Package: bayeslm 1.0.1
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) <arxiv:1806.05738>.
Authors:
bayeslm_1.0.1.tar.gz
bayeslm_1.0.1.tar.gz(r-4.5-noble)bayeslm_1.0.1.tar.gz(r-4.4-noble)
bayeslm_1.0.1.tgz(r-4.4-emscripten)bayeslm_1.0.1.tgz(r-4.3-emscripten)
bayeslm.pdf |bayeslm.html✨
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 2 years agofrom:afc4ce623c. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 21 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 21 2024 |
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 |
