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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>.

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

Documentation:

Reference manual: bayeslm.pdf
Vignettes: Demo of the bayeslm package

Downloads:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bayeslm 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.
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