update some alan-turing-institute links

JuliaAI · May 5, 2024 · e6726df · e6726df
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diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -18,7 +18,7 @@ below. The Committee will respond to reports on a case-by-case basis.
 following forums:
 
 - the GitHub repository
-  [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) (including
+  [MLJ.jl](https://github.com/JuliaAI/MLJ.jl) (including
   all issues, discussions, and pull requests)
 
 - all GitHub repositories in the [JuliaAI](https://github.com/JuliaAI)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -9,7 +9,7 @@ channel](https://julialang.org/slack/), #mlj.
 - [Code organization](ORGANIZATION.md)
 
 - Issues: Currently issues are split between [MLJ
-  issues](https://github.com/alan-turing-institute/MLJ.jl/issues) and
+  issues](https://github.com/JuliaAI/MLJ.jl/issues) and
   issues in all other repositories, collected in [this GitHub
   Project](https://github.com/orgs/JuliaAI/projects/1).
 
@@ -43,7 +43,7 @@ an internal state reflecting the outcomes of applying `fit!` and
 A generalization of machine, called a *nodal* machine, is a key
 element of *learning networks* which combine several models together,
 and form the basis for specifying new composite model types. See
-[here](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/)
+[here](https://JuliaAI.github.io/MLJ.jl/dev/composing_models/)
 for more on these.
 
 MLJ code is now spread over [multiple repositories](ORGANIZATION.md).

diff --git a/ORGANIZATION.md b/ORGANIZATION.md
@@ -8,7 +8,7 @@ connections do not currently exist but are planned/proposed.*
 Repositories of some possible interest outside of MLJ, or beyond
 its conventional use, are marked with a ⟂ symbol:
 
-* [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) is the
+* [MLJ.jl](https://github.com/JuliaAI/MLJ.jl) is the
   general user's point-of-entry for choosing, loading, composing,
   evaluating and tuning machine learning models. It pulls in most code
   from other repositories described below.  MLJ also hosts the [MLJ
@@ -100,9 +100,9 @@ its conventional use, are marked with a ⟂ symbol:
   models and measures (metrics).
 
 * (⟂)
-  [DataScienceTutorials](https://github.com/alan-turing-institute/DataScienceTutorials.jl)
+  [DataScienceTutorials](https://github.com/JuliaAI/DataScienceTutorials.jl)
   collects tutorials on how to use MLJ, which are deployed
-  [here](https://alan-turing-institute.github.io/DataScienceTutorials.jl/)
+  [here](https://JuliaAI.github.io/DataScienceTutorials.jl/)
 
 * [MLJTestIntegration](https://github.com/JuliaAI/MLJTestIntegration.jl)
   provides tests for implementations of the MLJ model interface, and

diff --git a/README.md b/README.md
@@ -4,11 +4,11 @@
 
 <h2 align="center">A Machine Learning Framework for Julia
 <p align="center">
-  <a href="https://github.com/alan-turing-institute/MLJ.jl/actions">
-    <img src="https://github.com/alan-turing-institute/MLJ.jl/workflows/CI/badge.svg"
+  <a href="https://github.com/JuliaAI/MLJ.jl/actions">
+    <img src="https://github.com/JuliaAI/MLJ.jl/workflows/CI/badge.svg"
          alt="Build Status">
   </a>
-  <a href="https://alan-turing-institute.github.io/MLJ.jl/dev/">
+  <a href="https://JuliaAI.github.io/MLJ.jl/dev/">
     <img src="https://img.shields.io/badge/docs-stable-blue.svg"
          alt="Documentation">
   </a>
@@ -28,13 +28,13 @@
 MLJ (Machine Learning in Julia) is a toolbox written in Julia
 providing a common interface and meta-algorithms for selecting,
 tuning, evaluating, composing and comparing about [200 machine learning
-models](https://alan-turing-institute.github.io/MLJ.jl/dev/model_browser/#Model-Browser)
+models](https://JuliaAI.github.io/MLJ.jl/dev/model_browser/#Model-Browser)
 written in Julia and other languages.
 
-**New to MLJ?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/).
+**New to MLJ?** Start [here](https://JuliaAI.github.io/MLJ.jl/dev/).
 
 **Integrating an existing machine learning model into the MLJ
-framework?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/quick_start_guide_to_adding_models/).
+framework?** Start [here](https://JuliaAI.github.io/MLJ.jl/dev/quick_start_guide_to_adding_models/).
 
 **Wanting to contribute?** Start [here](CONTRIBUTING.md). 
 

diff --git a/ROADMAP.md b/ROADMAP.md
@@ -45,7 +45,7 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
 - [ ] **Integrate deep learning** using [Flux.jl](https://github.com/FluxML/Flux.jl.git) deep learning.  [Done](https://github.com/FluxML/MLJFlux.jl) but can
   improve the experience by:
 
-  - [x] finishing iterative model wrapper [#139](https://github.com/alan-turing-institute/MLJ.jl/issues/139)
+  - [x] finishing iterative model wrapper [#139](https://github.com/JuliaAI/MLJ.jl/issues/139)
 
   - [ ] improving performance by implementing data front-end after (see [MLJBase
   #501](https://github.com/JuliaAI/MLJBase.jl/pull/501)) but see also [this relevant discussion](https://github.com/FluxML/MLJFlux.jl/issues/97).
@@ -55,7 +55,7 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
    [Turing.jl](https://github.com/TuringLang/Turing.jl),
    [Gen](https://github.com/probcomp/Gen),
    [Soss.jl](https://github.com/cscherrer/Soss.jl.git)
-   [#157](https://github.com/alan-turing-institute/MLJ.jl/issues/157)
+   [#157](https://github.com/JuliaAI/MLJ.jl/issues/157)
    [discourse
    thread](https://discourse.julialang.org/t/ppl-connection-to-mlj-jl/28736)
    [done](https://github.com/tlienart/SossMLJ.jl) but experimental and
@@ -66,20 +66,20 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
 
 -   [ ] Feature engineering (python featuretools?, recursive feature
 	elimination ✓ done in FeatureSelection.jl :)
-	[#426](https://github.com/alan-turing-institute/MLJ.jl/issues/426) [MLJModels #314](https://github.com/JuliaAI/MLJModels.jl/issues/314)
+	[#426](https://github.com/JuliaAI/MLJ.jl/issues/426) [MLJModels #314](https://github.com/JuliaAI/MLJModels.jl/issues/314)
 
 
 ### Enhancing core functionality
 
--   [x] Iterative model control [#139](https://github.com/alan-turing-institute/MLJ.jl/issues/139). [Done](https://github.com/JuliaAI/MLJIteration.jl)
+-   [x] Iterative model control [#139](https://github.com/JuliaAI/MLJ.jl/issues/139). [Done](https://github.com/JuliaAI/MLJIteration.jl)
 
 -   [ ] **†** Add more tuning
 	strategies. See [here](https://github.com/JuliaAI/MLJTuning.jl#what-is-provided-here)
 	for complete
 	wish-list. Particular focus on:
 
 	- [x] random search
-	([#37](https://github.com/alan-turing-institute/MLJ.jl/issues/37))
+	([#37](https://github.com/JuliaAI/MLJ.jl/issues/37))
 	(done)
 
 	- [x] Latin hypercube
@@ -88,37 +88,37 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
 	- [ ] Bayesian methods, starting with Gaussian Process methods a
 	  la PyMC3. Some preliminary research done.
 
-	- [ ] POC for AD-powered gradient descent [#74](https://github.com/alan-turing-institute/MLJ.jl/issues/74)
+	- [ ] POC for AD-powered gradient descent [#74](https://github.com/JuliaAI/MLJ.jl/issues/74)
 
 	- [ ] Tuning with adaptive resource allocation, as in
 	  Hyperband. This might be implemented elegantly with the help of
 	  the recent `IterativeModel` wrapper, which applies, in
 	  particular to `TunedModel` instances [see
-	  here](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Using-training-losses,-and-controlling-model-tuning).
+	  here](https://JuliaAI.github.io/MLJ.jl/dev/controlling_iterative_models/#Using-training-losses,-and-controlling-model-tuning).
 
 	- [ ] Genetic algorithms
-[#38](https://github.com/alan-turing-institute/MLJ.jl/issues/38)
+[#38](https://github.com/JuliaAI/MLJ.jl/issues/38)
 
 	- [ ] Particle Swarm Optimization (current WIP, GSoC project @lhnguyen-vn)
 
 	- [ ] tuning strategies for non-Cartesian spaces of models [MLJTuning
 	#18](https://github.com/JuliaAI/MLJTuning.jl/issues/18), architecture search, and other AutoML workflows
 
 - [ ]  Systematic benchmarking, probably modeled on
-	[MLaut](https://arxiv.org/abs/1901.03678) [#69](https://github.com/alan-turing-institute/MLJ.jl/issues/74)
+	[MLaut](https://arxiv.org/abs/1901.03678) [#69](https://github.com/JuliaAI/MLJ.jl/issues/74)
 
 - [ ]   Give `EnsembleModel` a more extendible API and extend beyond bagging
 	(boosting, etc) and migrate to a separate repository?
-	[#363](https://github.com/alan-turing-institute/MLJ.jl/issues/363)
+	[#363](https://github.com/JuliaAI/MLJ.jl/issues/363)
 
 - [ ]  **†** Enhance complex model composition:
 
 	- [x] Introduce a canned
-	stacking model wrapper ([POC](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/stacking/)). WIP @olivierlabayle
+	stacking model wrapper ([POC](https://JuliaAI.github.io/DataScienceTutorials.jl/getting-started/stacking/)). WIP @olivierlabayle
 
 	- [ ] Get rid of macros for creating pipelines and possibly
 	implement target transforms as wrappers ([MLJBase
-	#594](https://github.com/alan-turing-institute/MLJ.jl/issues/594))
+	#594](https://github.com/JuliaAI/MLJ.jl/issues/594))
 	WIP @CameronBieganek and @ablaom
 
 
@@ -147,11 +147,11 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
 	could use [NaiveBayes.jl](https://github.com/dfdx/NaiveBayes.jl)
 	as a POC (currently wrapped only for dense input) but the API
 	needs to be finalized first
-	{#731](https://github.com/alan-turing-institute/MLJ.jl/issues/731). Probably
+	{#731](https://github.com/JuliaAI/MLJ.jl/issues/731). Probably
 	need a new SparseTables.jl package.
 
 - [x] POC for implementation of time series models classification
-	[#303](https://github.com/alan-turing-institute/MLJ.jl/issues/303),
+	[#303](https://github.com/JuliaAI/MLJ.jl/issues/303),
 	[ScientificTypesBase #14](https://github.com/JuliaAI/ScientificTypesBase.jl/issues/14) POC is [here](https://github.com/JuliaAI/TimeSeriesClassification.jl)
 
 - [ ] POC for time series forecasting, along lines of sktime; probably needs [MLJBase
@@ -162,16 +162,16 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
 - [ ]   Add tools or a separate repository for visualization in MLJ.
 
 	- [x] Extend visualization of tuning plots beyond two-parameters
-	[#85](https://github.com/alan-turing-institute/MLJ.jl/issues/85)
+	[#85](https://github.com/JuliaAI/MLJ.jl/issues/85)
 	(closed).
-	[#416](https://github.com/alan-turing-institute/MLJ.jl/issues/416)
+	[#416](https://github.com/JuliaAI/MLJ.jl/issues/416)
 	[Done](https://github.com/JuliaAI/MLJTuning.jl/pull/121) but might be worth adding alternatives suggested in issue.
 
-	- [ ] visualizing decision boundaries? [#342](https://github.com/alan-turing-institute/MLJ.jl/issues/342)
+	- [ ] visualizing decision boundaries? [#342](https://github.com/JuliaAI/MLJ.jl/issues/342)
 
 	- [ ] provide visualizations that MLR3 provides via [mlr3viz](https://github.com/mlr-org/mlr3viz)
 
-- [ ] Extend API to accommodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/alan-turing-institute/MLJ.jl/issues/780) WIP @davn and @ablaom
+- [ ] Extend API to accommodate outlier detection, as provided by [OutlierDetection.jl](https://github.com/davnn/OutlierDetection.jl) [#780](https://github.com/JuliaAI/MLJ.jl/issues/780) WIP @davn and @ablaom
 
 - [ ] Add more pre-processing tools:
 
@@ -194,14 +194,14 @@ GH Project](https://github.com/orgs/JuliaAI/projects/1).
   is merged.
 
 - [ ]  Online learning support and distributed data
-	[#60](https://github.com/alan-turing-institute/MLJ.jl/issues/60)
+	[#60](https://github.com/JuliaAI/MLJ.jl/issues/60)
 
 - [ ]  DAG scheduling for learning network training
-	[#72](https://github.com/alan-turing-institute/MLJ.jl/issues/72)
+	[#72](https://github.com/JuliaAI/MLJ.jl/issues/72)
 	(multithreading first?)
 
 - [ ]  Automated estimates of cpu/memory requirements
-	[#71](https://github.com/alan-turing-institute/MLJ.jl/issues/71)
+	[#71](https://github.com/JuliaAI/MLJ.jl/issues/71)
 
 - [x] Add multithreading to tuning [MLJTuning
   #15](https://github.com/JuliaAI/MLJTuning.jl/issues/15)

diff --git a/docs/src/about_mlj.md b/docs/src/about_mlj.md
@@ -15,7 +15,7 @@ MLJ is released under the MIT license.
 
 A self-contained notebook and julia script of this demonstration is
 also available
-[here](https://github.com/alan-turing-institute/MLJ.jl/tree/dev/examples/lightning_tour).
+[here](https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/lightning_tour).
 
 The first code snippet below creates a new Julia environment
 `MLJ_tour` and installs just those packages needed for the tour. See
@@ -210,13 +210,13 @@ and to report issues.
 
 For a query to have maximum exposure to maintainers and users, start a
 discussion thread at [Julia Discourse Machine
-Learning](https://github.com/alan-turing-institute/MLJ.jl) and tag
+Learning](https://github.com/JuliaAI/MLJ.jl) and tag
 your issue "mlj". Queries can also be posted as
-[issues](https://github.com/alan-turing-institute/MLJ.jl/issues), or
+[issues](https://github.com/JuliaAI/MLJ.jl/issues), or
 on the `#mlj` slack workspace in the Julia Slack channel.
 
 Bugs, suggestions, and feature requests can be posted
-[here](https://github.com/alan-turing-institute/MLJ.jl/issues).
+[here](https://github.com/JuliaAI/MLJ.jl/issues).
 
 Users are also welcome to join the `#mlj` Julia slack channel to ask
 questions and make suggestions.
@@ -269,7 +269,7 @@ packages such as DecisionTree.jl, ScikitLearn.jl or XGBoost.jl.
 MLJ is supported by several satellite packages (MLJTuning,
 MLJModelInterface, etc) which the general user is *not* required to
 install directly. Developers can learn more about these
-[here](https://github.com/alan-turing-institute/MLJ.jl/blob/master/ORGANIZATION.md).
+[here](https://github.com/JuliaAI/MLJ.jl/blob/master/ORGANIZATION.md).
 
 See also the alternative installation instructions for [Modifying Behavior](@ref).
 

diff --git a/docs/src/benchmarking.md b/docs/src/benchmarking.md
@@ -2,4 +2,4 @@
 
 This feature not yet available.
 
-[CONTRIBUTE.md](https://github.com/alan-turing-institute/MLJ.jl/blob/master/CONTRIBUTE.md)
+[CONTRIBUTE.md](https://github.com/JuliaAI/MLJ.jl/blob/master/CONTRIBUTE.md)
diff --git a/docs/src/frequently_asked_questions.md b/docs/src/frequently_asked_questions.md
@@ -49,7 +49,7 @@ term:
 - **Clean probabilistic API.** The scikit-learn API does not specify a
   universal standard for the form of probabilistic predictions. By
   fixing a probabilistic API along the lines of the
-  [skpro](https://github.com/alan-turing-institute/skpro) project, MLJ
+  [skpro](https://github.com/JuliaAI/skpro) project, MLJ
   aims to improve support for Bayesian statistics and probabilistic
   graphical models.
 

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -4,12 +4,12 @@
 <div style="font-size:1.25em;font-weight:bold;">
   <a href="about_mlj"
     style="color: #389826;">About</a>           &nbsp;|&nbsp;
-  <a href="https://alan-turing-institute.github.io/MLJ.jl/dev/about_mlj/#Installation" 
+  <a href="https://JuliaAI.github.io/MLJ.jl/dev/about_mlj/#Installation" 
     style="color: #389826;">Install</a>         &nbsp;|&nbsp;
   <a href="learning_mlj"   style="color: #389826;">Learn</a>            &nbsp;|&nbsp;
   <a href="mlj_cheatsheet" style="color: #9558B2;">Cheatsheet</a>       &nbsp;|&nbsp;
   <a href="common_mlj_workflows" style="color: #9558B2;">Workflows</a>  &nbsp;|&nbsp;
-  <a href="https://github.com/alan-turing-institute/MLJ.jl/" style="color: #9558B2;">For Developers</a> &nbsp;|&nbsp;
+  <a href="https://github.com/JuliaAI/MLJ.jl/" style="color: #9558B2;">For Developers</a> &nbsp;|&nbsp;
   <a href="third_party_packages" style="color: #9558B2;">3rd Party Packages</a>
 </div>
 
@@ -26,11 +26,11 @@ To support MLJ development, please cite these works or star the repo:
 
 ```@raw html
 <a class="github-button" 
-  href="https://github.com/alan-turing-institute/MLJ.jl" 
+  href="https://github.com/JuliaAI/MLJ.jl" 
   data-icon="octicon-star" 
   data-size="large" 
   data-show-count="true" 
-  aria-label="Star alan-turing-institute/MLJ.jl on GitHub">
+  aria-label="Star JuliaAI/MLJ.jl on GitHub">
   Star</a>
 ```
 

diff --git a/docs/src/learning_mlj.md b/docs/src/learning_mlj.md
@@ -38,7 +38,7 @@ intended as a complete reference.
 
 - [Analyzing the Glass Dataset](https://towardsdatascience.com/part-i-analyzing-the-glass-dataset-c556788a496f): A gentle introduction to data science using Julia and MLJ (three-part blog post)
 
-- [Lightning Tour](https://github.com/alan-turing-institute/MLJ.jl/blob/dev/examples/lightning_tour/lightning_tour.ipynb): A compressed demonstration of key MLJ functionality
+- [Lightning Tour](https://github.com/JuliaAI/MLJ.jl/blob/dev/examples/lightning_tour/lightning_tour.ipynb): A compressed demonstration of key MLJ functionality
 
 - [MLJ JuliaCon2020 Workshop](https://github.com/ablaom/MachineLearningInJulia2020): older version of  [MLJTutorial](https://github.com/ablaom/MLJTutorial.jl) with [video](https://www.youtube.com/watch?time_continue=27&v=qSWbCn170HU&feature=emb_title)
 

diff --git a/docs/src/list_of_supported_models.md b/docs/src/list_of_supported_models.md
@@ -5,7 +5,7 @@ the [Model Browser](@ref).
 
 MLJ provides access to a wide variety of machine learning models.
 We are always looking for
-[help](https://github.com/alan-turing-institute/MLJ.jl/blob/master/CONTRIBUTING.md)
+[help](https://github.com/JuliaAI/MLJ.jl/blob/master/CONTRIBUTING.md)
 adding new models or testing existing ones.  Currently available
 models are listed below; for the most up-to-date list, run `using MLJ;
 models()`. 

diff --git a/docs/src/preparing_data.md b/docs/src/preparing_data.md
@@ -116,7 +116,7 @@ Workshop](https://github.com/ablaom/MachineLearningInJulia2020)
 (specifically,
 [here](https://github.com/ablaom/MachineLearningInJulia2020/blob/master/tutorials.md#fixing-scientific-types-in-tabular-data))
 and [this Data Science in Julia
-tutorial](https://alan-turing-institute.github.io/DataScienceTutorials.jl/data/scitype/).
+tutorial](https://JuliaAI.github.io/DataScienceTutorials.jl/data/scitype/).
 
 Also relevant is the section, [Working with Categorical Data](@ref).
 
@@ -136,5 +136,5 @@ MissingImputator = @load MissingImputator pkg=BetaML
 [This MLJ
 Workshop](https://github.com/ablaom/MachineLearningInJulia2020), and the "End-to-end
 examples" in [Data Science in Julia
-tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/)
+tutorials](https://JuliaAI.github.io/DataScienceTutorials.jl/)
 give further illustrations of data preprocessing in MLJ.
diff --git a/docs/src/third_party_packages.md b/docs/src/third_party_packages.md
@@ -6,7 +6,7 @@ Last updated December 2020.
 
 Pull requests to update this list are very welcome. Otherwise, you may
 post an issue requesting this
-[here](https://github.com/alan-turing-institute/MLJ.jl/issues).
+[here](https://github.com/JuliaAI/MLJ.jl/issues).
 
 ## Packages providing models in the MLJ model registry
 
@@ -16,7 +16,7 @@ See [List of Supported Models](@ref model_list)
 ## Providing unregistered models:
 
 - [SossMLJ.jl](https://github.com/cscherrer/SossMLJ.jl)
-- [TimeSeriesClassification](https://github.com/alan-turing-institute/TimeSeriesClassification.jl)
+- [TimeSeriesClassification](https://github.com/JuliaAI/TimeSeriesClassification.jl)
 
 ## Packages providing other kinds of functionality:
 

diff --git a/examples/lightning_tour/lightning_tour.md b/examples/lightning_tour/lightning_tour.md
@@ -5,7 +5,7 @@ EditURL = "<unknown>/../../../../MLJ/examples/lightning_tour/lightning_tour.jl"
 # Lightning tour of MLJ
 
 *For a more elementary introduction to MLJ, see [Getting
-Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/).*
+Started](https://JuliaAI.github.io/MLJ.jl/dev/getting_started/).*
 
 **Note.** Be sure this file has not been separated from the
 accompanying Project.toml and Manifest.toml files, which should not
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2,4 +2,4 @@

		This feature not yet available.

		[CONTRIBUTE.md](https://github.com/alan-turing-institute/MLJ.jl/blob/master/CONTRIBUTE.md)
		[CONTRIBUTE.md](https://github.com/JuliaAI/MLJ.jl/blob/master/CONTRIBUTE.md)