The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
| Version: | 1.41 |
| Depends: | R (≥ 3.4.0) |
| Imports: | Matrix, stats, snowfall, abind, splines2 |
| Published: | 2020-03-24 |
| Author: | Minsuk Shin and Ruoxuan Tian |
| Maintainer: | Minsuk Shin <minsuk000 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://arxiv.org/abs/1507.07106v4 |
| NeedsCompilation: | no |
| CRAN checks: | BayesS5 results |
| Reference manual: | BayesS5.pdf |
| Package source: | BayesS5_1.41.tar.gz |
| Windows binaries: | r-devel: BayesS5_1.41.zip, r-release: BayesS5_1.41.zip, r-oldrel: BayesS5_1.41.zip |
| macOS binaries: | r-release (arm64): BayesS5_1.41.tgz, r-oldrel (arm64): BayesS5_1.41.tgz, r-release (x86_64): BayesS5_1.41.tgz, r-oldrel (x86_64): BayesS5_1.41.tgz |
| Old sources: | BayesS5 archive |
Please use the canonical form https://CRAN.R-project.org/package=BayesS5 to link to this page.
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.
Health stats visible at Monitor.