diff --git a/Project.toml b/Project.toml
index fbcab26..fdee096 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "MixedModelsSim"
 uuid = "d5ae56c5-23ca-4a1f-b505-9fc4796fc1fe"
 authors = ["Phillip Alday", "Douglas Bates", "Lisa DeBruine", "Reinhold Kliegl"]
-version = "0.2.10"
+version = "0.2.11"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
diff --git a/docs/Project.toml b/docs/Project.toml
index dfaa096..e4f9ea1 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -1,6 +1,5 @@
 [deps]
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
-CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DataFrameMacros = "75880514-38bc-4a95-a458-c2aea5a3a702"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
@@ -16,9 +15,8 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-CairoMakie = "0.10"
-CategoricalArrays = "0.10"
-DataFrames = "1"
+CairoMakie = "0.12"
 DataFrameMacros = "0.1"
-Documenter = "1"
+DataFrames = "1"
+Documenter = "1.3"
 StableRNGs = "1"
diff --git a/src/utilities.jl b/src/utilities.jl
index 77bd5e5..4a8f4eb 100644
--- a/src/utilities.jl
+++ b/src/utilities.jl
@@ -215,6 +215,11 @@ Update the mixed model to use the random-effects covariance matrices.
 The `namedre` can be created using [`create_re`](@ref). The `namedre` are specified
 by the name of the blocking variable, e.g. `subj=create_re(...)`.
 
+!!! warning
+    The model's response must be initialized to a non-constant value before
+    calling this function, otherwise the model update will fail with a 
+    `PosDefException`.
+
 !!! warning
     Setting θ directly as a keyword-argument is deprecated.
 
@@ -273,6 +278,11 @@ Create the covariance factor for a random effect from the standard deviations an
 The `sigmas` should be specified in the same order as the random slopes in the
 output of `VarCorr(m)`.
 
+!!! note
+    The `sigmas` are specified **relative** to the residual standard deviation. 
+    Absolute values must be scaled by dividing by the assumed residual 
+    standard deviation.
+
 The correlation matrix defaults to the identity matrix, i.e. no correlation between
 random effects.