diff --git a/.gitignore b/.gitignore
index 0b2114508..18e9a4249 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,12 +4,15 @@
 docs/build
 docs/site
 docs/src/api/ordinarydiffeq
+docs/src/api/stochasticdiffeq
 docs/ordinarydiffeq_pages.jl
+docs/stochasticdiffeq_pages.jl
 docs/common_first_steps.jl
-/Manifest.toml
+docs/common_imex_first_steps.jl
 
 # vscode stuff
 .vscode
 .vscode/*
 
-Manifest.toml
\ No newline at end of file
+docs/src/assets/Project.toml
+Manifest.toml
diff --git a/docs/make.jl b/docs/make.jl
index 717425c60..0f3ca294f 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -28,10 +28,12 @@ if isdir(ordinartdiffeq_docs_path)
     ordinary_diffeq_pages_file = joinpath(ordinartdiffeq_docs_root, "pages.jl")
     cp(ordinary_diffeq_pages_file, ordinary_diffeq_pages_dest, force = true)
 
-    # Copy the common_first_steps.jl file from OrdinaryDiffEq.jl
-    common_first_steps_dest = joinpath(@__DIR__, "common_first_steps.jl")
-    common_first_steps_file = joinpath(ordinartdiffeq_docs_root, "common_first_steps.jl")
-    cp(common_first_steps_file, common_first_steps_dest, force = true)
+    # Copy the common_first_steps.jl and common_imex_first_steps.jl files from OrdinaryDiffEq.jl
+    for fname in ["common_first_steps.jl", "common_imex_first_steps.jl"]
+        common_first_steps_dest = joinpath(@__DIR__, fname)
+        common_first_steps_file = joinpath(ordinartdiffeq_docs_root, fname)
+        cp(common_first_steps_file, common_first_steps_dest, force = true)
+    end
 end
 
 # Copy StochasticDiffEq.jl documentation
diff --git a/docs/src/examples/classical_physics.md b/docs/src/examples/classical_physics.md
index 8cfc8f67d..dfe00ee01 100644
--- a/docs/src/examples/classical_physics.md
+++ b/docs/src/examples/classical_physics.md
@@ -152,7 +152,7 @@ p = Plots.plot(sol, vars = (1, 2), xlims = (-9, 9), title = "Phase Space Plot",
 function phase_plot(prob, u0, p, tspan = 2pi)
     _prob = ODE.ODEProblem(prob.f, u0, (0.0, tspan))
     sol = ODE.solve(_prob, ODE.Vern9()) # Use Vern9 solver for higher accuracy
-    Plots.plot!(p, sol, idxs = (1, 2), xlims = nothing, ylims = nothing)
+    Plots.plot!(p, sol, idxs = (1, 2))
 end
 for i in (-4pi):(pi / 2):(4π)
     for j in (-4pi):(pi / 2):(4π)
diff --git a/docs/src/features/dae_initialization.md b/docs/src/features/dae_initialization.md
index 6f8acd647..4cf10deb7 100644
--- a/docs/src/features/dae_initialization.md
+++ b/docs/src/features/dae_initialization.md
@@ -1,4 +1,4 @@
-# DAE Initialization
+# [DAE Initialization](@id dae_initialization)
 
 DAE (Differential-Algebraic Equation) problems often require special initialization procedures to ensure that the initial conditions are consistent with the algebraic constraints. The DifferentialEquations.jl ecosystem provides several initialization algorithms to handle this automatically or to verify that your provided initial conditions are already consistent.
 
@@ -213,4 +213,4 @@ sol = solve(prob, IDA(), initializealg = CheckInit())    # Sundials
 ## References
 
 - Brown, P. N., Hindmarsh, A. C., & Petzold, L. R. (1998). Consistent initial condition calculation for differential-algebraic systems. SIAM Journal on Scientific Computing, 19(5), 1495-1512.
-- Shampine, L. F. (2002). Consistent initial condition for differential-algebraic systems. SIAM Journal on Scientific Computing, 22(6), 2007-2026.
\ No newline at end of file
+- Shampine, L. F. (2002). Consistent initial condition for differential-algebraic systems. SIAM Journal on Scientific Computing, 22(6), 2007-2026.
diff --git a/docs/src/tutorials/dae_example.md b/docs/src/tutorials/dae_example.md
index 06daf85a4..1b236ba4e 100644
--- a/docs/src/tutorials/dae_example.md
+++ b/docs/src/tutorials/dae_example.md
@@ -165,7 +165,7 @@ sol_check = DE.solve(prob, Sundials.IDA(), initializealg = DiffEqBase.CheckInit(
 ```
 
 For more details on DAE initialization options, see the
-[DAE Initialization documentation](@ref dae_initialization).
+[DAE Initialization documentation](@ref "dae_initialization").
 
 In order to clearly see all the features of this solution, it should be plotted
 on a logarithmic scale. We'll also plot each on a different subplot, to allow