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bayeslm  

Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors
View on CRAN: Click here


Download and install bayeslm package within the R console
Install from CRAN:
install.packages("bayeslm")

Install from Github:
library("remotes")
install_github("cran/bayeslm")

Install by package version:
library("remotes")
install_version("bayeslm", "2.0")



Attach the package and use:
library("bayeslm")
Maintained by
Jingyu He
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-14
Latest Update: 2025-09-01
Description:
Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) .
How to cite:
Jingyu He (2017). bayeslm: Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors. R package version 2.0, https://cran.r-project.org/web/packages/bayeslm. Accessed 02 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:20), 0.1.0 (2017-07-14 18:31), 0.2.0 (2017-08-24 05:15), 0.3.0 (2017-09-27 05:21), 0.3.1 (2017-10-17 05:39), 0.5.0 (2017-11-12 05:58), 0.6.0 (2017-12-17 23:57), 0.7.0 (2018-02-22 00:20), 0.8.0 (2018-06-18 19:57), 1.0.1 (2022-06-28 00:50)
Other packages that cited bayeslm R package
View bayeslm citation profile
Other R packages that bayeslm depends, imports, suggests or enhances
Complete documentation for bayeslm
Functions, R codes and Examples using the bayeslm R package
Some associated functions: bayeslm-package . bayeslm . hs_gibbs . plot.MCMC . predict.bayeslm.fit . summary.bayeslm.fit . summary.mcmc . 
Some associated R codes: RcppExports.R . bayeslm.R . bayeslm.default.R . bayeslm.formula.R . plot.mcmc.R . predict.bayeslm.fit.R . summary.bayesm.fit.R . summary.mcmc.R .  Full bayeslm package functions and examples
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