diff --git a/.lintr b/.lintr
index a7539e42..fe9e7d45 100644
--- a/.lintr
+++ b/.lintr
@@ -6,6 +6,7 @@ linters: linters_with_tags(
     extraction_operator_linter = NULL,
     todo_comment_linter = NULL,
     function_argument_linter = NULL,
+    indentation_linter = NULL, # unstable as of lintr 3.1.0
     # Use minimum R declared in DESCRIPTION or fall back to current R version
     backport_linter(if (length(x <- etdev::extract_min_r_version())) x else getRversion())
   )
diff --git a/R/estimate_reporting.R b/R/estimate_reporting.R
index 07c272e4..2fca9307 100644
--- a/R/estimate_reporting.R
+++ b/R/estimate_reporting.R
@@ -25,20 +25,24 @@
 #' @export
 #'
 #' @examples
-#' library(cfr)
-#' library(epiparameter)
-#' library(covidregionaldata)
+#' # get data pre-loaded with {incidence2}
+#' df_covid_uk <- incidence2::covidregionaldataUK
 #'
-#' df_covid_uk <- get_national_data(
-#'   countries = "united kingdom", source = "who", verbose = FALSE
+#' # aggregate the covid data for the UK as a whole
+#' df_covid_uk <- aggregate(
+#'   data = df_covid_uk,
+#'   x = cbind(cases_new, deaths_new) ~ date,
+#'   FUN = function(x) sum(x, na.rm = TRUE)
 #' )
+#'
 #' # rename columns
 #' colnames(df_covid_uk)[colnames(df_covid_uk) == "cases_new"] <- "cases"
 #' colnames(df_covid_uk)[colnames(df_covid_uk) == "deaths_new"] <- "deaths"
 #'
 #' df_covid_uk_subset <- subset(df_covid_uk, date <= "2020-05-31")
 #'
-#' onset_to_death_covid <- epidist_db(
+#' # load epidist object from {epiparameter}
+#' onset_to_death_covid <- epiparameter::epidist_db(
 #'   disease = "COVID-19",
 #'   epi_dist = "onset_to_death",
 #'   author = "Linton_etal"
@@ -139,12 +143,11 @@ estimate_reporting <- function(data,
   # here, the estimate called "severity_me" translates to "reporting_me"
   # and the estimate "severity_hi" translates to "reporting_lo"
   # TODO: check if this is correct
-  df_out <- data.frame(
-    reporting_me = df_out$severity_me,
-    reporting_lo = df_out$severity_hi,
-    reporting_hi = df_out$severity_lo
+  df_out <- as.data.frame(df_out,
+    row.names = NULL,
+    col.names = c("reporting_me", "reporting_hi", "reporting_lo")
   )
 
-  # return data
-  df_out
+  # return data with columns in correct order
+  df_out[, c("reporting_me", "reporting_lo", "reporting_hi")]
 }
diff --git a/R/estimate_time_varying.R b/R/estimate_time_varying.R
index 54c47a5f..6386b492 100644
--- a/R/estimate_time_varying.R
+++ b/R/estimate_time_varying.R
@@ -33,32 +33,48 @@
 #' @export
 #'
 #' @examples
-#' # load package data
-#' data("ebola1976")
+#' # get data pre-loaded with {incidence2}
+#' df_covid_uk <- incidence2::covidregionaldataUK
 #'
-#' # get an onset to death distribution from the {epiparameter} package
-#' onset_to_death_ebola <- epiparameter::epidist_db(
-#'   disease = "Ebola Virus Disease",
+#' # aggregate the covid data for the UK as a whole
+#' df_covid_uk <- aggregate(
+#'   data = df_covid_uk,
+#'   x = cbind(cases_new, deaths_new) ~ date,
+#'   FUN = function(x) sum(x, na.rm = TRUE)
+#' )
+#'
+#' # rename columns
+#' colnames(df_covid_uk)[colnames(df_covid_uk) == "cases_new"] <- "cases"
+#' colnames(df_covid_uk)[colnames(df_covid_uk) == "deaths_new"] <- "deaths"
+#'
+#' df_covid_uk_subset <- subset(df_covid_uk, date <= "2020-05-31")
+#'
+#' # load epidist object from {epiparameter}
+#' onset_to_death_covid <- epiparameter::epidist_db(
+#'   disease = "COVID-19",
 #'   epi_dist = "onset_to_death",
-#'   author = "Barry_etal"
+#'   author = "Linton_etal"
 #' )
 #'
 #' # estimate time varying severity without correcting for delays
-#' estimate_time_varying(
-#'   data = ebola1976,
+#' cfr_time_varying <- estimate_time_varying(
+#'   data = df_covid_uk_subset,
 #'   smooth_inputs = TRUE,
 #'   burn_in_value = 7L,
 #'   correct_for_delays = FALSE
 #' )
+#' # View
+#' tail(cfr_time_varying)
 #'
 #' # estimate time varying severity while correcting for delays
-#' estimate_time_varying(
-#'   data = ebola1976,
-#'   epi_dist = onset_to_death_ebola,
+#' cfr_time_varying <- estimate_time_varying(
+#'   data = df_covid_uk_subset,
+#'   epi_dist = onset_to_death_covid,
 #'   smooth_inputs = TRUE,
 #'   burn_in_value = 7L,
 #'   correct_for_delays = TRUE
 #' )
+#' tail(cfr_time_varying)
 #'
 estimate_time_varying <- function(data,
                                   epi_dist = NULL,
diff --git a/R/prepare_data.R b/R/prepare_data.R
index 404d2c93..c92656f0 100644
--- a/R/prepare_data.R
+++ b/R/prepare_data.R
@@ -67,7 +67,7 @@ prepare_data <- function(data, ...) {
 #' )
 #'
 #' # View head of prepared data
-#' head(
+#' tail(
 #'   prepare_data(
 #'     covid_uk_incidence,
 #'     cases_variable = "cases_new",
diff --git a/man/estimate_reporting.Rd b/man/estimate_reporting.Rd
index c2676d8e..3debe98a 100644
--- a/man/estimate_reporting.Rd
+++ b/man/estimate_reporting.Rd
@@ -87,20 +87,24 @@ ascertainment estimate is calculated as the ratio of the baseline severity
 estimate and the delay-adjusted severity estimate
 }
 \examples{
-library(cfr)
-library(epiparameter)
-library(covidregionaldata)
+# get data pre-loaded with {incidence2}
+df_covid_uk <- incidence2::covidregionaldataUK
 
-df_covid_uk <- get_national_data(
-  countries = "united kingdom", source = "who", verbose = FALSE
+# aggregate the covid data for the UK as a whole
+df_covid_uk <- aggregate(
+  data = df_covid_uk,
+  x = cbind(cases_new, deaths_new) ~ date,
+  FUN = function(x) sum(x, na.rm = TRUE)
 )
+
 # rename columns
 colnames(df_covid_uk)[colnames(df_covid_uk) == "cases_new"] <- "cases"
 colnames(df_covid_uk)[colnames(df_covid_uk) == "deaths_new"] <- "deaths"
 
 df_covid_uk_subset <- subset(df_covid_uk, date <= "2020-05-31")
 
-onset_to_death_covid <- epidist_db(
+# load epidist object from {epiparameter}
+onset_to_death_covid <- epiparameter::epidist_db(
   disease = "COVID-19",
   epi_dist = "onset_to_death",
   author = "Linton_etal"
diff --git a/man/estimate_time_varying.Rd b/man/estimate_time_varying.Rd
index dc6098c2..5422e388 100644
--- a/man/estimate_time_varying.Rd
+++ b/man/estimate_time_varying.Rd
@@ -70,31 +70,47 @@ distribution representing the delay between case detection and death, then
 a case fatality ratio over time is estimated
 }
 \examples{
-# load package data
-data("ebola1976")
+# get data pre-loaded with {incidence2}
+df_covid_uk <- incidence2::covidregionaldataUK
 
-# get an onset to death distribution from the {epiparameter} package
-onset_to_death_ebola <- epiparameter::epidist_db(
-  disease = "Ebola Virus Disease",
+# aggregate the covid data for the UK as a whole
+df_covid_uk <- aggregate(
+  data = df_covid_uk,
+  x = cbind(cases_new, deaths_new) ~ date,
+  FUN = function(x) sum(x, na.rm = TRUE)
+)
+
+# rename columns
+colnames(df_covid_uk)[colnames(df_covid_uk) == "cases_new"] <- "cases"
+colnames(df_covid_uk)[colnames(df_covid_uk) == "deaths_new"] <- "deaths"
+
+df_covid_uk_subset <- subset(df_covid_uk, date <= "2020-05-31")
+
+# load epidist object from {epiparameter}
+onset_to_death_covid <- epiparameter::epidist_db(
+  disease = "COVID-19",
   epi_dist = "onset_to_death",
-  author = "Barry_etal"
+  author = "Linton_etal"
 )
 
 # estimate time varying severity without correcting for delays
-estimate_time_varying(
-  data = ebola1976,
+cfr_time_varying <- estimate_time_varying(
+  data = df_covid_uk_subset,
   smooth_inputs = TRUE,
   burn_in_value = 7L,
   correct_for_delays = FALSE
 )
+# View
+tail(cfr_time_varying)
 
 # estimate time varying severity while correcting for delays
-estimate_time_varying(
-  data = ebola1976,
-  epi_dist = onset_to_death_ebola,
+cfr_time_varying <- estimate_time_varying(
+  data = df_covid_uk_subset,
+  epi_dist = onset_to_death_covid,
   smooth_inputs = TRUE,
   burn_in_value = 7L,
   correct_for_delays = TRUE
 )
+tail(cfr_time_varying)
 
 }
diff --git a/man/prepare_data.incidence2.Rd b/man/prepare_data.incidence2.Rd
index 5de2c8dd..48114cb9 100644
--- a/man/prepare_data.incidence2.Rd
+++ b/man/prepare_data.incidence2.Rd
@@ -30,7 +30,7 @@ data.}
 }
 \value{
 A data.frame suitable for disease severity estimation functions
-provided in \code{{cfr}}, with the columns "date", "cases", and "deaths".
+provided in \pkg{cfr}, with the columns "date", "cases", and "deaths".
 Note that groups in \verb{<incidence2>} are not retained, and cases and deaths
 are summed by date.
 The result has a continuous sequence of dates between the start and end date
@@ -68,7 +68,7 @@ covid_uk_incidence <- incidence2::incidence(
 )
 
 # View head of prepared data
-head(
+tail(
   prepare_data(
     covid_uk_incidence,
     cases_variable = "cases_new",
diff --git a/tests/testthat/_snaps/estimate_reporting.md b/tests/testthat/_snaps/estimate_reporting.md
index aa86e4fe..9c1fbdcb 100644
--- a/tests/testthat/_snaps/estimate_reporting.md
+++ b/tests/testthat/_snaps/estimate_reporting.md
@@ -43,6 +43,6 @@
     Code
       reporting_estimate
     Output
-         reporting_me reporting_lo reporting_hi
-      73        0.014        0.014         0.56
+        reporting_me reporting_lo reporting_hi
+      1        0.014        0.014         0.56