Merge pull request #383 from ArnoStrouwen/canon

canonize docs

Author: ChrisRackauckas

README.md

@@ -1,19 +1,22 @@
 # DataDrivenDiffEq.jl
 
-[![Build Status](https://github.com/SciML/DataDrivenDiffEq.jl/workflows/CI/badge.svg)](https://github.com/SciML/DataDrivenDiffEq.jl/actions?query=workflow%3ACI)
-[![pkgeval](https://juliahub.com/docs/DataDrivenDiffEq/pkgeval.svg)](https://juliahub.com/ui/Packages/DataDrivenDiffEq/EZBji)
-[![codecov.io](http://codecov.io/github/SciML/DataDrivenDiffEq.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaDiffEq/DataDrivenDiffEq.jl?branch=master)
-[![DOI](https://zenodo.org/badge/212827023.svg)](https://zenodo.org/badge/latestdoi/212827023)
+[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
 [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/DataDrivenDiffEq/stable/)
+[![DOI](https://zenodo.org/badge/212827023.svg)](https://zenodo.org/badge/latestdoi/212827023)
+
+[![codecov](https://codecov.io/gh/SciML/DataDrivenDiffEq.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/DataDrivenDiffEq.jl)
+[![Build Status](https://github.com/SciML/DataDrivenDiffEq.jl/workflows/CI/badge.svg)](https://github.com/SciML/DataDrivenDiffEq.jl/actions?query=workflow%3ACI)
+
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+[![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 DataDrivenDiffEq.jl is a package in the SciML ecosystem for data-driven differential equation
 structural estimation and identification. These tools include automatically discovering equations
 from data and using this to simulate perturbed dynamics.
 
 For information on using the package,
 [see the stable documentation](https://docs.sciml.ai/DataDrivenDiffEq/stable/). Use the
-[in-development documentation](https://datadriven.sciml.ai/dev/) for the version of
+[in-development documentation](https://docs.sciml.ai/DataDrivenDiffEq/dev/) for the version of
 the documentation which contains the un-released features.
 
 ## Quick Demonstration

docs/examples/8_symbolic_regression.jl

@@ -6,7 +6,7 @@
 #   Hence, the performance might differ and depends strongly on the hyperparameters of the optimization. 
 #   This example might not recover the groundtruth, but is showing off the use within `DataDrivenDiffEq.jl`.
 #
-# DataDrivenDiffEq offers an interface to [`SymbolicRegression.jl`](https://github.com/MilesCranmer/SymbolicRegression.jl) to infer more complex functions. To 
+# DataDrivenDiffEq offers an interface to [`SymbolicRegression.jl`](https://docs.sciml.ai/SymbolicRegression/stable/) to infer more complex functions. To
 # use it, simply load a sufficient version of `SymbolicRegression` (currently we support version >= 0.9).
 
 using DataDrivenDiffEq

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://github.com/MilesCranmer/SymbolicRegression.jl) API, please cite
+If you are using the [SymbolicRegression.jl](https://docs.sciml.ai/SymbolicRegression/stable/) API, please cite
 
 ```bibtex
 @software{pysr,

docs/src/index.md

@@ -1,6 +1,6 @@
 # DataDrivenDiffEq.jl
 
-[DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl) is a package for finding the governing equations of motion automatically from a dataset.
+**DataDrivenDiffEq.jl** is a package for finding the governing equations of motion automatically from a dataset.
 
 The methods in this package take in data and return the differential equation model which generated the data. A known model is not required as input. These methods can estimate equation-free and equation-based models for discrete and continuous differential equations.
 
@@ -10,7 +10,7 @@ There are two main types of estimation, depending on if you need the result to b
 
 ## Package Overview
 
-[DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl) currently implements the following algorithms for structural estimation and identification. Please note that all the algorithms have been unified under a single mathematical framework, so the interface might be a little different than what you expect.
+Currently, the following algorithms for structural estimation and identification are implemented. Please note that all the algorithms have been unified under a single mathematical framework, so the interface might be a little different than what you expect.
 
 + Dynamic Mode Decomposition (DMD)
 + Extended Dynamic Mode Decomposition
@@ -23,6 +23,6 @@ There are two main types of estimation, depending on if you need the result to b
 To use [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl), install via:
 
 ```julia
-]add DataDrivenDiffEq
-using DataDrivenDiffEq
+using Pkg
+Pkg.add("DataDrivenDiffEq")
 ```

docs/src/solvers/common.md

@@ -8,8 +8,8 @@ The table below provides an overview, which class of algorithms support which cl
 |:---------- | ---------- |:------------:|:------------:|:------------:|:------------:|
 | [Koopman](@ref koopman_algorithms)    |     -      | +             |  +        | Optional | 
 | [Sparse Regression](@ref sparse_optimization)    | +  | + | + | Necessary | 
-| [EQSearch](@ref eqsearch_api)    | +  | + | + | No | [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl)
-| [OccamNet](@ref occamnet_api)    | +  | + | + | No | [Flux.jl](https://github.com/FluxML/Flux.jl)
+| [EQSearch](@ref eqsearch_api)    | +  | + | + | No | [SymbolicRegression.jl](https://docs.sciml.ai/SymbolicRegression/stable/)
+| [OccamNet](@ref occamnet_api)    | +  | + | + | No | [Flux.jl](https://docs.sciml.ai/Flux.jl/stable/)
 
 All of the above methods return a [`DataDrivenSolution`](@ref) if not enforced otherwise.
 

docs/src/solvers/symbolic_regression.md

@@ -5,14 +5,14 @@
 ## [EQSearch](@id eqsearch_api)
 
 !!! warning
-    This feature requires the explicit loading of [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl) in addition to `DataDrivenDiffEq`. It will _only_ be useable if loaded like:
+    This feature requires the explicit loading of [SymbolicRegression.jl](https://docs.sciml.ai/SymbolicRegression/stable/) in addition to `DataDrivenDiffEq`. It will _only_ be useable if loaded like:
     ```julia
     using DataDrivenDiffEq
     using SymbolicRegression
     ```
     Currently `DataDrivenDiffEq` supports version >= 0.9.
 
-This algorithm wraps [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl).
+This algorithm wraps [SymbolicRegression.jl](https://docs.sciml.ai/SymbolicRegression/stable/).
 ### Symbolic Regression
 See the [tutorial](@ref symbolic_regression_simple).