Package: BayesRS 0.1.3

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, 2003, <doi:10.1.1.13.3406>). 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.zip(r-4.5)BayesRS_0.1.3.zip(r-4.4)BayesRS_0.1.3.zip(r-4.3)
BayesRS_0.1.3.tgz(r-4.4-any)BayesRS_0.1.3.tgz(r-4.3-any)
BayesRS_0.1.3.tar.gz(r-4.5-noble)BayesRS_0.1.3.tar.gz(r-4.4-noble)
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BayesRS.pdf |BayesRS.html✨
BayesRS/json (API)

# Install 'BayesRS' in R:

install.packages('BayesRS', repos = c('https://mirkoth.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mirkoth/bayesrs/issues

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

bayesrsdata - Example Data Set

On CRAN:

3.95 score 6 scripts 217 downloads 9 mentions 1 exports 37 dependencies

Last updated 7 years agofrom:ed4bafe952. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-win	OK	Nov 05 2024
R-4.5-linux	OK	Nov 05 2024
R-4.4-win	OK	Nov 05 2024
R-4.4-mac	OK	Nov 05 2024
R-4.3-win	OK	Nov 05 2024
R-4.3-mac	OK	Nov 05 2024