diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
new file mode 100644
index 00000000..6c53d9cc
--- /dev/null
+++ b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 0.11.0
+Date: 2024-06-19 12:12:13 UTC
+SHA: d85d13034a8e067409c1566fe0edd7a84ed1455b
diff --git a/DESCRIPTION b/DESCRIPTION
index 5440843e..b6a3f6ec 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: PKNCA
 Type: Package
 Title: Perform Pharmacokinetic Non-Compartmental Analysis
-Version: 0.10.2.9000
+Version: 0.11.0
 Authors@R: c(
     person("Bill", "Denney", email="wdenney@humanpredictions.com", role=c("aut", "cre"), comment=c(ORCID="0000-0002-5759-428X")),
     person("Clare", "Buckeridge", email="clare.buckeridge@pfizer.com", role="aut"),
diff --git a/NEWS.md b/NEWS.md
index bfb1ee70..8c4472d2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,7 +4,7 @@ will continue until then.  These will be especially noticeable around
 the inclusion of IV NCA parameters and additional specifications of
 the dosing including dose amount and route.
 
-# PKNCA development version
+# PKNCA 0.11.0
 
 * PKNCA will now indicate the number of observations included in a summary ("n")
   when it is not the same as the number of subjects included in the summary
diff --git a/R/intervals.R b/R/intervals.R
deleted file mode 100644
index ed4d47e0..00000000
--- a/R/intervals.R
+++ /dev/null
@@ -1,99 +0,0 @@
-#' Select primary NCA parameters for an interval
-#'
-#' @inheritParams assert_intervaltime_single
-#' @param parameters The NCA parameters to calculate during the interval
-#' @param groups The groups to use for parameter calculation
-#' @param extrapolation When using an extrapolation method (e.g. for the
-#'   parameter "aucint"), what extrapolation method should be used?
-#' @param clast_extrap When using an extrapolation method, what clast value
-#'   should be used:  "obs" = observed, "pred" = predicted
-#' @examples
-#' make_intervals(start = 0, end = Inf, parameters = c("cmax", "tmax", "half.life", "aucinf"))
-#'
-#' @export
-make_intervals <- function(start, end,
-                           parameters = NULL,
-                           groups = data.frame(),
-                           route = c("extravascular", "intravascular"),
-                           dose_duration = c("bolus", "infusion"),
-                           dose_type = c("single", "steady-state"),
-                           collection = c("point", "interval"),
-                           extrapolation = c("inf", "last", "all"),
-                           clast_extrap = c("obs", "pred")) {
-  interval <- assert_intervaltime_single(start, end)
-  if (!is.data.frame(groups)) {
-    stop("`groups` must be a data.frame")
-  }
-  route <- match.arg(route)
-  dose_duration <- match.arg(dose_duration)
-  dose_type <- match.arg(dose_type)
-  collection <- match.arg(collection)
-  extrapolation <- match.arg(extrapolation)
-  clast_extrap <- match.arg(clast_extrap)
-  if (is.null(parameters)) {
-    parameters <-
-      auto_parameters(
-        start = start, end = end,
-        route = route,
-        dose_duration = dose_duration,
-        dose_type = dose_type,
-        collection = collection
-      )
-    # auto-select parameters
-    parameters <- c("cmax", "tmax")
-    if (end == "inf") {
-      if (dose_type == "single") {
-        parameters <- c(parameters, "auclast", "aucinf")
-      }
-      parameters <- c(parameters, "half.life")
-    } else {
-      # end is not inf
-      parameters <- c(parameters, "aucint")
-    }
-    if (route == "extravascular") {
-      if (dose_duration == "bolus") {
-        parameters <- c(parameters, "c0")
-      }
-    }
-  }
-}
-
-#' Auto-select NCA parameters based on dosing type
-#'
-#' @inheritParams assert_intervaltime_single
-#' @param route What is the route of administration?
-#' @param dose_duration If `route = "intravascular"` how is the dose
-#'   administered? Ignored if `route = "extravascular"`.
-#' @param dose_type Is the data single-dose or steady-state?
-#' @param collection Is the collection at a "point" in time (like plasma, serum,
-#'   or blood) or an "interval" of time (like urine or feces)?
-#' @returns A character vector of parameters to calculate which can be passed to
-#'   `make_intervals()`
-#' @export
-auto_parameters <- function(start, end,
-                            route = c("extravascular", "intravascular"),
-                            dose_duration = c("bolus", "infusion"),
-                            dose_type = c("single", "steady-state"),
-                            collection = c("point", "interval")) {
-  if (collection == "point") {
-    ret <- c("cmax", "tmax")
-    if (is.infinite(end)) {
-      ret <- c(ret, "half.life")
-      if (dose_type == "single") {
-        ret <- c(ret, "aucinf.obs", "auclast")
-      }
-    } else {
-      ret <- c(ret, "aucint.inf.obs")
-    }
-    if (route == "intravascular") {
-      if (dose_duration == "bolus") {
-        ret <- c(ret, "c0")
-      } else if (dose_duration == "infusion") {
-        ret <- c(ret, "ceoi")
-      }
-    }
-  } else if (collection == "interval") {
-    ret <- c("ae", "fe")
-  }
-  ret
-}
diff --git a/R/sparse.R b/R/sparse.R
index 6173db18..eaad17e5 100644
--- a/R/sparse.R
+++ b/R/sparse.R
@@ -65,7 +65,7 @@ sparse_pk_attribute <- function(sparse_pk, ...) {
 #'
 #' Where:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{w_i}{w_i}}{is the weight at time i}
 #'   \item{\eqn{\delta_{time,i-1,i}}{d_time[i-1,i]} and \eqn{\delta_{time,i,i+1}}{d_time[i,i+1]}}{are the changes between time i-1 and i or i and i+1 (zero outside of the time range)}
 #'   \item{\eqn{t_i}{t_i}}{is the time at time i}
@@ -89,7 +89,7 @@ sparse_auc_weight_linear <- function(sparse_pk) {
 #' Choices for the method of calculation (the argument `sparse_mean_method`)
 #' are:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{"arithmetic mean"}{Arithmetic mean (ignoring number of BLQ samples)}
 #'   \item{"arithmetic mean, <=50% BLQ"}{If >= 50% of the measurements are BLQ, zero.  Otherwise, the arithmetic mean of all samples (including the BLQ as zero).}
 #' }
@@ -199,7 +199,7 @@ var_sparse_auc <- function(sparse_pk) {
 #' defined as zero (rather than dividing by zero).
 #'
 #' Where:
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{\hat{\sigma}_{ij}}{sigma_ij}}{The covariance of times i and j}
 #'   \item{\eqn{r_i}{r_i} and \eqn{r_j}{r_j}}{The number of subjects (usually animals) at times i and j, respectively}
 #'   \item{\eqn{r_{ij}{r_ij}}}{The number of subjects (usually animals) at both times i and j}
@@ -292,7 +292,7 @@ sparse_to_dense_pk <- function(sparse_pk) {
 #'
 #' Where:
 #'
-#' \itemize{
+#' \describe{
 #'   \item{\eqn{AUC}{AUC}}{is the estimated area under the concentration-time curve}
 #'   \item{\eqn{w_i}{w_i}}{is the weight applied to the concentration at time i (related to the time which it affects, see [sparse_auc_weight_linear()])}
 #'   \item{\eqn{\bar{C}_i}{Cbar_i}}{is the average concentration at time i}
diff --git a/R/time_calc.R b/R/time_calc.R
index d5f397f0..7f0643d4 100644
--- a/R/time_calc.R
+++ b/R/time_calc.R
@@ -4,7 +4,7 @@
 #' @param time_obs A vector of times for observations
 #' @param units Passed to `base::as.numeric.difftime()`
 #' @returns A data.frame with columns for:
-#' \itemize{
+#' \describe{
 #'   \item{event_number_before}{The index of `time_event` that is the last one before `time_obs` or `NA` if none are before.}
 #'   \item{event_number_after}{The index of `time_event` that is the first one after `time_obs` or `NA` if none are after.}
 #'   \item{time_before}{The minimum time that the current `time_obs` is before a `time_event`, 0 if at least one `time_obs == time_event`.}
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 8752480e..829a9b7b 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -11,6 +11,7 @@ AUMClast
 Analyte
 BIP
 BLQ
+Beal's
 Biometrics
 Biopharmaceutical
 CDISC
@@ -25,6 +26,7 @@ DL
 DOI
 Dixit
 Durations
+Extrap
 Gabrielsson
 Hadley
 Hsuan
@@ -96,7 +98,6 @@ conc
 ctrough
 customizable
 dbplyr
-difftime
 doBy
 doi
 dosetype
diff --git a/man/cov_holder.Rd b/man/cov_holder.Rd
index 6c0b8c04..9520ef3e 100644
--- a/man/cov_holder.Rd
+++ b/man/cov_holder.Rd
@@ -25,7 +25,7 @@ If \eqn{r_{ij} = 0}{r_ij = 0}, then \eqn{\hat{\sigma}_{ij}}{sigma_ij} is
 defined as zero (rather than dividing by zero).
 
 Where:
-\itemize{
+\describe{
 \item{\eqn{\hat{\sigma}_{ij}}{sigma_ij}}{The covariance of times i and j}
 \item{\eqn{r_i}{r_i} and \eqn{r_j}{r_j}}{The number of subjects (usually animals) at times i and j, respectively}
 \item{\eqn{r_{ij}{r_ij}}}{The number of subjects (usually animals) at both times i and j}
diff --git a/man/pk.calc.sparse_auc.Rd b/man/pk.calc.sparse_auc.Rd
index fe5d9c56..ab8b711a 100644
--- a/man/pk.calc.sparse_auc.Rd
+++ b/man/pk.calc.sparse_auc.Rd
@@ -44,7 +44,7 @@ The AUC is calculated as:
 
 Where:
 
-\itemize{
+\describe{
 \item{\eqn{AUC}{AUC}}{is the estimated area under the concentration-time curve}
 \item{\eqn{w_i}{w_i}}{is the weight applied to the concentration at time i (related to the time which it affects, see \code{\link[=sparse_auc_weight_linear]{sparse_auc_weight_linear()}})}
 \item{\eqn{\bar{C}_i}{Cbar_i}}{is the average concentration at time i}
diff --git a/man/sparse_auc_weight_linear.Rd b/man/sparse_auc_weight_linear.Rd
index 331b940a..1373dae9 100644
--- a/man/sparse_auc_weight_linear.Rd
+++ b/man/sparse_auc_weight_linear.Rd
@@ -23,7 +23,7 @@ The weight is used as the \eqn{w_i}{w_i} parameter in \code{\link[=pk.calc.spars
 
 Where:
 
-\itemize{
+\describe{
 \item{\eqn{w_i}{w_i}}{is the weight at time i}
 \item{\eqn{\delta_{time,i-1,i}}{d_time[i-1,i]} and \eqn{\delta_{time,i,i+1}}{d_time[i,i+1]}}{are the changes between time i-1 and i or i and i+1 (zero outside of the time range)}
 \item{\eqn{t_i}{t_i}}{is the time at time i}
diff --git a/man/sparse_mean.Rd b/man/sparse_mean.Rd
index 25cefd38..f59377f4 100644
--- a/man/sparse_mean.Rd
+++ b/man/sparse_mean.Rd
@@ -25,7 +25,7 @@ Choices for the method of calculation (the argument \code{sparse_mean_method})
 are:
 }
 \details{
-\itemize{
+\describe{
 \item{"arithmetic mean"}{Arithmetic mean (ignoring number of BLQ samples)}
 \item{"arithmetic mean, <=50\% BLQ"}{If >= 50\% of the measurements are BLQ, zero.  Otherwise, the arithmetic mean of all samples (including the BLQ as zero).}
 }
diff --git a/man/time_calc.Rd b/man/time_calc.Rd
index 27637ff7..7b432916 100644
--- a/man/time_calc.Rd
+++ b/man/time_calc.Rd
@@ -15,7 +15,7 @@ time_calc(time_event, time_obs, units = NULL)
 }
 \value{
 A data.frame with columns for:
-\itemize{
+\describe{
 \item{event_number_before}{The index of \code{time_event} that is the last one before \code{time_obs} or \code{NA} if none are before.}
 \item{event_number_after}{The index of \code{time_event} that is the first one after \code{time_obs} or \code{NA} if none are after.}
 \item{time_before}{The minimum time that the current \code{time_obs} is before a \code{time_event}, 0 if at least one \code{time_obs == time_event}.}
diff --git a/vignettes/v06-half-life-calculation-tobit.Rmd b/vignettes/v06-half-life-calculation-tobit.Rmd
index 51207a38..4f3ec8c4 100644
--- a/vignettes/v06-half-life-calculation-tobit.Rmd
+++ b/vignettes/v06-half-life-calculation-tobit.Rmd
@@ -88,7 +88,7 @@ The steps for Tobit regression are:
     2. The $\lambda_z$ value (slope for the half-life line) must be positive;
        in other words, the half-life slope must be decreasing.
 
-# Comparision of Tobit and semi-log regression
+# Comparison of Tobit and semi-log regression
 
 In almost all scenarios, Tobit regression using the algorithm above improves the
 half-life estimate compared to semi-log regression.  In the figure below,