[Diff since v0.38.3](v0.38.3...v0.38.4)

Improve performance of VarNamedVector. It should now be very nearly on par with Metadata for all models we've benchmarked on.

**Merged pull requests:**
- Improvements to VarNamedVector (#1098) (@mhauru)
- Print out intermediate benchmark results (#1100) (@mhauru)

[Diff since v0.38.2](v0.38.2...v0.38.3)

Add an implementation of `returned(::Model, ::AbstractDict{<:VarName})`.
Please note that although the NamedTuple method still exists, we generally recommend using Dict, as NamedTuples cannot correctly represent variables with indices / fields on the left-hand side of tildes, like `x[1]` or `x.a`.

The generic method `returned(::Model, values, keys)` is deprecated and will be removed in the next minor version.

**Merged pull requests:**
- Add `to_chains` and `from_chains` function (#1087) (@penelopeysm)
- Revert "Add `to_chains` and `from_chains` function (#1087)" (#1093) (@penelopeysm)
- tidy integration test workflow (#1094) (@penelopeysm)
- Implement `returned` for AbstractDict; deprecate {values, keys} method (#1096) (@penelopeysm)

**Closed issues:**
- Unable to compute sparse Hessians with ThreadSafeVarInfo (#1081)
- `returned` doesn't have a method for Dict (#1095)

[Diff since v0.38.1](v0.38.1...v0.38.2)

Added a compatibility entry for JET@0.11.

**Merged pull requests:**
- JET@0.11 compat (#1089) (@penelopeysm)

**Closed issues:**
- Add a `logdensity` specialization that takes `NamedTuple`s (#671)
- Add support for `ProductNamedTupleDistribution` (#801)
- Enzyme doesn't like accumulators (#947)
- rogue idea: make `SamplingContext` a leaf context + make a new `InitialisationContext` (#955)
- computation time of `returned` scales with number of model parameters (#1019)

[Diff since v0.38.0](v0.38.0...v0.38.1)

Added `DynamicPPL.from_linked_vec_transform` and `DynamicPPL.from_vec_transform` methods for `ProductNamedTupleDistribution`.
This patch allows sampling from `ProductNamedTupleDistribution` in DynamicPPL models.

**Merged pull requests:**
- ProductNamedTupleDistribution compatibility (#1079) (@penelopeysm)

**Closed issues:**
- Implement `to_vec_transform` for `ProductNamedTupleDistribution` (#1077)

[Diff since v0.37.5](v0.37.5...v0.38.0)

DynamicPPL 0.38 introduces a new evaluation context, `InitContext`.
It is used to generate fresh values for random variables in a model.

Evaluation contexts are stored inside a `DynamicPPL.Model` object, and control what happens with tilde-statements when a model is run.
The two major leaf (basic) contexts are `DefaultContext` and, now, `InitContext`.
`DefaultContext` is the default context, and it simply uses the values that are already stored in the `VarInfo` object passed to the model evaluation function.
On the other hand, `InitContext` ignores values in the VarInfo object and inserts new values obtained from a specified source.
(It follows also that the VarInfo being used may be empty, which means that `InitContext` is now also the way to obtain a fresh VarInfo for a model.)

DynamicPPL 0.38 provides three flavours of _initialisation strategies_, which are specified as the second argument to `InitContext`:

  - `InitContext(rng, InitFromPrior())`: New values are sampled from the prior distribution (on the right-hand side of the tilde).
  - `InitContext(rng, InitFromUniform(a, b))`: New values are sampled uniformly from the interval `[a, b]`, and then invlinked to the support of the distribution on the right-hand side of the tilde.
  - `InitContext(rng, InitFromParams(p, fallback))`: New values are obtained by indexing into the `p` object, which can be a `NamedTuple` or `Dict{<:VarName}`. If a variable is not found in `p`, then the `fallback` strategy is used, which is simply another of these strategies. In particular, `InitFromParams` enables the case where different variables are to be initialised from different sources.

(It is possible to define your own initialisation strategy; users who wish to do so are referred to the DynamicPPL API documentation and source code.)

**The main impact on the upcoming Turing.jl release** is that, instead of providing initial values for sampling, the user will be expected to provide an initialisation strategy instead.
This is a more flexible approach, and not only solves a number of pre-existing issues with initialisation of Turing models, but also improves the clarity of user code.
In particular:

  - When providing a set of fixed parameters (i.e. `InitFromParams(p)`), `p` must now either be a NamedTuple or a Dict. Previously Vectors were allowed, which is error-prone because the ordering of variables in a VarInfo is not obvious.
  - The parameters in `p` must now always be provided in unlinked space (i.e., in the space of the distribution on the right-hand side of the tilde). Previously, whether a parameter was expected to be in linked or unlinked space depended on whether the VarInfo was linked or not, which was confusing.

For developers working on DynamicPPL, `InitContext` now completely replaces what used to be `SamplingContext`, `SampleFromPrior`, and `SampleFromUniform`.
Evaluating a model with `SamplingContext(SampleFromPrior())` (e.g. with `DynamicPPL.evaluate_and_sample!!(model, VarInfo(), SampleFromPrior())` has a direct one-to-one replacement in `DynamicPPL.init!!(model, VarInfo(), InitFromPrior())`.
Please see the docstring of `init!!` for more details.
Likewise `SampleFromUniform()` can be replaced with `InitFromUniform()`.
`InitFromParams()` provides new functionality which was previously implemented in the roundabout way of manipulating the VarInfo (e.g. using `unflatten`, or even more hackily by directly modifying values in the VarInfo), and then evaluating using `DefaultContext`.

The main change that this is likely to create is for those who are implementing samplers or inference algorithms.
The exact way in which this happens will be detailed in the Turing.jl changelog when a new release is made.
Broadly speaking, though, `SamplingContext(MySampler())` will be removed so if your sampler needs custom behaviour with the tilde-pipeline you will likely have to define your own context.

`DynamicPPL.Sampler` and **all associated interface functions** have also been removed entirely.
If you were using these, the corresponding replacements are:

  - `DynamicPPL.Sampler(S)`: just don't wrap `S`; but make sure `S` subtypes `AbstractMCMC.AbstractSampler`
  - `DynamicPPL.initialstep`: directly implement `AbstractMCMC.step` and `AbstractMCMC.step_warmup` as per the AbstractMCMC interface
  - `DynamicPPL.loadstate`: `Turing.loadstate` (will be introduced in the next version)
  - `DynamicPPL.default_chain_type`: removed, just use the `chain_type` keyword argument directly
  - `DynamicPPL.initialsampler`: `Turing.Inference.init_strategy` (will be introduced in the next version; note that this function must return an `AbstractInitStrategy`, see above for explanation)
  - `DynamicPPL.default_varinfo`: `Turing.Inference.default_varinfo` (will be introduced in the next version)
  - `DynamicPPL.TestUtils.test_sampler` and related methods: removed, please implement your own testing utilities as needed

There are now only two functions in the tilde-pipeline that need to be overloaded to change the behaviour of tilde-statements, namely, `tilde_assume!!` and `tilde_observe!!`.
Other functions such as `tilde_assume` and `assume` (and their `observe` counterparts) have been removed.

Note that this was effectively already the case in DynamicPPL 0.37 (where they were just wrappers around each other).
The separation of these functions was primarily implemented to avoid performing extra work where unneeded (e.g. to not calculate the log-likelihood when `PriorContext` was being used). This functionality has since been replaced with accumulators (see the 0.37 changelog for more details).

Previously `VarInfo` (or more correctly, the `Metadata` object within a `VarInfo`), had a flag called `"del"` for all variables. If it was set to `true` the variable was to be overwritten with a new value at the next evaluation. The new `InitContext` and related changes above make this flag unnecessary, and it has been removed.

The only flag other than `"del"` that `Metadata` ever used was `"trans"`. Thus the generic functions `set_flag!`, `unset_flag!` and `is_flagged!` have also been removed in favour of more specific ones. We've also used this opportunity to name the `"trans"` flag and the corresponding `istrans` function to be more explicit. The new, exported interface consists of the `is_transformed` and `set_transformed!!` functions.

The `resume_from=chn` keyword argument to `sample` has been removed; please use the `initial_state` argument instead.
`loadstate` will be exported from Turing in the next release of Turing.

The functions `pointwise_prior_logdensities`, `pointwise_logdensities`, and `pointwise_loglikelihoods` when called on `MCMCChains.Chains` objects, now return new `MCMCChains.Chains` objects by default, instead of dictionaries of matrices.

If you want the old behaviour, you can pass `OrderedDict` as the third argument, i.e., `pointwise_logdensities(model, chain, OrderedDict)`.

The `include_all` keyword argument for `predict` now works even when no RNG is specified (previously it would only work when an RNG was explicitly passed).

This convenience method has been added to quickly modify the leaf context of a model.

A number of functions have been reimplemented and unified with the help of `InitContext`.
In particular, this release brings substantial performance improvements for `returned` and `predict`.
Their APIs are the same.

The implementation of the `varname_leaves` and `varname_and_value_leaves` functions have been moved to AbstractPPL.jl.
Their behaviour is otherwise identical, and they are still accessible from the DynamicPPL module (though still not exported).

**Merged pull requests:**
- v0.38 (#1018) (@penelopeysm)
- Remove `Sampler` and its interface (#1037) (@penelopeysm)
- Separate context code into smaller files; remove some dead code (#1050) (@penelopeysm)
- add Enzyme benchmarks and incorporate some CompatHelper bits (#1056) (@penelopeysm)
- Fixes for Turing 0.41 (#1057) (@penelopeysm)
- Delete the `"del"` flag (#1058) (@mhauru)
- no coveralls (#1059) (@penelopeysm)
- Replace `Metadata.flags` with `Metadata.trans` (#1060) (@mhauru)
- Remove `resume_from` and `default_chain_type` (#1061) (@penelopeysm)
- Allow more flexible `initial_params` (#1064) (@penelopeysm)
- Bump JET and SpecialFunctions test compat (#1065) (@penelopeysm)
- Fix CI for 1.12 (#1067) (@penelopeysm)
- fix include_all kwarg for predict, improve perf (#1068) (@penelopeysm)
- don't run Turing integration test on 1.12.0 (#1069) (@penelopeysm)
- Change pointwise_logdensities default key type to VarName (#1071) (@mhauru)
- Remove VarInfo internal docs from navigation (#1073) (@mhauru)
- Fix DynamicPPL / MCMCChains methods (#1076) (@penelopeysm)

**Closed issues:**
- Add scale parameter to SampleFromUniform (#375)
- Removed `ignorable` argument from `set_flag!` (#683)
- Return an AbstractChains object from appliying pointwise_logdensities to Abstractchains? (#688)
- Usage of `VarName` as keys (#712)
- Support using `OrderedDict` for initialization, also improve error message (#774)
- Inline (and more carefully use) `islinked` inside `initialize_parameters!!` (#797)
- `SampleFromPrior`, etc. cleanup (#859)
- `tovec` causing an error when sampling a Product named tuple distribution (#933)
- `del "del"`: remove `del` flag (#982)
- `setleafcontext(::Model, ::AbstractContext)` (#993)
- improve implementation of `predict(...; include_all)` (#1042)
- `loadstate` doesn't differentiate between single-chain sampling and multi-chain sampling with n_chains = 1 (#1049)
- make pointwise_logdensities return a dict of varname (#1070)
- chains methods claim to be generic but only work on MCMCChains (#1072)

[Diff since v0.37.4](v0.37.4...v0.37.5)

A minor optimisation for Enzyme AD on DynamicPPL models.

**Merged pull requests:**
- Don't use closure for enzyme (#1048) (@wsmoses)

[Diff since v0.37.3](v0.37.3...v0.37.4)

An extension for MarginalLogDensities.jl has been added.

Loading DynamicPPL and MarginalLogDensities now provides the `DynamicPPL.marginalize` function to marginalise out variables from a model.
This is useful for averaging out random effects or nuisance parameters while improving inference on fixed effects/parameters of interest.
The `marginalize` function returns a `MarginalLogDensities.MarginalLogDensity`, a function-like callable struct that returns the approximate log-density of a subset of the parameters after integrating out the rest of them.
By default, this uses the Laplace approximation and sparse AD, making the marginalisation computationally very efficient.
Note that the Laplace approximation relies on the model being differentiable with respect to the marginalised variables, and that their posteriors are unimodal and approximately Gaussian.

Please see [the MarginalLogDensities documentation](https://eloceanografo.github.io/MarginalLogDensities.jl/stable) and the [new Marginalisation section of the DynamicPPL documentation](https://turinglang.org/DynamicPPL.jl/v0.37/api/#Marginalisation) for further information.

**Merged pull requests:**
- pretty tables 3 (#1026) (@penelopeysm)
- MarginalLogDensities extension (#1036) (@penelopeysm)
- pin JET to <= 0.10.6 (#1046) (@penelopeysm)

**Closed issues:**
- Enzyme doesn't like accumulators (#947)
- JET 1.12 tests are failing (#1040)

[Diff since v0.37.2](v0.37.2...v0.37.3)

Prevents inlining of `DynamicPPL.istrans` with Enzyme, which allows Enzyme to differentiate models where `VarName`s have the same symbol but different types.

**Merged pull requests:**
- `InitContext`, part 4 - Use `init!!` to replace `evaluate_and_sample!!`, `predict`, `returned`, and `initialize_values` (#984) (@penelopeysm)
- Refactor inactive_noinl to inactive for istrans (#1043) (@wsmoses)

[Diff since v0.37.1](v0.37.1...v0.37.2)

Make the resume_from keyword work for multiple-chain (parallel) sampling as well. Prior to this version, it was silently ignored. Note that to get the correct behaviour you also need to have a recent version of MCMCChains (v7.2.1).

**Merged pull requests:**
- `InitContext`, part 3 - Introduce `InitContext` (#981) (@penelopeysm)
- Remove unneeded argument from default_accumulators() (#1025) (@penelopeysm)
- use `varname_leaves` from AbstractPPL instead (#1030) (@penelopeysm)
- tidy occurrences of varname_leaves as well (#1031) (@penelopeysm)
- Bump actions/checkout from 4 to 5 (#1032) (@dependabot[bot])
- Fix `resume_from` for parallel sampling (#1035) (@penelopeysm)

**Closed issues:**
- VarInfo with custom accumulators (#744)
- `varname_and_value_leaves` etc should be in AbstractPPL (#1028)
- `resume_from` silently no-ops with multiple-chain sampling (#1033)

[Diff since v0.37.0](v0.37.0...v0.37.1)

Update DynamicPPLMooncakeExt to work with Mooncake 0.4.147.

**Merged pull requests:**
- Fix MooncakeExt for 0.4.147 (#1021) (@penelopeysm)