Rtwalk 2.2.0

The algorithm is unchanged: it still follows Christen & Fox (2010) exactly, with the same default settings. Because random numbers are now drawn in a different order, chains differ from those of version 2.1.0 for the same seed.

Performance and memory

• The t-walk kernel is now implemented in C++ (via Rcpp). The overhead per iteration fell from about 65 to about 2 microseconds, so cheap targets sample about 30 times faster.
• Errors raised by log_posterior are caught without wrapping every evaluation in tryCatch(), which alone cost several times the evaluation of a simple target.
• The pure-R implementation is kept as a reference that follows the paper literally (twalk_control(engine = "R")); tests check that both engines produce the same chains draw by draw.
• New thin argument to store only every thin-th iteration.
• twalk_control(store_companion = FALSE) drops the companion trajectory, halving the memory used by the output.
• all_samples is no longer stored, which used as much memory as the two trajectories together. It is deprecated: accessing it rebuilds it, with a warning.

New features

• twalk_control() exposes the constants of the algorithm (n1, aw, at and the move probabilities), with the defaults of Christen & Fox (2010, Section 2.5), for sensitivity studies.
• twalk_continue() extends a previous run from the final state of the sampler.
• twalk_step() performs a single t-walk transition, so the kernel can be combined with Gibbs updates as described in Christen & Fox (2010, Section 5).
• The log posterior at the stored points is returned in log_posterior and companion_log_posterior.
• Parameter names: if x0 has names, log_posterior receives named vectors and the output columns are named.
• x0 and xp0 may be matrices with one row per chain, so that multiple chains start from dispersed points. A note is shown when all chains share the same starting points.
• Each chain now uses its own random-number stream, so multi-chain results are reproducible with set.seed() and do not depend on n_cores. With n_cores = 1, chains run sequentially without starting a cluster.
• Interrupting a sequential run returns the iterations completed so far.
• Errors raised by log_posterior at proposals are still treated as rejections, but a warning now reports how many occurred and the first error message.
• plot() method showing traces of the log posterior and of the parameters, one colour per chain.
• posterior::as_draws(), as_draws_array() and as_draws_df() methods.

Diagnostics

• calculate_diagnostics() and summary() now use the rank-normalized split-R-hat and the bulk and tail effective sample sizes of Vehtari et al. (2021); the estimates agree with those of the posterior package. R-hat is now also available for a single chain.
• summary() returns a summary.twalk data frame, printed with the sampler performance, instead of printing and returning the table invisibly.
• Diagnostics use the column names of the samples, if any.

Documentation

• New vignette reproducing the experiments of Christen & Fox (2010).
• New vignette comparing the t-walk with other general-purpose samplers.
• The validation vignette now shows its results next to the true values.

Other changes in this version

• Refactored sequential and parallel execution to use one shared chain engine.
• Limited progress-bar refreshes to approximately 100 per chain.
• Added an explicit error for identical initial points, which otherwise create a degenerate t-walk state.
• Added coda::as.mcmc() and coda::as.mcmc.list() methods for single and parallel t-walk results, with optional burn-in and companion conversion.
• Parallel workers load the same version of the package as the main process, including development versions loaded with pkgload::load_all().
• Added per-kernel proposal, acceptance, and actual-movement statistics for the Traverse, Walk, Blow, and Hop kernels.
• Corrected the Hop proposal-density helper so that only selected coordinates contribute to its continuous density.
• Added regression tests for Hop coordinate selection and the scalar affine invariance property established by Christen & Fox (2010).

Rtwalk 2.1.0

Algorithm correctness

• Corrected the Blow-kernel proposal density to include only coordinates selected for movement, matching Christen & Fox (2010).
• Bounded all Metropolis–Hastings acceptance probabilities to [0, 1] and moved their calculation to the log scale for numerical stability.
• Preserved accepted identity proposals from the original t-walk transition kernel while reporting them separately from actual state movements.
• Avoided repeated evaluations of the log posterior, reducing sequential execution to one evaluation per proposal plus the two initial evaluations.

Sampling and parallel execution

• Added reproducible independent random-number streams for parallel chains; set.seed() now controls both sequential and parallel runs.
• Parallel chains now preserve the supplied initial points and support constrained parameter spaces correctly.
• Parallel execution now exports direct and recursively referenced log-posterior dependencies to worker processes.
• Added consistent n_chains and total_iterations metadata in sequential and parallel results.

Output and diagnostics

• Added separate samples and companion_samples trajectories. The legacy all_samples field remains available for compatibility but is not treated as one time-ordered chain.
• Added move_rate and no_move_rate alongside the Metropolis–Hastings acceptance_rate.
• Applied burn-in independently to each parallel chain and computed effective sample sizes without joining chain boundaries.
• Unified sample and burn-in validation across diagnostics and visualization.

Interface and validation

• Added the public show_progress argument. Additional arguments, including an argument named internal_call, are now passed exclusively to log_posterior.
• Added clear validation for iteration counts, chains, cores, initial points, log-posterior return values, burn-in fractions, and parameter names.
• Expanded regression tests for kernel mathematics, parallel reproducibility, constrained supports, dependency export, diagnostics, and edge cases.

Rtwalk 2.0.2

• Added a comprehensive unit test suite using testthat, achieving 100% test coverage.
• Added testthat and covr to Suggested dependencies.

Rtwalk 2.0.1

• Added S3 methods for print() and summary() for t-walk output objects.
• Improved diagnostics reporting and user-facing summaries.
• Minor internal refactoring with no changes to core algorithm behavior.

Rtwalk 2.0.0

• Reintroduced the t-walk MCMC algorithm to CRAN.
• Modernized the codebase to comply with current CRAN policies.
• Updated documentation and examples.
• Ensured compatibility with recent versions of R.