diff --git a/src/leidenalg/Optimiser.py b/src/leidenalg/Optimiser.py
index b6c839e..409e5f5 100644
--- a/src/leidenalg/Optimiser.py
+++ b/src/leidenalg/Optimiser.py
@@ -4,7 +4,7 @@
 from math import log, sqrt
 
 class Optimiser(object):
-  """ Class for doing community detection using the Leiden algorithm.
+  r""" Class for doing community detection using the Leiden algorithm.
 
   The Leiden algorithm [1] derives from the Louvain algorithm [2]. The Louvain
   algorithm has an elegant formulation. It consists of two phases: (1) move
@@ -308,7 +308,7 @@ def optimise_partition(self, partition, n_iterations=2, is_membership_fixed=None
     return diff
 
   def optimise_partition_multiplex(self, partitions, layer_weights=None, n_iterations=2, is_membership_fixed=None):
-    """ Optimise the given partitions simultaneously.
+    r""" Optimise the given partitions simultaneously.
 
     Parameters
     ----------
diff --git a/src/leidenalg/VertexPartition.py b/src/leidenalg/VertexPartition.py
index 50372cf..6be4dad 100644
--- a/src/leidenalg/VertexPartition.py
+++ b/src/leidenalg/VertexPartition.py
@@ -385,7 +385,7 @@ def weight_from_comm(self, v, comm):
     return _c_leiden._MutableVertexPartition_weight_from_comm(self._partition, v, comm)
 
 class ModularityVertexPartition(MutableVertexPartition):
-  """ Implements modularity. This quality function is well-defined only for positive edge weights.
+  r""" Implements modularity. This quality function is well-defined only for positive edge weights.
 
   Notes
   -----
@@ -758,7 +758,7 @@ def __deepcopy__(self, memo):
     return new_partition
 
 class RBConfigurationVertexPartition(LinearResolutionParameterVertexPartition):
-  """ Implements Reichardt and Bornholdt's Potts model with a configuration null model.
+  r""" Implements Reichardt and Bornholdt's Potts model with a configuration null model.
   This quality function is well-defined only for positive edge weights.
   This quality function uses a linear resolution parameter.