Rtwalk: An MCMC Sampler Using the t-Walk Algorithm

Implements the t-walk algorithm, a general-purpose, self-adjusting Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires no tuning and is robust for a wide range of target distributions, including high-dimensional and multimodal problems. The sampler is implemented in C++ and validated against a pure R reference implementation of the published algorithm. The package supports reproducible parallel chains, continuation of runs, single transitions for hybrid samplers, and convergence diagnostics following Vehtari et al. (2021) <doi:10.1214/20-BA1221>.

Version:	2.2.0
Depends:	R (≥ 3.6.0)
Imports:	codetools, parallel, stats, utils, graphics, grDevices, coda, Rcpp
LinkingTo:	Rcpp
Suggests:	testthat (≥ 3.0.0), covr, mvtnorm, devtools, pkgload, posterior, roxygen2, knitr, rmarkdown, ellipse
Published:	2026-10-30
DOI:	10.32614/CRAN.package.Rtwalk
Author:	Rodrigo Fonseca Villa [aut, cre]
Maintainer:	Rodrigo Fonseca Villa <rodrigo03.villa at gmail.com>
BugReports:	https://github.com/rodrigosqrt3/Rtwalk/issues
License:	GPL-3
URL:	https://github.com/rodrigosqrt3/Rtwalk
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	Rtwalk results

Documentation:

Reference manual:	Rtwalk.html , Rtwalk.pdf
Vignettes:	Reproducing Christen & Fox (2010) (source) Benchmarking the t-walk against other general-purpose samplers (source) Validation and Simulation Study for the Rtwalk Package (source, R code)

Downloads:

Package source:	Rtwalk_2.2.0.tar.gz
Windows binaries:	r-devel: Rtwalk_2.1.0.zip, r-release: Rtwalk_2.1.0.zip, r-oldrel: Rtwalk_2.2.0.zip
macOS binaries:	r-release (arm64): Rtwalk_2.2.0.tgz, r-oldrel (arm64): Rtwalk_2.2.0.tgz, r-release (x86_64): Rtwalk_2.2.0.tgz, r-oldrel (x86_64): Rtwalk_2.2.0.tgz
Old sources:	Rtwalk archive

Linking:

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