fix merge conflicts

Author: shravanngoswamii

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2018-2024, Hong Ge, the Turing language team
+Copyright (c) 2018-2025, Hong Ge, the Turing language team
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Manifest.toml

@@ -5,6 +5,7 @@ AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 AbstractPPL = "7a57a42e-76ec-4ea3-a279-07e840d6d9cf"
 AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
 AdvancedMH = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
+AdvancedVI = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
@@ -13,6 +14,7 @@ DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
@@ -36,6 +38,7 @@ Memoization = "6fafb56a-5788-4b4e-91ca-c0cea6611c73"
 MicroCanonicalHMC = "234d2aa0-2291-45f7-9047-6fa6f316b0a8"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
@@ -51,7 +54,6 @@ StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-Turing = "0.38"
+Turing = "0.39"

Project.toml

@@ -32,19 +32,19 @@ website:
         text: Team
     right:
       # Current version
-      - text: "v0.38"
+      - text: "v0.39"
         menu:
           - text: Changelog
             href: https://turinglang.org/docs/changelog.html
           - text: All Versions
             href: https://turinglang.org/docs/versions.html
     tools:
       - icon: twitter
-        href: https://x.com/TuringLang
         text: Turing Twitter
+        href: https://x.com/TuringLang
       - icon: github
-        href: https://github.com/TuringLang/Turing.jl
         text: Turing GitHub
+        href: https://github.com/TuringLang
 
   sidebar:
     - text: documentation
@@ -116,12 +116,6 @@ website:
               - developers/inference/variational-inference/index.qmd
               - developers/inference/implementing-samplers/index.qmd
 
-  page-footer:
-    #background: "#073c44"
-    left: |
-      Turing is created by <a href="http://mlg.eng.cam.ac.uk/hong/" target="_blank">Hong Ge</a>, and maintained by the <a href="/team" target="_blank">core team of developers</a>. <br>
-      © 2025 under the terms of the <a href="https://github.com/TuringLang/Turing.jl/blob/master/LICENCE" target="_blank">MIT License</a>.
-
     right:
       - icon: twitter
         href: https://x.com/TuringLang
@@ -149,43 +143,90 @@ format:
     toc-title: "Table of Contents"
     code-fold: false
     code-overflow: scroll
+    include-in-header:
+      - text: |
+          <style>
+            a {
+              text-decoration: none;
+            }
+            a:hover {
+              text-decoration: underline;
+            }
+          </style>
+    include-after-body:
+      - text: |
+          <footer class="custom-footer">
+            <div class="footer-container">
+              <div class="footer-grid">
+                <div class="footer-links-wrapper">
+                  <div class="footer-column footer-explore">
+                    <h5>Explore</h5>
+                    <a href="https://turinglang.org/docs/getting-started/">Get Started</a>
+                    <a href="https://turinglang.org/docs/tutorials/">Tutorials</a>
+                    <a href="/library">Libraries</a>
+                    <a href="/news">News</a>
+                    <a href="/team">Team</a>
+                  </div>
+
+                  <div class="footer-column footer-connect">
+                    <h5>Connect</h5>
+                    <a href="https://github.com/TuringLang" target="_blank" rel="noopener"><i class="bi bi-github"></i> GitHub</a>
+                    <a href="https://x.com/TuringLang" target="_blank" rel="noopener"><i class="bi bi-twitter"></i> Twitter</a>
+                    <a href="https://julialang.slack.com/archives/CCYDC34A0" target="_blank" rel="noopener"><i class="bi bi-slack"></i> Slack</a>
+                    <a href="https://discourse.julialang.org/c/domain/probprog/48" target="_blank" rel="noopener"><i class="bi bi-chat-dots"></i> Discourse</a>
+                  </div>
+                </div>
+
+                <div class="footer-column footer-brands">
+                  <h5>Supported by leading researchers</h5>
+                  <p>Turing.jl is developed by researchers and engineers at the following research institutions.</p>
+                  <div class="logo-grid">
+                    <a href="https://mlg.eng.cam.ac.uk/" class="partner-logo" target="_blank" rel="noopener">
+                      <img src="https://www.cam.ac.uk/sites/default/files/university-cambridge-logo-black-example-640x132.png" alt="University of Cambridge Logo" class="brands-light-mode-logo">
+                      <img src="https://www.cam.ac.uk/sites/default/files/university-cambridge-logo-white-example-640x133.png" alt="University of Cambridge Logo Dark" class="brands-dark-mode-logo">
+                    </a>
+                    <a href="https://www.turing.ac.uk/" class="partner-logo" target="_blank" rel="noopener">
+                      <img src="/assets/images/brands/Turing_Logo_1000x400px_Black.webp" alt="The Alan Turing Institute Logo" class="brands-light-mode-logo">
+                      <img src="/assets/images/brands/Turing_Logo_1000x400px_White.webp" alt="The Alan Turing Institute Logo Dark" class="brands-dark-mode-logo">
+                    </a>
+                  </div>
+                </div>
+
+              </div>
+              <div class="footer-bottom">
+                <p>Turing is created by <a href="https://mlg.eng.cam.ac.uk/hong/" target="_blank" rel="noopener">Hong Ge</a>, and maintained by the core <a href="/team" target="_blank" rel="noopener">team</a> of developers and <a href="https://github.com/TuringLang/Turing.jl/graphs/contributors" target="_blank" rel="noopener">contributors</a>!<br>© 2025 The Turing Project Contributors. <a href="https://github.com/TuringLang/Turing.jl/blob/master/LICENCE" target="_blank" rel="noopener">MIT License</a>.</p>
+                <a href="https://github.com/TuringLang/turinglang.github.io/" target="_blank" rel="noopener" class="footer-source-link"><i class="bi bi-github"></i> Website Source</a>
+              </div>
+            </div>
+          </footer>
+          
 execute:
   echo: true
   output: true
   freeze: auto
-include-in-header:
-  - text: |
-      <style>
-        a {
-          text-decoration: none;
-        }
-        a:hover {
-          text-decoration: underline;
-        }
-      </style>
 
 # These variables can be used in any qmd files, e.g. for links:
 # the [Getting Started page]({{< meta get-started >}})
 # Note that you don't need to prepend `../../` to the link, Quarto will figure
 # it out automatically.
 
-get-started: tutorials/docs-00-getting-started
-tutorials-intro: tutorials/00-introduction
-gaussian-mixture-model: tutorials/01-gaussian-mixture-model
-logistic-regression: tutorials/02-logistic-regression
-bayesian-neural-network: tutorials/03-bayesian-neural-network
-hidden-markov-model: tutorials/04-hidden-markov-model
-linear-regression: tutorials/05-linear-regression
-infinite-mixture-model: tutorials/06-infinite-mixture-model
-poisson-regression: tutorials/07-poisson-regression
-multinomial-logistic-regression: tutorials/08-multinomial-logistic-regression
-variational-inference: tutorials/09-variational-inference
-bayesian-differential-equations: tutorials/10-bayesian-differential-equations
-probabilistic-pca: tutorials/11-probabilistic-pca
-gplvm: tutorials/12-gplvm
-seasonal-time-series: tutorials/13-seasonal-time-series
-using-turing-advanced: tutorials/docs-09-using-turing-advanced
-using-turing: tutorials/docs-12-using-turing-guide
+core-functionality: core-functionality
+get-started: getting-started
+
+tutorials-intro: tutorials/coin-flipping
+gaussian-mixture-model: tutorials/gaussian-mixture-models
+logistic-regression: tutorials/bayesian-logistic-regression
+bayesian-neural-network: tutorials/bayesian-neural-networks
+hidden-markov-model: tutorials/hidden-markov-models
+linear-regression: tutorials/bayesian-linear-regression
+infinite-mixture-model: tutorials/infinite-mixture-models
+poisson-regression: tutorials/bayesian-poisson-regression
+multinomial-logistic-regression: tutorials/multinomial-logistic-regression
+variational-inference: tutorials/variational-inference
+bayesian-differential-equations: tutorials/bayesian-differential-equations
+probabilistic-pca: tutorials/probabilistic-pca
+gplvm: tutorials/gaussian-process-latent-variable-models
+seasonal-time-series: tutorials/bayesian-time-series-analysis
 
 usage-automatic-differentiation: usage/automatic-differentiation
 usage-custom-distribution: usage/custom-distribution
@@ -204,7 +245,7 @@ dev-model-manual: developers/compiler/model-manual
 contexts: developers/compiler/minituring-contexts
 minituring: developers/compiler/minituring-compiler
 using-turing-compiler: developers/compiler/design-overview
-using-turing-variational-inference: developers/inference/variational-inference
+dev-variational-inference: developers/inference/variational-inference
 using-turing-implementing-samplers: developers/inference/implementing-samplers
 dev-transforms-distributions: developers/transforms/distributions
 dev-transforms-bijectors: developers/transforms/bijectors

_quarto.yml

@@ -294,7 +294,7 @@ Of course, using an MCMC algorithm to sample from the prior is unnecessary and s
 The use of contexts also goes far beyond just evaluating log probabilities and sampling. Some examples from Turing are
 
 * `FixedContext`, which fixes some variables to given values and removes them completely from the evaluation of any log probabilities. They power the `Turing.fix` and `Turing.unfix` functions.
-* `ConditionContext` conditions the model on fixed values for some parameters. They are used by `Turing.condition` and `Turing.uncondition`, i.e. the `model | (parameter=value,)` syntax. The difference between `fix` and `condition` is whether the log probability for the corresponding variable is included in the overall log density. 
+* `ConditionContext` conditions the model on fixed values for some parameters. They are used by `Turing.condition` and `Turing.decondition`, i.e. the `model | (parameter=value,)` syntax. The difference between `fix` and `condition` is whether the log probability for the corresponding variable is included in the overall log density. 
 
 * `PriorExtractorContext` collects information about what the prior distribution of each variable is.
 * `PrefixContext` adds prefixes to variable names, allowing models to be used within other models without variable name collisions.

developers/compiler/minituring-contexts/index.qmd

@@ -36,26 +36,26 @@ using DynamicPPL
 function gdemo2(model, varinfo, context, x)
     # Assume s² has an InverseGamma distribution.
     s², varinfo = DynamicPPL.tilde_assume!!(
-        context, InverseGamma(2, 3), Turing.@varname(s²), varinfo
+        context, InverseGamma(2, 3), @varname(s²), varinfo
     )
 
     # Assume m has a Normal distribution.
     m, varinfo = DynamicPPL.tilde_assume!!(
-        context, Normal(0, sqrt(s²)), Turing.@varname(m), varinfo
+        context, Normal(0, sqrt(s²)), @varname(m), varinfo
     )
 
     # Observe each value of x[i] according to a Normal distribution.
     for i in eachindex(x)
         _retval, varinfo = DynamicPPL.tilde_observe!!(
-            context, Normal(m, sqrt(s²)), x[i], Turing.@varname(x[i]), varinfo
+            context, Normal(m, sqrt(s²)), x[i], @varname(x[i]), varinfo
         )
     end
 
     # The final return statement should comprise both the original return
     # value and the updated varinfo.
     return nothing, varinfo
 end
-gdemo2(x) = Turing.Model(gdemo2, (; x))
+gdemo2(x) = DynamicPPL.Model(gdemo2, (; x))
 
 # Instantiate a Model object with our data variables.
 model2 = gdemo2([1.5, 2.0])

developers/compiler/model-manual/index.qmd

@@ -403,11 +403,11 @@ As we promised, all of this hassle of implementing our `MALA` sampler in a way t
 It also enables use with Turing.jl through the `externalsampler`, but we need to do one final thing first: we need to tell Turing.jl how to extract a vector of parameters from the "sample" returned in our implementation of `AbstractMCMC.step`. In our case, the "sample" is a `MALASample`, so we just need the following line:
 
 ```{julia}
-# Load Turing.jl.
 using Turing
+using DynamicPPL
 
 # Overload the `getparams` method for our "sample" type, which is just a vector.
-Turing.Inference.getparams(::Turing.Model, sample::MALASample) = sample.x
+Turing.Inference.getparams(::DynamicPPL.Model, sample::MALASample) = sample.x
 ```
 
 And with that, we're good to go!

developers/inference/implementing-samplers/index.qmd

@@ -92,5 +92,5 @@ The underlying theory of Bayesian machine learning is not explained in detail in
 A thorough introduction to the field is [*Pattern Recognition and Machine Learning*](https://www.springer.com/us/book/9780387310732) (Bishop, 2006); an online version is available [here (PDF, 18.1 MB)](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf).
 :::
 
-The next page on [Turing's core functionality]({{<meta using-turing>}}) explains the basic features of the Turing language.
+The next page on [Turing's core functionality]({{<meta core-functionality>}}) explains the basic features of the Turing language.
 From there, you can either look at [worked examples of how different models are implemented in Turing]({{<meta tutorials-intro>}}), or [specific tips and tricks that can help you get the most out of Turing]({{<meta usage-performance-tips>}}).

getting-started/index.qmd

@@ -344,7 +344,7 @@ import Mooncake
 import SciMLSensitivity
 
 # Define the AD backend to use
-adtype = AutoMooncake(; config=nothing)
+adtype = AutoMooncake()
 
 # Sample a single chain with 1000 samples using Mooncake
 sample(model, NUTS(; adtype=adtype), 1000; progress=false)

tutorials/bayesian-differential-equations/index.qmd

@@ -210,7 +210,7 @@ setprogress!(false)
 ```{julia}
 # Perform inference.
 n_iters = 2_000
-ch = sample(bayes_nn(reduce(hcat, xs), ts), NUTS(; adtype=AutoMooncake(; config=nothing)), n_iters);
+ch = sample(bayes_nn(reduce(hcat, xs), ts), NUTS(; adtype=AutoMooncake()), n_iters);
 ```
 
 Now we extract the parameter samples from the sampled chain as `θ` (this is of size `5000 x 20` where `5000` is the number of iterations and `20` is the number of parameters).