Fix documentation issues: broken links, typos, and placeholder text

- Fix malformed reference link in problems.md (missing opening bracket)
- Fix typo in example_04.jl: "nonlinear_continuos" -> "nonlinear_continuous"
- Fix copy-pasteable code IDs that didn't match example content:
  - example_04.jl: linear_discrete_copy_paste -> nonlinear_continuous_copy_paste
  - example_02.jl (SR): symbolic_regression_simple_copy_paste -> symbolic_regression_lifted_copy_paste
- Replace placeholder "**TEXT**" with actual content in example_04.jl (sparse)
- Update outdated SymbolicRegression.jl URLs from v0.6 to stable in examples
- Update citations.md SymbolicRegression.jl link from dev to stable
- Remove misleading comment about control signals in SR example_02.jl

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: ChrisRackauckas

docs/src/citations.md

@@ -15,7 +15,7 @@ If you are using `DataDrivenDiffEq.jl` for research, please cite
 }
 ```
 
-If you are using the [SymbolicRegression.jl](https://ai.damtp.cam.ac.uk/symbolicregression/dev/) API, please cite
+If you are using the [SymbolicRegression.jl](https://ai.damtp.cam.ac.uk/symbolicregression/stable/) API, please cite
 
 ```bibtex
 @misc{cranmerInterpretableMachineLearning2023,

docs/src/libs/datadrivendmd/example_04.jl

@@ -1,4 +1,4 @@
-# # [Nonlinear Time Continuous System](@id nonlinear_continuos)
+# # [Nonlinear Time Continuous System](@id nonlinear_continuous)
 #
 # Similarly, we can use the [Extended Dynamic Mode Decomposition](https://link.springer.com/article/10.1007/s00332-015-9258-5) via a nonlinear [`Basis`](@ref) of observables. Here, we will look at a rather [famous example](https://arxiv.org/pdf/1510.03007) with a finite dimensional solution.
 
@@ -66,7 +66,7 @@ res = solve(prob, Ψ, DMDPINV(), digits = 2)
 # And plot the results
 #md plot(res)
 
-#md # ## [Copy-Pasteable Code](@id linear_discrete_copy_paste)
+#md # ## [Copy-Pasteable Code](@id nonlinear_continuous_copy_paste)
 #md #
 #md # ```julia
 #md # @__CODE__

docs/src/libs/datadrivensparse/example_04.jl

@@ -46,7 +46,7 @@ basis = Basis(eqs, x, independent_variable = t, implicits = D.(x))
 
 #md plot(dd_prob)
 
-# Next to varying over different sparsity penalties, we also want to batch our data using the **TEXT**
+# Next to varying over different sparsity penalties, we also want to batch our data using [`DataProcessing`](@ref).
 
 sampler = DataProcessing(split = 0.8, shuffle = true, batchsize = 30)
 res = solve(dd_prob, basis, ImplicitOptimizer(STLSQ(1e-2:1e-2:1.0)),

docs/src/libs/datadrivensr/example_01.jl

@@ -33,7 +33,7 @@ prob = ContinuousDataDrivenProblem(X, t, U = U)
 
 #md plot(prob)
 
-# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://astroautomata.com/SymbolicRegression.jl/v0.6/api/#Options).
+# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options).
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 
 eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
@@ -44,7 +44,7 @@ eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
 alg = EQSearch(eq_options = eqsearch_options)
 
 # Again, we `solve` the problem to obtain a [`DataDrivenSolution`](@ref). Note that any additional keyword arguments are passed onto
-# symbolic regressions [`EquationSearch`](https://astroautomata.com/SymbolicRegression.jl/v0.6/api/#EquationSearch) with the exception of `niterations` which
+# symbolic regressions [`EquationSearch`](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#EquationSearch) with the exception of `niterations` which
 # is `maxiters`
 
 res = solve(prob, alg, options = DataDrivenCommonOptions(maxiters = 100))

docs/src/libs/datadrivensr/example_02.jl

@@ -19,14 +19,14 @@ tspan = (0.0, 10.0)
 sys = ODEProblem{true, SciMLBase.NoSpecialize}(pendulum!, u0, tspan)
 sol = solve(sys, Tsit5());
 
-# We will use the data provided by our problem, but add the control signal `U = sin(0.5*t)` to it. Instead of using a function, like in [another example](@ref linear_continuous_controls)
+# We will use the data provided by our problem.
 prob = DataDrivenProblem(sol)
 
 # And plot the problems data.
 
 #md plot(prob)
 
-# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://astroautomata.com/SymbolicRegression.jl/v0.6/api/#Options).
+# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options).
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 # Note that we do not include `sin`, but rather lift the search space of variables.
 
@@ -56,7 +56,7 @@ res = solve(prob, basis, alg, options = DataDrivenCommonOptions(maxiters = 100))
 system = get_basis(res)
 #md println(system) # hide
 
-#md # ## [Copy-Pasteable Code](@id symbolic_regression_simple_copy_paste)
+#md # ## [Copy-Pasteable Code](@id symbolic_regression_lifted_copy_paste)
 #md #
 #md # ```julia
 #md # @__CODE__

docs/src/problems.md

@@ -6,7 +6,7 @@ DataDrivenProblem
 
 ## Defining a Problem
 
-Problems of identification, estimation, or inference are defined by data. These data contain at least measurements of the states `X`, which would be sufficient to describe a `[DiscreteDataDrivenProblem`](@ref) with unit time steps similar to the first example on dynamic mode decomposition. Of course, we can extend this to include time points `t`, control signals `U` or a function describing those `u(x,p,t)`. Additionally, any parameters `p` known a priori can be included in the problem. In practice, this looks like:
+Problems of identification, estimation, or inference are defined by data. These data contain at least measurements of the states `X`, which would be sufficient to describe a [`DiscreteDataDrivenProblem`](@ref) with unit time steps similar to the first example on dynamic mode decomposition. Of course, we can extend this to include time points `t`, control signals `U` or a function describing those `u(x,p,t)`. Additionally, any parameters `p` known a priori can be included in the problem. In practice, this looks like:
 
 ```julia
 problem = DiscreteDataDrivenProblem(X)