[REVIEW]: Fix typos and add inline code chunks

doc/overview.rst

@@ -3,7 +3,7 @@
 Overview
 ========
 
-The data types in this library follow the mathematical definitions of various multilayer network types defined in the article `"Multilayer Networks", M. Kivela et al., arXiv:1309.7233 [physics.soc-ph] <http://arxiv.org/abs/1309.7233>`_. In the article a multilayer network is defined as a general mathematical structure and all the other types of networks are defined as special cases of that structure. Here we take e a similar approach and define a class MultilayerNetwork such that it represents the mathematical definition of the multilayer network. All the other network classes then inherit the MultilayerNetwork class. Currently we have the MultiplexNetwork class which represent multiplex networks as defined in the article. In the article there were several constraints defined for multiplex networks. Some of these constraints, such as "categorical" and "ordinal" couplings, are also implemented in this library. Instances of MultiplexNetwork that are constrained in this way can be implemented efficiently and the algorithms dealing with general multilayer networks can take advantage of the information that the network object is constrained. 
+The data types in this library follow the mathematical definitions of various multilayer network types defined in the article `"Multilayer Networks", M. Kivela et al., arXiv:1309.7233 [physics.soc-ph] <http://arxiv.org/abs/1309.7233>`_. In the article a multilayer network is defined as a general mathematical structure and all the other types of networks are defined as special cases of that structure. Here we take e a similar approach and define a class :code:`MultilayerNetwork` such that it represents the mathematical definition of the multilayer network. All the other network classes then inherit the :code:`MultilayerNetwork` class. Currently we have the :code:`MultiplexNetwork` class which represent multiplex networks as defined in the article. In the article there were several constraints defined for multiplex networks. Some of these constraints, such as "categorical" and "ordinal" couplings, are also implemented in this library. Instances of :code:`MultiplexNetwork` that are constrained in this way can be implemented efficiently and the algorithms dealing with general multilayer networks can take advantage of the information that the network object is constrained. 
 
 
 Computational efficiency

doc/tutorial/networktypes.rst

@@ -184,7 +184,7 @@ Multiplex networks
 
 The multilayer networks can in theory be used to represented multiplex networks, but in practice, it is often better to use a specialized class :code:`MultiplexNetwork` when dealing with multiplex networks.
 There are several reasons for this. First, the :code:`MultiplexNetwork` class offers an additional convenient interface for handling intra-layer networks.
-Second, the MultiplexNetwork class can take coupling rules as an input when it is constructed and use them to implicitly create the inter-layer edges when they are needed. This saves some memory and makes it easier to create networks with such coupling structures.
+Second, the :code:`MultiplexNetwork` class can take coupling rules as an input when it is constructed and use them to implicitly create the inter-layer edges when they are needed. This saves some memory and makes it easier to create networks with such coupling structures.
 Third, using a :code:`MultiplexNetwork` will let the functions in the library know that your multilayer network is a multiplex network. Some of the functions only work for multiplex networks, but even the ones that work for general multilayer networks can use the information to speed up the processing.
 
 The simplest multiplex network is the one with no coupling edges. You would create such an object with the following command:

doc/tutorial/nx.rst

@@ -18,7 +18,7 @@ This will produce a native Pymnet multilayer network object with 0 aspects (i.e.
 >>> net.aspects
 0
 
-For the sake of reproducability in the next example, let's explicitly seed the ranom number generator here:
+For the sake of reproducibility in the next example, let's explicitly seed the random number generator here:
 
 >>> import random
 >>> random.seed(42)