docs: remove StructuralIndentifiability dependency

Doc examples also commented out, to be added when
StructuralIndentifiability works with v9

Author: AayushSabharwal

docs/Project.toml

@@ -13,7 +13,6 @@ OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
-StructuralIdentifiability = "220ca800-aa68-49bb-acd8-6037fa93a544"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -32,7 +31,6 @@ OptimizationOptimJL = "0.1"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 StochasticDiffEq = "6"
-StructuralIdentifiability = "0.4, 0.5"
 SymbolicUtils = "1"
 Symbolics = "5"
 Unitful = "1.12"

docs/src/tutorials/parameter_identifiability.md

@@ -28,7 +28,7 @@ To define the ode system in Julia, we use `ModelingToolkit.jl`.
 
 We first define the parameters, variables, differential equations and the output equations.
 
-```@example SI
+```julia
 using StructuralIdentifiability, ModelingToolkit
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
@@ -66,7 +66,7 @@ end
 
 After that, we are ready to check the system for local identifiability:
 
-```@example SI
+```julia
 # query local identifiability
 # we pass the ode-system
 local_id_all = assess_local_identifiability(de, p = 0.99)
@@ -76,7 +76,7 @@ We can see that all unknowns (except $x_7$) and all parameters are locally ident
 
 Let's try to check specific parameters and their combinations
 
-```@example SI
+```julia
 to_check = [de.k5, de.k7, de.k10 / de.k9, de.k5 + de.k6]
 local_id_some = assess_local_identifiability(de, funcs_to_check = to_check, p = 0.99)
 ```
@@ -103,7 +103,7 @@ We will run a global identifiability check on this enzyme dynamics[^3] model. We
 
 Global identifiability needs information about local identifiability first, but the function we chose here will take care of that extra step for us.
 
-```@example SI2
+```julia
 using StructuralIdentifiability, ModelingToolkit
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
@@ -144,7 +144,7 @@ We can see that only parameters `a, g` are unidentifiable, and everything else c
 
 Let us consider the same system but with two inputs, and we will find out identifiability with probability `0.9` for parameters `c` and `b`:
 
-```@example SI3
+```julia
 using StructuralIdentifiability, ModelingToolkit
 using ModelingToolkit: t_nounits as t, D_nounits as D