modify readme to address numfocus comments

ReactiveBayes · Nov 25, 2024 · 9c51b65 · 9c51b65
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diff --git a/README.md b/README.md
@@ -171,51 +171,31 @@ Our high-level project roadmap outlines the key milestones and focus areas for t
 
 For a more granular view of our progress and ongoing tasks, check out our [project board](https://github.com/orgs/reactivebayes/projects/2/views/4) or join our 4-weekly [public meetings](https://dynalist.io/d/F4aA-Z2c8X-M1iWTn9hY_ndN).
 
-# External Contributors
 
-RxInfer has benefited from the contributions and development efforts of external collaborators and organizations. We're grateful for their involvement in advancing the project.
+# Join Our Community and Contribute to RxInfer
 
-## Active Inference Institute
+RxInfer is a community-driven project and we welcome all contributions! To get started:
+- Check out our [contributing guide](https://reactivebayes.github.io/RxInfer.jl/stable/contributing/guide)
+- Review the [contributing guidelines](https://reactivebayes.github.io/RxInfer.jl/stable/contributing/guidelines)
+- Browse [beginner-friendly issues](https://github.com/ReactiveBayes/RxInfer.jl/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) to find something that interests you
 
-Members of the [Active Inference Institute](https://www.activeinference.org/) have been working on improving the visualization capabilities of RxInfer/GraphPPL. Their efforts focus on developing better model visualization capabilities, creating various summary/subgraph visualization modalities, implementing different graph layout algorithms, and improving the ability to inspect and understand models.
+## Active Inference Institute Collaboration
 
-For more details on their ongoing work, see the [RxInfer development project board](https://coda.io/d/RxInfer-2024-Active-Inference-Institute_ddtS-XZ4BJb/Developing-RxInfer-jl_sufeCeIa#_lutfq_7F).
+The [Active Inference Institute](https://www.activeinference.org/) community members are enhancing RxInfer/GraphPPL's visualization capabilities. Their work includes:
+- Developing advanced model visualization features
+- Creating summary and subgraph visualization modalities
+- Implementing various graph layout algorithms
+- Improving model inspection and understanding tools
 
-## Educational Content
+For more details on ongoing work, see the [RxInfer development project board](https://coda.io/d/RxInfer-2024-Active-Inference-Institute_ddtS-XZ4BJb/Developing-RxInfer-jl_sufeCeIa#_lutfq_7F).
 
-Educational content and tutorials related to RxInfer are being developed and can be found on [Learnable Loop](https://learnableloop.com/#category=RxInfer). These resources cover a range of topics including visualizing Forney Factor Graphs, sales forecasting with time-varying autoregressive models, hidden Markov models with control, and various applications of Active Inference across different domains.
+## Learning Resources
 
-# Contributing
-
-We welcome contributions from the community. If you are interested in contributing to the development of `RxInfer.jl`, please check out our [contributing guide](https://reactivebayes.github.io/RxInfer.jl/stable/contributing/guide), the [contributing guidelines](https://reactivebayes.github.io/RxInfer.jl/stable/contributing/guidelines), or look at the [issues linked with the `good first issue` label](https://github.com/ReactiveBayes/RxInfer.jl/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) to get started.
-
-# Where to go next?
-
-There are a set of [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/) available in `RxInfer` repository that demonstrate the more advanced features of the package. Alternatively, you can head to the [documentation](https://reactivebayes.github.io/RxInfer.jl/stable/) that provides more detailed information of how to use `RxInfer` to specify more complex probabilistic models.
-
-## Ecosystem
-
-The `RxInfer` framework consists of three *core* packages developed by [ReactiveBayes](https://github.com/reactivebayes/):
-
-- [`ReactiveMP.jl`](https://github.com/reactivebayes/ReactiveMP.jl) - the underlying message passing-based inference engine
-- [`GraphPPL.jl`](https://github.com/reactivebayes/GraphPPL.jl) - model and constraints specification package
-- [`Rocket.jl`](https://github.com/reactivebayes/Rocket.jl) - reactive extensions package for Julia 
-
-## JuliaCon 2023 presentation
-
-Additionally, checkout our [video from JuliaCon 2023](https://www.youtube.com/watch?v=qXrvDVm_fnE) for a high-level overview of the package
-
-<p align="center">
-    <a href="https://www.youtube.com/watch?v=qXrvDVm_fnE"><img style="width: 100%" src="https://img.youtube.com/vi/qXrvDVm_fnE/0.jpg"></a>
-</p>
-
-## Our presentation at the Julia User Group Munich meetup
-
-Also check out the recorded presentation at the Julia User Group Munich meetup for a more detailed overview of the package
-
-<p align="center">
-    <a href="https://www.youtube.com/watch?v=KuluqEzFtm8"><img style="width: 100%" src="https://img.youtube.com/vi/KuluqEzFtm8/0.jpg"></a>
-</p>
+The community maintains educational content and tutorials on [Learnable Loop](https://learnableloop.com/#category=RxInfer), covering topics such as:
+- Visualizing Forney Factor Graphs
+- Sales forecasting with time-varying autoregressive models
+- Hidden Markov models with control
+- Applications of Active Inference across different domains
 
 # License
 

diff --git a/docs/make.jl b/docs/make.jl
@@ -69,10 +69,13 @@ foreach(vcat(ExamplesOverviewPath, ExamplesCategoriesOverviewPaths)) do path
         @warn "`$(path)` does not exist. Generating an empty overview. Use the `make examples` command to generate the overview and all examples."
         mkpath(dirname(path))
         open(path, "w") do f
-            write(f, """
-            $(isequal(path, ExamplesOverviewPath) ? "# [Examples overview](@id examples-overview)" : "")
-            The overview is missing. Use the `make examples` command to generate the overview and all examples.
-            """)
+            write(
+                f,
+                """
+       $(isequal(path, ExamplesOverviewPath) ? "# [Examples overview](@id examples-overview)" : "")
+       The overview is missing. Use the `make examples` command to generate the overview and all examples.
+       """
+            )
         end
     end
 end
@@ -105,22 +108,10 @@ makedocs(;
             "Model specification"       => "manuals/model-specification.md",
             "Constraints specification" => "manuals/constraints-specification.md",
             "Meta specification"        => "manuals/meta-specification.md",
-            "Inference specification"   => [
-                "Overview" => "manuals/inference/overview.md", 
-                "Static inference" => "manuals/inference/static.md", 
-                "Streamline inference" => "manuals/inference/streamlined.md",
-                "Initialization"   => "manuals/inference/initialization.md",
-                "Auto-updates"   => "manuals/inference/autoupdates.md",
-                "Deterministic nodes" => "manuals/inference/delta-node.md",
-                "Non-conjugate inference" => "manuals/inference/nonconjugate.md",
-                "Undefined message update rules" => "manuals/inference/undefinedrules.md"
-            ],
-            "Inference customization"   => [
-                "Defining a custom node and rules" => "manuals/customization/custom-node.md",
-                "Inference results postprocessing" => "manuals/customization/postprocess.md", 
-            ],
-            "Debugging" => "manuals/debugging.md",
-            "Migration from v2 to v3" => "manuals/migration-guide-v2-v3.md"
+            "Inference specification"   => ["Overview" => "manuals/inference/overview.md", "Static inference" => "manuals/inference/static.md", "Streamline inference" => "manuals/inference/streamlined.md", "Initialization" => "manuals/inference/initialization.md", "Auto-updates" => "manuals/inference/autoupdates.md", "Deterministic nodes" => "manuals/inference/delta-node.md", "Non-conjugate inference" => "manuals/inference/nonconjugate.md", "Undefined message update rules" => "manuals/inference/undefinedrules.md"],
+            "Inference customization"   => ["Defining a custom node and rules" => "manuals/customization/custom-node.md", "Inference results postprocessing" => "manuals/customization/postprocess.md"],
+            "Debugging"                 => "manuals/debugging.md",
+            "Migration from v2 to v3"   => "manuals/migration-guide-v2-v3.md"
         ],
         "Library" => [
             "Model construction" => "library/model-construction.md",
@@ -131,11 +122,11 @@ makedocs(;
         "Examples" => [
             "Overview" => "examples/overview.md", # This must be auto-generated with `make examples`
             ExamplesPages...,
-            "External examples" => "contributing/external-examples.md"
+            "Contribute by examples" => "contributing/examples.md"
         ],
         "Contributing" => [
             "Contribution guide" => "contributing/guide.md",
-            "Contribution guidelines"=> "contributing/guidelines.md",
+            "Contribution guidelines" => "contributing/guidelines.md",
             "Contributing to the documentation" => "contributing/new-documentation.md",
             "Contributing to the examples" => "contributing/new-example.md",
             "Publishing a new release" => "contributing/new-release.md"

diff --git a/docs/src/contributing/examples.md b/docs/src/contributing/examples.md
@@ -0,0 +1,11 @@
+# [Contributing by examples](@id examples)
+
+We welcome examples from the community that showcase RxInfer.jl in action! Whether you're working in machine learning, signal processing, robotics, computational biology, or any other domain - your contributions help others learn and explore probabilistic modeling with RxInfer.jl.
+
+## Featured Examples
+
+- **Active Inference with RxInfer.jl** - Dive into the realm of Active Inference guided by [Kobus Esterhuysen](https://www.linkedin.com/in/kobusesterhuysen/) at [Learnable Loop](https://learnableloop.com/#category=RxInfer).
+- **Tutorial Series on RxInfer.jl** - Explore a series of engaging tutorial videos on RxInfer.jl's functionalities, presented by [@doggotodjl](https://www.youtube.com/@doggodotjl/search?query=RxInfer).
+
+!!! note 
+    If you're interested in contributing an example, we'd love to hear from you! Please initiate an issue or start a new discussion on our [GitHub repository](https://github.com/reactivebayes/RxInfer.jl) to get involved.
diff --git a/docs/src/contributing/external-examples.md b/docs/src/contributing/external-examples.md
diff --git a/examples/.meta.jl b/examples/.meta.jl
@@ -3,22 +3,10 @@
 # Please carefully read the documentation section `Contributing: new example` for more information
 return (
     categories = (
-        basic_examples = (
-            title = "Basic examples",
-            description = "Basic examples contain \"Hello World!\" of Bayesian inference in RxInfer.",
-        ),
-        advanced_examples = (
-            title = "Advanced examples",
-            description = "Advanced examples contain more complex inference problems.",
-        ),
-        problem_specific = (
-            title = "Problem specific",
-            description = "Problem specific examples contain specialized models and inference for various domains.",
-        ),
-        hidden_examples = (
-            title = "",
-            description = ""
-        )
+        basic_examples = (title = "Basic examples", description = "Basic examples contain \"Hello World!\" of Bayesian inference in RxInfer."),
+        advanced_examples = (title = "Advanced examples", description = "Advanced examples contain more complex inference problems."),
+        problem_specific = (title = "Problem specific", description = "Problem specific examples contain specialized models and inference for various domains."),
+        hidden_examples = (title = "", description = "")
     ),
     examples = [
         (
@@ -39,16 +27,14 @@ return (
             description = "In this demo, we are interested in Bayesian state estimation in different types of State-Space Models, including linear, nonlinear, and cases with missing observations",
             category = :basic_examples
         ),
-
         (
             filename = "Predicting Bike Rental Demand.ipynb",
             title = "Predicting Bike Rental Demand",
             description = "An illustrative guide to implementing prediction mechanisms within RxInfer.jl, using bike rental demand forecasting as a contextual example.",
             category = :basic_examples
         ),
-
         (
-            filename  = "Hidden Markov Model.ipynb",
+            filename = "Hidden Markov Model.ipynb",
             title = "How to train your Hidden Markov Model",
             description = "An example of structured variational Bayesian inference in Hidden Markov Model with unknown transition and observational matrices.",
             category = :basic_examples
@@ -108,8 +94,8 @@ return (
             category = :advanced_examples
         ),
         (
-            filename  = "Autoregressive Models.ipynb", 
-            title = "Autoregressive Models", 
+            filename = "Autoregressive Models.ipynb",
+            title = "Autoregressive Models",
             description = "An example of Bayesian treatment of latent AR and ARMA models. Reference: [Albert Podusenko, Message Passing-Based Inference for Time-Varying Autoregressive Models](https://www.mdpi.com/1099-4300/23/6/683).",
             category = :problem_specific
         ),
@@ -135,7 +121,7 @@ return (
             filename = "Invertible Neural Network Tutorial.ipynb",
             title = "Invertible neural networks: a tutorial",
             description = "An example of variational Bayesian Inference with invertible neural networks. Reference: Bart van Erp, Hybrid Inference with Invertible Neural Networks in Factor Graphs.",
-            category = :problem_specific  
+            category = :problem_specific
         ),
         (
             filename = "Probit Model (EP).ipynb",
@@ -155,18 +141,7 @@ return (
             description = "In this example we create a non-conjugate model and use a nonlinear link function between variables. We show how to extend the functionality of `RxInfer` and to create a custom factor node with arbitrary message passing update rules.",
             category = :problem_specific
         ),
-        (
-            filename  = "Universal Mixtures.ipynb", 
-            title = "Universal Mixtures", 
-            description = "Universal mixture modeling.",
-            category = :problem_specific
-        ),
-
-        (
-            filename  = "Tiny Benchmark.ipynb", 
-            title = "Tiny Benchmark", 
-            description = "Tiny Benchmark for Internal testing.",
-            category = :hidden_examples
-        ),
+        (filename = "Universal Mixtures.ipynb", title = "Universal Mixtures", description = "Universal mixture modeling.", category = :problem_specific),
+        (filename = "Tiny Benchmark.ipynb", title = "Tiny Benchmark", description = "Tiny Benchmark for Internal testing.", category = :hidden_examples)
     ]
 )
diff --git a/ext/ProjectionExt/ProjectionExt.jl b/ext/ProjectionExt/ProjectionExt.jl
@@ -42,4 +42,4 @@ function ReactiveMP.constrain_form(constraint::ProjectedTo, context::ProjectionC
     return result
 end
 
-end
+end
diff --git a/paper/example.jl b/paper/example.jl
@@ -143,11 +143,11 @@ savefig(p, "inference.pdf")
 using BenchmarkTools
 
 # See the optimization flags in the `compile.sh` file
-benchmark = @benchmark experiment(data[3]) setup=(data = dataset(T = 1000; precision = 1.0)) seconds = 15
+benchmark = @benchmark experiment(data[3]) setup = (data = dataset(T = 1000; precision = 1.0)) seconds = 15
 
 @info "Writing the benchmark results..."
 
-open("benchmark.txt", "w") do file 
+open("benchmark.txt", "w") do file
     show(file, "text/plain", benchmark)
     versioninfo(file, verbose = false)
 end
diff --git a/paper/paper.md b/paper/paper.md
@@ -133,7 +133,7 @@ We present **RxInfer.jl**, a package for processing infinite data streams by rea
     inference solutions for linear Gaussian dynamical systems, autoregressive models, hierarchical models, 
     discrete-valued models, mixture models, invertible neural networks [@van_erp_hybrid_2022], arbitrary nonlinear state transition 
     functions, and conjugate pair primitives.
-- The inference procedure is auto-differentiable with external packages, such as **ForwardDiff.jl** [@revels_forward-mode_2016] 
+- The inference procedure is auto-differentiable with automatic differentiation packages of Julia community, such as **ForwardDiff.jl** [@revels_forward-mode_2016] 
     or **ReverseDiff.jl**.
 - The inference engine supports different types of floating-point numbers, such as `Float32`, `Float64`, and `BigFloat`.
 

diff --git a/scripts/examples.jl b/scripts/examples.jl
@@ -223,7 +223,7 @@ function Base.run(examplesrunner::ExamplesRunner)
             end
         end
 
-        write(io_main_overview, "- [External examples](@ref external-examples): Featured examples from the community.\n")
+        write(io_main_overview, "- [Contribute by examples](@ref examples): Featured examples from the community.\n")
 
         foreach(examples) do example
             mdname = replace(example[:filename], ".ipynb" => ".md")
-Original file line number
+Diff line change
@@ Expand Up @@
         return result
     end
-    end
+    end