Merge pull request #198 from ChrisRackauckas-Claude/fix-broken-analysis-links-issue-197

Fix broken analysis documentation links

Author: ChrisRackauckas

news/2018/01/24/Parameters.md

@@ -173,7 +173,7 @@ use this guide and request new additions as necessary!
 Adjoint sensitivity analysis lets you directly solve for the derivative of some
 functional of the differential equation solution, such as a cost function in
 an optimization problem.
-[DifferentialEquations.jl now has a package-independent adjoint sensitivity analysis implementation](https://diffeq.sciml.ai/latest/analysis/sensitivity) that lets you use any of the common
+[DifferentialEquations.jl now has a package-independent adjoint sensitivity analysis implementation](https://docs.sciml.ai/SciMLSensitivity/stable/) that lets you use any of the common
 interface ODE solvers to perform this analysis. While there are more optimizations
 which still need to be done in this area, this will be a useful feature for those
 looking to perform optimization on the ODE solver.
@@ -185,7 +185,7 @@ function approach. While L2-error of the solution against data corresponds to
 maximum likelihood estimation under the assumption of a Normal likelihood, this
 is constrained to very specific likelihood functions (Normal). Now our tools
 allow for giving a likelihood distribution associated with each time point.
-[We have some examples in the documentation showing how to use MLE estimation to get fitting distributions](https://diffeq.sciml.ai/latest/analysis/parameter_estimation).
+[We have some examples in the documentation showing how to use MLE estimation to get fitting distributions](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/).
 This process is a more precise approach to data fitting and thus should be an
 interesting new tool to use in cases where one wants to fit parameters against
 a lot of data.
@@ -198,7 +198,7 @@ requires that your function is defined via `@ode_def` and will write and run a
 code from [Stan](https://mc-stan.org/) to generate posterior distributions. The
 `turing_inference` function uses [Turing.jl](https://github.com/yebai/Turing.jl)
 and can work with any DifferentialEquations.jl object. These functions simply
-require your `DEProblem`, data, and prior distributions and the [rest of the inference setup is done for you](https://diffeq.sciml.ai/latest/analysis/parameter_estimation). Thus this is a very quick way to make use of
+require your `DEProblem`, data, and prior distributions and the [rest of the inference setup is done for you](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/). Thus this is a very quick way to make use of
 the power of Bayesian inference tools!
 
 ## Small Problem Speedups

news/2018/02/17/Reactions.md

@@ -77,7 +77,7 @@ is a combination that can be orders of magnitude faster than Stan.jl (though
 additional testing which takes into account accuracy differences will be
 needed for a more precise determination). Still, it's as simple to use as the
 other Bayesian functions (see
-[the example](https://diffeq.sciml.ai/latest/analysis/parameter_estimation))
+[the example](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/))
 and so give it a try if you're up for it.
 
 ## Livestream Tutorial

news/2020/03/29/SciML.md

@@ -110,11 +110,11 @@ It is very rare that someone thinks their model is perfect. Thus a large portion
 of the focus of our organization is to help scientific modelers derive equations
 and fit models. This includes tools for:
 
-- [Maximum likelihood and Bayesian parameter estimation](https://diffeq.sciml.ai/dev/analysis/parameter_estimation/)
-- [Forward and adjoint local sensitivity analysis](https://diffeq.sciml.ai/dev/analysis/sensitivity/) for fast gradients
-- [Global sensitivity analysis](https://diffeq.sciml.ai/dev/analysis/global_sensitivity/)
+- [Maximum likelihood and Bayesian parameter estimation](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/)
+- [Forward and adjoint local sensitivity analysis](https://docs.sciml.ai/SciMLSensitivity/stable/) for fast gradients
+- [Global sensitivity analysis](https://docs.sciml.ai/GlobalSensitivity/stable/)
 - [Building surrogates of models](https://surrogates.sciml.ai/latest/)
-- [Uncertainty quantification](https://diffeq.sciml.ai/dev/analysis/uncertainty_quantification/)
+- [Uncertainty quantification](https://docs.sciml.ai/Overview/stable/highlevels/uncertainty_quantification/)
 
 Some of our newer tooling like [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl)
 can even take in timeseries data and generate LaTeX code for the best fitting model
@@ -374,7 +374,7 @@ are the following:
   and its Physics-Informed Neural Networks (PINN) functionality,
   [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl), etc.
   Because it does not require differential equations, we plan to split out the
-  documentation of [Global Sensitivity Analysis](https://diffeq.sciml.ai/latest/analysis/global_sensitivity/)
+  documentation of [Global Sensitivity Analysis](https://docs.sciml.ai/GlobalSensitivity/stable/)
   to better facilitate its wider usage.
 - We plan to continue improving the [ModelingToolkit](https://github.com/SciML/ModelingToolkit.jl)
   ecosystem utilizing its symbolic nature for [generic specification of PDEs](https://github.com/SciML/DifferentialEquations.jl/issues/469).

news/2020/05/09/ModelDiscovery.md

@@ -150,7 +150,7 @@ probabilistic programming libraries ([ModelingToolkit.jl](https://github.com/Sci
 automatically transforms Julia differential equation code to Stan). Together,
 this serves as a very good resource for non-Bayesian-inclined users to utilize
 Bayesian parameter estimation with just one function.
-[See the parameter estimation documentation for more details](https://diffeq.sciml.ai/latest/analysis/parameter_estimation/).
+[See the parameter estimation documentation for more details](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/).
 
 As a quick update to the probabilistic programming space, we would like to note
 that the Turing.jl library performs exceptionally well in comparison to the

news/2020/06/01/ModellingToolkit.md

@@ -278,7 +278,7 @@ there is no modification that is required. When forward-mode automatic
 differentiation libraries are used, type handling will automatically
 promote to ensure the solution is differentiated properly. When reverse-mode
 automatic differentiation is used, the backpropogation will automatically
-be replaced with [adjoint sensitivity methods](https://diffeq.sciml.ai/latest/analysis/sensitivity/#solve-Differentiation-Examples-1)
+be replaced with [adjoint sensitivity methods](https://docs.sciml.ai/SciMLSensitivity/stable/getting_started/)
 which can be controlled through the `sensealg` keyword argument.
 **The result is full performance and flexibility, but no code changes
 required**.

roadmap.md

@@ -113,11 +113,11 @@ It is very rare that someone thinks their model is perfect. Thus a large portion
 of the focus of our organization is to help scientific modelers derive equations
 and fit models. This includes tools for:
 
-- [Maximum likelihood and Bayesian parameter estimation](https://diffeq.sciml.ai/dev/analysis/parameter_estimation/)
-- [Forward and adjoint local sensitivity analysis](https://diffeq.sciml.ai/dev/analysis/sensitivity/) for fast gradients
-- [Global sensitivity analysis](https://diffeq.sciml.ai/dev/analysis/global_sensitivity/)
+- [Maximum likelihood and Bayesian parameter estimation](https://docs.sciml.ai/Overview/stable/highlevels/inverse_problems/)
+- [Forward and adjoint local sensitivity analysis](https://docs.sciml.ai/SciMLSensitivity/stable/) for fast gradients
+- [Global sensitivity analysis](https://docs.sciml.ai/GlobalSensitivity/stable/)
 - [Building surrogates of models](https://surrogates.sciml.ai/latest/)
-- [Uncertainty quantification](https://diffeq.sciml.ai/dev/analysis/uncertainty_quantification/)
+- [Uncertainty quantification](https://docs.sciml.ai/Overview/stable/highlevels/uncertainty_quantification/)
 
 Some of our newer tooling like [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl)
 can even take in timeseries data and generate LaTeX code for the best fitting model
@@ -379,7 +379,7 @@ are the following:
   and its Physics-Informed Neural Networks (PINN) functionality,
   [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl), etc.
   Because it does not require differential equations, we plan to split out the
-  documentation of [Global Sensitivity Analysis](https://diffeq.sciml.ai/latest/analysis/global_sensitivity/)
+  documentation of [Global Sensitivity Analysis](https://docs.sciml.ai/GlobalSensitivity/stable/)
   to better facilitate its wider usage.
 - We plan to continue improving the [ModelingToolkit](https://github.com/SciML/ModelingToolkit.jl)
   ecosystem utilizing its symbolic nature for [generic specification of PDEs](https://github.com/SciML/DifferentialEquations.jl/issues/469).