bayestestR

R package bayestestR: Understand and Describe Bayesian Models and Posterior Distributions. Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors).


References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
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  1. Kelter, Riko: Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors (2022)
  2. Daniel Lüdecke, Dominique Makowski, Philip Waggoner, Mattan S. Ben-Shachar: see: An R Package for Visualizing Statistical Models (2021) not zbMATH
  3. Daniel Lüdecke; Mattan S. Ben-Shachar; Indrajeet Patil; Philip Waggoner; Dominique Makowski: performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021) not zbMATH
  4. Indrajeet Patil: Visualizations with statistical details: The ggstatsplot approach (2021) not zbMATH
  5. Indrajeet Patil: statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details (2021) not zbMATH
  6. Daniel Ludecke; Mattan S. Ben-Shachar; Indrajeet Patil; Dominique Makowski: Extracting, Computing and Exploring the Parameters of Statistical Models using R (2020) not zbMATH
  7. Jhwueng, Dwueng-Chwuan: Modeling rate of adaptive trait evolution using Cox-Ingersoll-Ross process: an approximate Bayesian computation approach (2020)
  8. Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Manh-Toan Ho, Manh-TungHo, Peter Mantello: Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package (2020) not zbMATH
  9. Riko Kelter: fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e-value (2020) arXiv
  10. Daniel Lüdecke, Philip D. Waggoner, Dominique Makowski: insight: A Unified Interface to Access Information fromModel Objects in R (2019) not zbMATH
  11. Dominique Makowski, Mattan S. Ben-Shachar, Daniel Lüdecke: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019) not zbMATH
  12. Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH