diff --git a/R/tar_nlmixr_multimodel.R b/R/tar_nlmixr_multimodel.R
index d5443b0..c6014de 100644
--- a/R/tar_nlmixr_multimodel.R
+++ b/R/tar_nlmixr_multimodel.R
@@ -4,10 +4,12 @@
 #'   modelFunction`
 #' @inheritParams nlmixr2est::nlmixr
 #' @inheritParams targets::tar_target
+#' @param envir The environment where models are defined (usually doesn't need
+#'   to be modified)
 #' @return A list of targets for the model simplification, data simplification,
 #'   and model estimation.
 #' @export
-tar_nlmixr_multimodel <- function(name, ..., data, est, control = list(), table = nlmixr2est::tableControl()) {
+tar_nlmixr_multimodel <- function(name, ..., data, est, control = list(), table = nlmixr2est::tableControl(), envir = parent.frame()) {
   if (is.null(est)) {
     stop("'est' must not be null")
   }
@@ -20,11 +22,12 @@ tar_nlmixr_multimodel <- function(name, ..., data, est, control = list(), table
     table = substitute(table),
     # This extracts the ... argument similarly to using `substitute()`.  From
     # https://stackoverflow.com/questions/55019441/deparse-substitute-with-three-dots-arguments
-    model_list = match.call(expand.dots = FALSE)$...
+    model_list = match.call(expand.dots = FALSE)$...,
+    envir = envir
   )
 }
 
-tar_nlmixr_multimodel_parse <- function(name, data, est, control, table, model_list) {
+tar_nlmixr_multimodel_parse <- function(name, data, est, control, table, model_list, envir) {
   checkmate::assert_named(model_list, type = "unique")
   ret_prep <-
     lapply(
@@ -34,7 +37,8 @@ tar_nlmixr_multimodel_parse <- function(name, data, est, control, table, model_l
       data = data,
       est = est,
       control = control,
-      table = table
+      table = table,
+      envir = envir
     )
   # Extract the targets to fit.  This will be a list of lists.  The inner list
   # will have the three targets for fitting the model, and the outer list will
@@ -42,7 +46,7 @@ tar_nlmixr_multimodel_parse <- function(name, data, est, control, table, model_l
   target_model_fitting <- lapply(X = unname(ret_prep), FUN = \(x) x[["target"]])
   # Generate the combined list with names
   combined_list <- lapply(X = ret_prep, FUN = \(x) as.name(x$name))
-  target_combined_list <- tar_target_raw(name = name, command = str2lang(deparse(combined_list)))
+  target_combined_list <- targets::tar_target_raw(name = name, command = str2lang(deparse(combined_list)))
   # Return the models to fit and the list-combining target
   append(
     target_model_fitting,
@@ -50,10 +54,10 @@ tar_nlmixr_multimodel_parse <- function(name, data, est, control, table, model_l
   )
 }
 
-tar_nlmixr_multimodel_single <- function(object, name, data, est, control, table) {
+tar_nlmixr_multimodel_single <- function(object, name, data, est, control, table, envir) {
   # Hash the model itself without its description.  Then, if the description
   # changes, the model will not need to rerun.
-  hash_long <- digest::digest(eval(object))
+  hash_long <- digest::digest(eval(object, envir = envir))
   hash <- substr(hash_long, 1, 8)
   name_hash <- paste(name, hash, sep = "_")
   tar_prep <-
diff --git a/man/tar_nlmixr_multimodel.Rd b/man/tar_nlmixr_multimodel.Rd
index 3ea303a..67ebc03 100644
--- a/man/tar_nlmixr_multimodel.Rd
+++ b/man/tar_nlmixr_multimodel.Rd
@@ -10,7 +10,8 @@ tar_nlmixr_multimodel(
   data,
   est,
   control = list(),
-  table = nlmixr2est::tableControl()
+  table = nlmixr2est::tableControl(),
+  envir = parent.frame()
 )
 }
 \arguments{
@@ -42,6 +43,9 @@ to be different for each type of estimation method}
 
 \item{table}{The output table control object (like
 `tableControl()`)}
+
+\item{envir}{The environment where models are defined (usually doesn't need
+to be modified)}
 }
 \value{
 A list of targets for the model simplification, data simplification,