diff --git a/R/design.R b/R/design.R
index 2b09095..c5b1948 100644
--- a/R/design.R
+++ b/R/design.R
@@ -39,8 +39,8 @@
 #' choice set given the sample from the prior preference distribution.
 #' Defaults to `FALSE`.
 #' @param method Which method to use for obtaining a Bayesian D-efficient
-#' design, `"Modfed"` or `"CEA"`? Defaults to `"Modfed"`. See `?idefix::Modfed`
-#' and `?idefix::CEA` for more details.
+#' design, `"CEA"` or `"Modfed"`? Defaults to `"CEA"`. See `?idefix::CEA`
+#' and `?idefix::Modfed` for more details.
 #' @param max_iter A numeric value indicating the maximum number allowed
 #' iterations when searching for a Bayesian D-efficient design. The default is
 #' 50.
@@ -89,20 +89,18 @@
 #' )
 #'
 #' # Make a Bayesian D-efficient design with a prior model specified
-#' # Note that by default method = "Modfed" and parallel = TRUE.
-#' # This example uses "CEA" because it is faster.
+#' # Note that by default parallel = TRUE.
 #' design_deff <- cbc_design(
 #'     profiles  = profiles,
-#'     n_resp    = 30, # Number of respondents
-#'     n_alts    = 2,  # Number of alternatives per question
-#'     n_q       = 5,  # Number of questions per respondent
+#'     n_resp    = 300, # Number of respondents
+#'     n_alts    = 3,  # Number of alternatives per question
+#'     n_q       = 6,  # Number of questions per respondent
 #'     n_start   = 1,
 #'     priors = list(
 #'         price     = -0.1,
 #'         type      = c(0.1, 0.2),
 #'         freshness = c(0.1, 0.2)
 #'     ),
-#'     method = "CEA",
 #'     parallel = FALSE
 #' )
 cbc_design <- function(
@@ -118,7 +116,7 @@ cbc_design <- function(
   priors = NULL,
   prior_no_choice = NULL,
   probs = FALSE,
-  method = "Modfed",
+  method = "CEA",
   max_iter = 50,
   parallel = TRUE
 ) {
@@ -388,15 +386,16 @@ make_design_deff <- function(
 
     profiles_restricted <- nrow(expand.grid(lvl.names)) > nrow(profiles)
 
+    if (profiles_restricted & (method == "CEA")) {
+      # "CEA" method only works with unrestricted profile set
+      method == "Modfed"
+      warning(
+        'The "CEA" algorithm requires the use of an unrestricted set of ',
+        'profiles, so "Modfed" is now being used instead.'
+      )
+    }
+
     if (method == "CEA") {
-        # "CEA" method only works with unrestricted profile set
-        if (profiles_restricted) {
-          stop(
-            'The CEA algorithm requires the use of an unrestricted set of ',
-            'profiles. Set \'method = "Modfed"\', which can use restricted ',
-            'profiles, or consider using an unrestricted set of profiles.'
-          )
-        }
         D <- idefix::CEA(
             lvls = lvls,
             coding = coding,
diff --git a/man/cbc_design.Rd b/man/cbc_design.Rd
index dfe6ce7..007fdb4 100644
--- a/man/cbc_design.Rd
+++ b/man/cbc_design.Rd
@@ -17,7 +17,7 @@ cbc_design(
   priors = NULL,
   prior_no_choice = NULL,
   probs = FALSE,
-  method = "Modfed",
+  method = "CEA",
   max_iter = 50,
   parallel = TRUE
 )
@@ -68,8 +68,8 @@ choice set given the sample from the prior preference distribution.
 Defaults to \code{FALSE}.}
 
 \item{method}{Which method to use for obtaining a Bayesian D-efficient
-design, \code{"Modfed"} or \code{"CEA"}? Defaults to \code{"Modfed"}. See \code{?idefix::Modfed}
-and \code{?idefix::CEA} for more details.}
+design, \code{"CEA"} or \code{"Modfed"}? Defaults to \code{"CEA"}. See \code{?idefix::CEA}
+and \code{?idefix::Modfed} for more details.}
 
 \item{max_iter}{A numeric value indicating the maximum number allowed
 iterations when searching for a Bayesian D-efficient design. The default is
@@ -128,20 +128,18 @@ design_rand_labeled <- cbc_design(
 )
 
 # Make a Bayesian D-efficient design with a prior model specified
-# Note that by default method = "Modfed" and parallel = TRUE.
-# This example uses "CEA" because it is faster.
+# Note that by default parallel = TRUE.
 design_deff <- cbc_design(
     profiles  = profiles,
-    n_resp    = 30, # Number of respondents
-    n_alts    = 2,  # Number of alternatives per question
-    n_q       = 5,  # Number of questions per respondent
+    n_resp    = 300, # Number of respondents
+    n_alts    = 3,  # Number of alternatives per question
+    n_q       = 6,  # Number of questions per respondent
     n_start   = 1,
     priors = list(
         price     = -0.1,
         type      = c(0.1, 0.2),
         freshness = c(0.1, 0.2)
     ),
-    method = "CEA",
     parallel = FALSE
 )
 }