Fix typos helpfully pointed out by @Fil (#49)

paper.md

@@ -96,17 +96,17 @@ reference material on the algorithms:
 software is automatically tested using a combination of doc (this paper is also
 tested), unit, integration and property based tests with 100% coverage.
 
-There are potential limitiations associated with Nashpy, these are due to the
+There are potential limitations associated with Nashpy, these are due to the
 complexity of the algorithms themselves. For example, support enumeration
 enumerates all potential pairs of strategy. For $n\times n$ sized square
 matrices it has complexity of the order of $\mathcal{O}\left({2^n}^2\right)$.
-All implementations provided in Nashpy ensure these affects are reduced: numpy
+All implementations provided in Nashpy ensure these effects are reduced: numpy
 [@scipy] provides C based implementations for vectorized performance.
 Furthermore, all algorithms are generators which ensures that not all equilibria
 must be found before one is returned. For example, below an 11 by 11 game is
 considered and timings computed are shown for relative comparison.  Using the
-more efficient Lemke-Howson algorithm [@lemke1964equilibrium] an equilibria is
-founds approximately 3000 times faster.
+more efficient Lemke-Howson algorithm [@lemke1964equilibrium] an equilibrium is
+found approximately 3000 times faster.
 
 ```
 >>> from pprint import pprint