fix links

README.md

@@ -1,9 +1,9 @@
 ![](docs/src/assets/biglogo-blacktheme.svg?raw=true&sanitize=true)
 
-[![Official page](https://img.shields.io/badge/official%20page%20-RxInfer-blue)](https://reactivebayes.github.io/rxinfer-website/)
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://reactivebayes.github.io/RxInfer.jl/stable/)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://reactivebayes.github.io/RxInfer.jl/dev/)
-[![Examples](https://img.shields.io/badge/examples-RxInfer-brightgreen)](https://reactivebayes.github.io/RxInferExamples.jl/)
+[![Official page](https://img.shields.io/badge/official%20page%20-RxInfer-blue)](https://rxinfer.ml)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://docs.rxinfer.ml/stable/)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://docs.rxinfer.ml/dev/)
+[![Examples](https://img.shields.io/badge/examples-RxInfer-brightgreen)](https://examples.rxinfer.ml/)
 [![Q&A](https://img.shields.io/badge/Q&A-RxInfer-orange)](https://github.com/reactivebayes/RxInfer.jl/discussions)
 [![Roadmap](https://img.shields.io/badge/roadmap-RxInfer-yellow)](#roadmap)
 [![Build Status](https://github.com/reactivebayes/RxInfer.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/reactivebayes/RxInfer.jl/actions/workflows/CI.yml?query=branch%3Amain)
@@ -20,18 +20,18 @@ Given a probabilistic model, RxInfer allows for an efficient message-passing bas
 
 ### Performance and scalability
 
-RxInfer.jl has been designed with a focus on efficiency, scalability and maximum performance for running Bayesian inference with message passing. Below is a comparison between RxInfer.jl and Turing.jl on latent state estimation in a linear multi-variate Gaussian state-space model. [Turing.jl](https://github.com/TuringLang/Turing.jl) is a state-of-the-art Julia-based general-purpose probabilistic programming package and is capable of running inference in a broader class of models. Still, RxInfer.jl executes the inference task in [various models](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/) faster and more accurately. RxInfer.jl accomplishes this by taking advantage of any conjugate likelihood-prior pairings in the model, which have analytical posteriors that are known by RxInfer.jl. As a result, in models with conjugate pairings, RxInfer.jl often beats general-purpose probabilistic programming packages in terms of computational load, speed, memory and accuracy. Note, however, that RxInfer.jl also supports non-conjugate inference and is continually improving in order to support a larger class of models.
+RxInfer.jl has been designed with a focus on efficiency, scalability and maximum performance for running Bayesian inference with message passing. Below is a comparison between RxInfer.jl and Turing.jl on latent state estimation in a linear multi-variate Gaussian state-space model. [Turing.jl](https://github.com/TuringLang/Turing.jl) is a state-of-the-art Julia-based general-purpose probabilistic programming package and is capable of running inference in a broader class of models. Still, RxInfer.jl executes the inference task in [various models](https://examples.rxinfer.ml) faster and more accurately. RxInfer.jl accomplishes this by taking advantage of any conjugate likelihood-prior pairings in the model, which have analytical posteriors that are known by RxInfer.jl. As a result, in models with conjugate pairings, RxInfer.jl often beats general-purpose probabilistic programming packages in terms of computational load, speed, memory and accuracy. Note, however, that RxInfer.jl also supports non-conjugate inference and is continually improving in order to support a larger class of models.
 
 Turing comparison             |  Scalability performance
 :-------------------------:|:-------------------------:
 ![](benchmarks/plots/lgssm_comparison.svg?raw=true&sanitize=true)  |  ![](benchmarks/plots/lgssm_scaling.svg?raw=true&sanitize=true)
 
 > [!NOTE]
-> See many more examples in the [RxInferExamples.jl](https://reactivebayes.github.io/RxInferExamples.jl/) repository.
+> See many more examples in the [RxInferExamples.jl](https://examples.rxinfer.ml/) repository.
 
 ### Faster inference with better results
 
-RxInfer.jl not only beats generic-purpose Bayesian inference methods in conjugate models, executes faster, and scales better, but also provides more accurate results. Check out the [documentation](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/) for more examples!
+RxInfer.jl not only beats generic-purpose Bayesian inference methods in conjugate models, executes faster, and scales better, but also provides more accurate results. Check out more examples [here](https://examples.rxinfer.ml)!
 
 Inference with RxInfer             |  Inference with HMC
 :-------------------------:|:-------------------------:
@@ -61,12 +61,12 @@ Optionally, use `] test RxInfer` to validate the installation by running the tes
 
 # Documentation
 
-For more information about `RxInfer.jl` please refer to the [documentation](https://reactivebayes.github.io/RxInfer.jl/stable/).
+For more information about `RxInfer.jl` please refer to the [documentation](https://docs.rxinfer.ml).
 
 # Getting Started
 
 > [!NOTE]
-> There are examples available to get you started in the [RxInferExamples.jl](https://reactivebayes.github.io/RxInferExamples.jl/) repository.
+> There are examples available to get you started in the [RxInferExamples.jl](https://examples.rxinfer.ml/) repository.
 
 ### Coin flip simulation
 
@@ -180,14 +180,14 @@ The `RxInfer` framework consists of four *core* packages developed by [ReactiveB
 
 # Where to go next?
 
-There are a set of [examples](https://reactivebayes.github.io/RxInferExamples.jl/) available in the `RxInferExamples.jl` 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.
+There are a set of [examples](https://examples.rxinfer.ml/) available in the `RxInferExamples.jl` repository that demonstrate the more advanced features of the package. Alternatively, you can head to the [documentation](https://docs.rxinfer.ml) that provides more detailed information of how to use `RxInfer` to specify more complex probabilistic models.
 
 
 # Join Our Community and Contribute to RxInfer
 
 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)
+- Check out our [contributing guide](https://docs.rxinfer.ml/stable/contributing/guide)
+- Review the [contributing guidelines](https://docs.rxinfer.ml/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
 
 ## Active Inference Institute Collaboration
@@ -232,7 +232,7 @@ RxInfer collects completely anonymous telemetry data regarding package usage. Th
 - Entirely anonymous
 - (Opt-out) Can be disabled for a single Julia session or permanently
 
-You can learn more about it and how to opt-out by visiting our [documentation](https://reactivebayes.github.io/RxInfer.jl/stable/).
+You can learn more about it and how to opt-out by visiting our [documentation](https://docs.rxinfer.ml).
 
 ## Session Sharing
 
@@ -249,7 +249,7 @@ This information is invaluable for debugging issues and improving RxInfer. Sessi
 - Only shared when you explicitly choose to do so
 - (Opt-in) Can be enabled to send reports automatically when an error occurs. When enabled, still entirely anonymous.
 
-If you're opening a GitHub issue, we encourage you to share your session ID with us - it helps us understand your use case better and provide more accurate support. Learn more about session sharing and how to opt-in in our [documentation](https://reactivebayes.github.io/RxInfer.jl/stable/).
+If you're opening a GitHub issue, we encourage you to share your session ID with us - it helps us understand your use case better and provide more accurate support. Learn more about session sharing and how to opt-in in our [documentation](https://docs.rxinfer.ml).
 
 # License
 

codemeta.json

@@ -13,7 +13,7 @@
     "description": "Julia package for automated, scalable and efficient Bayesian inference on factor graphs with reactive message passing. ",
     "applicationCategory": "Statistics",
     "developmentStatus": "active",
-    "readme": "https://reactivebayes.github.io/RxInfer.jl/stable/",
+    "readme": "https://rxinfer.ml",
     "softwareVersion": "4.0.1",
     "keywords": [
         "Bayesian inference",
@@ -36,8 +36,10 @@
         "MacOS"
     ],
     "relatedLink": [
-        "https://reactivebayes.github.io/rxinfer-website/",
-        "https://reactivebayes.github.io/RxInfer.jl/stable/",
+        "https://rxinfer.ml",
+        "https://docs.rxinfer.ml",
+        "https://examples.rxinfer.ml",
+        "https://github.com/ReactiveBayes/RxInfer.jl",
         "https://app.codecov.io/gh/reactivebayes/RxInfer.jl"
     ],
     "author": [

docs/make.jl

@@ -19,7 +19,7 @@ makedocs(;
     sitename = "RxInfer.jl",
     format = Documenter.HTML(;
         prettyurls = get(ENV, "CI", "false") == "true",
-        canonical = "https://reactivebayes.github.io/RxInfer.jl",
+        canonical = "https://docs.rxinfer.ml",
         edit_link = "main",
         warn_outdated = true,
         assets = ["assets/theme.css", "assets/header.css", "assets/header.js", "assets/chat.js", "assets/favicon.ico"],
@@ -79,7 +79,7 @@ makedocs(;
 function inject_keywords_meta()
     # Define keywords for the documentation
     keywords = "Julia, Bayesian inference, factor graph, message passing, probabilistic programming, reactive programming, RxInfer"
-    base_url = "https://reactivebayes.github.io/RxInfer.jl/stable"
+    base_url = "https://docs.rxinfer.ml/stable"
 
     # Meta tags to inject
     meta_tags = """
@@ -108,7 +108,7 @@ end
 
 # Function to generate sitemap.xml
 function generate_sitemap()
-    base_url = "https://reactivebayes.github.io/RxInfer.jl"
+    base_url = "https://docs.rxinfer.ml/stable"
     sitemap_content = """<?xml version="1.0" encoding="UTF-8"?>
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">"""
 

docs/src/assets/header.js

@@ -31,9 +31,9 @@ window.onload = function() {
 
                 const items = [
                     { title: "Home", link: "http://www.rxinfer.ml", icon: [ "fas", "fa-diagram-project" ] },
-                    { title: "Get Started", link: "https://reactivebayes.github.io/RxInfer.jl/stable/manuals/getting-started/", icon: [ "fas", "fa-person-chalkboard" ] },
-                    { title: "Documentation", link: "https://reactivebayes.github.io/RxInfer.jl/stable/", icon: [ "fas", "fa-book" ] },
-                    { title: "Examples", link: "https://reactivebayes.github.io/RxInferExamples.jl/", icon: [ "fas", "fa-laptop-code" ] },
+                    { title: "Get Started", link: "https://docs.rxinfer.ml/stable/manuals/getting-started/", icon: [ "fas", "fa-person-chalkboard" ] },
+                    { title: "Documentation", link: "https://docs.rxinfer.ml/stable/", icon: [ "fas", "fa-book" ] },
+                    { title: "Examples", link: "https://examples.rxinfer.ml/", icon: [ "fas", "fa-laptop-code" ] },
                     { title: "Papers", link: "https://biaslab.github.io/publication/", icon: [ "far", "fa-book-open" ] },
                     { title: "Team", link: "https://github.com/orgs/ReactiveBayes/people", icon: [ "fas", "fa-people-group" ] },
                     { title: "Discussions", link: "https://github.com/orgs/ReactiveBayes/discussions", icon: [ "far", "fa-comment" ] },

docs/src/contributing/examples.md

@@ -1,6 +1,6 @@
 # [Contribute with examples](@id examples)
 
-The examples for RxInfer.jl are now maintained in a dedicated repository at [RxInferExamples.jl](https://reactivebayes.github.io/RxInferExamples.jl/). Please visit the examples repository to:
+The examples for RxInfer.jl are now maintained in a dedicated repository at [RxInferExamples.jl](https://examples.rxinfer.ml/). Please visit the examples repository to:
 - Browse existing examples
 - Learn how to contribute your own examples
 - Get started with RxInfer.jl
@@ -10,5 +10,5 @@ The examples for RxInfer.jl are now maintained in a dedicated repository at [RxI
 - **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, please visit the [RxInferExamples.jl](https://reactivebayes.github.io/RxInferExamples.jl/) repository. We welcome all contributions that help demonstrate the capabilities of RxInfer.jl!
+!!! note
+    If you're interested in contributing an example, please visit the [RxInferExamples.jl](https://examples.rxinfer.ml/) repository. We welcome all contributions that help demonstrate the capabilities of RxInfer.jl!

docs/src/contributing/new-example.md

@@ -1,5 +1,5 @@
 # [Contributing to the examples](@id contributing-new-example)
 
-The examples have been moved to a dedicated repository at [RxInferExamples.jl](https://reactivebayes.github.io/RxInferExamples.jl/).
+The examples have been moved to a dedicated repository at [RxInferExamples.jl](https://examples.rxinfer.ml/).
 
 To contribute new examples, please visit the examples repository and follow the contribution guidelines there. We welcome all contributions that help demonstrate the capabilities of RxInfer.jl!

docs/src/examples/overview.md

@@ -1,5 +1,5 @@
 # [Examples](@id examples-overview)
 
-All examples for the `RxInfer` package are available at [https://reactivebayes.github.io/RxInferExamples.jl/](https://reactivebayes.github.io/RxInferExamples.jl/).
+All examples for the `RxInfer` package are available at [https://examples.rxinfer.ml/](https://examples.rxinfer.ml/).
 
-We welcome community contributions! If you have an interesting example or application of RxInfer, please consider sharing it with the community by submitting it to our examples repository. Visit our [contribution guidelines](https://reactivebayes.github.io/RxInferExamples.jl/) to learn how to contribute.
+We welcome community contributions! If you have an interesting example or application of RxInfer, please consider sharing it with the community by submitting it to our examples repository. Visit our [contribution guidelines](https://examples.rxinfer.ml/how_to_contribute/) to learn how to contribute.

docs/src/manuals/inference/overview.md

@@ -336,4 +336,4 @@ RxInfer.enable_inference_error_hint!
 
 ## Where to go next?
 
-Read more explanation about the other keyword arguments in the [Streamlined (online) inference](@ref manual-online-inference)section or check out the [Static Inference](@ref manual-static-inference) section or check some more advanced [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+Read more explanation about the other keyword arguments in the [Streamlined (online) inference](@ref manual-online-inference)section or check out the [Static Inference](@ref manual-static-inference) section or check some more advanced [examples](https://examples.rxinfer.ml/).

docs/src/manuals/inference/static.md

@@ -2,7 +2,7 @@
 
 This guide explains how to use the [`infer`](@ref) function for static datasets. We'll show how `RxInfer` can estimate posterior beliefs given a set of observations. We'll use a simple Beta-Bernoulli model as an example, which has been covered in the [Getting Started](@ref user-guide-getting-started) section, but keep in mind that these techniques can apply to any model.
 
-Also read about [Streaming Inference](@ref manual-online-inference) or checkout more complex [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+Also read about [Streaming Inference](@ref manual-online-inference) or checkout more complex [examples](https://examples.rxinfer.ml/).
 
 ## [Model specification](@id manual-static-inference-model-spec)
 
@@ -496,4 +496,4 @@ nothing #hide
 This guide covered some fundamental usages of the [`infer`](@ref) function in the context of inference with static datasets, 
 but did not cover all the available keyword arguments of the function. Read more explanation about the other keyword arguments 
 in the [Overview](@ref user-guide-inference-execution) section or check out the [Streaming Inference](@ref manual-online-inference) section.
-Also check out more complex [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+Also check out more complex [examples](https://examples.rxinfer.ml/).

docs/src/manuals/inference/streamlined.md

@@ -3,7 +3,7 @@
 This guide explains how to use the [`infer`](@ref) function for dynamic datasets. We show how `RxInfer` can continuously update beliefs asynchronously whenever a new observation arrives. We use a simple Beta-Bernoulli model as an example, which has been covered in the [Getting Started](@ref user-guide-getting-started) section, 
 however, these techniques can be applied to any model
 
-Also read about [Static Inference](@ref manual-static-inference) or checkout more complex [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+Also read about [Static Inference](@ref manual-static-inference) or checkout more complex [examples](https://examples.rxinfer.ml/).
 
 ## [Model specification](@id manual-online-inference-model-spec)
 
@@ -919,4 +919,4 @@ end
 This guide covered some fundamental usages of the [`infer`](@ref) function in the context of streamline inference, 
 but did not cover all the available keyword arguments of the function. Read more explanation about the other keyword arguments 
 in the [Overview](@ref user-guide-inference-execution) section or check out the [Static Inference](@ref manual-static-inference) section.
-Also check out more complex [examples](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+Also check out more complex [examples](https://examples.rxinfer.ml/).

paper/paper.md

@@ -137,7 +137,7 @@ We present **RxInfer.jl**, a package for processing infinite data streams by rea
     or **ReverseDiff.jl**.
 - The inference engine supports different types of floating-point numbers, such as `Float32`, `Float64`, and `BigFloat`.
 
-A large collection of examples is available at [https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/](https://reactivebayes.github.io/RxInfer.jl/stable/examples/overview/).
+A large collection of examples is available at [https://examples.rxinfer.ml/](https://examples.rxinfer.ml/).
 
 # Example usage