chore: update SMC links to JuliaDiff repo

DifferentiationInterface/README.md

@@ -91,7 +91,7 @@ Whenever you refer to this package or the ideas it contains, please cite:
 
  1. our JMLR paper [*A Common Interface for Automatic Differentiation*](https://jmlr.org/papers/v27/25-1024.html);
  2. our inspiration [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl).
- 3. if you use sparse differentiation capabilities, our companion packages [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) and [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) which provide these capabilities (see their respective repositories for guidelines).
+ 3. if you use sparse differentiation capabilities, our companion packages [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) and [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) which provide these capabilities (see their respective repositories for guidelines).
 
 For the first two items, you can use the provided [`CITATION.cff`](https://github.com/JuliaDiff/DifferentiationInterface.jl/blob/main/CITATION.cff) file or the following BibTeX entries:
 

DifferentiationInterface/docs/make.jl

@@ -11,7 +11,7 @@ using Zygote: Zygote
 links = InterLinks(
     "ADTypes" => "https://sciml.github.io/ADTypes.jl/stable/",
     "SparseConnectivityTracer" => "https://adrianhill.de/SparseConnectivityTracer.jl/stable/",
-    "SparseMatrixColorings" => "https://gdalle.github.io/SparseMatrixColorings.jl/stable/",
+    "SparseMatrixColorings" => "https://juliadiff.org/SparseMatrixColorings.jl/stable/",
     "Symbolics" => "https://symbolics.juliasymbolics.org/stable/",
 )
 

DifferentiationInterface/docs/src/explanation/advanced.md

@@ -23,7 +23,7 @@ An `AutoSparse` backend must be constructed from three ingredients:
       + [`DenseSparsityDetector`](@ref) from DifferentiationInterface.jl (beware that this detector only gives a locally valid pattern)
       + [`KnownJacobianSparsityDetector`](@extref ADTypes.KnownJacobianSparsityDetector) or [`KnownHessianSparsityDetector`](@extref ADTypes.KnownHessianSparsityDetector) from [ADTypes.jl](https://github.com/SciML/ADTypes.jl) (if you already know the pattern)
 
- 3. A coloring algorithm following the [`ADTypes.AbstractColoringAlgorithm`](@extref ADTypes.AbstractColoringAlgorithm) interface, such as those from [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl):
+ 3. A coloring algorithm following the [`ADTypes.AbstractColoringAlgorithm`](@extref ADTypes.AbstractColoringAlgorithm) interface, such as those from [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl):
 
       + [`GreedyColoringAlgorithm`](@extref SparseMatrixColorings.GreedyColoringAlgorithm) (our generic recommendation, don't forget to tune the `order` parameter)
       + [`ConstantColoringAlgorithm`](@extref SparseMatrixColorings.ConstantColoringAlgorithm) (if you have already computed the optimal coloring and always want to return it)
@@ -48,7 +48,7 @@ But after preparation, the more zeros are present in the matrix, the greater the
 The complexity of sparse Jacobians or Hessians grows with the number of distinct colors in a coloring of the sparsity pattern.
 To reduce this number of colors, [`GreedyColoringAlgorithm`](@extref SparseMatrixColorings.GreedyColoringAlgorithm) has two main settings: the order used for vertices and the decompression method.
 Depending on your use case, you may want to modify either of these options to increase performance.
-See the documentation of [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) for details.
+See the documentation of [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) for details.
 
 ### Mixed mode
 

DifferentiationInterface/docs/src/tutorials/advanced.md

@@ -127,7 +127,7 @@ In the examples above, we didn't use preparation.
 Sparse preparation is more costly than dense preparation, but it is even more essential.
 Indeed, once preparation is done, sparse differentiation is much faster than dense differentiation, because it makes fewer calls to the underlying function.
 
-Some result analysis functions from [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) can help you figure out what the preparation contains.
+Some result analysis functions from [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) can help you figure out what the preparation contains.
 First, it records the sparsity pattern itself (the one returned by the detector).
 
 ```@example tuto_advanced