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. This implementation includes an
option for running multiple chains in parallel to accelerate sampling and
facilitate convergence diagnostics.
| Version: |
2.0.1 |
| Imports: |
parallel, stats, utils, graphics, grDevices, coda |
| Suggests: |
mvtnorm, devtools, roxygen2, knitr, rmarkdown, ellipse |
| Published: |
2026-02-05 |
| 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: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
Rtwalk results |
Documentation:
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