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>.

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
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
Old sources: bayeslm archive

Linking:

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