typos CI

.github/dependabot.yml

@@ -5,3 +5,6 @@ updates:
     directory: "/" # Location of package manifests
     schedule:
       interval: "weekly"
+    ignore:
+      - dependency-name: "crate-ci/typos"
+        update-types: ["version-update:semver-patch"]

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v3
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1,4 @@
+[default.extend-words]
+quations = "quations"
+IIF = "IIF"
+padd = "padd"

docs/src/solvers/bvp_solve.md

@@ -29,5 +29,5 @@ off via the keyword argument `adaptive = false`.
 
 ODEInterface.jl can be used seamlessly with BoundaryValueDiffEq.jl, after we define our model using `BVProblem` or `TwoPointBVProblem`, we can directly call the solvers from ODEInterface.jl.
 
-  - `BVPM2` - FORTRAN code for solving two-point boundary value problems. `BVPM2` is only compatiable with `TwoPointBVProblem`.
+  - `BVPM2` - FORTRAN code for solving two-point boundary value problems. `BVPM2` is only compatible with `TwoPointBVProblem`.
   - `BVPSOL` - FORTRAN77 code which solves highly nonlinear two point boundary value problems using a local linear solver (condensing algorithm) or a global sparse linear solver for the solution of the arising linear subproblems, by Peter Deuflhard, Georg Bader, Lutz Weimann. `BVPSOL` should be used with `TwoPointBVProblem` and initial guess.

docs/src/tutorials/sde_example.md

@@ -46,7 +46,7 @@ plot(sol)
 
 One unique feature of DifferentialEquations.jl is that higher-order methods for
 stochastic differential equations are included. To illustrate it, let us compare the 
-accuray of the `EM()` method and a higher-order method `SRIW1()` with the analytical solution.
+accuracy of the `EM()` method and a higher-order method `SRIW1()` with the analytical solution.
 This is a good way to judge the accuracy of a given algorithm, and is also useful
 to test convergence of new methods being developed. To setup our problem, we define
 `u_analytic(u₀, p, t, W)` and pass it to the `SDEFunction` as:
@@ -143,7 +143,7 @@ du = f(u,p,t)dt + g(u,p,t)dW,
 ```
 
 where `g` is now a matrix of values, is numerically integrated in the
-same way as ODEs. A common scenario is when we have diagnol noise, which
+same way as ODEs. A common scenario is when we have diagonal noise, which
 is the default for DifferentialEquations.jl. Physically this means that
 every variable in the system gets a different random kick. Consequently, `g` is a
 diagonal matrix and we can handle this in a simple manner by defining