NEWS

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gpumetropolis 0.2.0

• gpu_metropolis(backend = "auto") is now the default. The selector picks "cuda" when its feature is compiled in, then "vulkan", and falls back to "cpu" with a one-shot per-session notice pointing at the source-install recipe.
• Adaptive warmup, on by default. When adapt = TRUE and `warmup

  0, the warmup runs in batches; between batches, Welford updates the per-chain running variance per dimension while Robbins-Monro updates a per-chain scalar toward the asymptotic optimum acceptance (0.234 in d >= 2, 0.44 in d = 1; Roberts-Gelman-Gilks 1997). Adaptation stops cleanly at the warmup boundary, so the sampling phase is stationary. proposal_sdbecomes a seed for the warmup rather than a knob to sintonise;adapt = FALSEkeeps the trim-only warmup of 0.1.x.proposal_sdalso accepts ann_chainsbyn_params` matrix for the explicit per-chain initialisation.

• gpum_diagnose(fit): one-call diagnostic. Prints a per-parameter table (mean, sd, 2.5%, 50%, 97.5% quantiles, split R-hat, ESS, MCSE) and a convergence verdict from the canonical thresholds (R-hat < 1.01 and ESS >= 400). When plot = TRUE, opens a multi-panel plot per parameter (trace, pooled density, running mean per chain, pooled ACF), plus, when the fit is adaptive, an extra row showing the acceptance rate per chain by warmup batch with the asymptotic optimum as a reference.
• The fit object now carries fit$adaptation when the warmup was adaptive, with final_proposal_sd, final_scales, n_batches, batch_sizes and accept_history.

gpumetropolis 0.1.3

• On attach, the package now notifies the user when a newer version is available on the R-universe channel of the maintainer. The check is active only in interactive sessions, silent on any network or parse failure, and opt-out via the GPUMETROPOLIS_NO_VERSION_CHECK environment variable. The hint includes the source-install command, so the auto-detected GPU backend kicks in on the upgrade.

gpumetropolis 0.1.2

• gpu_metropolis() now discards a warmup portion before returning. The new warmup argument defaults to floor(n_iter / 2), following the convention of Stan and nimble, so fit$draws is post-warmup by default and is suitable for direct plotting and posterior summaries. Set warmup = 0 to keep every iteration, useful for trace plots that show the burn-in trajectory. The trim is plain; an adaptive warmup that also tunes the proposal during the burn-in is the next release.
• The gpum_fit object now carries n_iter (kept), n_iter_total (raw) and warmup so the raw and discarded counts are recoverable.

gpumetropolis 0.1.1

• Auto-detect GPU backends at install time. The configure step probes the build host for the CUDA toolkit (nvcc) and the Vulkan tools (vulkaninfo) and adds the matching Cargo features to the build, so a source install on a machine with the toolchains present produces a binary that exposes the GPU backends without the user passing any flag. Hosts with no GPU toolchain build CPU-only, unchanged. Override with the GPUMETROPOLIS_BACKENDS, GPUMETROPOLIS_CUDA and GPUMETROPOLIS_VULKAN environment variables.

gpumetropolis 0.1.0

First release. The package is distributed through R-universe as a focused generic sampler.

Sampler

• gpum_model() declares a model from a log-likelihood and an optional log-prior written as one-sided formulas, in a restricted operation set (+, -, *, /, ^, unary -, exp, log, sqrt). The formulas are compiled to a stack-machine bytecode.
• gpu_metropolis() runs a batched random-walk Metropolis sampler over the compiled model. One kernel source runs on three backends: cpu, cuda and vulkan. backend = "auto" selects the CPU for few chains and CUDA for many.
• The GPU kernel assigns one block of threads to each chain; the threads of a block share the sum over observations through a tree reduction. The CPU backend is native Rust, with the log-likelihood JIT-compiled to machine code and worked in double precision.
• Each chain advances a counter-based stream from a triple32 hash. The seed argument is itself hashed before the chain offset is applied, so runs with consecutive integer seeds get independent streams rather than streams that overlap by a one-counter shift.

Diagnostics

• rhat(), the split potential scale reduction factor.
• ess(), the effective sample size by Geyer's initial positive sequence.
• ks_equivalence(), a two-sample Kolmogorov-Smirnov check of distributional equivalence, thinned to the effective sample size.
• gaussian_mean_posterior(), the closed-form posterior used to check parameter recovery.