diff --git a/R/setup_rank_data.R b/R/setup_rank_data.R
index 110e5794..a55b8976 100644
--- a/R/setup_rank_data.R
+++ b/R/setup_rank_data.R
@@ -8,7 +8,9 @@
 #'   optional initial value of the rankings. If `rankings` has column names,
 #'   these are assumed to be the names of the items. `NA` values in rankings are
 #'   treated as missing data and automatically augmented; to change this
-#'   behavior, see the `na_action` argument to [set_model_options()].
+#'   behavior, see the `na_action` argument to [set_model_options()]. A vector
+#'   length `n_items` is silently converted to a matrix of length `1 x n_items`,
+#'   and names (if any), are used as column names.
 #'
 #' @param preferences A data frame with one row per pairwise comparison, and
 #'   columns `assessor`, `top_item`, and `bottom_item`. Each column contains the
@@ -138,10 +140,19 @@ setup_rank_data <- function(
     if (na_action == "fail" && any(is.na(rankings))) {
       stop("rankings matrix contains NA values")
     }
+    if (!is.matrix(rankings)) {
+      rankings <- matrix(rankings,
+        nrow = 1,
+        dimnames = list(NULL, names(rankings))
+      )
+    }
 
     if (na_action == "omit" && any(is.na(rankings))) {
       keeps <- apply(rankings, 1, function(x) !any(is.na(x)))
-      print(paste("Omitting", sum(!keeps), "row(s) from rankings due to NA values"))
+      print(paste(
+        "Omitting", sum(!keeps),
+        "row(s) from rankings due to NA values"
+      ))
       rankings <- rankings[keeps, , drop = FALSE]
     }
   } else {
@@ -153,7 +164,10 @@ setup_rank_data <- function(
   if (!is.null(observation_frequency)) {
     validate_positive_vector(observation_frequency)
     if (nrow(rankings) != length(observation_frequency)) {
-      stop("observation_frequency must be of same length as the number of rows in rankings")
+      stop(
+        "observation_frequency must be of same ",
+        "length as the number of rows in rankings"
+      )
     }
   } else {
     observation_frequency <- rep(1, nrow(rankings))
diff --git a/man/setup_rank_data.Rd b/man/setup_rank_data.Rd
index a1a14dd2..74b09f9b 100644
--- a/man/setup_rank_data.Rd
+++ b/man/setup_rank_data.Rd
@@ -24,7 +24,9 @@ converted to rankings. If \code{preferences} is provided, \code{rankings} is an
 optional initial value of the rankings. If \code{rankings} has column names,
 these are assumed to be the names of the items. \code{NA} values in rankings are
 treated as missing data and automatically augmented; to change this
-behavior, see the \code{na_action} argument to \code{\link[=set_model_options]{set_model_options()}}.}
+behavior, see the \code{na_action} argument to \code{\link[=set_model_options]{set_model_options()}}. A vector
+length \code{n_items} is silently converted to a matrix of length \verb{1 x n_items},
+and names (if any), are used as column names.}
 
 \item{preferences}{A data frame with one row per pairwise comparison, and
 columns \code{assessor}, \code{top_item}, and \code{bottom_item}. Each column contains the
diff --git a/tests/testthat/test-setup_rank_data.R b/tests/testthat/test-setup_rank_data.R
index cd8ec36e..a7c777f9 100644
--- a/tests/testthat/test-setup_rank_data.R
+++ b/tests/testthat/test-setup_rank_data.R
@@ -45,6 +45,14 @@ test_that("setup_rank_data works with rankings", {
 
   dat <- setup_rank_data(rr, validate_rankings = FALSE)
   expect_equal(dat$rankings, rr)
+
+  input1 <- potato_weighing[2, , drop = FALSE]
+  rownames(input1) <- NULL
+  input2 <- potato_weighing[2, ]
+  expect_equal(
+    setup_rank_data(input1),
+    setup_rank_data(input2)
+  )
 })
 
 test_that("setup_rank_data works for preferences", {
diff --git a/tests/testthat/test-smc_update_correctness.R b/tests/testthat/test-smc_update_correctness.R
index eb97c1e4..14287b49 100644
--- a/tests/testthat/test-smc_update_correctness.R
+++ b/tests/testthat/test-smc_update_correctness.R
@@ -247,7 +247,7 @@ test_that("update_mallows works for data one at a time", {
   for (i in seq_len(200)) {
     mod <- update_mallows(
       model = mod,
-      new_data = setup_rank_data(sushi_rankings[i, , drop = FALSE])
+      new_data = setup_rank_data(sushi_rankings[i, ])
     )
   }
   expect_equal(