bayestestR-package: bayestestR: Describing Effects and their Uncertainty,...

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework

Description

Existing R packages allow users to easily fit a large variety of models and extract and visualize the posterior draws. However, most of these packages only return a limited set of indices (e.g., point-estimates and CIs). bayestestR provides a comprehensive and consistent set of functions to analyze and describe posterior distributions generated by a variety of models objects, including popular modeling packages such as rstanarm, brms or BayesFactor.

References:

• Makowski et al. (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.21105/joss.01541")}

• Makowski et al. (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2019.02767")}

Details

bayestestR

Author(s)

Maintainer: Dominique Makowski [e-mail] (ORCID) (@Dom_Makowski)

Authors:

• Daniel Lüdecke [e-mail] (ORCID) (@strengejacke)

• Mattan S. Ben-Shachar [e-mail] (ORCID) (@mattansb)

• Indrajeet Patil [e-mail] (ORCID) (@patilindrajeets)

• Michael D. Wilson [e-mail] (ORCID)

• Brenton M. Wiernik [e-mail] (ORCID) (@bmwiernik)

Other contributors:

• Paul-Christian Bürkner [e-mail] [reviewer]

• Tristan Mahr [e-mail] (ORCID) [reviewer]

• Henrik Singmann [e-mail] (ORCID) [contributor]

• Quentin F. Gronau (ORCID) [contributor]

• Sam Crawley [e-mail] (ORCID) [contributor]

See Also

Useful links:

• https://easystats.github.io/bayestestR/

• Report bugs at https://github.com/easystats/bayestestR/issues

bayestestR documentation built on April 7, 2023, 5:09 p.m.