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Rosenbrock  

Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo (MCMC) Sampler Benchmarking
View on CRAN: Click here


Download and install Rosenbrock package within the R console
Install from CRAN:
install.packages("Rosenbrock")

Install from Github:
library("remotes")
install_github("cran/Rosenbrock")

Install by package version:
library("remotes")
install_version("Rosenbrock", "0.1.0")



Attach the package and use:
library("Rosenbrock")
Maintained by
Martin Wiegand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-15
Latest Update: 2020-03-15
Description:
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) .
How to cite:
Martin Wiegand (2020). Rosenbrock: Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo (MCMC) Sampler Benchmarking. R package version 0.1.0, https://cran.r-project.org/web/packages/Rosenbrock. Accessed 30 Jan. 2025.
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Complete documentation for Rosenbrock
Functions, R codes and Examples using the Rosenbrock R package
Some associated functions: deven . dfull . dhybrid . reven . rhybrid . 
Some associated R codes: main.R .  Full Rosenbrock package functions and examples
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