diff --git a/DESCRIPTION b/DESCRIPTION
index 13f82df..6885a54 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -20,6 +20,7 @@ Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 Imports:
+    checkmate,
     cli,
     copula,
     dplyr,
@@ -51,9 +52,10 @@ Suggests:
     tidyr,
     roxygen2,
     usethis,
-    testthat,
+    testthat (>= 3.0.0),
     rcmdcheck,
-    httptest
+    httptest,
+    withr
 VignetteBuilder:
     knitr
 URL: https://cdcgov.github.io/forecasttools, https://github.com/CDCgov/forecasttools
@@ -63,3 +65,4 @@ LazyData: true
 Remotes:
     hubverse-org/hubData
 BugReports: https://github.com/CDCgov/forecasttools/issues
+Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index 6b37075..1d612ed 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(bottom_up_aggregation)
+export(categorize_vector)
 export(copula2tbl)
 export(count_trajectories)
 export(create_table_for_scoring)
@@ -13,6 +14,7 @@ export(gather_hub_forecast_data)
 export(gather_location_data)
 export(gather_target_data)
 export(get_hubverse_table)
+export(get_prism_cutpoints)
 export(inferencedata_to_tidy_draws)
 export(location_lookup)
 export(nhsn_soda_query)
diff --git a/R/categorize_prism.R b/R/categorize_prism.R
new file mode 100644
index 0000000..32bbbce
--- /dev/null
+++ b/R/categorize_prism.R
@@ -0,0 +1,75 @@
+#' Get PRISM activity level cutpoints for given
+#' diseases and locations
+#'
+#' @param diseases disease(s) for which to return the cutpoints.
+#' One of `"ARI"`, `"COVID-19"`, `"Influenza"`, or `"RSV"`, or
+#' an array of those values.
+#' @param locations location(s) for which to return the cutpoints.
+#' A location two-letter abbreviation as in the `short_name`
+#' column of [forecasttools::us_location_table], or an array
+#' of those abbreviations.
+#' @return The cutpoints, as an ordered list of vectors.
+#'
+#' @examples
+#' get_prism_cutpoints("WA", "Influenza")
+#'
+#' get_prism_cutpoints(c("US", "WA"), "COVID-19")
+#'
+#' get_prism_cutpoints(c("US", "WA"), c("ARI", "RSV"))
+#' @export
+get_prism_cutpoints <- function(locations, diseases) {
+  checkmate::assert_names(
+    diseases,
+    subset.of =
+      dimnames(forecasttools::prism_thresholds)$disease,
+    what = "disease"
+  )
+  checkmate::assert_names(
+    locations,
+    subset.of =
+      dimnames(forecasttools::prism_thresholds)$location,
+    what = "location"
+  )
+
+  return(purrr::map2(locations, diseases, \(x, y) {
+    forecasttools::prism_thresholds[x, y, ]
+  }))
+}
+
+
+
+#' Categorize a vector of values into PRISM
+#' activity level bins.
+#'
+#' @param values values to categorize
+#' @param locations vector of locations of length equal to
+#' `values` or a single location for all `values`.
+#' @param diseases vector of diseases of length equal to
+#' `values` or a single disease for all `values`.
+#' @param prism_bin_names Bin names for the PRISM bins.
+#' in order from lowest to highest. Must be a vector of
+#' length 5. `list(prism_bin_names)` will be passed as the
+#' `label_sets` argument to [categorize_vector()].
+#' Defaults to the standard PRISM bin names in title case:
+#' `c("Very Low", "Low", "Moderate", "High", "Very High")`.
+#' @return A factor vector equal in length to `values` of
+#' the categories, as the output of [categorize_vector()].
+#' @export
+categorize_prism <- function(values,
+                             locations,
+                             diseases,
+                             prism_bin_names = c(
+                               "Very Low",
+                               "Low",
+                               "Moderate",
+                               "High",
+                               "Very High"
+                             )) {
+  cutpoints <- get_prism_cutpoints(locations, diseases)
+
+  return(categorize_vector(
+    values,
+    break_sets = cutpoints,
+    label_sets = list(prism_bin_names)
+  ))
+}
diff --git a/R/categorize_vector.R b/R/categorize_vector.R
new file mode 100644
index 0000000..c1fa340
--- /dev/null
+++ b/R/categorize_vector.R
@@ -0,0 +1,58 @@
+#' Catgeorize entries in a numeric vector according
+#' to a list of breakpoints and labels.
+#'
+#' Vectorized version of [base::cut()] that
+#' allows each entry in the vector to use different
+#' break points and labels.
+#'
+#' @param values Vector of values to categorize.
+#' @param break_sets sets of category breakpoints,
+#' with one set for each entry of `values`, as a list of
+#' numeric vectors. Alternatively, a single set of category
+#' breakpoints to use for all values, as a one-entry list
+#' containing a single numeric vector.
+#' @param label_sets sets of labels to associate,
+#' with the corresponding breakpoints in `break_sets`,
+#' with one set for each entry of `values`, as a list of
+#' character vectors. Alternatively, a single set of labels
+#' to use for all values, as a one-entry list containing a
+#' single character vector.
+#' @param include.lowest Passed to [base::cut()]. Default `TRUE`.
+#' @param order Passed to [base::cut()]. Default `TRUE`.
+#' @param right Passed to [base::cut()]. Default `TRUE`.
+#' @param ... Additional keyword arguments passed to
+#' [base::cut()].
+#' @return A categorized vector, as vector of ordered
+#' factors.
+#' @export
+categorize_vector <- function(values,
+                              break_sets,
+                              label_sets,
+                              include.lowest = TRUE, # nolint
+                              order = TRUE,
+                              right = TRUE,
+                              ...) {
+  n_vals <- length(values)
+
+  checkmate::assert_list(break_sets,
+    types = "numeric"
+  )
+  checkmate::assert_list(label_sets,
+    types = "character"
+  )
+  checkmate::qassert(break_sets, c("l1", glue::glue("l{n_vals}")))
+  checkmate::qassert(label_sets, c("l1", glue::glue("l{n_vals}")))
+
+  return(purrr::pmap_vec(
+    list(
+      x = values,
+      breaks = break_sets,
+      labels = label_sets,
+      include.lowest = include.lowest,
+      order = order,
+      right = right,
+      ...
+    ),
+    cut
+  ))
+}
diff --git a/R/data.R b/R/data.R
index c371a47..b57dae3 100644
--- a/R/data.R
+++ b/R/data.R
@@ -41,3 +41,25 @@
 #' A data frame with 40 rows and 20 columns:
 #' @source <data-raw/ex_inferencedata_dataframe.R>
 "ex_inferencedata_dataframe"
+
+#' PRISM respiratory virus activity level thresholds
+#'
+#' A multi-dimensional array with PRISM
+#' respiratory virus activity level thresholds.
+#' Dimensions, in order, are `location`,
+#' `disease`, and `breaks`.
+#'
+#' Values of `disease` are `Influenza`, `COVID-19`,
+#' `RSV`, and `ARI` (acute respiratory infections).
+#'
+#' Values of `breaks` are `prop_lower_bound`,
+#' `prop_low`, `prop_moderate`, `prop_high`,
+#' `prop_very_high`, and `prop_upper_bound`.
+#'
+#' Values of `location` are US jurisdictions
+#' and the United States as a whole, using
+#' USPS two-letter codes (the values of `short_name`)
+#' in [us_location_table].
+#'
+#' @source <data-raw/prism_thresholds.R>
+"prism_thresholds"
diff --git a/data-raw/prism_thresholds.R b/data-raw/prism_thresholds.R
new file mode 100644
index 0000000..fa657d9
--- /dev/null
+++ b/data-raw/prism_thresholds.R
@@ -0,0 +1,53 @@
+#' When there is an appropriate online endpoint for these
+#' thresholds, this this script will be updated to point there.
+#' For now, it reads from a bundled .tsv in inst/extdata
+
+thresholds <- readr::read_tsv(
+  fs::path(
+    "inst",
+    "extdata",
+    "prism_thresholds",
+    ext = "tsv"
+  ),
+  show_col_types = FALSE
+)
+
+## Transform thresholds from percentage to proprotion
+prop_thresholds <- thresholds |>
+  dplyr::transmute(
+    disease,
+    location = state_abb,
+    prop_lower_bound = 0,
+    prop_low = perc_level_low / 100,
+    prop_moderate = perc_level_moderate / 100,
+    prop_high = perc_level_high / 100,
+    prop_very_high = perc_level_very_high / 100,
+    prop_upper_bound = 1
+  )
+
+#' Transform thresholds from flat table to
+#' multi-dimensional array, via a nested list.
+#'
+#' Method for conversion to multi-dim array:
+#' 1. Transform the long-form tabular data to a
+#' nested named list (via nest() and deframe())
+#' 2. Transform the nested named list to a multi-dimensional
+#' array with dimension names (via simplify2array() and unlist())
+#' 3. Order and name the dimensions of that array.
+
+thresholds_nested_list <- prop_thresholds |>
+  tidyr::nest(breaks = dplyr::starts_with("prop_")) |>
+  tidyr::nest(loc_breaks = c(location, breaks)) |>
+  tibble::deframe() |>
+  purrr::map(deframe) # yields a nested list of tibbles
+
+prism_thresholds <- thresholds_nested_list |>
+  simplify2array() |> # yields a 2D array of length-1 tibbles
+  apply(1:2, unlist) |> # yields a 3D array
+  aperm(c(2, 3, 1)) # orders 3D array dimensions as desired
+
+names(dimnames(prism_thresholds)) <- c("location", "disease", "breaks")
+
+usethis::use_data(prism_thresholds,
+  overwrite = TRUE
+)
diff --git a/data/prism_thresholds.rda b/data/prism_thresholds.rda
new file mode 100644
index 0000000..e3db6f5
Binary files /dev/null and b/data/prism_thresholds.rda differ
diff --git a/inst/extdata/prism_thresholds.tsv b/inst/extdata/prism_thresholds.tsv
new file mode 100644
index 0000000..6657c52
--- /dev/null
+++ b/inst/extdata/prism_thresholds.tsv
@@ -0,0 +1,213 @@
+disease	state_abb	state_code	state_name	perc_level_low	perc_level_moderate	perc_level_high	perc_level_very_high
+Influenza	US	US	United States	0.339653993544693	1.92323278764894	3.5068115817532	5.09039037585745
+COVID-19	US	US	United States	0.601509069090881	1.70419326288427	2.80687745667766	3.90956165047104
+RSV	US	US	United States	0.0716383969527895	0.35553747427476	0.63943655159673	0.9233356289187
+ARI	US	US	United States	10.4648426922243	13.8181574261492	17.1714721600741	20.524786893999
+Influenza	AK	02	Alaska	0.980271843148604	3.27706539993575	5.57385895672289	7.87065251351003
+COVID-19	AK	02	Alaska	0.910853966945788	2.45917900621476	4.00750404548373	5.5558290847527
+RSV	AK	02	Alaska	0.108836758958285	0.776941155646192	1.4450455523341	2.11314994902201
+ARI	AK	02	Alaska	12.0325208608167	15.7896879739664	19.5468550871162	23.3040222002659
+Influenza	AL	01	Alabama	0.405774191534674	3.37678721933339	6.3478002471321	9.31881327493081
+COVID-19	AL	01	Alabama	0.721251202776328	2.46576569857216	4.210280194368	5.9547946901638404
+RSV	AL	01	Alabama	0.0549766487333309	0.48933369382138	0.923690738909429	1.35804778399748
+ARI	AL	01	Alabama	11.0707571553582	15.9540807957783	20.8374044361984	25.7207280766185
+Influenza	AR	05	Arkansas	0.407918012977306	2.61327017610197	4.81862233922664	7.02397450235131
+COVID-19	AR	05	Arkansas	0.45144584816555	1.40132733439487	2.35120882062419	3.30109030685351
+RSV	AR	05	Arkansas	0.0751933833915953	0.633327398862675	1.19146141433375	1.74959542980483
+ARI	AR	05	Arkansas	8.61531194725447	11.9832893748361	15.3512668024178	18.7192442299994
+Influenza	AZ	04	Arizona	0.416088204662123	2.14117700133523	3.86626579800833	5.59135459468143
+COVID-19	AZ	04	Arizona	0.84685830428958	2.16651677001998	3.48617523575039	4.80583370148079
+RSV	AZ	04	Arizona	0.0409779572441818	0.383211885015569	0.725445812786956	1.06767974055834
+ARI	AZ	04	Arizona	11.4533628016604	15.3024693224721	19.1515758432838	23.0006823640955
+Influenza	CA	06	California	0.226327183316214	1.69160209129706	3.15687699927791	4.62215190725875
+COVID-19	CA	06	California	0.558108660515558	1.73564597703968	2.9131832935638	4.09072061008792
+RSV	CA	06	California	0.0344265530499923	0.362341041789881	0.690255530529769	1.01817001926966
+ARI	CA	06	California	11.0228425209899	14.9338128652802	18.8447832095704	22.7557535538606
+Influenza	CO	08	Colorado	0.185509100201764	1.61218388046304	3.03885866072432	4.4655334409856
+COVID-19	CO	08	Colorado	0.697231245966627	1.74398516098764	2.79073907600865	3.83749299102966
+RSV	CO	08	Colorado	0.0431537609053263	0.668897107306689	1.29464045370805	1.92038380010942
+ARI	CO	08	Colorado	12.4096908218521	16.6268464364414	20.8440020510308	25.0611576656201
+Influenza	CT	09	Connecticut	0.20507343794368	2.16808276690581	4.13109209586794	6.09410142483008
+COVID-19	CT	09	Connecticut	0.56429242884738	1.78858048168432	3.01286853452126	4.23715658735821
+RSV	CT	09	Connecticut	0.027742021145623	0.254310764673641	0.48087950820166	0.707448251729679
+ARI	CT	09	Connecticut	12.183243737408	16.562508840949	20.94177394449	25.3210390480311
+Influenza	DC	11	District of Columbia	0.159476150800101	1.86715984989864	3.57484354899718	5.28252724809572
+COVID-19	DC	11	District of Columbia	0.526433374147159	1.7120674978765	2.89770162160584	4.08333574533518
+RSV	DC	11	District of Columbia	0.098625078652519	0.601922910551889	1.10522074245126	1.60851857435063
+ARI	DC	11	District of Columbia	11.2120287686789	15.15450620356	19.0969836384412	23.0394610733224
+Influenza	DE	10	Delaware	0.109073949476632	2.17023351649464	4.23139308351265	6.29255265053066
+COVID-19	DE	10	Delaware	0.500070197051349	1.50823954874698	2.51640890044261	3.52457825213824
+RSV	DE	10	Delaware	0.0929108216428634	0.723376592241813	1.35384236284076	1.98430813343971
+ARI	DE	10	Delaware	9.86691707533298	13.6141171814386	17.3613172875442	21.1085173936498
+Influenza	FL	12	Florida	0.836011839170292	2.08527355145154	3.3345352637328	4.58379697601405
+COVID-19	FL	12	Florida	1.1049827880024	2.55967138225653	4.01435997651066	5.46904857076479
+RSV	FL	12	Florida	0.090842972515709	0.276793454076287	0.462743935636866	0.648694417197444
+ARI	FL	12	Florida	13.682909199968	16.0678095777651	18.4527099555622	20.8376103333594
+Influenza	GA	13	Georgia	0.332114011595581	2.16634716346359	4.0005803153316	5.83481346719961
+COVID-19	GA	13	Georgia	0.448875073863044	1.20225812238962	1.95564117091619	2.70902421944277
+RSV	GA	13	Georgia	0.0464097808652688	0.228273481893565	0.410137182921861	0.592000883950157
+ARI	GA	13	Georgia	8.78163783501836	12.0715811692079	15.3615245033975	18.651467837587
+Influenza	GU	66	Guam	1.36987145456657	2.3958246435565	3.42177783254644	4.44773102153638
+COVID-19	GU	66	Guam	1.70022224239727	4.32394716960283	6.94767209680839	9.57139702401395
+RSV	GU	66	Guam	0.0434653845684039	0.264617599911222	0.48576981525404	0.706922030596857
+ARI	GU	66	Guam	14.7132748188422	17.539021154782102	20.3647674907219	23.1905138266618
+Influenza	HI	15	Hawaii	1.20634438328273	2.05152708478552	2.89670978628831	3.7418924877911
+COVID-19	HI	15	Hawaii	1.3848702406628	2.95422375691452	4.52357727316624	6.09293078941796
+RSV	HI	15	Hawaii	0.0691583103604069	0.454787996872479	0.840417683384552	1.22604736989662
+ARI	HI	15	Hawaii	15.8733762946673	17.6805026947188	19.4876290947704	21.2947554948219
+Influenza	IA	19	Iowa	0.310883509962675	2.24681588824838	4.18274826653409	6.1186806448198
+COVID-19	IA	19	Iowa	0.705216900119656	1.94461409079116	3.18401128146266	4.42340847213416
+RSV	IA	19	Iowa	0.114465550559083	0.617363744292784	1.12026193802648	1.62316013176018
+ARI	IA	19	Iowa	11.3216203348727	15.2047392453378	19.0878581558028	22.9709770662679
+Influenza	ID	16	Idaho	0.173518705997438	2.21977276605722	4.266026826117	6.31228088617678
+COVID-19	ID	16	Idaho	0.517549129078217	1.88616706393311	3.254784998788	4.62340293364289
+RSV	ID	16	Idaho	0.0452887440033941	0.789012847272541	1.53273695054169	2.27646105381083
+ARI	ID	16	Idaho	9.26000256906029	13.2441844464682	17.2283663238762	21.2125482012841
+Influenza	IL	17	Illinois	0.219024609883691	2.67487242237358	5.13072023486347	7.58656804735336
+COVID-19	IL	17	Illinois	0.629502061203994	1.64890993897819	2.66831781675239	3.68772569452659
+RSV	IL	17	Illinois	0.0807410991366061	0.56458990601095	1.04843871288529	1.53228751975964
+ARI	IL	17	Illinois	9.85408533909644	14.0655294261757	18.276973513255	22.4884176003343
+Influenza	IN	18	Indiana	0.190640113402513	2.95231071385513	5.71398131430775	8.47565191476036
+COVID-19	IN	18	Indiana	0.439965735656953	1.72767209988354	3.01537846411012	4.3030848283367
+RSV	IN	18	Indiana	0.0737012308096612	0.468568107412954	0.863434984016248	1.25830186061954
+ARI	IN	18	Indiana	9.89493187733481	15.2034842359254	20.512036594516	25.8205889531065
+Influenza	KS	20	Kansas	0.160408294434422	2.10476923765141	4.0491301808684	5.99349112408539
+COVID-19	KS	20	Kansas	0.355420758935411	1.55048130444068	2.74554184994595	3.94060239545123
+RSV	KS	20	Kansas	0.0500403520982661	0.435440231919359	0.820840111740451	1.20623999156154
+ARI	KS	20	Kansas	9.38425870686798	14.0270751464575	18.6698915860469	23.3127080256364
+Influenza	KY	21	Kentucky	0.269116155766922	3.49935688483254	6.72959761389816	9.95983834296378
+COVID-19	KY	21	Kentucky	0.909576325747603	2.17315690355804	3.43673748136848	4.70031805917892
+RSV	KY	21	Kentucky	0.068022259600647	0.560698318234278	1.05337437686791	1.54605043550154
+ARI	KY	21	Kentucky	12.0384252216093	16.629820375167	21.2212155287246	25.8126106822823
+Influenza	LA	22	Louisiana	0.525503676488859	2.92567479862962	5.32584592077039	7.72601704291115
+COVID-19	LA	22	Louisiana	0.654250121285289	2.6066092423007	4.55896836331611	6.51132748433152
+RSV	LA	22	Louisiana	0.0510041480553787	0.446905958630119	0.842807769204859	1.2387095797796
+ARI	LA	22	Louisiana	9.57073099615822	13.4692855010584	17.3678400059585	21.2663945108586
+Influenza	MA	25	Massachusetts	0.214661386007536	2.07116867971577	3.927675973424	5.78418326713223
+COVID-19	MA	25	Massachusetts	0.651185991880751	1.82948468746477	3.0077833830488	4.18608207863282
+RSV	MA	25	Massachusetts	0.0670209420307515	0.665273786348602	1.26352663066645	1.8617794749843
+ARI	MA	25	Massachusetts	10.1755348538362	13.5477614985896	16.919988143343	20.2922147880964
+Influenza	MD	24	Maryland	0.166566858410208	2.04985748878942	3.93314811916863	5.81643874954784
+COVID-19	MD	24	Maryland	0.546305741007218	1.60834266260778	2.67037958420835	3.73241650580891
+RSV	MD	24	Maryland	0.0591043672668986	0.613415081927477	1.16772579658805	1.72203651124863
+ARI	MD	24	Maryland	8.50635457808995	11.8662937603706	15.2262329426513	18.5861721249319
+Influenza	ME	23	Maine	0.156056147912831	2.72861981621678	5.30118348452072	7.87374715282467
+COVID-19	ME	23	Maine	0.69510471408335	1.76495953583068	2.83481435757801	3.90466917932534
+RSV	ME	23	Maine	0.0345178372991826	0.51783502909311	1.00115222088704	1.48446941268097
+ARI	ME	23	Maine	10.602944735988	14.718016839629	18.8330889432699	22.9481610469109
+Influenza	MI	26	Michigan	0.198899383681348	2.44068490779078	4.68247043190021	6.92425595600964
+COVID-19	MI	26	Michigan	0.564815218447782	1.62113070346039	2.677446188473	3.7337616734856
+RSV	MI	26	Michigan	0.0391618200145449	0.615730594729324	1.1922993694441	1.76886814415888
+ARI	MI	26	Michigan	9.32339611319278	13.414057815787	17.5047195183812	21.5953812209754
+Influenza	MN	27	Minnesota	0.227893615527177	3.04451571726336	5.86113781899954	8.67775992073573
+COVID-19	MN	27	Minnesota	0.586214555636831	1.57378125785207	2.56134796006731	3.54891466228255
+RSV	MN	27	Minnesota	0.0905385802732976	0.666491269096988	1.24244395792068	1.81839664674437
+ARI	MN	27	Minnesota	10.4202177684303	14.9752756858733	19.5303336033163	24.0853915207593
+Influenza	MO	29	Missouri	0.0020277041100999	0.0508479335129959	0.099668162915892	0.148488392318788
+COVID-19	MO	29	Missouri	0.00793462807198301	0.0312817613746453	0.0546288946773075	0.0779760279799697
+RSV	MO	29	Missouri	7.71391329087799e-4	0.0178666474123378	0.0349619034955877	0.0520571595788377
+ARI	MO	29	Missouri	0.146132016728597	0.244833011704006	0.343534006679416	0.442235001654825
+Influenza	MS	28	Mississippi	0.458529612426411	3.04871049911109	5.63889138579577	8.22907227248045
+COVID-19	MS	28	Mississippi	0.6964788841527	2.14702473660205	3.59757058905141	5.04811644150076
+RSV	MS	28	Mississippi	0.0532949022520541	0.350134209266904	0.646973516281753	0.943812823296603
+ARI	MS	28	Mississippi	11.0886183437294	15.3454286875145	19.6022390312996	23.8590493750847
+Influenza	MT	30	Montana	0.10444777891344	2.56765207350132	5.0308563680892	7.49406066267708
+COVID-19	MT	30	Montana	0.818830191937599	2.03213980315244	3.24544941436727	4.45875902558211
+RSV	MT	30	Montana	0.0544391758221658	0.569642941188658	1.08484670655515	1.60005047192164
+ARI	MT	30	Montana	10.2923694704229	14.0093412053813	17.7263129403396	21.443284675298
+Influenza	NC	37	North Carolina	0.53944866721049	3.91000410366721	7.28055954012393	10.6511149765807
+COVID-19	NC	37	North Carolina	0.957637497938248	2.34620243383482	3.73476736973139	5.12333230562796
+RSV	NC	37	North Carolina	0.052673744506241	0.41527639169893	0.77787903889162	1.14048168608431
+ARI	NC	37	North Carolina	12.5462641511737	17.6298354412814	22.713406731389	27.7969780214967
+Influenza	ND	38	North Dakota	0.112930343017489	2.62452986279678	5.13612938257607	7.64772890235537
+COVID-19	ND	38	North Dakota	0.699611618838267	1.867781287173	3.03595095550773	4.20412062384246
+RSV	ND	38	North Dakota	0.0646653141036112	0.819442963648635	1.57422061319366	2.32899826273868
+ARI	ND	38	North Dakota	9.4044814603834	13.8762153012153	18.3479491420471	22.819682982879
+Influenza	NE	31	Nebraska	0.142123247520877	2.04338657077044	3.94464989402	5.84591321726956
+COVID-19	NE	31	Nebraska	0.308022398798573	1.3132531948107	2.31848399082282	3.32371478683494
+RSV	NE	31	Nebraska	0.0601351478758112	0.499493821357885	0.938852494839959	1.37821116832203
+ARI	NE	31	Nebraska	7.89373963676057	11.3460008242845	14.7982620118084	18.2505231993323
+Influenza	NH	33	New Hampshire	0.187345932755749	2.07974215749176	3.97213838222778	5.86453460696379
+COVID-19	NH	33	New Hampshire	0.555696355548634	1.47316381552908	2.39063127550954	3.30809873548999
+RSV	NH	33	New Hampshire	0.0452020647941643	0.439866751578225	0.834531438362285	1.22919612514635
+ARI	NH	33	New Hampshire	8.14785825720061	11.5925514918363	15.037244726472	18.4819379611078
+Influenza	NJ	34	New Jersey	0.321586577636629	2.11829624218902	3.91500590674142	5.71171557129381
+COVID-19	NJ	34	New Jersey	0.429078579510625	1.40677814171534	2.38447770392005	3.36217726612477
+RSV	NJ	34	New Jersey	0.0501750296729801	0.326255204094114	0.602335378515248	0.878415552936382
+ARI	NJ	34	New Jersey	9.36969876260443	12.8014294345102	16.2331601064161	19.6648907783219
+Influenza	NM	35	New Mexico	0.378159575817063	3.45109157421842	6.52402357261978	9.59695557102114
+COVID-19	NM	35	New Mexico	1.20519051983787	2.8005977044009	4.39600488896392	5.99141207352695
+RSV	NM	35	New Mexico	0.0948330729151471	0.948925940795856	1.80301880867657	2.65711167655727
+ARI	NM	35	New Mexico	14.2370777902424	19.5583117690265	24.8795457478107	30.2007797265949
+Influenza	NV	32	Nevada	0.331848165602097	1.74076298114575	3.14967779668941	4.55859261223307
+COVID-19	NV	32	Nevada	0.554330593283608	1.9522803599949	3.35023012670618	4.74817989341747
+RSV	NV	32	Nevada	0.0386034631585323	0.455543135704951	0.87248280825137	1.28942248079779
+ARI	NV	32	Nevada	12.6346692559538	16.9407175282059	21.2467658004581	25.5528140727103
+Influenza	NY	36	New York	0.239330551323172	1.4905015902329	2.74167262914263	3.99284366805236
+COVID-19	NY	36	New York	0.450918945714812	1.26656212007167	2.08220529442853	2.89784846878539
+RSV	NY	36	New York	0.0353350085642889	0.297119813124248	0.558904617684206	0.820689422244165
+ARI	NY	36	New York	8.88556129222044	12.1839014529949	15.4822416137694	18.7805817745438
+Influenza	OH	39	Ohio	0.133365036676197	2.07837795667362	4.0233908766710504	5.96840379666847
+COVID-19	OH	39	Ohio	0.428391015969636	1.5444863442969	2.66058167262416	3.77667700095142
+RSV	OH	39	Ohio	0.031697956459707	0.347938585023004	0.6641792135863	0.980419842149597
+ARI	OH	39	Ohio	8.94046717769431	12.977330814341	17.0141944509877	21.0510580876345
+Influenza	OK	40	Oklahoma	0.194519088918796	2.26880517554912	4.34309126217945	6.41737734880977
+COVID-19	OK	40	Oklahoma	0.451722004139253	1.60750109481465	2.76328018549005	3.91905927616545
+RSV	OK	40	Oklahoma	0.0665630227990645	0.505625255974784	0.944687489150504	1.38374972232622
+ARI	OK	40	Oklahoma	9.64048917383551	13.9926778536221	18.3448665334086	22.6970552131952
+Influenza	OR	41	Oregon	0.184733555894893	2.63694594852427	5.08915834115365	7.54137073378303
+COVID-19	OR	41	Oregon	0.721723680396138	1.90299855149265	3.08427342258916	4.26554829368567
+RSV	OR	41	Oregon	0.0369486523403626	0.518469000442146	0.999989348543929	1.48150969664571
+ARI	OR	41	Oregon	10.2315408639826	14.4130813884515	18.5946219129204	22.7761624373892
+Influenza	PA	42	Pennsylvania	0.109848618133185	1.66112000752232	3.21239139691146	4.7636627863006
+COVID-19	PA	42	Pennsylvania	0.400993530421904	1.22890460427557	2.05681567812923	2.88472675198289
+RSV	PA	42	Pennsylvania	0.0332074441883945	0.402715867407431	0.772224290626467	1.1417327138455
+ARI	PA	42	Pennsylvania	7.64881877824774	10.8527592171087	14.0566996559697	17.2606400948307
+Influenza	RI	44	Rhode Island	0.160757569379592	1.63884952386386	3.11694147834813	4.5950334328324
+COVID-19	RI	44	Rhode Island	0.42262534696292	1.33998691303549	2.25734847910806	3.17471004518063
+RSV	RI	44	Rhode Island	0.0352095675786539	0.351174190651514	0.667138813724374	0.983103436797233
+ARI	RI	44	Rhode Island	8.22210598236906	11.3953545332548	14.5686030841405	17.7418516350263
+Influenza	SC	45	South Carolina	0.366504838565552	3.3035836912866	6.24066254400765	9.1777413967287
+COVID-19	SC	45	South Carolina	0.779678647853532	2.21360504445429	3.64753144105504	5.0814578376558
+RSV	SC	45	South Carolina	0.0437758036567582	0.349666927241627	0.655558050826495	0.961449174411364
+ARI	SC	45	South Carolina	11.2393482868308	16.1646313545215	21.0899144222122	26.0151974899029
+Influenza	SD	46	South Dakota	0.107663520150527	1.4517450637419	2.79582660733327	4.13990815092464
+COVID-19	SD	46	South Dakota	0.463047734076824	1.25261668291056	2.04218563174429	2.83175458057803
+RSV	SD	46	South Dakota	0.0556838174146855	0.429426543281552	0.803169269148418	1.17691199501528
+ARI	SD	46	South Dakota	7.09293991662318	9.85081799618781	12.6086960757524	15.3665741553171
+Influenza	TN	47	Tennessee	0.332859566854169	2.79116438433375	5.24946920181332	7.7077740192929
+COVID-19	TN	47	Tennessee	0.738046426680436	1.92739752851377	3.1167486303471	4.30609973218043
+RSV	TN	47	Tennessee	0.0743188919664283	0.477605612759586	0.880892333552745	1.2841790543459
+ARI	TN	47	Tennessee	11.159036448309	15.2813460920594	19.4036557358097	23.5259653795601
+Influenza	TX	48	Texas	0.520174196798285	2.54353442584249	4.5668946548867	6.59025488393091
+COVID-19	TX	48	Texas	0.565954504897554	2.0777207241502	3.58948694340284	5.10125316265549
+RSV	TX	48	Texas	0.0816356369911876	0.486860055469191	0.892084473947194	1.2973088924252
+ARI	TX	48	Texas	11.6516815193029	15.4725592639722	19.2934370086415	23.1143147533108
+Influenza	UT	49	Utah	0.204051288549113	2.02694474679294	3.84983820503676	5.67273166328058
+COVID-19	UT	49	Utah	0.801491794024371	2.27392276043664	3.7463537268489	5.21878469326117
+RSV	UT	49	Utah	0.0453125500085216	0.885549389631906	1.72578622925529	2.56602306887868
+ARI	UT	49	Utah	10.8931248279202	14.916684224283	18.9402436206458	22.9638030170086
+Influenza	VA	51	Virginia	0.269996393322792	2.46251085628429	4.65502531924579	6.84753978220729
+COVID-19	VA	51	Virginia	0.613311182232651	1.96038096836806	3.30745075450347	4.65452054063888
+RSV	VA	51	Virginia	0.0554625861287223	0.332493376801982	0.609524167475242	0.886554958148502
+ARI	VA	51	Virginia	12.1658602931845	16.7785328242972	21.3912053554099	26.0038778865227
+Influenza	VT	50	Vermont	0.161478759526905	2.50512968820188	4.84878061687685	7.19243154555183
+COVID-19	VT	50	Vermont	0.575327309281069	1.50341128784659	2.43149526641212	3.35957924497764
+RSV	VT	50	Vermont	0.0838052625684766	0.829157176714467	1.57450909086046	2.31986100500645
+ARI	VT	50	Vermont	10.9177788316109	15.0677948691483	19.2178109066857	23.3678269442231
+Influenza	WA	53	Washington	0.259309741488702	3.02574862465439	5.79218750782007	8.55862639098575
+COVID-19	WA	53	Washington	0.871115374009525	2.2004050199449	3.52969466588027	4.85898431181564
+RSV	WA	53	Washington	0.0455148517416957	0.61158985349889	1.17766485525608	1.74373985701328
+ARI	WA	53	Washington	12.2895232596611	17.3144751055576	22.3394269514541	27.3643787973506
+Influenza	WI	55	Wisconsin	0.140496439155569	1.76157489086172	3.38265334256787	5.00373179427401
+COVID-19	WI	55	Wisconsin	0.527507482921962	1.43534356986904	2.34317965681611	3.25101574376319
+RSV	WI	55	Wisconsin	0.0457745963981928	0.416852705416996	0.7879308144358	1.1590089234546
+ARI	WI	55	Wisconsin	10.604721131927	14.4005766476894	18.1964321634519	21.9922876792143
+Influenza	WV	54	West Virginia	0.125027003643313	2.35494682914699	4.58486665465066	6.81478648015433
+COVID-19	WV	54	West Virginia	0.57679671134205	1.98858985883762	3.4003830063332	4.81217615382877
+RSV	WV	54	West Virginia	0.072610173823666	0.87339747551367	1.67418477720367	2.47497207889368
+ARI	WV	54	West Virginia	11.0197618734323	15.6814338737168	20.3431058740013	25.0047778742858
+Influenza	WY	56	Wyoming	0.18207320882403	2.03349714897363	3.88492108912323	5.73634502927283
+COVID-19	WY	56	Wyoming	0.791321117375274	2.25557531691173	3.7198295164482	5.18408371598466
+RSV	WY	56	Wyoming	0.067830399921913	0.85301183737459	1.63819327482727	2.42337471227994
+ARI	WY	56	Wyoming	9.62666199404063	13.6752626336958	17.723863273351	21.7724639130062
diff --git a/man/categorize_vector.Rd b/man/categorize_vector.Rd
new file mode 100644
index 0000000..514c71b
--- /dev/null
+++ b/man/categorize_vector.Rd
@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/categorize_vector.R
+\name{categorize_vector}
+\alias{categorize_vector}
+\title{Catgeorize entries in a numeric vector according
+to a list of breakpoints and labels.}
+\usage{
+categorize_vector(
+  values,
+  break_sets,
+  label_sets,
+  include.lowest = TRUE,
+  order = TRUE,
+  right = TRUE,
+  ...
+)
+}
+\arguments{
+\item{values}{Vector of values to categorize.}
+
+\item{break_sets}{sets of category breakpoints,
+with one set for each entry of \code{values}, as a list of
+numeric vectors. Alternatively, a single set of category
+breakpoints to use for all values, as a one-entry list
+containing a single numeric vector.}
+
+\item{label_sets}{sets of labels to associate,
+with the corresponding breakpoints in \code{break_sets},
+with one set for each entry of \code{values}, as a list of
+character vectors. Alternatively, a single set of labels
+to use for all values, as a one-entry list containing a
+single character vector.}
+
+\item{include.lowest}{Passed to \code{\link[base:cut]{base::cut()}}. Default \code{TRUE}.}
+
+\item{order}{Passed to \code{\link[base:cut]{base::cut()}}. Default \code{TRUE}.}
+
+\item{right}{Passed to \code{\link[base:cut]{base::cut()}}. Default \code{TRUE}.}
+
+\item{...}{Additional keyword arguments passed to
+\code{\link[base:cut]{base::cut()}}.}
+}
+\value{
+A categorized vector, as vector of ordered
+factors.
+}
+\description{
+Vectorized version of \code{\link[base:cut]{base::cut()}} that
+allows each entry in the vector to use different
+break points and labels.
+}
diff --git a/man/get_prism_cutpoints.Rd b/man/get_prism_cutpoints.Rd
new file mode 100644
index 0000000..25cde3d
--- /dev/null
+++ b/man/get_prism_cutpoints.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/categorize_prism.R
+\name{get_prism_cutpoints}
+\alias{get_prism_cutpoints}
+\title{Get PRISM activity level cutpoints for given
+diseases and locations}
+\usage{
+get_prism_cutpoints(locations, diseases)
+}
+\arguments{
+\item{locations}{location(s) for which to return the cutpoints.
+A location two-letter abbreviation as in the \code{short_name}
+column of \link{us_location_table}, or an array
+of such abbreviations.}
+
+\item{diseases}{disease(s) for which to return the cutpoints.
+One of \code{"ARI"}, \code{"COVID-19"}, \code{"Influenza"}, or \code{"RSV"}, or
+an array of those values.}
+}
+\value{
+The cutpoints, as a single vector or array of vectors.
+}
+\description{
+Get PRISM activity level cutpoints for given
+diseases and locations
+}
+\details{
+Note that if both \code{locations} and \code{diseases} are vectors,
+the result will be a 3-dimensional array whose first dimension
+is location, second is disease, and third is the cutpoints.
+}
+\examples{
+get_prism_cutpoints("WA", "Influenza")
+
+get_prism_cutpoints(c("US", "WA"), "COVID-19")
+
+get_prism_cutpoints(c("US", "WA"), c("ARI", "RSV"))
+}
diff --git a/man/prism_thresholds.Rd b/man/prism_thresholds.Rd
new file mode 100644
index 0000000..d261d51
--- /dev/null
+++ b/man/prism_thresholds.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{prism_thresholds}
+\alias{prism_thresholds}
+\title{PRISM respiratory virus activity level thresholds}
+\format{
+An object of class \code{array} of dimension 53 x 4 x 6.
+}
+\source{
+<data-raw/prism_thresholds.R>
+}
+\usage{
+prism_thresholds
+}
+\description{
+A multi-dimensional array with PRISM
+respiratory virus activity level thresholds.
+Dimensions, in order, are \code{location},
+\code{disease}, and \code{breaks}.
+}
+\details{
+Values of \code{disease} are \code{Influenza}, \code{COVID-19},
+\code{RSV}, and \code{ARI} (acute respiratory infections).
+
+Values of \code{breaks} are \code{prop_lower_bound},
+\code{prop_low}, \code{prop_moderate}, \code{prop_high},
+\code{prop_very_high}, and \code{prop_upper_bound}.
+
+Values of \code{location} are US jurisdictions
+and the United States as a whole, using
+USPS two-letter codes (the values of \code{short_name})
+in \link{us_location_table}.
+}
+\keyword{datasets}
diff --git a/tests/testthat.R b/tests/testthat.R
new file mode 100644
index 0000000..a328e6d
--- /dev/null
+++ b/tests/testthat.R
@@ -0,0 +1,12 @@
+# This file is part of the standard setup for testthat.
+# It is recommended that you do not modify it.
+#
+# Where should you do additional test configuration?
+# Learn more about the roles of various files in:
+# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
+# * https://testthat.r-lib.org/articles/special-files.html
+
+library(testthat)
+library(forecasttools)
+
+test_check("forecasttools")
diff --git a/tests/testthat/test_categorize_prism.R b/tests/testthat/test_categorize_prism.R
new file mode 100644
index 0000000..0af71ac
--- /dev/null
+++ b/tests/testthat/test_categorize_prism.R
@@ -0,0 +1,75 @@
+test_that(paste0(
+  "get_prism_cutpoints() works identically ",
+  "to a manual read from the table "
+), {
+  locations <- names(forecasttools::prism_thresholds[, 1, 1])
+  diseases <- names(forecasttools::prism_thresholds[1, , 1])
+  locs_diseases <- tidyr::expand_grid(
+    locations,
+    diseases
+  )
+
+  purrr::pmap(
+    locs_diseases,
+    \(locations, diseases) {
+      result <- get_prism_cutpoints(locations, diseases)
+      expected <- forecasttools::prism_thresholds[
+        locations, diseases,
+      ]
+      expect_equal(result, expected)
+    }
+  )
+})
+
+test_that(paste0(
+  "get_prism_cutpoints() gives the expected ",
+  "values for particular examples"
+), {
+  test_cases <- list(
+    list(
+      diseases = c(
+        "Influenza"
+      ),
+      locations = c("US", "MA"),
+      expected = array(
+        c(
+          0, 0,
+          0.00339654, 0.002146614,
+          0.019232328, 0.020711687,
+          0.035068116, 0.03927676,
+          0.050903904, 0.057841833,
+          1, 1
+        ),
+        dim = c(2, 6),
+        dimnames = list(
+          location = c("US", "MA"),
+          breaks = c(
+            "prop_lower_bound",
+            "prop_low",
+            "prop_moderate",
+            "prop_high",
+            "prop_very_high",
+            "prop_upper_bound"
+          )
+        )
+      ),
+      list(
+        diseases = "RSV",
+        locations = "UT",
+        expected = c(
+          prop_lower_bound = 0,
+          prop_low = 0.0004531255,
+          prop_moderate = 0.0088554939,
+          prop_high = 0.0172578623,
+          prop_very_high = 0.0256602307,
+          prop_upper_bound = 1
+        )
+      )
+    )
+  )
+
+  purrr::map(test_cases, \(x) {
+    result <- get_prism_cutpoints(x$locations, x$diseases)
+    expect_equal(result, x$expected)
+  })
+})
diff --git a/tests/testthat/test_categorize_vector.R b/tests/testthat/test_categorize_vector.R
new file mode 100644
index 0000000..33fbd3a
--- /dev/null
+++ b/tests/testthat/test_categorize_vector.R
@@ -0,0 +1,231 @@
+n_categories <- 10
+n_cutpoints <- n_categories + 1
+n_to_categorize <- 100
+
+withr::with_seed(5, {
+  to_categorize <- runif(n_to_categorize, -100, 100)
+  cutpoints <- runif(n_to_categorize * n_cutpoints, -200, 200) |>
+    array(dim = c(n_to_categorize, n_cutpoints)) |>
+    apply(1, sort) |>
+    t()
+  ## need lower and upper bound cutpoints in addition to one
+  ## for each label
+  lab_no <- sample(1:(5 * n_to_categorize * n_categories),
+    n_to_categorize * n_categories,
+    replace = FALSE
+  )
+})
+
+labs <- glue::glue("L_{lab_no}") |>
+  array(dim = c(n_to_categorize, n_categories))
+
+testthat::test_that("test data has appropriate dimensions", {
+  expect_equal(dim(cutpoints), c(n_to_categorize, n_cutpoints))
+  expect_equal(dim(labs), c(n_to_categorize, n_categories))
+  expect_equal(dim(array(to_categorize)), n_to_categorize)
+})
+
+
+testthat::test_that("categorize_vector() argument checking works", {
+  ## this works
+  expect_no_error(categorize_vector(
+    to_categorize,
+    list(cutpoints[1, ]),
+    list(labs[1, ])
+  ))
+
+  ## these error even though the entries are the right
+  ## shapes and types because one or the other is
+  ## not wrapped into a length-1 list
+  expect_error(categorize_vector(
+    to_categorize,
+    list(cutpoints[1, ]),
+    labs[1, ]
+  ))
+  expect_error(categorize_vector(
+    to_categorize,
+    cutpoints[1, ],
+    list(labs[1, ])
+  ))
+
+  ## these errors because a dimension for breaks or
+  ## labels is neither 1 nor the length of to_categorize
+  expect_error(categorize_vector(
+    to_categorize,
+    list(
+      cutpoints[1, ],
+      cutpoints[2, ]
+    ),
+    list(labs[1, ])
+  ))
+  expect_error(categorize_vector(
+    to_categorize,
+    list(
+      cutpoints[1, ]
+    ),
+    list(
+      labs[1, ],
+      labs[2, ]
+    )
+  ))
+
+  ## this errors because the labels are not a list of
+  ## character vectors
+  expect_error(categorize_vector(
+    to_categorize,
+    list(cutpoints[1, ]),
+    list(a = rep(1L, length(labs[1, ])))
+  ))
+
+  ## this errors because the cutpoints are not numeric
+  expect_error(categorize_vector(
+    to_categorize,
+    list(as.character(cutpoints[1, ])),
+    list(labs[1, ])
+  ))
+})
+
+testthat::test_that(
+  paste0(
+    "categorize_vector() is equivalent to cut() ",
+    "for a single set of bins and cutpoints"
+  ),
+  {
+    expected <- cut(
+      to_categorize,
+      breaks = cutpoints[1, ],
+      labels = labs[1, ],
+      include.lowest = TRUE,
+      order = TRUE,
+      right = TRUE
+    )
+
+    result <- categorize_vector(
+      to_categorize,
+      list(cutpoints[1, ]),
+      list(labs[1, ])
+    )
+    expect_equal(expected, result)
+  }
+)
+
+testthat::test_that(
+  paste0(
+    "categorize_vector() is equivalent to iterated ",
+    "cut() with paired sets of cutpoints ",
+    "and labels"
+  ),
+  {
+    expected_vec_both <- replicate(n_to_categorize, NA, simplify = FALSE)
+    for (i in 1:n_to_categorize) {
+      expected_vec_both[[i]] <- cut(
+        to_categorize[i],
+        breaks = cutpoints[i, ],
+        labels = labs[i, ],
+        include.lowest = TRUE,
+        order = FALSE,
+        right = TRUE
+      )
+    }
+    result_vec_both <- categorize_vector(to_categorize,
+      purrr::array_branch(cutpoints, 1),
+      purrr::array_branch(labs, 1),
+      order = FALSE
+    )
+
+    expect_equal(
+      unlist(expected_vec_both),
+      result_vec_both
+    )
+  }
+)
+
+testthat::test_that(
+  paste0(
+    "categorize_vector() is equivalent to iterated ",
+    "cut() with paired sets of cutpoints ",
+    "and labels, and can produce ordered factors ",
+    "provided all label sets are the same."
+  ),
+  {
+    expected_vec_both <- replicate(n_to_categorize, NA, simplify = FALSE)
+    for (i in 1:n_to_categorize) {
+      expected_vec_both[[i]] <- cut(
+        to_categorize[i],
+        breaks = cutpoints[i, ],
+        labels = labs[1, ],
+        include.lowest = TRUE,
+        order = TRUE,
+        right = TRUE
+      )
+    }
+    result_vec_both <- categorize_vector(
+      to_categorize,
+      purrr::array_branch(cutpoints, 1),
+      replicate(n_to_categorize,
+        labs[1, ],
+        simplify = FALSE
+      )
+    )
+
+    expect_equal(
+      factor(unlist(expected_vec_both), ordered = TRUE),
+      result_vec_both
+    )
+  }
+)
+
+
+
+
+
+testthat::test_that(
+  paste0(
+    "categorize_vector() is equivalent to iterated ",
+    "cut() with vectorized cutpoints and single ",
+    "sets of labels, or the reverse."
+  ),
+  {
+    expected_vec_bins <- replicate(n_to_categorize, NA, simplify = FALSE)
+    expected_vec_labs <- replicate(n_to_categorize, NA, simplify = FALSE)
+
+    for (i in 1:n_to_categorize) {
+      expected_vec_bins[[i]] <- cut(
+        to_categorize[i],
+        breaks = cutpoints[i, ],
+        labels = labs[25, ],
+        include.lowest = TRUE,
+        order = TRUE,
+        right = TRUE
+      )
+      expected_vec_labs[[i]] <- cut(
+        to_categorize[i],
+        breaks = cutpoints[7, ],
+        labels = labs[i, ],
+        include.lowest = TRUE,
+        order = FALSE,
+        right = TRUE
+      )
+    }
+    result_vec_labs <- categorize_vector(to_categorize,
+      list(cutpoints[7, ]),
+      purrr::array_branch(labs, 1),
+      order = FALSE
+    )
+
+    result_vec_bins <- categorize_vector(
+      to_categorize,
+      purrr::array_branch(cutpoints, 1),
+      list(labs[25, ])
+    )
+
+    expect_equal(
+      factor(unlist(expected_vec_bins), ordered = TRUE),
+      result_vec_bins
+    )
+    expect_equal(
+      unlist(expected_vec_labs),
+      result_vec_labs
+    )
+  }
+)
diff --git a/tests/testthat/test_to_hubverse.R b/tests/testthat/test_to_hubverse.R
index 0a11854..fd7bac5 100644
--- a/tests/testthat/test_to_hubverse.R
+++ b/tests/testthat/test_to_hubverse.R
@@ -9,7 +9,7 @@ testthat::test_that(
         tibble::tibble(),
         "2025-01-01"
       ),
-      "which is day number 4"
+      "4"
     )
     testthat::expect_error(
       forecasttools::get_hubverse_table(
@@ -17,7 +17,7 @@ testthat::test_that(
         "2025-01-01",
         week_start = 3
       ),
-      "which is day number 1"
+      "1"
     )
     testthat::expect_error(
       forecasttools::get_hubverse_table(
@@ -25,7 +25,7 @@ testthat::test_that(
         "2025-01-01",
         reference_dow = 3
       ),
-      "to be day number 3"
+      "3"
     )
   }
 )