Fix CI

.github/workflows/Documentation.yml

@@ -41,7 +41,7 @@ jobs:
       - name: Install Julia
         uses: julia-actions/setup-julia@latest
         with:
-          version: '1'
+          version: '1.11'
 
       # Cache to speed up subsequent runs
       - uses: julia-actions/cache@v2

Project.toml

@@ -57,7 +57,7 @@ EnumX = "1"
 GraphMakie = "0.5"
 Graphs = "1.4"
 HomotopyContinuation = "2.9"
-JumpProcesses = "9.13.2"
+JumpProcesses = "9.19.2"
 LaTeXStrings = "1.3.0"
 Latexify = "0.16.6"
 MacroTools = "0.5.5"

docs/Project.toml

@@ -49,7 +49,7 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
 BenchmarkTools = "1.5"
-BifurcationKit = "0.4.4"
+BifurcationKit = "0.4.17"
 CairoMakie = "0.12, 0.13"
 Catalyst = "15"
 DataFrames = "1.6"
@@ -64,14 +64,14 @@ GraphMakie = "0.5"
 Graphs = "1.11.1"
 HomotopyContinuation = "2.9"
 IncompleteLU = "0.2"
-JumpProcesses = "9.13.2"
+JumpProcesses = "9.19.2"
 Latexify = "0.16.5"
 LinearSolve = "2.30, 3"
 ModelingToolkit = "9.69"
 NetworkLayout = "0.4"
 NonlinearSolve = "3.12, 4"
 Optim = "1.9"
-Optimization = "4, 5"
+Optimization = "4"
 OptimizationBBO = "0.4"
 OptimizationEvolutionary = "0.4"
 OptimizationNLopt = "0.3"
@@ -83,7 +83,7 @@ OrdinaryDiffEqRosenbrock = "1"
 OrdinaryDiffEqSDIRK = "1"
 OrdinaryDiffEqTsit5 = "1"
 OrdinaryDiffEqVerner = "1"
-PEtab = "3.8"
+PEtab = "3.11.1"
 Plots = "1.40"
 QuasiMonteCarlo = "0.3"
 SciMLBase = "2.46"

docs/src/inverse_problems/petab_ode_param_fitting.md

@@ -435,14 +435,14 @@ And two additional optional argument:
 
 Because `calibrate_multistart` handles initial guess sampling, unlike for `calibrate`, no initial guess has to be provided.
 
-Here, we fit parameters through 10 independent optimisation runs, using QuasiMonteCarlo's `SobolSample` method, and save the result to the OptimisationRuns folder:
+Here, we fit parameters through 10 independent optimisation runs, using QuasiMonteCarlo's `LatinHypercubeSample` method, and save the result to the OptimisationRuns folder:
 ```@example petab1
 using Optim
 using QuasiMonteCarlo
 mkdir("OptimisationRuns") # hide
 res_ms = calibrate_multistart(petab_problem, IPNewton(), 10; dirsave = "OptimisationRuns",
-    sampling_method = QuasiMonteCarlo.SobolSample())
-res_ms = calibrate_multistart(petab_problem, IPNewton(), 10; dirsave = "OptimisationRuns", sampling_method = QuasiMonteCarlo.SobolSample()) # hide
+    sampling_method = QuasiMonteCarlo.LatinHypercubeSample())
+res_ms = calibrate_multistart(petab_problem, IPNewton(), 10; dirsave = "OptimisationRuns", sampling_method = QuasiMonteCarlo.LatinHypercubeSample()) # hide
 nothing # hide
 ```
 The best result across all runs can still be retrieved using `get_ps(res_ms, petab_problem)`, with the results of the individual runs being stored in the `res_ms.runs` field.

test/simulation_and_solving/hybrid_models.jl

@@ -106,12 +106,12 @@ let
     Nsims = 4000
     for n in 1:Nsims
         sol = solve(jprob, Tsit5(); saveat = tspan[2], seed)
-        @test sol.retcode == ReturnCode.Terminated
+        @test SciMLBase.successful_retcode(sol)
         Xsamp += sol[1, end]
         seed += 1
     end
     Xsamp /= Nsims
-    @test abs(Xsamp - Xf(0.2, p) < 0.05 * Xf(0.2, p))
+    @test abs(Xsamp - Xf(0.2, p)) < 0.05 * Xf(0.2, p)
 end
 
 # Checks that a disjoint hybrid model (i.e. where the Jump and ODE parts do not interact) gives the

test/upstream/mtk_structure_indexing.jl

@@ -52,7 +52,7 @@ begin
     sint = init(sprob, ImplicitEM(); save_everystep = false)
     jint = init(jprob, SSAStepper())
     nint = init(nprob, NewtonRaphson(); save_everystep = false)
-    @test_broken ssint = init(ssprob, DynamicSS(Tsit5()); save_everystep = false) # https://github.com/SciML/SciMLBase.jl/issues/660
+    ssint = init(ssprob, DynamicSS(Tsit5()); save_everystep = false)
     integrators = [oint, sint, jint, nint]
 
     # Creates solutions.