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Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <doi:10.48550/arXiv.1806.05738>.
Version: | 1.0.1 |
Depends: | R (≥ 2.10) |
Imports: | Rcpp (≥ 0.12.7), stats, graphics, grDevices, coda, methods, RcppParallel |
LinkingTo: | Rcpp, RcppArmadillo, RcppParallel |
Suggests: | rmarkdown, knitr |
Published: | 2022-06-27 |
DOI: | 10.32614/CRAN.package.bayeslm |
Author: | Jingyu He [aut, cre], P. Richard Hahn [aut], Hedibert Lopes [aut], Andrew Herren [ctb] |
Maintainer: | Jingyu He <jingyuhe at cityu.edu.hk> |
License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)] |
URL: | https://github.com/JingyuHe/bayeslm |
NeedsCompilation: | yes |
SystemRequirements: | GNU make |
Materials: | README |
CRAN checks: | bayeslm results |
Reference manual: | bayeslm.pdf |
Vignettes: |
Demo of the bayeslm package |
Package source: | bayeslm_1.0.1.tar.gz |
Windows binaries: | r-devel: bayeslm_1.0.1.zip, r-release: bayeslm_1.0.1.zip, r-oldrel: bayeslm_1.0.1.zip |
macOS binaries: | r-release (arm64): bayeslm_1.0.1.tgz, r-oldrel (arm64): bayeslm_1.0.1.tgz, r-release (x86_64): bayeslm_1.0.1.tgz, r-oldrel (x86_64): bayeslm_1.0.1.tgz |
Old sources: | bayeslm archive |
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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.