Simulate data | Run BayesSUR model with random effects | Referrence
This package is considered a duplicate. The official version of this package is found at:https://mbant.r-universe.dev/BayesSUR
Package: BayesSUR 2.3-3
Zhi Zhao
BayesSUR: Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.
Authors:
BayesSUR_2.3-3.tar.gz
BayesSUR_2.3-3.zip(r-4.7-x86_64)BayesSUR_2.3-3.zip(r-4.7-arm64)BayesSUR_2.3-3.zip(r-4.6-x86_64)BayesSUR_2.3-3.zip(r-4.6-arm64)
BayesSUR_2.3-3.tar.gz(r-4.7-arm64)BayesSUR_2.3-3.tar.gz(r-4.7-x86_64)BayesSUR_2.3-3.tar.gz(r-4.6-arm64)BayesSUR_2.3-3.tar.gz(r-4.6-x86_64)
BayesSUR_2.3-3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BayesSUR/json (API)
| # Install 'BayesSUR' in R: |
| install.packages('BayesSUR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mbant/bayessur/issues
Uses libs:
Datasets:
- exampleEQTL - Simulated data set to mimic a small expression quantitative trait loci (eQTL) example
- exampleGDSC - Preprocessed data set to mimic a small pharmacogenomic example
- targetGene - TargetGene
Last updated from:721ae078ea. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 181 | ||
| linux-devel-x86_64 | OK | 238 | ||
| source / vignettes | OK | 345 | ||
| linux-release-arm64 | OK | 179 | ||
| linux-release-x86_64 | OK | 181 | ||
| windows-devel-arm64 | OK | 314 | ||
| windows-devel-x86_64 | OK | 243 | ||
| windows-release-arm64 | OK | 338 | ||
| windows-release-x86_64 | OK | 190 | ||
| wasm-release | OK | 142 |
Exports:BayesSURelpdgetEstimatorplotCPOplotEstimatorplotGraphplotManhattanplotMCMCdiagplotNetwork
Dependencies:clicpp11digestfilehashglueigraphlatticelifecyclemagrittrMatrixpkgconfigpngRcppRcppArmadillorlangtikzDevicevctrs
BayesSUR with random effects
Last update: 2025-03-27
Started: 2023-12-01
BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression
Introduction | Models specification | The R package BayesSUR | Quick start with a simple example | Two extended examples based on real data | Conclusion | The elpd
Last update: 2019-12-07
Started: 2019-12-07
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fitting BayesSUR models | BayesSUR-package BayesSUR |
| BayesSUR_internal | BayesSUR_internal |
| coef method for class 'BayesSUR' | coef.BayesSUR |
| expected log pointwise predictive density | elpd |
| Simulated data set to mimic a small expression quantitative trait loci (eQTL) example | exampleEQTL |
| Preprocessed data set to mimic a small pharmacogenomic example | exampleGDSC |
| get fitted responses | fitted.BayesSUR |
| extract the posterior mean of parameters | getEstimator |
| create a selection of plots | plot.BayesSUR |
| plot conditional predictive ordinate | plotCPO |
| plot heatmap of estimators | plotEstimator |
| plot graph for response variables | plotGraph |
| plot Manhattan-like plots | plotManhattan |
| plot MCMC diagnostic plots | plotMCMCdiag |
| plot network representation of the associations between responses and predictors | plotNetwork |
| predict method for class 'BayesSUR' | predict.BayesSUR |
| print method for class 'BayesSUR' | print.BayesSUR |
| summary method for class 'BayesSUR' | summary.BayesSUR |
| targetGene | targetGene |