Update docs/src/model_creation/functional_parameters.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/model_creation/functional_parameters.md

@@ -2,7 +2,7 @@
 Catalyst supports the usage of "functional parameters". In practice these are not parameters, but a way to inject custom functions into models. This can be used when rates depend on real data, or to represent complicated functions ( which uses e.g. `for` loops or random number generation). Here, the function's values are declared as a data interpolation, which is then used as the functional parameter's value in the simulation. On this page, we first show how to create time-dependent functional parameters, and then give an example where the functional parameter depends on a species value.
 
 ## [Basic example](@id functional_parameters_basic_example)
-Let us first consider an easy, quick-start example. We will consider a simple [birth-death model](@ref basic_CRN_library_bd), but where the birth rate is determined by an input parameter (which value depends on time). First, we [define the input parameter programmatically](@ref programmatic_CRN_construction), and its values across all time values using the [DataInterpolations.jl](https://github.com/SciML/DataInterpolations.jl) package. In this example we will use the input function $pIn(t) = (2 + t)/(1 + t)$.
+Let us first consider an easy, quick-start example. We will consider a simple [birth-death model](@ref basic_CRN_library_bd), but where the birth rate is determined by an input parameter (for which the value depends on time). First, we [define the input parameter programmatically](@ref programmatic_CRN_construction), and its values across all time points using the [DataInterpolations.jl](https://github.com/SciML/DataInterpolations.jl) package. In this example we will use the input function $pIn(t) = (2 + t)/(1 + t)$.
 ```@example functional_parameters_basic_example
 using Catalyst, DataInterpolations
 t = default_t()