Version bumps

docs/Project.toml

@@ -20,12 +20,12 @@ FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
-JumpProblemLibrary = "faf0f6d7-8cee-47cb-b27c-1eb80cef534e"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 ODEProblemLibrary = "fdc4e326-1af4-4b90-96e7-779fcce2daa5"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 SDEProblemLibrary = "c72e72a9-a271-4b2b-8966-303ed956772e"
@@ -37,7 +37,7 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [compat]
-AlgebraicMultigrid = "0.5, 0.6"
+AlgebraicMultigrid = "0.6"
 BSON = "0.3"
 BVProblemLibrary = "0.1.2"
 BenchmarkTools = "1"
@@ -49,26 +49,25 @@ DASKR = "2.9.1"
 DDEProblemLibrary = "0.1"
 DataFrames = "1.4"
 DiffEqBase = "6.106"
-DiffEqCallbacks = "2.24"
+DiffEqCallbacks = "3"
 DifferentialEquations = "7.6"
 Distributions = "0.25"
 Documenter = "1"
 FiniteDiff = "2"
 ForwardDiff = "0.10"
 IncompleteLU = "0.2"
 JLD2 = "0.4"
-JumpProblemLibrary = "0.1, 1"
-ModelingToolkit = "8"
+ModelingToolkit = "9"
 ODEProblemLibrary = "0.1"
 Optimization = "3"
-OptimizationNLopt = "0.1, 0.2"
+OptimizationNLopt = "0.2"
 OrdinaryDiffEq = "6.76"
 Plots = "1"
-RecursiveArrayTools = "2, 3"
+RecursiveArrayTools = "3"
 SDEProblemLibrary = "0.1"
 SciMLBase = "2"
 SciMLOperators = "0.3"
 StaticArrays = "1"
 Sundials = "4.11.3"
-Symbolics = "4, 5, 6"
+Symbolics = "6"
 Unitful = "1"

docs/make.jl

@@ -1,4 +1,4 @@
-using Documenter, DiffEqBase, SciMLBase, OrdinaryDiffEq
+using Documenter, DiffEqBase, SciMLBase, OrdinaryDiffEq, OrdinaryDiffEqCore
 import ODEProblemLibrary,
        SDEProblemLibrary, DDEProblemLibrary, DAEProblemLibrary, BVProblemLibrary
 using Sundials, DASKR
@@ -21,6 +21,7 @@ makedocs(
         DAEProblemLibrary,
         BVProblemLibrary,
         OrdinaryDiffEq,
+        OrdinaryDiffEqCore,
         Sundials, DASKR
     ],
     linkcheck = true,

docs/src/basics/solution.md

@@ -191,9 +191,10 @@ Extra fields for solutions of specific problems are specified in the appropriate
 problem definition page.
 
 ## Solution Function Stripping
-By default solution objects store functions, making them difficult to serialize. Using the function 
+
+By default solution objects store functions, making them difficult to serialize. Using the function
 `strip_solution(sol)`, a copy of the solution that does not contain any functions is created.
 
 ```@docs
 SciMLBase.strip_solution
-```
+```

docs/src/extras/timestepping.md

@@ -1,7 +1,7 @@
 # [Timestepping Method Descriptions](@id timestepping)
 
 ```@meta
-CurrentModule = OrdinaryDiffEq
+CurrentModule = OrdinaryDiffEqCore
 ```
 
 ## Common Setup
@@ -54,7 +54,7 @@ one step to the next, it can effect the stability of explicit methods. Thus,
 it's only applied by default to low order implicit solvers.
 
 ```@docs
-IController
+OrdinaryDiffEqCore.IController
 ```
 
 ## Proportional-Integral Controller (PI Controller)
@@ -84,13 +84,13 @@ q
 history portion. `qoldinit` is the initialized value for the gain history.
 
 ```@docs
-PIController
+OrdinaryDiffEqCore.PIController
 ```
 
 ## Proportional-Integral-Derivative Controller (PID Controller)
 
 ```@docs
-PIDController
+OrdinaryDiffEqCore.PIDController
 ```
 
 ## Gustafsson Acceleration
@@ -148,7 +148,7 @@ end
 ```
 
 ```@docs
-PredictiveController
+OrdinaryDiffEqCore.PredictiveController
 ```
 
 ## Abstract Controller

docs/src/features/callback_functions.md

@@ -245,7 +245,7 @@ It's clear from the plot how the controls affected the outcome.
 
 ### Example 3: AutoAbstol
 
-MATLAB's Simulink has the option for [an automatic absolute tolerance](https://www.mathworks.com/help/simulink/gui/absolute-tolerance.html).
+MATLAB's Simulink has the option for [an automatic absolute tolerance](https://www.mathworks.com/help/simulink/gui/absolutetolerance.html).
 In this example we will implement a callback which will add this behavior to
 any JuliaDiffEq solver which implements the `integrator` and callback interface.
 

docs/src/tutorials/advanced_ode_example.md

@@ -340,7 +340,7 @@ function. We will use [ModelingToolkit.jl](https://mtk.sciml.ai/dev/)'s
 ```@example stiff1
 using ModelingToolkit
 prob_ode_brusselator_2d_mtk = ODEProblem(
-    modelingtoolkitize(prob_ode_brusselator_2d_sparse),
+    complete(modelingtoolkitize(prob_ode_brusselator_2d_sparse)),
     [], (0.0, 11.5), jac = true, sparse = true);
 # @btime solve(prob_ode_brusselator_2d_mtk,CVODE_BDF(linear_solver=:KLU),save_everystep=false); # compiles very slowly
 nothing # hide

docs/src/tutorials/faster_ode_example.md

@@ -310,7 +310,7 @@ the Julia code for this.
 
 ```@example faster_ode2
 using ModelingToolkit
-de = modelingtoolkitize(prob)
+de = complete(modelingtoolkitize(prob))
 ```
 
 We can tell it to compute the Jacobian if we want to see the code:
@@ -663,7 +663,7 @@ r0[:, :, 1] .= uss .+ 0.1 .* rand.()
 r0[:, :, 2] .= vss
 
 prob = ODEProblem(basic_version!, r0, (0.0, 0.1), p)
-de = modelingtoolkitize(prob)
+de = complete(modelingtoolkitize(prob))
 
 # Note jac=true,sparse=true makes it automatically build sparse Jacobian code
 # as well!