This package is considered a duplicate. The official version of this package is found at:https://jingyuhe.r-universe.dev/bayeslm
Package: bayeslm 2.0
Jingyu He
bayeslm: Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors
Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <doi:10.48550/arXiv.1806.05738>.
Authors:
bayeslm_2.0.tar.gz
bayeslm_2.0.tar.gz(r-4.7-arm64)bayeslm_2.0.tar.gz(r-4.7-x86_64)bayeslm_2.0.tar.gz(r-4.6-arm64)bayeslm_2.0.tar.gz(r-4.6-x86_64)
bayeslm_2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bayeslm/json (API)
| # Install 'bayeslm' in R: |
| install.packages('bayeslm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jingyuhe/bayeslm/issues
Last updated from:3784e47108. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 201 | ||
| linux-devel-x86_64 | OK | 187 | ||
| source / vignettes | OK | 318 | ||
| linux-release-arm64 | OK | 200 | ||
| linux-release-x86_64 | OK | 160 | ||
| wasm-release | OK | 171 |
Exports:bayeslmhs_gibbsplot.MCMCsummary.bayeslm.fitsummary.MCMC
Dependencies:codalatticeRcppRcppArmadilloRcppParallel
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Efficient sampling for Gaussian linear regression with arbitrary priors | bayeslm-package |
| Efficient sampling for Gaussian linear model with arbitrary priors | bayeslm bayeslm.default bayeslm.formula |
| Gibbs sampler of horseshoe regression | hs_gibbs |
| Plot posterior summary | plot.MCMC |
| Predict new data | predict.bayeslm.fit |
| Summarize fitted object of 'bayeslm' | summary.bayeslm.fit |
| Summarize posterior draws | summary.MCMC |
