diff --git a/Project.toml b/Project.toml
index 400f733..2346c1f 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "Uniformization"
 uuid = "f44d862a-bb5d-4651-8b42-7616042c4b61"
 authors = ["Garrett Smith <gasmith@uni-potsdam.de> and contributors"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
diff --git a/README.md b/README.md
index 97b78be..74e2fbd 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,9 @@ Engineering and Informational Sciences*) are implemented.
 Begin by creating a generator matrix $\mathbf{Q}$ for the problem. The $i,j$-th entry
 specifies the transition rate per unit time from state $j$ to state $i$. We also need to
 specify the initial conditions, $\mathbf{p}(0)$, which must be a probabilty distribution
-over all states.
+over all states. Using the `TransitionRateMatrix` type is optional; ensuring that other
+types of matrices have the correct properties can also be done using the
+`transitionratematrix` and `transitionratematrix!` functions.
 
 ```julia
 using Uniformization.jl
@@ -42,6 +44,9 @@ The parameter $k$ controls the accuracy of the approximation; the default is $2^
 The `erlangization` method is the default, as it seems to be a good compromise between
 efficiency and robustness on stiff problems.
 
+Automatic differentiation using `ForwardDiff.jl` and `ReverseDiff.jl` should work as
+expected.
+
 Further information is available in the docstrings.
 
 ## Citing