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New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <doi:10.48550/arXiv.1903.09556>.
| Version: | 0.1.0 |
| Imports: | MASS |
| Published: | 2020-03-15 |
| DOI: | 10.32614/CRAN.package.Rosenbrock |
| Author: | Martin Wiegand |
| Maintainer: | Martin Wiegand <Martin.Wiegand at mrc-bsu.cam.ac.uk> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | Rosenbrock results |
| Reference manual: | Rosenbrock.pdf |
| Package source: | Rosenbrock_0.1.0.tar.gz |
| Windows binaries: | r-devel: Rosenbrock_0.1.0.zip, r-release: Rosenbrock_0.1.0.zip, r-oldrel: Rosenbrock_0.1.0.zip |
| macOS binaries: | r-release (arm64): Rosenbrock_0.1.0.tgz, r-oldrel (arm64): Rosenbrock_0.1.0.tgz, r-release (x86_64): Rosenbrock_0.1.0.tgz, r-oldrel (x86_64): Rosenbrock_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.