Package: BayesRS 0.1.3

Mirko Thalmann

BayesRS: Bayes Factors for Hierarchical Linear Models with Continuous Predictors

Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).

Authors:Mirko Thalmann [aut, cre], Marcel Niklaus [aut], Klaus Oberauer [ths], John Kruschke [ctb]

BayesRS_0.1.3.tar.gz
BayesRS_0.1.3.tar.gz(r-4.5-noble)BayesRS_0.1.3.tar.gz(r-4.4-noble)
BayesRS_0.1.3.tgz(r-4.4-emscripten)BayesRS_0.1.3.tgz(r-4.3-emscripten)
BayesRS.pdf |BayesRS.html✨
BayesRS/json (API)

# Install 'BayesRS' in R:
install.packages('BayesRS', repos = 'https://cloud.r-project.org')
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jagscpp

2.95 score 245 downloads 9 mentions 1 exports 37 dependencies

Last updated 7 years agofrom:c839a7bbe5. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-linuxOKMar 26 2025
R-4.4-linuxOKMar 26 2025

Exports:modelrun

Dependencies:clicodacolorspaceDEoptimRfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmetRologymgcvmunsellnlmenumDerivpillarpkgconfigplyrR6RColorBrewerRcppreshaperjagsrlangrobustbasescalestibbleutf8vctrsviridisLitewithr

An Introduction to BayesRS

Rendered fromBayesRS_overview.pdf.asisusingR.rsp::asison Mar 26 2025.

Last update: 2018-04-06
Started: 2017-10-18

Citation

To cite package ‘BayesRS’ in publications use:

Thalmann M, Niklaus M (2018). BayesRS: Bayes Factors for Hierarchical Linear Models with Continuous Predictors. R package version 0.1.3, https://CRAN.R-project.org/package=BayesRS.

Corresponding BibTeX entry: 

  @Manual{,
    title = {BayesRS: Bayes Factors for Hierarchical Linear Models with
      Continuous Predictors},
    author = {Mirko Thalmann and Marcel Niklaus},
    year = {2018},
    note = {R package version 0.1.3},
    url = {https://CRAN.R-project.org/package=BayesRS},
  }