Understand and Describe Bayesian Models and Posterior Distributions
select numerics columns
Point-estimates of posterior distributions
Objects exported from other packages
Area under the Curve (AUC)
Coerce to a Data Frame
Convert to Numeric
Inclusion Bayes Factors for testing predictors across Bayesian models
Bayes Factors (BF) for model comparison
Bayes Factors (BF) for a Single Parameter
Bayes Factors (BF) for Order Restricted Models
Bayes Factors (BF)
bayestestR: Describing Effects and their Uncertainty, Existence and Si...
Bias Corrected and Accelerated Interval (BCa)
Convert BIC indices to Bayes Factors via the BIC-approximation method.
Check if Prior is Informative
Confidence/Credible/Compatibility Interval (CI)
Contrast Matrices for Equal Marginal Priors in Bayesian Estimation
Convert (refit) a Bayesian model to frequentist
Curvewise Intervals (CWI)
Density Probability at a Given Value
Describe Posterior Distributions
Describe Priors
Diagnostic values for each iteration
Posteriors Sampling Diagnostic
Empirical Distributions
Extract and Returns the priors formatted for rstanarm
Set a new location for a prior
Effective Sample Size (ESS)
Test for Practical Equivalence
Density Estimation
Equal-Tailed Interval (ETI)
Highest Density Interval (HDI)
Maximum A Posteriori probability estimate (MAP)
Monte-Carlo Standard Error (MCSE)
Summary of Bayesian multivariate-response mediation-models
Convert model's posteriors to priors (EXPERIMENTAL)
Overlap Coefficient
Probability of Direction (pd)
Bayesian p-value based on the density at the Maximum A Posteriori (MAP...
Probability of being in the ROPE
Practical Significance (ps)
Convert p-values to (pseudo) Bayes Factors
Convert between Probability of Direction (pd) and p-value.
Reshape estimations with multiple iterations (draws) to long format
Find Default Equivalence (ROPE) Region Bounds
Region of Practical Equivalence (ROPE)
Sensitivity to Prior
Find Effect Size Thresholds
Sequential Effect eXistence and sIgnificance Testing (SEXIT)
Compute Support Intervals
Data Simulation
Returns Priors of a Model as Empirical Distributions
Simpson's paradox dataset simulation
Shortest Probability Interval (SPI)
Un-update Bayesian models to their prior-to-data state
Generate posterior distributions weighted across models
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: Makowski et al. (2021) <doi:10.21105/joss.01541>.