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Ratio-of-Uniforms Simulation with Transformation
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Download and install rust package within the R console
Install from CRAN:
install.packages("rust")

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

Install by package version:
library("remotes")
install_version("rust", "1.4.4")



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library("rust")
Maintained by
Paul J. Northrop
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-10-10
Latest Update: 2024-08-17
Description:
Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) , Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package can be used to improve efficiency.
How to cite:
Paul J. Northrop (2016). rust: Ratio-of-Uniforms Simulation with Transformation. R package version 1.4.4, https://cran.r-project.org/web/packages/rust. Accessed 09 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:54), 1.0.0 (2016-10-10 18:18), 1.1.0 (2016-11-18 12:43), 1.2.0 (2017-05-27 18:07), 1.2.1 (2017-06-01 07:48), 1.2.2 (2017-06-01 13:05), 1.2.3 (2017-08-28 13:37), 1.3.3 (2017-12-17 23:56), 1.3.4 (2017-12-22 15:44), 1.3.5 (2018-05-16 12:00), 1.3.6 (2019-02-10 06:20), 1.3.7 (2019-11-27 14:50), 1.3.8 (2019-12-20 09:40), 1.3.9 (2020-07-21 19:20), 1.3.10 (2020-09-02 12:00), 1.3.11 (2021-02-08 18:10), 1.3.12 (2021-06-03 19:10), 1.3.13 (2021-10-31 07:00), 1.4.0 (2022-11-16 13:10), 1.4.1 (2023-09-02 16:20), 1.4.2 (2023-12-02 18:40), 1.4.3 (2024-08-17 08:30)
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