diff --git a/DESCRIPTION b/DESCRIPTION
index 02dbd5da..df455a75 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: brms.mmrm
 Title: Bayesian MMRMs using 'brms'
-Version: 1.0.1.9003
+Version: 1.0.1.9004
 Authors@R: c(
   person(
     given = c("William", "Michael"),
diff --git a/NAMESPACE b/NAMESPACE
index d1b11499..b8eff9c7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -25,6 +25,7 @@ export(brm_archetype_successive_cells)
 export(brm_archetype_successive_effects)
 export(brm_data)
 export(brm_data_change)
+export(brm_data_chronologize)
 export(brm_formula)
 export(brm_formula_sigma)
 export(brm_marginal_data)
@@ -56,8 +57,10 @@ importFrom(brms,make_standata)
 importFrom(brms,prior)
 importFrom(brms,unstr)
 importFrom(dplyr,across)
+importFrom(dplyr,arrange)
 importFrom(dplyr,bind_cols)
 importFrom(dplyr,bind_rows)
+importFrom(dplyr,distinct)
 importFrom(dplyr,left_join)
 importFrom(dplyr,rename)
 importFrom(dplyr,select)
diff --git a/NEWS.md b/NEWS.md
index 3c7acb8c..980381d3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,9 +1,10 @@
-# brms.mmrm 1.0.1.9003 (development)
+# brms.mmrm 1.0.1.9004 (development)
 
 * Add `brm_marginal_grid()`.
 * Show posterior samples of `sigma` in `brm_marginal_draws()` and `brm_marginal_summaries()`.
 * Allow `outcome = "response"` with `reference_time = NULL`. Sometimes raw response is analyzed but the data has no baseline time point.
-* Preserve factors in `brm_data()` and encourage ordered factors for the time variable (#113). 
+* Preserve factors in `brm_data()` and encourage ordered factors for the time variable (#113).
+* Add `brm_data_chronologize()` to ensure the correctness of the time variable.
 
 # brms.mmrm 1.0.1
 
diff --git a/R/brm_data.R b/R/brm_data.R
index 6d58cb0b..452f7e5e 100644
--- a/R/brm_data.R
+++ b/R/brm_data.R
@@ -41,7 +41,11 @@
 #'   character vector or unordered factor.
 #' @param time Character of length 1, name of the discrete time variable.
 #'   For most analyses, please use an ordered factor for the `time` column
-#'   in the data. This ensures the time points sort in chronological order,
+#'   in the data. You can easily turn
+#'   the time variable into an ordered factor using
+#'   [brm_data_chronologize()], either before or immediately after
+#'   [brm_data()] (but before any `brm_archetype_*()` functions).
+#'   This ensures the time points sort in chronological order,
 #'   which ensures the correctness of informative prior archetypes and
 #'   autoregressive / moving average correlation structures.
 #'
diff --git a/R/brm_data_chronologize.R b/R/brm_data_chronologize.R
new file mode 100644
index 00000000..e115914f
--- /dev/null
+++ b/R/brm_data_chronologize.R
@@ -0,0 +1,134 @@
+#' @title Chronologize a dataset
+#' @export
+#' @family data
+#' @description Convert the discrete time variable into an ordered factor.
+#' @details Most MMRMs should use an ordered factor for the `time` column
+#'   in the data. This way, individual time points are treated as
+#'   distinct factor levels for the purposes of fixed effect parameterizations
+#'   (see the "Contrasts" section), and the explicit ordering ensures
+#'   that informative prior archetypes and ARMA-like correlation structures
+#'   are expressed correctly. Without the ordering, problems can arise when
+#'   character vectors are sorted: e.g. if `AVISIT` has levels
+#'   `"VISIT1", "VISIT2", ..., "VISIT10"`, then `brms` will mistake the
+#'   the order of scheduled study visits to be
+#'   `"VISIT1", "VISIT10", "VISIT2", ...`, which is not chronological.
+#'
+#'   You can easily turn
+#'   the time variable into an ordered factor using
+#'   [brm_data_chronologize()]. Either supply an explicit character vector
+#'   of chronologically-ordered factor levels in the `levels` argument,
+#'   or supply the name of a time-ordered variable in the `order` argument.
+#'
+#'   [brm_data_chronologize()] can be called either before or just after
+#'   [brm_data()], but in the former case, the discrete time variable
+#'   needs to be specified explicitly in `time` argument. And in the latter,
+#'   [brm_data_chronologize()] must be called before any of the informative
+#'   prior archetype functions such as [brm_archetype_successive_cells()].
+#' @section Contrasts:
+#'   Ordinarily, ordered factors automatically use polynomial contrasts from
+#'   [contr.poly()]. This is undesirable for MMRMs, so if the time variable
+#'   is an ordered factor, then [brm_data()]
+#'   manually sets `contrasts(data[[time]])` to a set of treatment contrasts
+#'   using [contr.treatment()]. If you prefer different contrasts, please
+#'   manually set `contrasts(data[[time]])` to something else after
+#'   calling [brm_data()].
+#' @return A data frame with the time column as an ordered factor.
+#' @inheritParams brm_data
+#' @param order Optional character string with the name of a variable in
+#'   the data for ordering the time variable.
+#'   Either `order` or `levels` must be supplied, but not both together.
+#'   If `order` is supplied,
+#'   the levels of `data[[order]]` must have a 1:1 correspondence with
+#'   those of `data[[time]]`, and `sort(unique(data[[order]]))` must
+#'   reflect the desired order of the levels of `data[[time]]`. For example,
+#'   suppose you have a CDISC dataset with categorical time variable `AVISIT`
+#'   and integer variable `AVISITN`. Then,
+#'   `brm_data_chronologize(time = "AVISIT", order = "AVISITN")` will turn
+#'   `AVISIT` into an ordered factor with levels that respect the ordering
+#'   in `AVISITN`.
+#' @param levels Optional character vector of levels of `data[[time]]`
+#'   in chronological order. Used to turn `data[[time]]` into an
+#'   ordered factor.
+#'   Either `order` or `levels` must be supplied, but not both together.
+#' @param time Character string with the name of the discrete time
+#'   variable in the data. This is the variable that [brm_data_chronologize()]
+#'   turns into an ordered factor. It needs to be specified explicitly
+#'   if and only if the `data` argument was not produced by a call to
+#'   [brm_data()].
+#' @examples
+#' data <- brm_simulate_outline(n_time = 12, n_patient = 4)
+#' data$AVISIT <- gsub("_0", "_", data$time)
+#' data$AVISITN <- as.integer(gsub("time_", "", data$time))
+#' data[, c("AVISIT", "AVISITN")]
+#' sort(unique(data$AVISIT)) # wrong order
+#' data1 <- brm_data_chronologize(data, time = "AVISIT", order = "AVISITN")
+#' sort(unique(data1$AVISIT)) # correct order
+#' levels <- paste0("time_", seq_len(12))
+#' data2 <- brm_data_chronologize(data, time = "AVISIT", levels = levels)
+#' sort(unique(data2$AVISIT)) # correct order
+brm_data_chronologize <- function(
+  data,
+  order = NULL,
+  levels = NULL,
+  time = attr(data, "brm_time")
+) {
+  if_any(
+    inherits(data, "brms_mmrm_data"),
+    brm_data_validate(data),
+    assert(is.data.frame(data), message = "data must be a data frame")
+  )
+  assert_chr(time, message = "time must be a character string")
+  assert_chr(
+    order %|||% "x",
+    message = "order must be NULL or a character string"
+  )
+  assert(
+    time %in% colnames(data),
+    message = "time must be a column name in the data"
+  )
+  assert(
+    (order %|||% time) %in% colnames(data),
+    message = "order must be NULL or a column name in the data"
+  )
+  assert_chr_vec(levels %|||% data[[time]][1L])
+  assert(
+    (levels %|||% data[[time]][1L]) %in% data[[time]],
+    message = "all elements of levels must be in data[[time]]"
+  )
+  assert(
+    !(is.null(order) && is.null(levels)),
+    message = "at least one of 'order' or 'levels' must be given"
+  )
+  assert(
+    !(!is.null(order) && !is.null(levels)),
+    message = paste(
+      "'order' and 'levels' cannot both be given.",
+      "Please choose one or the other."
+    )
+  )
+  if (!is.null(order)) {
+    grid <- dplyr::distinct(data[, c(time, order)])
+    assert(
+      !anyDuplicated(grid[[time]]),
+      !anyDuplicated(grid[[order]]),
+      as.character(sort(grid[[time]])) ==
+        as.character(sort(unique(data[[time]]))),
+      as.character(sort(grid[[order]])) ==
+        as.character(sort(unique(data[[order]]))),
+      message = paste(
+        "Cannot create an ordered factor for the discrete time variable",
+        "because the elements of the discrete time variable do not have a",
+        "1:1 correspondence with the elements of the ordering variable.",
+        "Please make sure variables",
+        time,
+        "and",
+        order,
+        "have a 1:1 correspondence between their levels."
+      )
+    )
+    grid <- grid[order(grid[[order]]),, drop = FALSE] # nolint
+    levels <- grid[[time]]
+  }
+  data[[time]] <- ordered(data[[time]], levels = levels)
+  data
+}
diff --git a/R/brm_package.R b/R/brm_package.R
index 07205ba1..e1b55441 100644
--- a/R/brm_package.R
+++ b/R/brm_package.R
@@ -26,8 +26,8 @@
 #'     CRC Press, Taylor & Francis Group.
 #' @family help
 #' @importFrom brms brm brmsformula get_prior make_standata prior unstr
-#' @importFrom dplyr across bind_cols bind_rows left_join rename select
-#'   summarize
+#' @importFrom dplyr across arrange bind_cols bind_rows distinct left_join
+#'   rename select summarize
 #' @importFrom ggplot2 aes facet_wrap geom_point geom_errorbar ggplot
 #'   position_dodge theme_gray xlab ylab
 #' @importFrom ggridges geom_density_ridges2
diff --git a/_pkgdown.yml b/_pkgdown.yml
index a7cded7e..7b3418b6 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -27,6 +27,7 @@ reference:
   contents:
   - 'brm_data'
   - 'brm_data_change'
+  - 'brm_data_chronologize'
 - title: Simulation
   contents:
   - 'brm_simulate_categorical'
diff --git a/man/brm_data.Rd b/man/brm_data.Rd
index c19029e5..57157281 100644
--- a/man/brm_data.Rd
+++ b/man/brm_data.Rd
@@ -47,7 +47,11 @@ character vector or unordered factor.}
 
 \item{time}{Character of length 1, name of the discrete time variable.
 For most analyses, please use an ordered factor for the \code{time} column
-in the data. This ensures the time points sort in chronological order,
+in the data. You can easily turn
+the time variable into an ordered factor using
+\code{\link[=brm_data_chronologize]{brm_data_chronologize()}}, either before or immediately after
+\code{\link[=brm_data]{brm_data()}} (but before any \verb{brm_archetype_*()} functions).
+This ensures the time points sort in chronological order,
 which ensures the correctness of informative prior archetypes and
 autoregressive / moving average correlation structures.
 
@@ -153,6 +157,7 @@ brm_data(
 }
 \seealso{
 Other data: 
-\code{\link{brm_data_change}()}
+\code{\link{brm_data_change}()},
+\code{\link{brm_data_chronologize}()}
 }
 \concept{data}
diff --git a/man/brm_data_change.Rd b/man/brm_data_change.Rd
index eabd184a..81545ea2 100644
--- a/man/brm_data_change.Rd
+++ b/man/brm_data_change.Rd
@@ -58,6 +58,7 @@ attr(data, "brm_reference_time")
 }
 \seealso{
 Other data: 
-\code{\link{brm_data}()}
+\code{\link{brm_data}()},
+\code{\link{brm_data_chronologize}()}
 }
 \concept{data}
diff --git a/man/brm_data_chronologize.Rd b/man/brm_data_chronologize.Rd
new file mode 100644
index 00000000..ebe447fd
--- /dev/null
+++ b/man/brm_data_chronologize.Rd
@@ -0,0 +1,99 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/brm_data_chronologize.R
+\name{brm_data_chronologize}
+\alias{brm_data_chronologize}
+\title{Chronologize a dataset}
+\usage{
+brm_data_chronologize(
+  data,
+  order = NULL,
+  levels = NULL,
+  time = attr(data, "brm_time")
+)
+}
+\arguments{
+\item{data}{Data frame or tibble with longitudinal data.}
+
+\item{order}{Optional character string with the name of a variable in
+the data for ordering the time variable.
+Either \code{order} or \code{levels} must be supplied, but not both together.
+If \code{order} is supplied,
+the levels of \code{data[[order]]} must have a 1:1 correspondence with
+those of \code{data[[time]]}, and \code{sort(unique(data[[order]]))} must
+reflect the desired order of the levels of \code{data[[time]]}. For example,
+suppose you have a CDISC dataset with categorical time variable \code{AVISIT}
+and integer variable \code{AVISITN}. Then,
+\code{brm_data_chronologize(time = "AVISIT", order = "AVISITN")} will turn
+\code{AVISIT} into an ordered factor with levels that respect the ordering
+in \code{AVISITN}.}
+
+\item{levels}{Optional character vector of levels of \code{data[[time]]}
+in chronological order. Used to turn \code{data[[time]]} into an
+ordered factor.
+Either \code{order} or \code{levels} must be supplied, but not both together.}
+
+\item{time}{Character string with the name of the discrete time
+variable in the data. This is the variable that \code{\link[=brm_data_chronologize]{brm_data_chronologize()}}
+turns into an ordered factor. It needs to be specified explicitly
+if and only if the \code{data} argument was not produced by a call to
+\code{\link[=brm_data]{brm_data()}}.}
+}
+\value{
+A data frame with the time column as an ordered factor.
+}
+\description{
+Convert the discrete time variable into an ordered factor.
+}
+\details{
+Most MMRMs should use an ordered factor for the \code{time} column
+in the data. This way, individual time points are treated as
+distinct factor levels for the purposes of fixed effect parameterizations
+(see the "Contrasts" section), and the explicit ordering ensures
+that informative prior archetypes and ARMA-like correlation structures
+are expressed correctly. Without the ordering, problems can arise when
+character vectors are sorted: e.g. if \code{AVISIT} has levels
+\verb{"VISIT1", "VISIT2", ..., "VISIT10"}, then \code{brms} will mistake the
+the order of scheduled study visits to be
+\verb{"VISIT1", "VISIT10", "VISIT2", ...}, which is not chronological.
+
+You can easily turn
+the time variable into an ordered factor using
+\code{\link[=brm_data_chronologize]{brm_data_chronologize()}}. Either supply an explicit character vector
+of chronologically-ordered factor levels in the \code{levels} argument,
+or supply the name of a time-ordered variable in the \code{order} argument.
+
+\code{\link[=brm_data_chronologize]{brm_data_chronologize()}} can be called either before or just after
+\code{\link[=brm_data]{brm_data()}}, but in the former case, the discrete time variable
+needs to be specified explicitly in \code{time} argument. And in the latter,
+\code{\link[=brm_data_chronologize]{brm_data_chronologize()}} must be called before any of the informative
+prior archetype functions such as \code{\link[=brm_archetype_successive_cells]{brm_archetype_successive_cells()}}.
+}
+\section{Contrasts}{
+
+Ordinarily, ordered factors automatically use polynomial contrasts from
+\code{\link[=contr.poly]{contr.poly()}}. This is undesirable for MMRMs, so if the time variable
+is an ordered factor, then \code{\link[=brm_data]{brm_data()}}
+manually sets \code{contrasts(data[[time]])} to a set of treatment contrasts
+using \code{\link[=contr.treatment]{contr.treatment()}}. If you prefer different contrasts, please
+manually set \code{contrasts(data[[time]])} to something else after
+calling \code{\link[=brm_data]{brm_data()}}.
+}
+
+\examples{
+data <- brm_simulate_outline(n_time = 12, n_patient = 4)
+data$AVISIT <- gsub("_0", "_", data$time)
+data$AVISITN <- as.integer(gsub("time_", "", data$time))
+data[, c("AVISIT", "AVISITN")]
+sort(unique(data$AVISIT)) # wrong order
+data1 <- brm_data_chronologize(data, time = "AVISIT", order = "AVISITN")
+sort(unique(data1$AVISIT)) # correct order
+levels <- paste0("time_", seq_len(12))
+data2 <- brm_data_chronologize(data, time = "AVISIT", levels = levels)
+sort(unique(data2$AVISIT)) # correct order
+}
+\seealso{
+Other data: 
+\code{\link{brm_data}()},
+\code{\link{brm_data_change}()}
+}
+\concept{data}
diff --git a/tests/testthat/test-brm_data_chronologize.R b/tests/testthat/test-brm_data_chronologize.R
new file mode 100644
index 00000000..c9a46f4e
--- /dev/null
+++ b/tests/testthat/test-brm_data_chronologize.R
@@ -0,0 +1,25 @@
+test_that("brm_data_chronologize() with class", {
+  data <- brm_simulate_outline(n_time = 12, n_patient = 4)
+  data$time <- gsub("_0", "_", data$time)
+  attr(data, "brm_reference_time") <- "time_1"
+  data$AVISITN <- as.integer(gsub("time_", "", data$time))
+  data[, c("time", "AVISITN")]
+  data1 <- brm_data_chronologize(data, time = "time", order = "AVISITN")
+  levels <- paste0("time_", seq_len(12))
+  expect_equal(as.character(sort(unique(data1$time))), levels)
+  data2 <- brm_data_chronologize(data, time = "time", levels = levels)
+  expect_equal(as.character(sort(unique(data2$time))), levels)
+})
+
+test_that("brm_data_chronologize() without class", {
+  data <- brm_simulate_outline(n_time = 12, n_patient = 4)
+  data <- tibble::as_tibble(data)
+  data$AVISIT <- gsub("_0", "_", data$time)
+  data$AVISITN <- as.integer(gsub("time_", "", data$time))
+  data[, c("AVISIT", "AVISITN")]
+  data1 <- brm_data_chronologize(data, time = "AVISIT", order = "AVISITN")
+  levels <- paste0("time_", seq_len(12))
+  expect_equal(as.character(sort(unique(data1$AVISIT))), levels)
+  data2 <- brm_data_chronologize(data, time = "AVISIT", levels = levels)
+  expect_equal(as.character(sort(unique(data2$AVISIT))), levels)
+})
diff --git a/vignettes/usage.Rmd b/vignettes/usage.Rmd
index 03ee7c82..869ac0f3 100644
--- a/vignettes/usage.Rmd
+++ b/vignettes/usage.Rmd
@@ -49,6 +49,17 @@ raw_data
 #> # ℹ 1,190 more rows
 ```
 
+It is good practice to convert the time variable to an ordered factor so individual discrete time points sort in the correct chronological order.^[Ordered factors usually have polynomial contrasts (`contr.poly()`), which makes the fixed effect parameterization counterintuitive. However, `brm_data()` manually sets the contrasts of the time variable to be treatment contrasts (`contr.treatment()`) so there are individual fixed effects for individual discrete time points. To revert back to polynomial contrasts, use `contr.poly()` after the call to `brm_data()`.]
+
+
+```r
+raw_data <- raw_data |>
+  brm_data_chronologize(time = "time", levels = paste0("time_", seq_len(4)))
+
+str(raw_data$time)
+#>  Ord.factor w/ 4 levels "time_1"<"time_2"<..: 1 2 3 4 1 2 3 4 1 2 ...
+```
+
 Next, create a special classed dataset that the package will recognize. The classed data object contains a pre-processed version of the data, along with attributes to declare the outcome variable, whether the outcome is response or change from baseline, the treatment group variable, the discrete time point variable, control group, baseline time point, and the covariates selected for analysis.
 
 
@@ -68,7 +79,7 @@ data <- brm_data(
 data
 #> # A tibble: 1,200 × 6
 #>    response group   time   patient     biomarker1 biomarker2
-#>       <dbl> <chr>   <chr>  <chr>            <dbl>      <dbl>
+#>       <dbl> <chr>   <ord>  <chr>            <dbl>      <dbl>
 #>  1    1.11  group_1 time_1 patient_001      1.31      -0.361
 #>  2    2.15  group_1 time_2 patient_001      1.31      -0.361
 #>  3    2.54  group_1 time_3 patient_001      1.31      -0.361
@@ -87,9 +98,8 @@ class(data)
 roles <- attributes(data)
 roles$row.names <- NULL
 str(roles)
-#> List of 14
+#> List of 10
 #>  $ names              : chr [1:6] "response" "group" "time" "patient" ...
-#>  $ class              : chr [1:4] "brms_mmrm_data" "tbl_df" "tbl" "data.frame"
 #>  $ brm_outcome        : chr "response"
 #>  $ brm_role           : chr "response"
 #>  $ brm_group          : chr "group"
@@ -98,10 +108,7 @@ str(roles)
 #>  $ brm_covariates     : chr [1:2] "biomarker1" "biomarker2"
 #>  $ brm_reference_group: chr "group_1"
 #>  $ brm_reference_time : chr "time_1"
-#>  $ brm_levels_group   : chr [1:3] "group_1" "group_2" "group_3"
-#>  $ brm_labels_group   : chr [1:3] "group_1" "group_2" "group_3"
-#>  $ brm_levels_time    : chr [1:4] "time_1" "time_2" "time_3" "time_4"
-#>  $ brm_labels_time    : chr [1:4] "time_1" "time_2" "time_3" "time_4"
+#>  $ class              : chr [1:4] "brms_mmrm_data" "tbl_df" "tbl" "data.frame"
 ```
 
 # Formula
@@ -165,34 +172,20 @@ The `make_standata()` function lets you see the data that `brms` will generate f
 
 ```r
 head(brms::make_standata(formula = formula, data = data)$X)
-#>   Intercept groupgroup_1 groupgroup_2 timetime_1 timetime_2 timetime_3
-#> 1         1            1            0          1          0          0
-#> 2         1            1            0          0          1          0
-#> 3         1            1            0          0          0          1
-#> 4         1            1            0          0          0          0
-#> 5         1            1            0          1          0          0
-#> 6         1            1            0          0          1          0
-#>   biomarker1 biomarker2 groupgroup_1:timetime_1 groupgroup_2:timetime_1
-#> 1  1.3126508 -0.3608809                       1                       0
-#> 2  1.3126508 -0.3608809                       0                       0
-#> 3  1.3126508 -0.3608809                       0                       0
-#> 4  1.3126508 -0.3608809                       0                       0
-#> 5  0.1068624 -2.4441488                       1                       0
-#> 6  0.1068624 -2.4441488                       0                       0
-#>   groupgroup_1:timetime_2 groupgroup_2:timetime_2 groupgroup_1:timetime_3
-#> 1                       0                       0                       0
-#> 2                       1                       0                       0
-#> 3                       0                       0                       1
-#> 4                       0                       0                       0
-#> 5                       0                       0                       0
-#> 6                       1                       0                       0
-#>   groupgroup_2:timetime_3
-#> 1                       0
-#> 2                       0
-#> 3                       0
-#> 4                       0
-#> 5                       0
-#> 6                       0
+#>   Intercept groupgroup_1 groupgroup_2 time2 time3 time4 biomarker1 biomarker2 groupgroup_1:time2
+#> 1         1            1            0     0     0     0  1.3126508 -0.3608809                  0
+#> 2         1            1            0     1     0     0  1.3126508 -0.3608809                  1
+#> 3         1            1            0     0     1     0  1.3126508 -0.3608809                  0
+#> 4         1            1            0     0     0     1  1.3126508 -0.3608809                  0
+#> 5         1            1            0     0     0     0  0.1068624 -2.4441488                  0
+#> 6         1            1            0     1     0     0  0.1068624 -2.4441488                  1
+#>   groupgroup_2:time2 groupgroup_1:time3 groupgroup_2:time3 groupgroup_1:time4 groupgroup_2:time4
+#> 1                  0                  0                  0                  0                  0
+#> 2                  0                  0                  0                  0                  0
+#> 3                  0                  1                  0                  0                  0
+#> 4                  0                  0                  0                  1                  0
+#> 5                  0                  0                  0                  0                  0
+#> 6                  0                  0                  0                  0                  0
 ```
 
 If you choose a different contrast method, a different model matrix may result.
@@ -204,34 +197,20 @@ options(
 )
 # different model matrix than before:
 head(brms::make_standata(formula = formula, data = data)$X)
-#>   Intercept groupgroup_2 groupgroup_3 timetime_2 timetime_3 timetime_4
-#> 1         1            0            0          0          0          0
-#> 2         1            0            0          1          0          0
-#> 3         1            0            0          0          1          0
-#> 4         1            0            0          0          0          1
-#> 5         1            0            0          0          0          0
-#> 6         1            0            0          1          0          0
-#>   biomarker1 biomarker2 groupgroup_2:timetime_2 groupgroup_3:timetime_2
-#> 1  1.3126508 -0.3608809                       0                       0
-#> 2  1.3126508 -0.3608809                       0                       0
-#> 3  1.3126508 -0.3608809                       0                       0
-#> 4  1.3126508 -0.3608809                       0                       0
-#> 5  0.1068624 -2.4441488                       0                       0
-#> 6  0.1068624 -2.4441488                       0                       0
-#>   groupgroup_2:timetime_3 groupgroup_3:timetime_3 groupgroup_2:timetime_4
-#> 1                       0                       0                       0
-#> 2                       0                       0                       0
-#> 3                       0                       0                       0
-#> 4                       0                       0                       0
-#> 5                       0                       0                       0
-#> 6                       0                       0                       0
-#>   groupgroup_3:timetime_4
-#> 1                       0
-#> 2                       0
-#> 3                       0
-#> 4                       0
-#> 5                       0
-#> 6                       0
+#>   Intercept groupgroup_2 groupgroup_3 time2 time3 time4 biomarker1 biomarker2 groupgroup_2:time2
+#> 1         1            0            0     0     0     0  1.3126508 -0.3608809                  0
+#> 2         1            0            0     1     0     0  1.3126508 -0.3608809                  0
+#> 3         1            0            0     0     1     0  1.3126508 -0.3608809                  0
+#> 4         1            0            0     0     0     1  1.3126508 -0.3608809                  0
+#> 5         1            0            0     0     0     0  0.1068624 -2.4441488                  0
+#> 6         1            0            0     1     0     0  0.1068624 -2.4441488                  0
+#>   groupgroup_3:time2 groupgroup_2:time3 groupgroup_3:time3 groupgroup_2:time4 groupgroup_3:time4
+#> 1                  0                  0                  0                  0                  0
+#> 2                  0                  0                  0                  0                  0
+#> 3                  0                  0                  0                  0                  0
+#> 4                  0                  0                  0                  0                  0
+#> 5                  0                  0                  0                  0                  0
+#> 6                  0                  0                  0                  0                  0
 ```
 
 # Priors
@@ -241,50 +220,28 @@ Some analyses require informative priors, others require non-informative ones. P
 
 ```r
 brms::get_prior(data = data, formula = formula)
-#>                   prior     class                    coef group resp  dpar
-#>                  (flat)         b                                         
-#>                  (flat)         b              biomarker1                 
-#>                  (flat)         b              biomarker2                 
-#>                  (flat)         b            groupgroup_2                 
-#>                  (flat)         b groupgroup_2:timetime_2                 
-#>                  (flat)         b groupgroup_2:timetime_3                 
-#>                  (flat)         b groupgroup_2:timetime_4                 
-#>                  (flat)         b            groupgroup_3                 
-#>                  (flat)         b groupgroup_3:timetime_2                 
-#>                  (flat)         b groupgroup_3:timetime_3                 
-#>                  (flat)         b groupgroup_3:timetime_4                 
-#>                  (flat)         b              timetime_2                 
-#>                  (flat)         b              timetime_3                 
-#>                  (flat)         b              timetime_4                 
-#>                  lkj(1)   cortime                                         
-#>  student_t(3, 0.9, 2.5) Intercept                                         
-#>                  (flat)         b                                    sigma
-#>                  (flat)         b              timetime_1            sigma
-#>                  (flat)         b              timetime_2            sigma
-#>                  (flat)         b              timetime_3            sigma
-#>                  (flat)         b              timetime_4            sigma
-#>  nlpar lb ub       source
-#>                   default
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>                   default
-#>                   default
-#>                   default
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
-#>              (vectorized)
+#>                   prior     class               coef group resp  dpar nlpar lb ub       source
+#>                  (flat)         b                                                      default
+#>                  (flat)         b         biomarker1                              (vectorized)
+#>                  (flat)         b         biomarker2                              (vectorized)
+#>                  (flat)         b       groupgroup_2                              (vectorized)
+#>                  (flat)         b groupgroup_2:time2                              (vectorized)
+#>                  (flat)         b groupgroup_2:time3                              (vectorized)
+#>                  (flat)         b groupgroup_2:time4                              (vectorized)
+#>                  (flat)         b       groupgroup_3                              (vectorized)
+#>                  (flat)         b groupgroup_3:time2                              (vectorized)
+#>                  (flat)         b groupgroup_3:time3                              (vectorized)
+#>                  (flat)         b groupgroup_3:time4                              (vectorized)
+#>                  (flat)         b              time2                              (vectorized)
+#>                  (flat)         b              time3                              (vectorized)
+#>                  (flat)         b              time4                              (vectorized)
+#>                  lkj(1)   cortime                                                      default
+#>  student_t(3, 0.9, 2.5) Intercept                                                      default
+#>                  (flat)         b                               sigma                  default
+#>                  (flat)         b         timetime_1            sigma             (vectorized)
+#>                  (flat)         b         timetime_2            sigma             (vectorized)
+#>                  (flat)         b         timetime_3            sigma             (vectorized)
+#>                  (flat)         b         timetime_4            sigma             (vectorized)
 ```
 
 # Model
@@ -312,60 +269,34 @@ model
 #>          total post-warmup draws = 4000
 #> 
 #> Correlation Structures:
-#>                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
-#> cortime(time_1,time_2)     0.42      0.05     0.33     0.51 1.00     3325
-#> cortime(time_1,time_3)    -0.80      0.02    -0.84    -0.76 1.00     2161
-#> cortime(time_2,time_3)    -0.54      0.04    -0.62    -0.46 1.00     3218
-#> cortime(time_1,time_4)    -0.29      0.05    -0.39    -0.18 1.00     4082
-#> cortime(time_2,time_4)     0.03      0.06    -0.09     0.14 1.00     3227
-#> cortime(time_3,time_4)    -0.10      0.06    -0.21     0.01 1.00     3697
-#>                        Tail_ESS
-#> cortime(time_1,time_2)     2911
-#> cortime(time_1,time_3)     2816
-#> cortime(time_2,time_3)     3092
-#> cortime(time_1,time_4)     2939
-#> cortime(time_2,time_4)     2861
-#> cortime(time_3,time_4)     2853
+#>                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
+#> cortime(time_1,time_2)     0.42      0.05     0.33     0.51 1.00     3325     2911
+#> cortime(time_1,time_3)    -0.80      0.02    -0.84    -0.76 1.00     2161     2816
+#> cortime(time_2,time_3)    -0.54      0.04    -0.62    -0.46 1.00     3218     3092
+#> cortime(time_1,time_4)    -0.29      0.05    -0.39    -0.18 1.00     4082     2939
+#> cortime(time_2,time_4)     0.03      0.06    -0.09     0.14 1.00     3227     2861
+#> cortime(time_3,time_4)    -0.10      0.06    -0.21     0.01 1.00     3697     2853
 #> 
 #> Regression Coefficients:
-#>                         Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
-#> Intercept                   1.21      0.09     1.04     1.39 1.00     1319
-#> groupgroup_2               -1.53      0.12    -1.77    -1.29 1.00     1394
-#> groupgroup_3                0.19      0.13    -0.06     0.44 1.00     1386
-#> timetime_2                  1.32      0.10     1.11     1.51 1.00     1994
-#> timetime_3                  0.52      0.17     0.17     0.87 1.00     1479
-#> timetime_4                 -1.44      0.17    -1.79    -1.11 1.00     1484
-#> biomarker1                 -0.01      0.01    -0.03     0.01 1.00     6061
-#> biomarker2                  0.02      0.01     0.00     0.04 1.00     4965
-#> groupgroup_2:timetime_2     0.04      0.14    -0.24     0.32 1.00     2310
-#> groupgroup_3:timetime_2     0.02      0.14    -0.26     0.31 1.00     2186
-#> groupgroup_2:timetime_3    -0.17      0.25    -0.65     0.31 1.00     1589
-#> groupgroup_3:timetime_3    -0.21      0.25    -0.69     0.28 1.00     1596
-#> groupgroup_2:timetime_4    -0.08      0.25    -0.55     0.42 1.00     1544
-#> groupgroup_3:timetime_4    -0.27      0.25    -0.75     0.21 1.00     1609
-#> sigma_timetime_1           -0.12      0.04    -0.20    -0.04 1.00     2469
-#> sigma_timetime_2            0.01      0.04    -0.08     0.09 1.00     3347
-#> sigma_timetime_3           -0.05      0.04    -0.13     0.03 1.00     2168
-#> sigma_timetime_4            0.21      0.04     0.13     0.29 1.00     3483
-#>                         Tail_ESS
-#> Intercept                   2071
-#> groupgroup_2                1918
-#> groupgroup_3                2158
-#> timetime_2                  2641
-#> timetime_3                  2113
-#> timetime_4                  1840
-#> biomarker1                  2949
-#> biomarker2                  2885
-#> groupgroup_2:timetime_2     2727
-#> groupgroup_3:timetime_2     2725
-#> groupgroup_2:timetime_3     2149
-#> groupgroup_3:timetime_3     2363
-#> groupgroup_2:timetime_4     2053
-#> groupgroup_3:timetime_4     2129
-#> sigma_timetime_1            2791
-#> sigma_timetime_2            3135
-#> sigma_timetime_3            2622
-#> sigma_timetime_4            2850
+#>                    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
+#> Intercept              1.21      0.09     1.04     1.39 1.00     1319     2071
+#> groupgroup_2          -1.53      0.12    -1.77    -1.29 1.00     1394     1918
+#> groupgroup_3           0.19      0.13    -0.06     0.44 1.00     1386     2158
+#> time2                  1.32      0.10     1.11     1.51 1.00     1994     2641
+#> time3                  0.52      0.17     0.17     0.87 1.00     1479     2113
+#> time4                 -1.44      0.17    -1.79    -1.11 1.00     1484     1840
+#> biomarker1            -0.01      0.01    -0.03     0.01 1.00     6061     2949
+#> biomarker2             0.02      0.01     0.00     0.04 1.00     4965     2885
+#> groupgroup_2:time2     0.04      0.14    -0.24     0.32 1.00     2310     2727
+#> groupgroup_3:time2     0.02      0.14    -0.26     0.31 1.00     2186     2725
+#> groupgroup_2:time3    -0.17      0.25    -0.65     0.31 1.00     1589     2149
+#> groupgroup_3:time3    -0.21      0.25    -0.69     0.28 1.00     1596     2363
+#> groupgroup_2:time4    -0.08      0.25    -0.55     0.42 1.00     1544     2053
+#> groupgroup_3:time4    -0.27      0.25    -0.75     0.21 1.00     1609     2129
+#> sigma_timetime_1      -0.12      0.04    -0.20    -0.04 1.00     2469     2791
+#> sigma_timetime_2       0.01      0.04    -0.08     0.09 1.00     3347     3135
+#> sigma_timetime_3      -0.05      0.04    -0.13     0.03 1.00     2168     2622
+#> sigma_timetime_4       0.21      0.04     0.13     0.29 1.00     3483     2850
 #> 
 #> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
 #> and Tail_ESS are effective sample size measures, and Rhat is the potential
@@ -390,111 +321,108 @@ draws <- brm_marginal_draws(data = data, formula = formula, model = model)
 draws
 #> $response
 #> # A draws_df: 1000 iterations, 4 chains, and 12 variables
-#>    group_1|time_1 group_1|time_2 group_1|time_3 group_1|time_4 group_2|time_1
-#> 1             1.2            2.5            1.8         -0.386          -0.25
-#> 2             1.2            2.6            1.7         -0.083          -0.23
-#> 3             1.2            2.6            1.8         -0.278          -0.31
-#> 4             1.2            2.6            1.7         -0.150          -0.32
-#> 5             1.2            2.5            1.8         -0.160          -0.26
-#> 6             1.1            2.5            1.8         -0.288          -0.19
-#> 7             1.1            2.4            1.9         -0.181          -0.30
-#> 8             1.2            2.6            1.8         -0.207          -0.33
-#> 9             1.1            2.5            1.7         -0.070          -0.35
-#> 10            1.2            2.6            1.6         -0.022          -0.44
-#>    group_2|time_2 group_2|time_3 group_2|time_4
-#> 1            1.05        -0.0050           -1.9
-#> 2            1.16         0.0115           -2.0
-#> 3            0.99         0.0939           -2.0
-#> 4            1.12        -0.0090           -1.9
-#> 5            1.02        -0.0371           -1.8
-#> 6            1.00        -0.0146           -2.0
-#> 7            1.08        -0.1218           -1.7
-#> 8            1.01         0.0043           -1.9
-#> 9            1.16        -0.0098           -1.8
-#> 10           0.91         0.1413           -1.9
+#>    group_1|time_1 group_1|time_2 group_1|time_3 group_1|time_4 group_2|time_1 group_2|time_2
+#> 1             1.2            2.5            1.8         -0.386          -0.25           1.05
+#> 2             1.2            2.6            1.7         -0.083          -0.23           1.16
+#> 3             1.2            2.6            1.8         -0.278          -0.31           0.99
+#> 4             1.2            2.6            1.7         -0.150          -0.32           1.12
+#> 5             1.2            2.5            1.8         -0.160          -0.26           1.02
+#> 6             1.1            2.5            1.8         -0.288          -0.19           1.00
+#> 7             1.1            2.4            1.9         -0.181          -0.30           1.08
+#> 8             1.2            2.6            1.8         -0.207          -0.33           1.01
+#> 9             1.1            2.5            1.7         -0.070          -0.35           1.16
+#> 10            1.2            2.6            1.6         -0.022          -0.44           0.91
+#>    group_2|time_3 group_2|time_4
+#> 1         -0.0050           -1.9
+#> 2          0.0115           -2.0
+#> 3          0.0939           -2.0
+#> 4         -0.0090           -1.9
+#> 5         -0.0371           -1.8
+#> 6         -0.0146           -2.0
+#> 7         -0.1218           -1.7
+#> 8          0.0043           -1.9
+#> 9         -0.0098           -1.8
+#> 10         0.1413           -1.9
 #> # ... with 3990 more draws, and 4 more variables
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 #> 
 #> $difference_time
 #> # A draws_df: 1000 iterations, 4 chains, and 9 variables
-#>    group_1|time_2 group_1|time_3 group_1|time_4 group_2|time_2 group_2|time_3
-#> 1             1.3           0.55           -1.6            1.3           0.25
-#> 2             1.3           0.47           -1.3            1.4           0.24
-#> 3             1.4           0.59           -1.5            1.3           0.41
-#> 4             1.4           0.52           -1.3            1.4           0.31
-#> 5             1.4           0.60           -1.3            1.3           0.23
-#> 6             1.4           0.68           -1.4            1.2           0.17
-#> 7             1.3           0.77           -1.3            1.4           0.18
-#> 8             1.4           0.57           -1.4            1.3           0.33
-#> 9             1.3           0.61           -1.2            1.5           0.34
-#> 10            1.4           0.44           -1.2            1.4           0.58
-#>    group_2|time_4 group_3|time_2 group_3|time_3
-#> 1            -1.7            1.2          0.107
-#> 2            -1.8            1.3          0.227
-#> 3            -1.7            1.3          0.244
-#> 4            -1.5            1.4          0.410
-#> 5            -1.5            1.3          0.574
-#> 6            -1.9            1.3          0.350
-#> 7            -1.4            1.4          0.391
-#> 8            -1.6            1.2          0.043
-#> 9            -1.4            1.5          0.377
-#> 10           -1.4            1.5          0.297
+#>    group_1|time_2 group_1|time_3 group_1|time_4 group_2|time_2 group_2|time_3 group_2|time_4
+#> 1             1.3           0.55           -1.6            1.3           0.25           -1.7
+#> 2             1.3           0.47           -1.3            1.4           0.24           -1.8
+#> 3             1.4           0.59           -1.5            1.3           0.41           -1.7
+#> 4             1.4           0.52           -1.3            1.4           0.31           -1.5
+#> 5             1.4           0.60           -1.3            1.3           0.23           -1.5
+#> 6             1.4           0.68           -1.4            1.2           0.17           -1.9
+#> 7             1.3           0.77           -1.3            1.4           0.18           -1.4
+#> 8             1.4           0.57           -1.4            1.3           0.33           -1.6
+#> 9             1.3           0.61           -1.2            1.5           0.34           -1.4
+#> 10            1.4           0.44           -1.2            1.4           0.58           -1.4
+#>    group_3|time_2 group_3|time_3
+#> 1             1.2          0.107
+#> 2             1.3          0.227
+#> 3             1.3          0.244
+#> 4             1.4          0.410
+#> 5             1.3          0.574
+#> 6             1.3          0.350
+#> 7             1.4          0.391
+#> 8             1.2          0.043
+#> 9             1.5          0.377
+#> 10            1.5          0.297
 #> # ... with 3990 more draws, and 1 more variables
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 #> 
 #> $difference_group
 #> # A draws_df: 1000 iterations, 4 chains, and 6 variables
-#>    group_2|time_2 group_2|time_3 group_2|time_4 group_3|time_2 group_3|time_3
-#> 1          0.0069          -0.30         -0.028         -0.089         -0.447
-#> 2          0.0631          -0.23         -0.498          0.016         -0.244
-#> 3         -0.1212          -0.19         -0.173         -0.123         -0.347
-#> 4          0.0026          -0.21         -0.211         -0.081         -0.115
-#> 5         -0.1005          -0.37         -0.212         -0.037         -0.027
-#> 6         -0.2115          -0.51         -0.460         -0.044         -0.329
-#> 7          0.0383          -0.59         -0.153          0.064         -0.377
-#> 8         -0.1074          -0.24         -0.215         -0.229         -0.529
-#> 9          0.1657          -0.27         -0.214          0.148         -0.238
-#> 10        -0.0431           0.14         -0.233          0.060         -0.147
-#>    group_3|time_4
-#> 1           -0.19
-#> 2           -0.40
-#> 3           -0.27
-#> 4           -0.53
-#> 5           -0.37
-#> 6           -0.19
-#> 7           -0.42
-#> 8           -0.47
-#> 9           -0.38
-#> 10          -0.40
+#>    group_2|time_2 group_2|time_3 group_2|time_4 group_3|time_2 group_3|time_3 group_3|time_4
+#> 1          0.0069          -0.30         -0.028         -0.089         -0.447          -0.19
+#> 2          0.0631          -0.23         -0.498          0.016         -0.244          -0.40
+#> 3         -0.1212          -0.19         -0.173         -0.123         -0.347          -0.27
+#> 4          0.0026          -0.21         -0.211         -0.081         -0.115          -0.53
+#> 5         -0.1005          -0.37         -0.212         -0.037         -0.027          -0.37
+#> 6         -0.2115          -0.51         -0.460         -0.044         -0.329          -0.19
+#> 7          0.0383          -0.59         -0.153          0.064         -0.377          -0.42
+#> 8         -0.1074          -0.24         -0.215         -0.229         -0.529          -0.47
+#> 9          0.1657          -0.27         -0.214          0.148         -0.238          -0.38
+#> 10        -0.0431           0.14         -0.233          0.060         -0.147          -0.40
 #> # ... with 3990 more draws
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
 #> 
 #> $effect
 #> # A draws_df: 1000 iterations, 4 chains, and 6 variables
-#>    group_2|time_2 group_2|time_3 group_2|time_4 group_3|time_2 group_3|time_3
-#> 1          0.0070          -0.32         -0.022         -0.090         -0.465
-#> 2          0.0624          -0.25         -0.426          0.016         -0.258
-#> 3         -0.1264          -0.20         -0.138         -0.128         -0.373
-#> 4          0.0026          -0.22         -0.178         -0.082         -0.118
-#> 5         -0.0994          -0.39         -0.183         -0.036         -0.028
-#> 6         -0.2190          -0.58         -0.370         -0.046         -0.375
-#> 7          0.0403          -0.59         -0.128          0.067         -0.379
-#> 8         -0.1078          -0.25         -0.164         -0.230         -0.544
-#> 9          0.1702          -0.26         -0.192          0.152         -0.226
-#> 10        -0.0462           0.15         -0.195          0.064         -0.158
-#>    group_3|time_4
-#> 1           -0.15
-#> 2           -0.34
-#> 3           -0.22
-#> 4           -0.45
-#> 5           -0.32
-#> 6           -0.15
-#> 7           -0.35
-#> 8           -0.36
-#> 9           -0.34
-#> 10          -0.33
+#>    group_2|time_2 group_2|time_3 group_2|time_4 group_3|time_2 group_3|time_3 group_3|time_4
+#> 1          0.0070          -0.32         -0.022         -0.090         -0.465          -0.15
+#> 2          0.0624          -0.25         -0.426          0.016         -0.258          -0.34
+#> 3         -0.1264          -0.20         -0.138         -0.128         -0.373          -0.22
+#> 4          0.0026          -0.22         -0.178         -0.082         -0.118          -0.45
+#> 5         -0.0994          -0.39         -0.183         -0.036         -0.028          -0.32
+#> 6         -0.2190          -0.58         -0.370         -0.046         -0.375          -0.15
+#> 7          0.0403          -0.59         -0.128          0.067         -0.379          -0.35
+#> 8         -0.1078          -0.25         -0.164         -0.230         -0.544          -0.36
+#> 9          0.1702          -0.26         -0.192          0.152         -0.226          -0.34
+#> 10        -0.0462           0.15         -0.195          0.064         -0.158          -0.33
 #> # ... with 3990 more draws
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
+#> 
+#> $sigma
+#> # A tibble: 4,000 × 12
+#>    `group_1|time_1` `group_1|time_2` `group_1|time_3` `group_1|time_4` `group_2|time_1`
+#>               <dbl>            <dbl>            <dbl>            <dbl>            <dbl>
+#>  1            0.908            0.988            0.962             1.27            0.908
+#>  2            0.869            1.01             0.949             1.17            0.869
+#>  3            0.888            0.959            0.931             1.26            0.888
+#>  4            0.883            0.989            0.976             1.18            0.883
+#>  5            0.880            1.01             0.967             1.16            0.880
+#>  6            0.900            0.966            0.876             1.24            0.900
+#>  7            0.847            0.951            0.995             1.20            0.847
+#>  8            0.940            0.996            0.973             1.31            0.940
+#>  9            0.966            0.973            1.05              1.12            0.966
+#> 10            0.900            0.933            0.925             1.19            0.900
+#> # ℹ 3,990 more rows
+#> # ℹ 7 more variables: `group_2|time_2` <dbl>, `group_2|time_3` <dbl>, `group_2|time_4` <dbl>,
+#> #   `group_3|time_1` <dbl>, `group_3|time_2` <dbl>, `group_3|time_3` <dbl>,
+#> #   `group_3|time_4` <dbl>
 ```
 
 If you need samples from these marginals averaged across time points, e.g. an "overall effect size", `brm_marginal_draws_average()` can average the draws above across discrete time points (either all or a user-defined subset).
@@ -565,6 +493,22 @@ brm_marginal_draws_average(draws = draws, data = data)
 #> 10          -0.032           -0.14
 #> # ... with 3990 more draws
 #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
+#> 
+#> $sigma
+#> # A tibble: 4,000 × 3
+#>    `group_1|average` `group_2|average` `group_3|average`
+#>                <dbl>             <dbl>             <dbl>
+#>  1             1.03              1.03              1.03 
+#>  2             1.00              1.00              1.00 
+#>  3             1.01              1.01              1.01 
+#>  4             1.01              1.01              1.01 
+#>  5             1.00              1.00              1.00 
+#>  6             0.996             0.996             0.996
+#>  7             0.998             0.998             0.998
+#>  8             1.05              1.05              1.05 
+#>  9             1.03              1.03              1.03 
+#> 10             0.988             0.988             0.988
+#> # ℹ 3,990 more rows
 ```
 
 The `brm_marginal_summaries()` function produces posterior summaries of these marginals, and it includes the Monte Carlo standard error (MCSE) of each estimate.
@@ -574,7 +518,7 @@ The `brm_marginal_summaries()` function produces posterior summaries of these ma
 summaries <- brm_marginal_summaries(draws, level = 0.95)
 
 summaries
-#> # A tibble: 165 × 6
+#> # A tibble: 225 × 6
 #>    marginal         statistic group   time     value    mcse
 #>    <chr>            <chr>     <chr>   <chr>    <dbl>   <dbl>
 #>  1 difference_group lower     group_2 time_2 -0.240  0.00686
@@ -587,7 +531,7 @@ summaries
 #>  8 difference_group mean      group_2 time_3 -0.174  0.00625
 #>  9 difference_group mean      group_2 time_4 -0.0783 0.00634
 #> 10 difference_group mean      group_3 time_2  0.0213 0.00307
-#> # ℹ 155 more rows
+#> # ℹ 215 more rows
 ```
 
 The `brm_marginal_probabilities()` function shows posterior probabilities of the form, 
@@ -639,7 +583,7 @@ summaries_data <- brm_marginal_data(data = data, level = 0.95)
 summaries_data
 #> # A tibble: 84 × 4
 #>    statistic group   time     value
-#>    <chr>     <chr>   <chr>    <dbl>
+#>    <chr>     <chr>   <ord>    <dbl>
 #>  1 lower     group_1 time_1  1.40  
 #>  2 lower     group_1 time_2  2.71  
 #>  3 lower     group_1 time_3  1.91  
diff --git a/vignettes/usage.Rmd.source b/vignettes/usage.Rmd.source
index 56ce46d4..6a57db61 100644
--- a/vignettes/usage.Rmd.source
+++ b/vignettes/usage.Rmd.source
@@ -46,6 +46,15 @@ raw_data <- brm_simulate_simple(
 raw_data
 ```
 
+It is good practice to convert the time variable to an ordered factor so individual discrete time points correctly represent correct chronological order.^[Ordered factors usually have polynomial contrasts (`contr.poly()`), which makes the fixed effect parameterization counterintuitive. However, `brm_data()` manually sets the contrasts of the time variable to be treatment contrasts (`contr.treatment()`) so there are individual fixed effects for individual discrete time points. To revert back to polynomial contrasts, use `contr.poly()` after the call to `brm_data()`.]
+
+```{r}
+raw_data <- raw_data |>
+  brm_data_chronologize(time = "time", levels = paste0("time_", seq_len(4)))
+
+str(raw_data$time)
+```
+
 Next, create a special classed dataset that the package will recognize. The classed data object contains a pre-processed version of the data, along with attributes to declare the outcome variable, whether the outcome is response or change from baseline, the treatment group variable, the discrete time point variable, control group, baseline time point, and the covariates selected for analysis.
 
 ```{r}