v0.0.1

ParallelMCMC v0.0.1

v0.0.1 (2026-05-02) — Initial Release

Features:

• DEER solver — Parallel-across-the-sequence MALA with Newton iteration. Solves an entire trajectory of $T$ correlated steps simultaneously via affine scan (prefix scan) with $O(\log T)$ work.
• ParallelMALASampler — Primary parallel sampler supporting stochastic diagonal Jacobian estimation via pushforward (Jacobian-vector products), with jacobian options for diagonal and dense modes.
• Sequential baselines — MALASampler and AdaptiveMALASampler for comparison / fallback use.
• GPU support — CUDA-accelerated DEER solver with GPU-compatible indexing and passing GPU tests.
• DynamicPPL / Turing.jl integration — Extension module ext/DynamicPPLExt provides interoperability with Turing.jl via the LogDensityProblemsAD backend.
• Multi-AD-backend support — Uses DifferentiationInterface and Enzyme for autodiff, with support for multiple backends including pullbacks and pushforwards.
• Preconditioning — Support for preconditioned MALA proposals.
• AbstractMCMC interface — All samplers implement AbstractMCMC and return MCMCChains.Chains objects.
• Benchmarking suite — benchmarks/ module for performance evaluation.
• BlockMALA sampler — Blocked version of the MALA sampler included.
  Dependencies
  Requires Julia >= 1.10, AbstractMCMC, CUDA, DifferentiationInterface, Enzyme, and MCMCChains, with optional DynamicPPL / LogDensityProblems / LogDensityProblemsAD for Turing integration.