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HISTORY.md

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 # Release 0.30.0
 
-- Replaced global variable-based AD backend mechanism with [`ADTypes.jl`](https://github.com/SciML/ADTypes.jl). Users should now specify the desired `ADType` directly in sampler constructors, e.g., `HMC(0.1, 10; adtype=AutoForwardDiff(; chunksize))`, or `HMC(0.1, 10; adtype=AutoReverseDiff(false))` (`false` indicates to not use compiled tape).
+- Replaced specifying a global AD backend with [`ADTypes.jl`](https://github.com/SciML/ADTypes.jl). Users should now specify the desired `ADType` directly in sampler constructors, e.g., `HMC(0.1, 10; adtype=AutoForwardDiff(; chunksize))`, or `HMC(0.1, 10; adtype=AutoReverseDiff(false))` (`false` indicates not to use compiled tape).
 - Interface functions such as `ADBackend`, `setadbackend`, `setadsafe`, `setchunksize`, and `setrdcache` are deprecated and will be removed in a future release.
 - Removed the outdated `verifygrad` function.
 - Updated to a newer version of `LogDensityProblemsAD` (v1.7).
 
 # Release 0.12.0
 
-- The interface for defining new distributions with a constrained support and making them compatible with `Turing` has changed. To make a custom distribution type `CustomDistribution` compatible with `Turing`, the user needs to define the method `bijector(d::CustomDistribution)` that returns an instance of type `Bijector` implementing the `Bijectors.Bijector` API.
+- The interface for defining new distributions with constrained support and making them compatible with `Turing` has changed. To make a custom distribution type `CustomDistribution` compatible with `Turing`, the user needs to define the method `bijector(d::CustomDistribution)` that returns an instance of type `Bijector` implementing the `Bijectors.Bijector` API.
 - `~` is now thread-safe when used for observations, but not assumptions yet.
-- There were some performance improvements in the automatic differentiation of functions in `DistributionsAD` and `Bijectors` leading to speeds closer to and sometimes faster than Stan's.
+- There were some performance improvements in the automatic differentiation of functions in `DistributionsAD` and `Bijectors`, leading to speeds closer to and sometimes faster than Stan's.
 - An HMC initialization bug was fixed. HMC initialization in Turing is now consistent with Stan's.
 - Sampling from the prior is now possible using `sample`.
-- `psample` is now deprecated, in favor of `sample(model, sampler, parallel_method, n_samples, n_chains)` where `parallel_method` can be either `MCMCThreads()` or `MCMCDistributed()`. `MCMCThreads` will use your available threads to sample each chain (ensure that you have the environment variable `JULIA_NUM_THREADS` set to the number of threads you want to use) and `MCMCDistributed` will dispatch chain sampling to each available processes (you can add processes with `addprocs()`).
-- Turing now uses AdvancedMH v0.5, which mostly provides behind-the -scenes restructuring.
-- Custom expressions and macros can be interpolated in the `@model` definition with `$` and it is possible to use `@.` also for assumptions and observations.
-- The macros `@varinfo`, `@logpdf`, and `@sampler` are removed. Instead one can access the internal variables `_varinfo`, `_model`, `_sampler`, and `_context` in the `@model` definition.
+- `psample` is now deprecated in favour of `sample(model, sampler, parallel_method, n_samples, n_chains)` where `parallel_method` can be either `MCMCThreads()` or `MCMCDistributed()`. `MCMCThreads` will use your available threads to sample each chain (ensure that you have the environment variable `JULIA_NUM_THREADS` set to the number of threads you want to use), and `MCMCDistributed` will dispatch chain sampling to each available process (you can add processes with `addprocs()`).
+- Turing now uses AdvancedMH v0.5, which mostly provides behind-the-scenes restructuring.
+- Custom expressions and macros can be interpolated in the `@model` definition with `$`; it is possible to use `@.` also for assumptions and observations.
+- The macros `@varinfo`, `@logpdf`, and `@sampler` are removed. Instead, one can access the internal variables `_varinfo`, `_model`, `_sampler`, and `_context` in the `@model` definition.
 - Additional constructors for SMC and PG make it easier to choose the resampling method and threshold.
 
 # Release 0.11.0