[Diff since v0.39.6](v0.39.6...v0.39.7)
Update compatibility to AdvancedPS 0.7 and Libtask 0.9.
These new libraries provide significant speedups for particle MCMC methods.
**Merged pull requests:**
- AdvancedPS v0.7 (and thus Libtask v0.9) support (#2585) (@mhauru)
- Add changelog for AdvancedPS/Libtask #2585 (#2622) (@penelopeysm)
- Bump patch in Project.toml (#2623) (@penelopeysm)
**Closed issues:**
- Dirac not working properly when assigned in a vector (#2621)

[Diff since v0.39.5](v0.39.5...v0.39.6)
Bumped compatibility of AbstractPPL to include 0.13.
**Merged pull requests:**
- AbstractPPL@0.13 compat (#2620) (@penelopeysm)

[Diff since v0.39.4](v0.39.4...v0.39.5)
Fixed a bug where sampling with an `externalsampler` would not set the log probability density inside the resulting chain.
Note that there are still potentially bugs with the log-Jacobian term not being correctly included.
A fix is being worked on.
**Merged pull requests:**
- Update logp in varinfo when external samplers are used (#2616) (@penelopeysm)
**Closed issues:**
- Gibbs sampler does not carry through log-prob from an external sampler (#2583)

[Diff since v0.39.3](v0.39.3...v0.39.4)
Bumped compatibility of AbstractPPL to include 0.12.
**Merged pull requests:**
- Modernise README (#2575) (@penelopeysm)
- Gibbs test |  Fix dynamic model test in Gibbs sampler suite (#2579) (@AoifeHughes)
- Replace 2018 AISTATS conference paper with the latest 2025 ACM paper (#2608) (@sunxd3)
- Bump AbstractPPL to 0.12 (#2614) (@penelopeysm)
**Closed issues:**
- HMM example model with missing data yields Malformed dims error (#1359)
- Help with intializing the `getq` function from the tutorials (#1515)
- Sampling from prior using return value doesn't work with MvNormal (#1559)
- Needs a method to get generated_quantities from the result of an optimization (#1886)
- Turing Sampling specified parameters (#1911)
- OrdinalLogistic - wrong support? (#1970)
- Arraydist and Dirichlet cause stackoverflow error (#1989)
- Hidden Markov tutorial crashes (#2074)
- Cannot sample from bounded distribution when using Dual types (#2088)
- missing keyword arguments not property processed (#2259)
- ReverseDiff Error with Vector{Real} Input in Turing Model (#2364)
- Remove `hmc.jl` and `mh.jl` in light of upstreamed "getparams" into AbstractMCMC (#2367)
- Improve Gibbs dynamic model test (#2402)
- Question: MCMChain after VI sampling (#2462)
- `parameters` macro for specifying trace manually (aka Turing's `VarInfo`) (#2492)
- Automate formatter on PRs (#2596)
- Add a support_policy.md (#2601)
- How to mimick a discrete bayes net? (#2606)
- arraydist() errors with non <:Real data (#2611)
- arraydist() errors when custom distribution uses non-Real data (#2612)
- Remove DistributionsAD (#2613)

[Diff since v0.39.2](v0.39.2...v0.39.3)
Improved the performance of `Turing.Inference.getparams` when called with an untyped VarInfo as the second argument, by first converting to a typed VarInfo.
This makes, for example, the post-sampling Chains construction for `Prior()` run much faster.
**Merged pull requests:**
- Fix performance of `getparams` on untyped vi (#2605) (@penelopeysm)
**Closed issues:**
- Prior() sampling takes longer than NUTS() (#2604)

[Diff since v0.39.1](v0.39.1...v0.39.2)
Fixed a bug in the support of `OrderedLogistic` (by changing the minimum from 0 to 1).
**Merged pull requests:**
- clean up some more imports (#2589) (@penelopeysm)
- Remove `DynamicPPL.alg_str` tests (#2591) (@penelopeysm)
- Update Project.toml (#2598) (@yebai)
**Closed issues:**
- Slice sampling as a `Gibbs` sampler (#2300)
- Regroup tests in test suite (#2441)
- ELBO trace during VI (#2543)

[Diff since v0.39.0](v0.39.0...v0.39.1)
No changes from 0.39.0 — this patch is released just to re-trigger a Documenter.jl run.
**Merged pull requests:**
- Bump tag for GHA (#2586) (@penelopeysm)
**Closed issues:**
- Chain construction errors when no variables are recorded (#1436)
- Difficulty sampling a model with truncated normal Likelihood (#1722)
- documentation for init_params kwarg (#1983)

[Diff since v0.38.6](v0.38.6...v0.39.0)
Breaking changes:
Turing's variational inference interface was updated to match version 0.4 version of AdvancedVI.jl.
AdvancedVI v0.4 introduces various new features:
  - location-scale families with dense scale matrices,
  - parameter-free stochastic optimization algorithms like `DoG` and `DoWG`,
  - proximal operators for stable optimization,
  - the sticking-the-landing control variate for faster convergence, and
  - the score gradient estimator for non-differentiable targets.
Please see the [Turing API documentation](https://turinglang.org/Turing.jl/stable/api/#Variational-inference), and [AdvancedVI's documentation](https://turinglang.org/AdvancedVI.jl/stable/), for more details.
The Turing.Essential module has been removed.
Anything exported from there can be imported from either `Turing` or `DynamicPPL`.
The `@addlogprob!` macro is now exported from Turing, making it officially part of the public interface.
**Merged pull requests:**
- Release: Turing.jl@0.39 (#2517) (@penelopeysm)

[Diff since v0.38.5](v0.38.5...v0.38.6)
Added compatibility with AdvancedHMC 0.8.
**Merged pull requests:**
- CompatHelper: bump compat for AdvancedHMC to 0.8, (keep existing compat) (#2582) (@github-actions[bot])

[Diff since v0.38.4](v0.38.4...v0.38.5)
Added compatibility with ForwardDiff v1.
**Merged pull requests:**
- Update to the AdvancedVI@0.4 interface (#2506) (@Red-Portal)
- CompatHelper: bump compat for ForwardDiff to 1 for package test, (keep existing compat) (#2523) (@github-actions[bot])
- CompatHelper: add new compat entry for ADTypes at version 1 for package test, (keep existing compat) (#2571) (@github-actions[bot])
- Reorganise code in `src/mcmc/Inference.jl` (#2573) (@penelopeysm)
- Clean up old code (#2574) (@penelopeysm)
- Rename `mcmc/sample.jl` to `mcmc/abstractmcmc.jl` (#2576) (@yebai)
**Closed issues:**
- AdvancedVI 0.3 and compatibility with Turing.jl (#2430)
- Get rid of src/essential (#2512)
- Messy code in literature[Turing.jl: a general-purpose probabilistic programming language] (#2580)