Merge branch 'master' into compathelper/new_version/2023-06-20-00-28-…

…18-752-01921173878

Project.toml

@@ -1,6 +1,7 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.27"
+version = "0.28.1"
+
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -38,7 +39,7 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 
 [compat]
 AbstractMCMC = "4"
-AdvancedHMC = "0.3.0, 0.4"
+AdvancedHMC = "0.3.0, 0.4.0, 0.5.2"
 AdvancedMH = "0.6.8, 0.7"
 AdvancedPS = "0.4"
 AdvancedVI = "0.2"
@@ -48,6 +49,7 @@ DataStructures = "0.18"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
+DynamicHMC = "3.4"
 DynamicPPL = "0.23"
 EllipticalSliceSampling = "0.5, 1"
 ForwardDiff = "0.10.3"
@@ -68,11 +70,14 @@ StatsFuns = "0.8, 0.9, 1"
 Tracker = "0.2.3"
 julia = "1.7"
 
-[weakdeps]
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-
 [extensions]
+TuringDynamicHMCExt = "DynamicHMC"
 TuringOptimExt = "Optim"
 
 [extras]
+DynamicHMC="bbc10e6e-7c05-544b-b16e-64fede858acb"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+
+[weakdeps]
+DynamicHMC="bbc10e6e-7c05-544b-b16e-64fede858acb"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"

README.md

@@ -6,19 +6,10 @@
 [![Documentation](https://img.shields.io/badge/doc-latest-blue.svg)](https://turinglang.org/dev/docs/using-turing/)
 [![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)
 
-**Turing.jl** is a Julia library for general-purpose [probabilistic programming](https://en.wikipedia.org/wiki/Probabilistic_programming_language). Turing allows the user to write models using standard Julia syntax, and provides a wide range of sampling-based inference methods for solving problems across probabilistic machine learning, Bayesian statistics, and data science. Compared to other probabilistic programming languages, Turing has a special focus on modularity, and decouples the modelling language (i.e. the compiler) and inference methods. This modular design, together with the use of a high-level numerical language Julia, makes Turing particularly extensible: new model families and inference methods can be easily added.
-
-Current features include:
-
-- [General-purpose](https://turinglang.org/dev/tutorials/06-infinite-mixture-model/) probabilistic programming with an [intuitive modelling interface](https://turinglang.org/dev/tutorials/00-introduction/)
-- Robust, efficient [Hamiltonian Monte Carlo (HMC)](https://github.com/TuringLang/AdvancedHMC.jl) sampling for differentiable posterior distributions
-- [Particle MCMC](https://github.com/TuringLang/AdvancedPS.jl) sampling for complex posterior distributions involving discrete variables and stochastic control flows
-- Compositional inference via Gibbs sampling that combines particle MCMC, HMC, [Random-Walk MH (RWMH)](https://github.com/TuringLang/AdvancedMH.jl) and [Elliptical Slice Sampling](https://github.com/TuringLang/Turing.jl/blob/master/src/inference/ess.jl)
-- Advanced variational inference based on [ADVI](https://github.com/TuringLang/AdvancedVI.jl) and [Normalising Flows](https://github.com/TuringLang/Bijectors.jl)
 
 ## Getting Started
 
-Turing's home page, with links to everything you'll need to use Turing is:
+Turing's home page, with links to everything you'll need to use Turing, is available at:
 
 https://turinglang.org/dev/docs/using-turing/get-started
 
@@ -30,17 +21,11 @@ See [releases](https://github.com/TuringLang/Turing.jl/releases).
 ## Want to contribute?
 
 Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include [Hong Ge](http://mlg.eng.cam.ac.uk/hong/), [Kai Xu](http://mlg.eng.cam.ac.uk/?portfolio=kai-xu), [Martin Trapp](http://martint.blog), [Mohamed Tarek](https://github.com/mohamed82008), [Cameron Pfiffer](https://business.uoregon.edu/faculty/cameron-pfiffer), [Tor Fjelde](http://retiredparkingguard.com/about.html).
-You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.
+You can see the complete list on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.
 
-Turing is an open source project so if you feel you have some relevant skills and are interested in contributing then please do get in touch. See the [Contributing](https://turinglang.org/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github or implementing things yourself and making a pull request. We would also appreciate example models written using Turing.
+Turing is an open source project so if you feel you have some relevant skills and are interested in contributing, please get in touch. See the [Contributing](https://turinglang.org/dev/docs/contributing/guide) page for details on the process. You can contribute by opening issues on Github, implementing things yourself, and making a pull request. We would also appreciate example models written using Turing.
 
 ## Issues and Discussions
-Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should can place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not already have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/).
+Issues related to bugs and feature requests are welcome on the [issues page](https://github.com/TuringLang/Turing.jl/issues), while discussions and questions about statistical applications and theory should place on the [Discussions page](https://github.com/TuringLang/Turing.jl/discussions) or [our channel](https://julialang.slack.com/messages/turing/) (`#turing`) in the Julia Slack chat. If you do not have an invitation to Julia's Slack, you can get one by going [here](https://julialang.org/slack/).
 
-## Related projects
-- The Stan language for probabilistic programming - [Stan.jl](https://github.com/StanJulia/Stan.jl)
-- Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods - [DynamicHMC.jl](https://github.com/tpapp/DynamicHMC.jl)
-- Comparing performance and results of mcmc options using Julia - [MCMCBenchmarks.jl](https://github.com/StatisticalRethinkingJulia/MCMCBenchmarks.jl)
 
-## Citing Turing.jl ##
-If you use **Turing** for your own research, please consider citing the following publication: Hong Ge, Kai Xu, and Zoubin Ghahramani: **Turing: a language for flexible probabilistic inference.** AISTATS 2018 [pdf](http://proceedings.mlr.press/v84/ge18b.html) [bibtex](https://github.com/TuringLang/Turing.jl/blob/master/CITATION.bib)

docs/README.md

@@ -1 +1,5 @@
-Turing's documentation in this directory is in markdown format. On how to build the documentation, please refer to the [README](https://github.com/TuringLang/turinglang.github.io) file in [turinglang.github.io](https://github.com/TuringLang/turinglang.github.io).
+Turing's documentation in this directory is in markdown format. 
+
+If you want to build the doc locally, please refer to the [README](https://github.com/TuringLang/turinglang.github.io) file in [turinglang.github.io](https://github.com/TuringLang/turinglang.github.io).
+
+Please also visit [this repo](https://github.com/TuringLang/TuringTutorials/tree/master/tutorials) for the docs.