Function reference • bayestestR

Posterior Description
`describe_posterior()`	Describe Posterior Distributions
`point_estimate()`	Point-estimates of posterior distributions
`map_estimate()`	Maximum A Posteriori probability estimate (MAP)
`eti()`	Equal-Tailed Interval (ETI)
`hdi()`	Highest Density Interval (HDI)
`ci()`	Confidence/Credible/Compatibility Interval (CI)
`reshape_ci()`	Reshape CI between wide/long formats
Effect Existence and Significance Functions for Bayesian Inference
Posterior Based Methods
`mhdior()`	Maximum HDI level inside/outside ROPE (MHDIOR)
`p_direction()` `pd()`	Probability of Direction (pd)
`p_rope()`	Probability of not being in ROPE
`p_map()` `p_pointnull()`	Bayesian p-value based on the density at the Maximum A Posteriori (MAP)
`p_significance()`	Practical Significance (ps)
`rope()`	Region of Practical Equivalence (ROPE)
`rope_range()`	Find Default Equivalence (ROPE) Region Bounds
`equivalence_test()`	Test for Practical Equivalence
`pd_to_p()` `p_to_pd()` `convert_p_to_pd()` `convert_pd_to_p()`	Convert between Probability of Direction (pd) and p-value.
Bayes factors
`bayesfactor()`	Bayes Factors (BF)
`bayesfactor_parameters()` `bayesfactor_pointull()` `bayesfactor_rope()` `bf_parameters()` `bf_pointull()` `bf_rope()`	Bayes Factors (BF) for a Single Parameter
`bayesfactor_models()` `bf_models()`	Bayes Factors (BF) for model comparison
`bayesfactor_inclusion()` `bf_inclusion()`	Inclusion Bayes Factors for testing predictors across Bayesian models
`bayesfactor_restricted()` `bf_restricted()`	Bayes Factors (BF) for Order Restricted Models
`si()`	Compute Support Intervals
`weighted_posteriors()`	Generate posterior distributions weighted across models
Model Diagnostics
`diagnostic_posterior()`	Posteriors Sampling Diagnostic
`describe_prior()`	Describe Priors
`sensitivity_to_prior()`	Sensitivity to Prior
`check_prior()`	Check if Prior is Informative
`simulate_prior()`	Returns Priors of a Model as Empirical Distributions
`unupdate()`	Un-update Bayesian models to their prior-to-data state
`effective_sample()`	Effective Sample Size (ESS)
`mcse()`	Monte-Carlo Standard Error (MCSE)
Density Estimation
`estimate_density()`	Density Estimation
`density_at()`	Density Probability at a Given Value
`area_under_curve()` `auc()`	Area under the Curve (AUC)
`overlap()`	Overlap Coefficient
Distributions
`distribution()` `distribution_custom()` `distribution_beta()` `distribution_binomial()` `distribution_binom()` `distribution_cauchy()` `distribution_chisquared()` `distribution_chisq()` `distribution_gamma()` `distribution_mixture_normal()` `distribution_normal()` `distribution_gaussian()` `distribution_poisson()` `distribution_student()` `distribution_t()` `distribution_student_t()` `distribution_tweedie()` `distribution_uniform()` `rnorm_perfect()`	Empirical Distributions
Utilities
`simulate_correlation()` `simulate_ttest()` `simulate_difference()`	Data Simulation
`mediation()`	Summary of Bayesian multivariate-response mediation-models
`convert_bayesian_as_frequentist()` `bayesian_as_frequentist()`	Convert (refit) a Bayesian model to frequentist
`contr.bayes()`	Orthonormal Contrast Matrices for Bayesian Estimation
`as.numeric(<map_estimate>)` `as.numeric(<mhdior>)` `as.numeric(<p_direction>)` `as.numeric(<p_map>)` `as.numeric(<p_significance>)`	Convert to Numeric
`as.data.frame(<density>)`	Coerce to a Data Frame
`update(<bayesfactor_models>)` `as.matrix(<bayesfactor_models>)`	Methods for `bayesfactor_models`

Reference

Posterior Description

Effect Existence and Significance

Posterior Based Methods

Bayes factors

Model Diagnostics

Density Estimation

Distributions

Utilities