = "qr")
-  data$time_scale = time_scale_map[as.character(data$time_scale)]
-  order = c("wk", "2wk", "mo", "qr", "yr")
-
-  return(mutate(data, time_scale = factor(data$time_scale, levels = order, ordered = TRUE)))
-}
-
-#' Filter out Time Scales OLD
-#'
-#' Choose a single best `time_scale` for each year in a dataset, grouped by
-#' nesting disease. This best `time_scale` is defined as the longest
-#' of the shortest time scales in each location and sub-disease.
-#'
-#' @param data A tidy data set with a `time_scale` column.
-#' @param initial_group Character vector naming columns for defining
-#' the initial grouping used to compute the shortest time scales.
-#' @param final_group Character vector naming columns for defining the final
-#' grouping used to compute the longest of the shortest time scales.
-#' @param cleanup Should intermediate columns be removed before returning the
-#' output
-#'
-#' @return A data set only containing records with the best time scale.
-#'
-#' @importFrom lubridate year
-#' @noRd
-filter_out_time_scales_old = function(data
-      , initial_group = c("iso_3166", "iso_3166_2", "disease", "nesting_disease")
-      , final_group = c("basal_disease")
-      , cleanup = TRUE
-    ) {
-  time_scale_map = c(
-      wk = "wk", yr = "yr", mo = "mo", `2wk` = "2wk", mt = "mo"
-    , `two-wks` = "2wk", qrtr = "qr", qr = "qr"
-  )
-  data$time_scale = time_scale_map[as.character(data$time_scale)]
-  if (length(unique(data$time_scale)) == 1L) return(data)
-  new_data = (data
-    |> mutate(year = year(period_end_date))
-    |> group_by(across(all_of(c("year", initial_group))))
-    |> mutate(shortest_time_scale = time_scale_chooser(time_scale, which.min))
-    |> ungroup()
-    |> group_by(across(all_of(c("year", final_group))))
-    |> mutate(best_time_scale = time_scale_chooser(shortest_time_scale, which.max))
-    |> ungroup()
-    |> filter(as.character(time_scale) == best_time_scale)
-  )
-  if (isTRUE(cleanup)) {
-    new_data = select(new_data
-      , -year, -shortest_time_scale, -best_time_scale
-    )
-  }
-  new_data
-}
-
-
-#' Normalize Time Scales
-#'
-#' Choose a single best `time_scale` for each year in a dataset, grouped by
-#' nesting disease. This best `time_scale` is defined as the longest
-#' of the shortest time scales in each location and sub-disease.
-#'
-#' @param data A tidy data set with a `time_scale` and `year` column
-#' @param initial_group Character vector naming columns for defining
-#' the initial grouping used to compute the shortest time scales.
-#' @param final_group Character vector naming columns for defining the final
-#' grouping used to compute the longest of the shortest time scales.
-#' @param get_implied_zeros Add zeros that are implied by a '0' reported at a coarser timescale.
-#' @param aggregate_if_unavailable If a location is not reporting for the determined
-#' 'best timescale', but is reporting at a finer timescale, aggregate this finer
-#' timescale to the 'best timescale'
-#'
-#' @return A data set only containing records with the optimal time scale.
-#'
-#' @importFrom lubridate year
-#' @export
-normalize_time_scales = function(data
-    , initial_group = c("year", "iso_3166", "iso_3166_2", "disease", "nesting_disease", "basal_disease")
-    , final_group = c("basal_disease")
-    , get_implied_zeros = TRUE
-    , aggregate_if_unavailable = TRUE
-) {
-
-  if(get_implied_zeros) data = get_implied_zeros(data)
-
-  if (length(unique(data$time_scale)) == 1L) return(data)
-
-  if (!"year" %in% colnames(data)) {stop("The column 'year' does not exist in the dataset.")}
-
-  new_data = (data
-    # remove '_unaccounted' cases when deciding best time_scale
-    |> factor_time_scale()
-    |> filter(!grepl("_unaccounted$", disease))
-   # |> mutate(year = year(period_end_date))
-    |> group_by(across(all_of(c("year", initial_group))))
-    |> mutate(shortest_time_scale = time_scale_chooser(time_scale, which.min))
-    |> ungroup()
-    |> group_by(across(all_of(c("year", final_group))))
-    |> mutate(best_time_scale = time_scale_chooser(shortest_time_scale, which.max))
-    |> ungroup()
-    |> filter(as.character(time_scale) == best_time_scale)
-    |> select(-best_time_scale, -shortest_time_scale)
-  )
-
-  # adding "unaccounted" data back, at the best_time_scale
-  all_new_data = (data
-    |> filter(grepl("_unaccounted$", disease))
-    |> mutate(year = year(period_end_date))
-    |> semi_join(select(new_data, "year", "time_scale", "disease", "nesting_disease", "basal_disease") |> unique(),
-                 by = c("year", "time_scale", final_group))
-    |> rbind(new_data)
-  )
-
-  if(aggregate_if_unavailable) {
-
-    # coarse scales to aggregate to
-    scales = (all_new_data
-    |> select(period_start_date, period_end_date, disease, nesting_disease, basal_disease)
-    |> unique()
-    |> rename(coarser_start_date = period_start_date,
-              coarser_end_date = period_end_date)
-    )
-
-    # data which isn't available at 'best_time_scale' for the year, but is
-    # available at a finer timescale
-    data_to_aggregate = (data
-     |> factor_time_scale()
-   # |> mutate(year = year(period_end_date))
-     |> left_join(select(all_new_data, "year","disease", "nesting_disease", "basal_disease", "time_scale") |> unique(),
-                  by = c("year", "disease", "nesting_disease", "basal_disease"),
-                  suffix = c('_old', '_new'))
-     |> filter(time_scale_old < time_scale_new)
-     |> mutate(period_start_date = as.Date(period_start_date),
-               period_end_date = as.Date(period_end_date))
-
-     # keep only data which isn't available at the 'best time scale' (which is now the timescale in all_new_data)
-     |> anti_join(select(all_new_data,"iso_3166_2", "year","disease", "nesting_disease", "basal_disease", "time_scale") |> unique()
-                  , by = c('time_scale_new' = 'time_scale', 'disease', 'year', 'nesting_disease','basal_disease', 'iso_3166_2'))
-    )
-
-    aggregated_unavailable_data = (scales
-     |> inner_join(data_to_aggregate, by = c("disease", "nesting_disease", "basal_disease"), relationship = 'many-to-many')
-     |> filter(period_end_date > coarser_start_date & period_end_date <= coarser_end_date)
-     |> select(names(data_to_aggregate), coarser_start_date, coarser_end_date)
-
-     |> group_by(iso_3166, iso_3166_2, disease, nesting_disease, basal_disease, coarser_start_date, coarser_end_date)
-     |> mutate(cases_coarse_period = sum(as.numeric(cases_this_period)))
-     |> mutate(population = round(mean(as.numeric(population))),
-               population_reporting = round(mean(as.numeric(population_reporting))))
-     |> ungroup()
-
-     |> select(-cases_this_period, -period_start_date, -period_end_date,
-               -days_this_period, -period_mid_date)
-     |> rename(time_scale = time_scale_new,
-               cases_this_period = cases_coarse_period,
-               period_start_date = coarser_start_date,
-               period_end_date = coarser_end_date)
-
-     |> distinct(iso_3166, iso_3166_2, disease, nesting_disease, basal_disease,
-                 period_start_date, period_end_date, .keep_all = TRUE)
-
-     # add back days_this_period and period_mid_date for the coarser start and end dates
-     # FIXME: apparently using iidda analysis functions will cause issues. oops
-     |> mutate(days_this_period = iidda.analysis::num_days(period_start_date, period_end_date))
-     |> mutate(period_mid_date = iidda.analysis::mid_dates(period_start_date, period_end_date, days_this_period))
-     |> select(-time_scale_old)
-     |> mutate(record_origin = 'derived-aggregated-timescales')
-    )
-
-    final = (all_new_data
-     |> mutate(record_origin = ifelse("record_origin" %in% names(all_new_data), record_origin, 'historical'))
-     |> rbind(aggregated_unavailable_data)
-    )
-
-    return(final)
-  } else{
-    return(all_new_data)
-  }
-}
-
-#' Get Implied Zeros
-#'
-#' Add zeros to data set that are implied by a '0' reported at a coarser timescale.
-#'
-#' @param data A tidy data set
-#'
-#' @return A tidy data set with inferred 0s
-#'
-#' @export
-get_implied_zeros = function(data){
-
-  starting_data = (data
-     |> mutate(year = year(as.Date(period_end_date)))
-     |> factor_time_scale()
-
-     |> group_by(iso_3166_2, disease, year, original_dataset_id)
-     |> mutate(all_zero = ifelse(sum(as.numeric(cases_this_period)) == 0, TRUE, FALSE))
-     |> ungroup()
-
-     |> group_by(disease, year, original_dataset_id)
-     |> mutate(finest_timescale = min(time_scale))
-     |> ungroup()
-    )
-
-  scales = (starting_data
-     |> filter(time_scale == finest_timescale)
-     |> distinct(disease, nesting_disease, basal_disease,
-                year, time_scale, period_start_date, period_end_date,
-                period_mid_date, days_this_period, original_dataset_id)
-)
-
-  # records for which all_zero = true and finest_timescale isn't available
-  get_new_zeros = (starting_data
-     |> filter(time_scale > finest_timescale, all_zero)
-
-     # filter for timescales that are not in the original data
-     |> anti_join(starting_data
-                  , by = c('iso_3166_2', 'year', 'finest_timescale' = 'time_scale',
-                           'disease', 'nesting_disease', 'basal_disease', 'dataset_id'
-                  )) # nesting/basal too? see if that changes result!
-
-     |> select(-period_start_date, -period_end_date, -period_mid_date,
-               -days_this_period)
-  )
-
-  # for rows in get_new_records, find the periods (i.e. start and end dates)
-  # for the finest_timescale for a given year, disease, and original_dataset_id
-  new_zeros = (get_new_zeros
-     |> left_join(scales, by = c('disease', 'year', 'finest_timescale' = 'time_scale',
-                                 'nesting_disease', 'basal_disease', 'original_dataset_id'),
-                  relationship = "many-to-many")
-     |> select(-time_scale, -year, -all_zero)
-     |> rename(time_scale = finest_timescale)
-
-     |> mutate(record_origin = 'derived-implied-zero')
-    )
-
-  # join back to original data
-  (data
-    |> mutate(record_origin = ifelse("record_origin" %in% names(data), record_origin, 'historical'))
-    |> rbind(new_zeros)
-  )
-}
-
-
-#' Find Unaccounted Cases
-#'
-#' Make new records for instances when the sum of leaf diseases is less than
-#' the reported total for their basal disease. The difference between these
-#' counts gets disease name 'basal_disease'_unaccounted'.
-#'
-#'
-#' @param data A tidy data set with a `basal_disease` column.
-#'
-#' @return A data set containing records that are the difference between a
-#' reported total for a basal_disease and the sum of their leaf diseases
-#'
-#' @export
-find_unaccounted_cases = function(data){
-
-  # check if sum of leaf diseases = reported sum of basal disease
-  sum_of_leaf = (
-    data
-    %>% filter(!disease %in% unique(nesting_disease))
-    %>% filter(disease != basal_disease)
-    %>% group_by(iso_3166, iso_3166_2, period_start_date, period_end_date, nesting_disease)
-    %>% summarise(cases_this_period = sum(as.numeric(cases_this_period)))
-  )
-
-  reported_totals = (
-    data
-    %>% filter(nesting_disease == '')
-    %>% filter(disease %in% sum_of_leaf$nesting_disease)
-    %>% select(-nesting_disease)
-    %>% rename(nesting_disease = disease)
-  )
-
-  # if sum of leaf diseases is < reported sum of basal disease,
-  # make new sub-disease called 'disease-name'_unaccounted, which contains
-  # the difference between sum of leaf diseases and the reported sum of the disease
-  unaccounted_data =
-    (inner_join(sum_of_leaf, reported_totals, by =
-                  c('iso_3166', 'iso_3166_2', 'period_start_date', 'period_end_date', 'nesting_disease'),
-              suffix = c('_sum', '_reported'))
-
-   %>% mutate(cases_this_period_reported = as.numeric(cases_this_period_reported),
-              cases_this_period_sum = as.numeric(cases_this_period_sum))
-
-   %>% filter(cases_this_period_sum < cases_this_period_reported)
-   %>% mutate(diff = cases_this_period_reported - cases_this_period_sum)
-   %>% rename(cases_this_period = diff)
-   %>% mutate(disease = paste(nesting_disease, 'unaccounted', sep = '_'))
-
-   %>% select(-cases_this_period_reported, -cases_this_period_sum)
-
-   %>% mutate(original_dataset_id = '')
-   %>% mutate(historical_disease = '')
-   %>% mutate(record_origin = 'derived-unaccounted-cases')
-    )
-
-  (data
-    %>% mutate(record_origin = ifelse("record_origin" %in% names(data), record_origin, 'historical'))
-    %>% rbind(unaccounted_data)
-  )
-}
-
diff --git a/R/iidda/man/factor_time_scale.Rd b/R/iidda/man/factor_time_scale.Rd
deleted file mode 100644
index 22e7784..0000000
--- a/R/iidda/man/factor_time_scale.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{factor_time_scale}
-\alias{factor_time_scale}
-\title{Factor Time Scale}
-\usage{
-factor_time_scale(data)
-}
-\arguments{
-\item{data}{A tidy data set with a `time_scale` column.}
-}
-\value{
-A data set with a factored time_scale column.
-}
-\description{
-Factor Time Scale
-}
diff --git a/R/iidda/man/find_unaccounted_cases.Rd b/R/iidda/man/find_unaccounted_cases.Rd
deleted file mode 100644
index 815b9fa..0000000
--- a/R/iidda/man/find_unaccounted_cases.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{find_unaccounted_cases}
-\alias{find_unaccounted_cases}
-\title{Find Unaccounted Cases}
-\usage{
-find_unaccounted_cases(data)
-}
-\arguments{
-\item{data}{A tidy data set with a `basal_disease` column.}
-}
-\value{
-A data set containing records that are the difference between a
-reported total for a basal_disease and the sum of their leaf diseases
-}
-\description{
-Make new records for instances when the sum of leaf diseases is less than
-the reported total for their basal disease. The difference between these
-counts gets disease name 'basal_disease'_unaccounted'.
-}
diff --git a/R/iidda/man/flatten_disease_hierarchy.Rd b/R/iidda/man/flatten_disease_hierarchy.Rd
deleted file mode 100644
index 832a967..0000000
--- a/R/iidda/man/flatten_disease_hierarchy.Rd
+++ /dev/null
@@ -1,44 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{flatten_disease_hierarchy}
-\alias{flatten_disease_hierarchy}
-\title{Flatten Disease Hierarchy}
-\usage{
-flatten_disease_hierarchy(
-  data,
-  disease_lookup,
-  grouping_columns = c("period_start_date", "period_end_date", "location"),
-  basal_diseases_to_prune = character(),
-  specials_pattern = "_unaccounted$"
-)
-}
-\arguments{
-\item{data}{A tidy data set with the following minimal set of columns:
-`disease`, `nesting_disease`, `period_start_date`, `period_end_date`,
-and `location`. Note that the latter three can be modified with
-`grouping_columns`.}
-
-\item{disease_lookup}{A lookup table with `disease` and `nesting_disease`
-columns that describe a global disease hierarchy that will be applied
-locally to flatten disease hierarchy at each point in time and space
-in the tidy data set in the `data` argument.}
-
-\item{grouping_columns}{Character vector of column names to use when
-grouping to determine the context.}
-
-\item{basal_diseases_to_prune}{Character vector of `disease`s to
-remove from `data`.}
-
-\item{specials_pattern}{Optional regular expression to use to match
-`disease` names in `data` that should be added to the lookup table. This
-is useful for disease names that are not historical and produced for
-harmonization purposes. The most common example is `"_unaccounted$"`,
-which is the default. Setting this argument to `NULL` avoids adding
-any special disease names to the lookup table.}
-}
-\description{
-Take a tidy data set with a potentially complex disease hierarchy
-and flatten this hierarchy so that, at any particular time and location
-(or some other context), all diseases in the `disease` column have the
-same `nesting_disease`.
-}
diff --git a/R/iidda/man/get_implied_zeros.Rd b/R/iidda/man/get_implied_zeros.Rd
deleted file mode 100644
index d645247..0000000
--- a/R/iidda/man/get_implied_zeros.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{get_implied_zeros}
-\alias{get_implied_zeros}
-\title{Get Implied Zeros}
-\usage{
-get_implied_zeros(data)
-}
-\arguments{
-\item{data}{A tidy data set}
-}
-\value{
-A tidy data set with inferred 0s
-}
-\description{
-Add zeros to data set that are implied by a '0' reported at a coarser timescale.
-}
diff --git a/R/iidda/man/is_leaf_disease.Rd b/R/iidda/man/is_leaf_disease.Rd
deleted file mode 100644
index d140485..0000000
--- a/R/iidda/man/is_leaf_disease.Rd
+++ /dev/null
@@ -1,22 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{is_leaf_disease}
-\alias{is_leaf_disease}
-\title{Is Leaf Disease}
-\usage{
-is_leaf_disease(disease, nesting_disease)
-}
-\arguments{
-\item{disease}{Disease name vector.}
-
-\item{nesting_disease}{Vector of the same length as \code{disease} giving
-the nesting diseases of element in \code{disease}.}
-}
-\value{
-True if disease is never a nesting disease (it is a leaf disease),
-False if disease is a nesting disease.
-}
-\description{
-Given a set of `disease`-`nesting_disease` pairs that all share the same
-\code{\link{basal_disease}},
-}
diff --git a/R/iidda/man/normalize_time_scales.Rd b/R/iidda/man/normalize_time_scales.Rd
deleted file mode 100644
index d58d140..0000000
--- a/R/iidda/man/normalize_time_scales.Rd
+++ /dev/null
@@ -1,38 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_prep_tools.R
-\name{normalize_time_scales}
-\alias{normalize_time_scales}
-\title{Normalize Time Scales}
-\usage{
-normalize_time_scales(
-  data,
-  initial_group = c("year", "iso_3166", "iso_3166_2", "disease", "nesting_disease",
-    "basal_disease"),
-  final_group = c("basal_disease"),
-  get_implied_zeros = TRUE,
-  aggregate_if_unavailable = TRUE
-)
-}
-\arguments{
-\item{data}{A tidy data set with a `time_scale` and `year` column}
-
-\item{initial_group}{Character vector naming columns for defining
-the initial grouping used to compute the shortest time scales.}
-
-\item{final_group}{Character vector naming columns for defining the final
-grouping used to compute the longest of the shortest time scales.}
-
-\item{get_implied_zeros}{Add zeros that are implied by a '0' reported at a coarser timescale.}
-
-\item{aggregate_if_unavailable}{If a location is not reporting for the determined
-'best timescale', but is reporting at a finer timescale, aggregate this finer
-timescale to the 'best timescale'}
-}
-\value{
-A data set only containing records with the optimal time scale.
-}
-\description{
-Choose a single best `time_scale` for each year in a dataset, grouped by
-nesting disease. This best `time_scale` is defined as the longest
-of the shortest time scales in each location and sub-disease.
-}