Package: bayeslm 1.0.1

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:Jingyu He [aut, cre], P. Richard Hahn [aut], Hedibert Lopes [aut], Andrew Herren [ctb]

bayeslm_1.0.1.tar.gz
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bayeslm.pdf |bayeslm.html✨
bayeslm/json (API)

# Install 'bayeslm' in R:
install.packages('bayeslm', repos = c('https://jingyuhe.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jingyuhe/bayeslm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

5.01 score 9 stars 23 scripts 301 downloads 5 exports 5 dependencies

Last updated 2 years agofrom:34420942d7. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64NOTENov 05 2024
R-4.5-linux-x86_64NOTENov 05 2024
R-4.4-win-x86_64NOTENov 05 2024
R-4.4-mac-x86_64NOTENov 05 2024
R-4.4-mac-aarch64NOTENov 05 2024
R-4.3-win-x86_64NOTENov 05 2024
R-4.3-mac-x86_64NOTENov 05 2024
R-4.3-mac-aarch64NOTENov 05 2024

Exports:bayeslmhs_gibbsplot.MCMCsummary.bayeslm.fitsummary.MCMC

Dependencies:codalatticeRcppRcppArmadilloRcppParallel

Demo of the bayeslm package

Rendered frombayeslm_demo.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-06-26
Started: 2022-06-26