[Docs] : Fix typos (#1095)

* Update index.md

* Update predator_prey.jl

* Update optim.jl

* Update event_rock_paper_scissors.jl

Author: FarukhS52

docs/src/index.md

@@ -123,7 +123,7 @@ This is an example of emergent behavior from simple interactions of agents that
 
 ## Getting help
 
-You're looking for support for Agents.jl? Look no further! Here's some things you can do to resolve your questions about Agents.jl:
+You're looking for support for Agents.jl? Look no further! Here are some things you can do to resolve your questions about Agents.jl:
 
 1. Read the online documentation! It is likely that the thing you want to know is already documented, so use the search bar and search away!
 2. Chat with us in the channel `#dynamics-bridged` in the [Julia Slack](https://julialang.org/slack/)!

examples/event_rock_paper_scissors.jl

@@ -120,7 +120,7 @@ end
 # Another way to think of propensities is the relative probability mass
 # for an event to happen.
 # The propensities may be constants or functions of the
-# currently actived agent and the model.
+# currently active agent and the model.
 
 # Here, the propensities for moving and attacking will be constants,
 attack_propensity = 1.0

examples/optim.jl

@@ -1,7 +1,7 @@
 # # Optimizing agent based models
 
 # Sometimes we need to fine-tune our ABMs parameters to a specific outcome.
-# The brute-force solution can quickly become infeasible for even for a few different
+# The brute-force solution can quickly become infeasible even for a few different
 # parameter settings over a number of valid scan ranges. Most of the time,
 # ABMs are also stochastic, so the effect of a parameter setting should be derived from
 # taking the average value only after running the model several times.
@@ -23,7 +23,7 @@
 # To look for optimal parameters, we need to define a cost function.
 # The cost function takes as arguments the model parameters that we want to tune;
 # in a SIR model, that would be the migration rate, death rate, transmission rate,
-# when an infected person has been detected (`β_det`), or when the remain undetected
+# when an infected person has been detected (`β_det`), or when they remain undetected
 # (`β_und`), infection period, reinfection probability, and time until the infection
 # is detected. The function returns an *objective*: this value takes the form one
 # or more numbers, which the optimiser will attempt to minimize.

examples/predator_prey.jl

@@ -43,7 +43,7 @@
 # First we define the agent types
 # (here you can see that it isn't really that much
 # of an advantage to have two different agent types. Like in the [Rabbit, Fox, Wolf](@ref)
-# example, we could have only one type and one additional filed to separate them.
+# example, we could have only one type and one additional field to separate them.
 # Nevertheless, for the sake of example, we will use two different types.)
 using Agents, Random