doc: replace urls with inter-links

Author: franckgaga

docs/src/manual/mtk.md

@@ -11,8 +11,8 @@ old_logger = global_logger(); global_logger(errlogger);
 
 ## Pendulum Model
 
-This example integrates the simple pendulum model of the [last section](@ref man_nonlin) in the
-[`ModelingToolkit.jl`](https://docs.sciml.ai/ModelingToolkit/stable/) (MTK) framework and
+This example integrates the simple pendulum model of the [last section](@ref man_nonlin) in
+[`ModelingToolkit`](https://docs.sciml.ai/ModelingToolkit/stable/) (MTK) framework and
 extracts appropriate `f!` and `h!` functions to construct a [`NonLinModel`](@ref). An
 [`NonLinMPC`](@ref) is designed from this model and simulated to reproduce the results of
 the last section.

src/estimator/kalman.jl

@@ -802,7 +802,7 @@ See [`init_ukf`](@ref) for the definition of the constants ``\mathbf{m̂, Ŝ}``
 superscript in e.g. ``\mathbf{X̂}_{k-1}^j(k)`` refers the vector at the ``j``th column of 
 ``\mathbf{X̂}_{k-1}(k)``. The symbol ``\mathbf{0}`` is a vector with zeros. The number of
 sigma points is ``n_σ = 2 n_\mathbf{x̂} + 1``. The matrices ``\sqrt{\mathbf{P̂}_{k-1}(k)}``
-and ``\sqrt{\mathbf{P̂}_{k}(k)}`` are the the lower triangular factors of [`cholesky`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#LinearAlgebra.cholesky)
+and ``\sqrt{\mathbf{P̂}_{k}(k)}`` are the the lower triangular factors of [`cholesky`](@extref Julia LinearAlgebra.cholesky)
 results. The correction and prediction step equations are provided below. The correction
 step is skipped if `estim.direct == true` since it's already done by the user.
 

src/estimator/mhe/construct.jl

@@ -1318,13 +1318,13 @@ This method is really intricate and I'm not proud of it. That's because of 3 ele
 - These functions are used inside the nonlinear optimization, so they must be type-stable
   and as efficient as possible.
 - The `JuMP` NLP syntax forces splatting for the decision variable, which implies use
-  of `Vararg{T,N}` (see the [performance tip](https://docs.julialang.org/en/v1/manual/performance-tips/#Be-aware-of-when-Julia-avoids-specializing))
+  of `Vararg{T,N}` (see the [performance tip](@extref Julia Be-aware-of-when-Julia-avoids-specializing))
   and memoization to avoid redundant computations. This is already complex, but it's even
   worse knowing that most automatic differentiation tools do not support splatting.
 - The signature of gradient and hessian functions is not the same for univariate (`nZ̃ == 1`)
   and multivariate (`nZ̃ > 1`) operators in `JuMP`. Both must be defined.
 
-Inspired from: [User-defined operators with vector outputs](https://jump.dev/JuMP.jl/stable/tutorials/nonlinear/tips_and_tricks/#User-defined-operators-with-vector-outputs)
+Inspired from: [User-defined operators with vector outputs](@extref JuMP User-defined-operators-with-vector-outputs)
 """
 function get_optim_functions(
     estim::MovingHorizonEstimator, 

src/estimator/mhe/execute.jl

@@ -379,7 +379,7 @@ where ``\mathbf{ŵ}_{k-1}(k-j)`` is the input increment for time ``k-j`` comput
 last time step ``k-1``. It then calls `JuMP.optimize!(estim.optim)` and extract the
 solution. A failed optimization prints an `@error` log in the REPL and returns the 
 warm-start value. A failed optimization also prints [`getinfo`](@ref) results in
-the debug log [if activated](https://docs.julialang.org/en/v1/stdlib/Logging/#Example:-Enable-debug-level-messages).
+the debug log [if activated](@extref Julia Example:-Enable-debug-level-messages).
 """
 function optim_objective!(estim::MovingHorizonEstimator{NT}) where NT<:Real
     model, optim = estim.model, estim.optim

src/sim_model.jl

@@ -301,10 +301,10 @@ end
 
 Sleep for `model.Ts` s minus the time elapsed since the last call to [`savetime!`](@ref).
 
-It calls [`sleep`](https://docs.julialang.org/en/v1/base/parallel/#Base.sleep) if `busywait`
-is `false`. Else, a simple `while` loop implements busy-waiting. As a rule-of-thumb,
-busy-waiting should be used if `model.Ts < 0.1` s, since the accuracy of `sleep` is around 1
-ms. Can be used to implement simple soft real-time simulations, see the example below.
+It calls [`sleep`](@extref Julia Base.sleep) if `busywait` is `false`. Else, a simple 
+`while` loop implements busy-waiting. As a rule-of-thumb, busy-waiting should be used if 
+`model.Ts < 0.1` s, since the accuracy of `sleep` is around 1 ms. Can be used to implement
+simple soft real-time simulations, see the example below.
 
 !!! warning
     The allocations in Julia are garbage-collected (GC) automatically. This can affect the