fixes

Author: TorkelE

docs/src/assets/Project.toml

@@ -35,7 +35,7 @@ DataFrames = "1"
 DiffEqParamEstim = "2.1"
 DifferentialEquations = "7.7"
 Distributions = "0.25"
-Documenter = "0.27"
+Documenter = "1.4.1"
 HomotopyContinuation = "2.6"
 Latexify = "0.15, 0.16"
 ModelingToolkit = "9.5"

docs/src/introduction_to_catalyst/catalyst_for_new_julia_users.md

@@ -67,7 +67,7 @@ using Plots
 ```
 Here, if we restart Julia, these commands *do need to be rerun.
 
-A more comprehensive (but still short) introduction to package management in Julia is provided at [the end of this documentation page](catalyst_for_new_julia_users_packages). It contains some useful information and is hence highly recommended reading. For a more detailed introduction to Julia package management, please read [the Pkg documentation](https://docs.julialang.org/en/v1/stdlib/Pkg/).
+A more comprehensive (but still short) introduction to package management in Julia is provided at [the end of this documentation page](@ref catalyst_for_new_julia_users_packages). It contains some useful information and is hence highly recommended reading. For a more detailed introduction to Julia package management, please read [the Pkg documentation](https://docs.julialang.org/en/v1/stdlib/Pkg/).
 
 ## Simulating a basic Catalyst model
 Now that we have some basic familiarity with Julia, and have installed and imported the required packages, we will create and simulate a basic chemical reaction network model using Catalyst.

docs/src/model_creation/dsl_description.md

@@ -671,6 +671,7 @@ sol[:Xtot]
 ```
 similarly, we can plot the values of $Xtot$ and $Ytot$ using
 ```@example obs1
+using plots
 plot(sol; idxs=[:Xtot, :Ytot], label=["Total X" "Total Y"])
 ```
 

docs/src/model_simulation/TOBEREMOVED_advanced_simulations.md

@@ -177,7 +177,7 @@ Here, we access the system's unknown `X` through the `integrator`, and add
 `5.0` to its amount. We can now simulate our system using the
 callback:
 ```@example ex2
-sol = solve(oprob; callback = ps_cb)
+sol = solve(oprob, Tsit5(); callback = ps_cb)
 plot(sol)
 ```
 
@@ -194,16 +194,16 @@ p = [:k => 1.0]
 oprob = ODEProblem(rn, u0, tspan, p)
 
 condition = [5.0]
-affect!(integrator) = integrator[:k] = 5.0
+affect!(integrator) = integrator.ps[:k] = 5.0
 ps_cb = PresetTimeCallback(condition, affect!)
 
-sol = solve(oprob; callback = ps_cb)
+sol = solve(oprob, Tsit5(); callback = ps_cb)
 plot(sol)
 ```
 The result looks as expected. However, what happens if we attempt to run the
 simulation again?
 ```@example ex2
-sol = solve(oprob; callback = ps_cb)
+sol = solve(oprob, Tsit5(); callback = ps_cb)
 plot(sol)
 ```
 The plot looks different, even though we simulate the same problem. Furthermore,
@@ -229,12 +229,12 @@ condition = [5.0]
 affect!(integrator) = integrator.ps[:k] = 5.0
 ps_cb = PresetTimeCallback(condition, affect!)
 
-sol = solve(deepcopy(oprob); callback = ps_cb)
+sol = solve(deepcopy(oprob), Tsit5(); callback = ps_cb)
 plot(sol)
 ```
 where we parse a copy of our `ODEProblem` to the solver (using `deepcopy`). We can now run
 ```@example ex2
-sol = solve(deepcopy(oprob); callback = ps_cb)
+sol = solve(deepcopy(oprob), Tsit5(); callback = ps_cb)
 plot(sol)
 ```
 and get the expected result.
@@ -253,7 +253,7 @@ oprob = ODEProblem(rn, u0, tspan, p)
 ps_cb_1 = PresetTimeCallback([3.0, 7.0], integ -> integ[:X1] += 5.0)
 ps_cb_2 = PresetTimeCallback([5.0], integ -> integ[:k] = 5.0)
 
-sol = solve(deepcopy(oprob); callback=CallbackSet(ps_cb_1, ps_cb_2))
+sol = solve(deepcopy(oprob), Tsit5(); callback=CallbackSet(ps_cb_1, ps_cb_2))
 plot(sol)
 ```
 
@@ -355,7 +355,7 @@ end
 
 u0_4 = [:X => 10.0, :Y => 10.0]
 tspan = (0.0, 10.0)
-p_4 = [p => 10.0, d => 1.]
+p_4 = [:p => 10.0, :d => 1.]
 
 sprob_4 = SDEProblem(rn_4, u0_4, tspan, p_4)
 sol_4 = solve(sprob_4, ImplicitEM())

docs/src/model_simulation/simulation_structure_interfacing.md

@@ -109,6 +109,7 @@ Finally, we note that we cannot change the values of solution unknowns or parame
 
 Catalyst is built on an *intermediary representation* implemented by (ModelingToolkit.jl)[https://github.com/SciML/ModelingToolkit.jl]. ModelingToolkit is a modelling framework where one first declares a set of symbolic variables and parameters using e.g.
 ```@example ex2
+using Catalyst # hide
 using ModelingToolkit
 t = default_t()
 @parameters σ ρ β

docs/src/steady_state_functionality/nonlinear_solve.md

@@ -66,7 +66,7 @@ Like previously, the found steady state is unaffected by the initial `u` guess.
 
 
 ## [Systems with conservation laws](@id nonlinear_solve_conservation_laws)
-As described in the section on homotopy continuation, when finding the steady states of systems with conservation laws, [additional considerations have to be taken](homotopy_continuation_conservation_laws). E.g. consider the following two-state system:
+As described in the section on homotopy continuation, when finding the steady states of systems with conservation laws, [additional considerations have to be taken](@ref homotopy_continuation_conservation_laws). E.g. consider the following two-state system:
 ```@example nonlinear_solve2
 using Catalyst, NonlinearSolve # hide
 two_state_model = @reaction_network begin